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The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for predicting one or more future activities of a user on a social media platform, said method comprising: extracting, by one or more processors, a first time series of one or more historical activities performed by said user on said social media platform from a social media platform server; receiving, by said one or more processors, a second time series of one or more future events from a computing device; determining, by said one or more processors, a first set of forecast values pertaining to said one or more future activities based on said first time series, wherein said first set of forecast values is determined using a first forecasting technique; determining, by said one or more processors, a second set of forecast values pertaining to said one or more future activities based on said first time series and said second time series, wherein said second set of forecast values is determined using a second forecasting technique; predicting, by said one or more processors, said one or more future activities of said user based on said first set of forecast values and said second set of forecast values; and recommending, by said one or more processors, one or more products/services based on said predicted one or more future activities of said user. 2. The method of claim 1, wherein said one or more historical activities performed by said user comprise one or more of one or more messages posted, shared, or followed by said user during a first predefined time duration and one or more products or services liked or disliked by said user during said first predefined time duration. 3. The method of claim 1, wherein said one or more future events comprise one or more periodic events or one or more non-periodic events, wherein said one or more periodic events comprise one or more of a festival, a sport event, and an exam session, and wherein said one or more non-periodic events comprise one or more of a musical event, an election campaign, and a natural catastrophes. 4. The method of claim 1, wherein said first time series comprises at least a count of said one or more historical activities performed by said user during a first predefined time duration. 5. The method of claim 4, wherein a mean and a variance of said count of said one or more historical activities are constant during said first predefined time duration. 6. The method of claim 1, wherein said first forecasting technique corresponds to an auto regressive integrated moving average (ARIMA) technique, and wherein said second forecasting technique is based on a regression modelling technique. 7. The method of claim 1, wherein said one or more future activities may comprise one or more of a frequency of visit of said user to said social media platform during a second predefined time duration, a count of messages to be posted, shared, or followed by said user during said second predefined time duration, and a like or a dislike of said user towards a product or a service during said second predefined time duration. 8. The method of claim 7, wherein said one or more future activities of said user are predicted, by said one or more processors, based on an aggregation of at least said first set of forecast values and said second set of forecast values. 9. The method of claim 8, wherein said aggregation corresponds to an average or a weighted average of at least said first set of forecast values and said second set of forecast values. 10. A system for predicting one or more future activities of a user on a social media platform, said system comprising: one or more processors configured to: extract a first time series of one or more historical activities performed by said user on said social media platform during a first predefined time duration from a social media platform server; receive a second time series of one or more future events for a second predefined time duration from a requestor-computing device; determine a first set of forecast values pertaining to said one or more future activities based on said first time series, wherein said first set of forecast values is determined using a first forecasting technique; determine a second set of forecast values pertaining to said one or more future activities based on said first time series and said second time series, wherein said second set of forecast values is determined using a second forecasting technique; predict said one or more future activities of said user for said second predefined time duration based on said first set of forecast values and said second set of forecast values; and recommend one or more products/services based on said predicted one or more future activities of said user during said second predefined time duration. 11. The system of claim 10, wherein said one or more historical activities performed by said user comprise one or more of one or more messages posted, shared, or followed by said user during said first predefined time duration and one or more products or services liked or disliked by said user during said first predefined time duration. 12. The system of claim 10, wherein said one or more future events comprise one or more periodic events or one or more non-periodic events, wherein said one or more periodic events comprise one or more of a festival, a sport event, and an exam session, and wherein said one or more non-periodic events comprise one or more of a musical event, an election campaign, and a natural catastrophes. 13. The system of claim 10, wherein said first forecasting technique corresponds to an auto regressive integrated moving average (ARIMA) technique, and wherein said second forecasting technique is based on a regression modelling technique. 14. The system of claim 10, wherein said one or more future activities may comprise one or more of a frequency of visit of said user to said social media platform during said second predefined time duration, a count of messages to be posted, shared, or followed by said user during said second predefined time duration, and a like or a dislike of said user towards a product or a service during said second predefined time duration. 15. The system of claim 14, wherein said one or more processors are further configured to predict said one or more future activities of said user based on an aggregation of at least said first set of forecast values and said second set of forecast values. 16. The system of claim 15, wherein said aggregation corresponds to an average or a weighted average of at least said first set of forecast values and said second set of forecast values. 17. A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for predicting one or more future activities of a user on a social media platform, wherein the computer program code is executable by one or more processors to: extract a first time series of one or more historical activities performed by said user on said social media platform during a first predefined time duration from a social media platform server; receive a second time series of one or more future events for a second predefined time duration from a requestor-computing device; determine a first set of forecast values pertaining to said one or more future activities based on said first time series, wherein said first set of forecast values is determined using an auto regressive integrated moving average (ARIMA) technique; determine a second set of forecast values pertaining to said one or more future activities based on said first time series and said second time series, wherein said second set of forecast values is determined based on a regression modelling technique; predict said one or more future activities of said user for said second predefined time duration based on said first set of forecast values and said second set of forecast values; and recommend one or more products/services based on said predicted one or more future activities of said user during said second predefined time duration. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values. |
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G06N308 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The disclosed embodiments illustrate a method and a system for predicting future activities of a user on a social media platform. The method includes extracting a first time series of one or more historical activities performed by the user from a social media platform server. The method further includes receiving a second time series of one or more future events from a requestor-computing device. The method further includes determining a first set of forecast values and a second set of forecast values based on the first time series and/or the second time series, wherein the first set of forecast values is determined using an ARIMA technique, and the second set of forecast values is determined using a regression modelling technique. The method further includes predicting the future activities of the user based on the first set of forecast values and the second set of forecast values. |
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A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph, links among the set of nodes being weighted to provide a weighted cognitive graph; associating a user with the travel-related cognitive persona; and, performing a cognitive computing operation based upon the travel-related cognitive persona associated with the user. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph, links among the set of nodes being weighted to provide a weighted cognitive graph; associating a user with the travel-related cognitive persona; and, performing a cognitive computing operation based upon the travel-related cognitive persona associated with the user. 2. The system of claim 1, wherein: the travel-related cognitive persona represents a set of attributes, each of the set of attributes corresponding to a node of the set of nodes; and, an amount of weighting between nodes of the set of nodes corresponds to a degree of relevance between the persona and the attributes. 3. The system of claim 2, wherein: the set of attributes comprise at least one of demographic attributes, geographic attributes, psychographic attributes, and behavioristic attributes. 4. The system of claim 1, wherein: a link between a first node of the set of nodes and a second node of the set of nodes is represented by an attribute weight, the attribute weight indicating a degree of relevance between the attributes corresponding to the first node and the second node. 5. The system of claim 1, wherein the instructions executable by the processor further comprise instructions for: receiving feedback from the user relating to the travel-related cognitive persona associated with the user; and, revising weighting of the links among the set of nodes of the weighted cognitive graph based upon the feedback. 6. The system of claim 5, wherein the instructions executable by the processor further comprise instructions for: using the revised weighting to generate a second travel-related cognitive persona. 7. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph, links among the set of nodes being weighted to provide a weighted cognitive graph; associating a user with the travel-related cognitive persona; and, performing a cognitive computing operation based upon the travel-related cognitive persona associated with the user. 8. The non-transitory, computer-readable storage medium of claim 7, wherein: the travel-related cognitive persona represents a set of attributes, each of the set of attributes corresponding to a node of the set of nodes; and, an amount of weighting between nodes of the set of nodes corresponds to a degree of relevance between the persona and the attributes. 9. The non-transitory, computer-readable storage medium of claim 8, wherein: the set of attributes comprise at least one of demographic attributes, geographic attributes, psychographic attributes, and behavioristic attributes. 10. The non-transitory, computer-readable storage medium of claim 7, wherein: a link between a first node of the set of nodes and a second node of the set of nodes is represented by an attribute weight, the attribute weight indicating a degree of relevance between the attributes corresponding to the first node and the second node. 11. The non-transitory, computer-readable storage medium of claim 7, wherein the instructions executable by the processor further comprise instructions for: receiving feedback from the user relating to the travel-related cognitive persona associated with the user; and, revising weighting of the links among the set of nodes of the weighted cognitive graph based upon the feedback. 12. The non-transitory, computer-readable storage medium of claim 11, wherein the instructions executable by the processor further comprise instructions for: using the revised weighting to generate a second travel-related cognitive persona. 13. The non-transitory, computer-readable storage medium of claim 7, wherein the computer executable instructions are deployable to a client system from a server system at a remote location. 14. The non-transitory, computer-readable storage medium of claim 7, wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph, links among the set of nodes being weighted to provide a weighted cognitive graph; associating a user with the travel-related cognitive persona; and, performing a cognitive computing operation based upon the travel-related cognitive persona associated with the user. |
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G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph, links among the set of nodes being weighted to provide a weighted cognitive graph; associating a user with the travel-related cognitive persona; and, performing a cognitive computing operation based upon the travel-related cognitive persona associated with the user. |
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Systems and methods are provided for modifying a knowledge representation based on a machine-learning classifier. The knowledge representation is synthesized based on an object of interest. The machine-learning classifier is applied to predict relevance of validation data items. The knowledge representation is modified based on the results of the machine-learning classifier and the validation data. The modified knowledge representation can be used in subsequent applications of the classifier. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of modifying a knowledge representation based on a machine-learning classifier, the method comprising: receiving a knowledge representation encoded as a non-transitory computer-readable data structure, based on an object of interest, the knowledge representation comprising at least one concept and/or relationship between two or more concepts; receiving validation data, the validation data comprising a first set of one or more labeled content items having a label that classifies each content item into one or more categories including a first category known to be relevant to the object of interest and a second category known to not be relevant to the object of interest; predicting, with a machine-learning classifier that uses at least one attribute derived from the knowledge representation as a feature, each of the one or more labeled content items as one of: a) relevant to the object of interest or b) not relevant to the object of interest; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 2. The method of claim 1, further comprising synthesizing the knowledge representation based on contents of the object of interest. 3. The method of claim 2, wherein the synthesizing further comprises generating the at least one concept and/or relationship between two or more concepts, wherein the concepts and/or relationships are not recited in the object of interest. 4. The method of claim 1, wherein the knowledge representation includes weights associated with the at least one concept. 5. The method of claim 1, wherein the predicting is based on an intersection of the one or more labeled content items and the feature. 6. The method of claim 1, wherein the object of interest comprises a topic, a tweet, a webpage, a website, a document, a collection of documents, a document title, a message, an advertisement, and/or a search query. 7. The method of claim 1, further comprising: after modifying the knowledge representation: re-predicting each of the first set of one or more labeled content items using the modified knowledge representation; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 8. The method of claim 7, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of labeled content items in the first category is equal to or exceeds a precision threshold. 9. The method of claim 7, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is equal to or exceeds a recall threshold. 10. The method of claim 1, wherein modifying the knowledge representation comprises modifying weights associated with the at least one concept in the knowledge representation, and/or adding additional concepts to the knowledge representation. 11. The method of claim 1, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items in the first category is less than a threshold precision value. 12. The method of claim 1, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is less than a threshold recall value. 13. The method of claim 1, wherein the at least one attribute comprises at least one of: a total number of concepts intersecting between the knowledge representation and the one or more labeled content items, a number of broader concepts intersecting between the knowledge representation and the one or more labeled content items, a sum of weights of concepts intersecting between the knowledge representation and the one or more labeled content items, and/or a number of narrower concepts intersecting between the knowledge representation and the one or more labeled content items. 14. A system for modifying a knowledge representation based on a machine-learning classifier, the system comprising: at least one processor configured to perform a method comprising: receiving a knowledge representation encoded as a non-transitory computer-readable data structure, based on an object of interest, the knowledge representation comprising at least one concept and/or relationship between two or more concepts; receiving validation data, the validation data comprising a first set of one or more labeled content items having a label that classifies each content item into one or more categories including a first category known to be relevant to the object of interest and a second category known to not be relevant to the object of interest; predicting, with a machine-learning classifier that uses at least one attribute derived from the knowledge representation as a feature, each of the one or more labeled content items as one of: a) relevant to the object of interest or b) not relevant to the object of interest; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 15. The system of claim 14, wherein the method further comprises synthesizing the knowledge representation based on contents of the object of interest. 16. The system of claim 15, wherein the synthesizing further comprises generating the at least one concept and/or relationship between two or more concepts, wherein the concepts and/or relationships are not recited in the object of interest. 17. The system of claim 14, wherein the knowledge representation includes weights associated with the at least one concept. 18. The system of claim 14, wherein the predicting is based on an intersection of the one or more labeled content items and the feature. 19. The system of claim 14, wherein the object of interest comprises a topic, a tweet, a webpage, a website, a document, a collection of documents, a document title, a message, an advertisement, and/or a search query. 20. The system of claim 14, wherein the method further comprises: after modifying the knowledge representation: re-predicting each of the first set of one or more labeled content items using the modified knowledge representation; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 21. The system of claim 20, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of labeled content items in the first category is equal to or exceeds a precision threshold. 22. The system of claim 20, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is equal to or exceeds a recall threshold. 23. The system of claim 14, wherein modifying the knowledge representation comprises modifying weights associated with the at least one concept in the knowledge representation, and/or adding additional concepts to the knowledge representation. 24. The system of claim 14, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items in the first category is less than a threshold precision value. 25. The system of claim 14, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is less than a threshold recall value. 26. The system of claim 14, wherein the at least one attribute comprises at least one of: a total number of concepts intersecting between the knowledge representation and the one or more labeled content items, a number of broader concepts intersecting between the knowledge representation and the one or more labeled content items, a sum of weights of concepts intersecting between the knowledge representation and the one or more labeled content items, and/or a number of narrower concepts intersecting between the knowledge representation and the one or more labeled content items. 27. At least one non-transitory computer readable storage medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method of modifying a knowledge representation based on a machine-learning classifier, the method comprising: receiving a knowledge representation encoded as a non-transitory computer-readable data structure, based on an object of interest, the knowledge representation comprising at least one concept and/or relationship between two or more concepts; receiving validation data, the validation data comprising a first set of one or more labeled content items having a label that classifies each content item into one or more categories including a first category known to be relevant to the object of interest and a second category known to not be relevant to the object of interest; predicting, with a machine-learning classifier that uses at least one attribute derived from the knowledge representation as a feature, each of the one or more labeled content items as one of: a) relevant to the object of interest or b) not relevant to the object of interest; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 28. The at least one non-transitory computer readable storage medium of claim 27, wherein the method further comprises synthesizing the knowledge representation based on contents of the object of interest. 29. The at least one non-transitory computer readable storage medium of claim 28, wherein the synthesizing further comprises generating the at least one concept and/or relationship between two or more concepts, wherein the concepts and/or relationships are not recited in the object of interest. 30. The at least one non-transitory computer readable storage medium of claim 27, wherein the knowledge representation includes weights associated with the at least one concept. 31. The at least one non-transitory computer readable storage medium of claim 27, wherein the predicting is based on an intersection of the one or more labeled content items and the feature. 32. The at least one non-transitory computer readable storage medium of claim 27, wherein the object of interest comprises a topic, a tweet, a webpage, a website, a document, a collection of documents, a document title, a message, an advertisement, and/or a search query. 33. The at least one non-transitory computer readable storage medium of claim 27, wherein the method further comprises: after modifying the knowledge representation: re-predicting each of the first set of one or more labeled content items using the modified knowledge representation; and modifying the knowledge representation based on a comparison of the prediction by the machine-learning classifier for each content item of the first set to the label of each respective content item. 34. The at least one non-transitory computer readable storage medium of claim 33, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of labeled content items in the first category is equal to or exceeds a precision threshold. 35. The at least one non-transitory computer readable storage medium of claim 33, wherein the re-predicting and the modifying are repeated until a ratio of a number of the one or more labeled content items correctly predicted as being relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is equal to or exceeds a recall threshold. 36. The at least one non-transitory computer readable storage medium of claim 27, wherein modifying the knowledge representation comprises modifying weights associated with the at least one concept in the knowledge representation, and/or adding additional concepts to the knowledge representation. 37. The at least one non-transitory computer readable storage medium of claim 27, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items in the first category is less than a threshold precision value. 38. The at least one non-transitory computer readable storage medium of claim 27, wherein modifying the knowledge representation based on the comparing comprises modifying the knowledge representation when a ratio of a number of the one or more labeled content items correctly predicted to be relevant to the object of interest to a total number of the one or more labeled content items predicted to be relevant to the object of interest is less than a threshold recall value. 39. The at least one non-transitory computer readable storage medium of claim 27, wherein the at least one attribute comprises at least one of: a total number of concepts intersecting between the knowledge representation and the one or more labeled content items, a number of broader concepts intersecting between the knowledge representation and the one or more labeled content items, a sum of weights of concepts intersecting between the knowledge representation and the one or more labeled content items, and/or a number of narrower concepts intersecting between the knowledge representation and the one or more labeled content items. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems and methods are provided for modifying a knowledge representation based on a machine-learning classifier. The knowledge representation is synthesized based on an object of interest. The machine-learning classifier is applied to predict relevance of validation data items. The knowledge representation is modified based on the results of the machine-learning classifier and the validation data. The modified knowledge representation can be used in subsequent applications of the classifier. |
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G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems and methods are provided for modifying a knowledge representation based on a machine-learning classifier. The knowledge representation is synthesized based on an object of interest. The machine-learning classifier is applied to predict relevance of validation data items. The knowledge representation is modified based on the results of the machine-learning classifier and the validation data. The modified knowledge representation can be used in subsequent applications of the classifier. |
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Systems methods and media for preference clustering are provided. In one example, a clustering system for analyzing a cluster comprises processors and a memory storing instructions that cause the system to calculate a Distance Angular Measure (DAM) for the cluster, the (DAM) comprising a distance component and an angular component of the cluster. In one example, the distance component of the (DAM) includes one of a cluster variation and a cluster radius. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A clustering system for analyzing a cluster, the clustering system comprising: processors; and a memory storing instructions that, when executed by at least one processor among the processors, cause the system to perform operations comprising, at least: calculating a Distance Angular Measure (DAM) for the cluster, the (DAM) comprising a distance component and an angular component of the cluster. 2. The clustering system of claim 1, wherein the distance component of the (DAM) includes one of a cluster variation and a cluster radius. 3. The clustering system of claim 2, wherein the cluster variation is defined by an algorithm comprising: σ C j 2 = ∑ x → ∈ C j x → - c → j N C j , where {right arrow over (c)}j is a center of the cluster Cj,{right arrow over (x)}εCj and NCj is a number of observations or points in Cj. 4. The clustering system of claim 2, wherein the cluster radius is defined by an algorithm comprising: φCj=max{right arrow over (x)}εCjd({right arrow over (x)},{right arrow over (c)}j) 5. The clustering system of claim 4, wherein a distance between two points in the cluster is defined by an algorithm comprising: ρ({right arrow over (x)},{right arrow over (y)})=√{square root over (∥{right arrow over (x)}∥+∥{right arrow over (y)}∥)} 1. 6. The clustering system of claim 1, wherein the angular component of the (DAM) includes a cosine function. 7. A method for analyzing a cluster, the method comprising: calculating a Distance Angular Measure (DAM) for the cluster, the (DAM) comprising a distance component and an angular component of the cluster. 8. The method of claim 7, wherein the distance component of the (DAM) includes one of a cluster variation and a cluster radius. 9. The method of claim 8, further comprising defining the cluster variation by an algorithm comprising: σ C j 2 = ∑ x → ∈ C j x → - c → j N C j , where {right arrow over (c)}j is a center of the cluster Cj,{right arrow over (x)}εCj and NCj is a number of observations or points in Cj. 10. The method of claim 8, further comprising defining the cluster radius by an algorithm comprising: φCj=max{right arrow over (x)}εCjd({right arrow over (x)},{right arrow over (c)}j) 11. The method of claim 10, further comprising defining a distance between two points in the duster by an algorithm comprising: φ({right arrow over (x)},{right arrow over (y)})=√{square root over (∥{right arrow over (x)}∥+∥{right arrow over (y)}∥)} 1. 12. The method of claim 7, further comprising including a cosine function into the angular component of the (DAM) 13. A machine-readable medium carrying instructions which, when read by a machine, cause the machine to perform operations comprising, at least: calculating a Distance Angular Measure (DAM) for the cluster, the (DAM) comprising a distance component and an angular component of the cluster. 14. The medium of claim 13, wherein the distance component of the (DAM) includes one of a cluster variation and a cluster radius. 15. The medium of claim 14, wherein the cluster variation is defined by an algorithm comprising: σ C j 2 = ∑ x → ∈ C j x → - c → j N C j , where {right arrow over (c)}j is a center of the cluster Cj,{right arrow over (x)}εCj and NCj is a number of observations or points in Cj. 16. The medium of claim 14, wherein the cluster radius is defined by an algorithm comprising: φCj=max{right arrow over (x)}εCjd({right arrow over (x)},{right arrow over (c)}j) 17. The medium of claim 16, wherein a distance between two points in the cluster is defined by an algorithm comprising: φ({right arrow over (x)},{right arrow over (y)})=√{square root over (∥{right arrow over (x)}∥+∥{right arrow over (y)}∥)} 1. 18. The medium of claim 13, wherein the angular component of the (DAM) includes a cosine function. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems methods and media for preference clustering are provided. In one example, a clustering system for analyzing a cluster comprises processors and a memory storing instructions that cause the system to calculate a Distance Angular Measure (DAM) for the cluster, the (DAM) comprising a distance component and an angular component of the cluster. In one example, the distance component of the (DAM) includes one of a cluster variation and a cluster radius. |
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G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems methods and media for preference clustering are provided. In one example, a clustering system for analyzing a cluster comprises processors and a memory storing instructions that cause the system to calculate a Distance Angular Measure (DAM) for the cluster, the (DAM) comprising a distance component and an angular component of the cluster. In one example, the distance component of the (DAM) includes one of a cluster variation and a cluster radius. |
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This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network. The invention discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, such as a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a “thought network”. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for generating a semantic network characterized in that it comprises the steps of: (a) providing an information domain; (b) representing the information domain as a data set, the data set being defined by data entities and one or more relationships between the data entities; (c) providing, by means of an agent, data representing one or more thoughts; and (d) synthesizing, or facilitating the synthesizing, by one or more computer processors, a semantic network based on one or more interactions between the data entities and the one or more thoughts. 2. The computer implemented method of claim 1, characterized in that it comprises the further step of enabling one or more of: (a) interactions between the data entities and the one or more thoughts; and (b) interactions between the agent and the data entities based on the one or more thoughts, by one or more synthesis operations. 3. The computer implemented method of claim 1, characterized in that it comprises the further step of integrating the one or more thoughts with the data entities. 4. The computer implemented method of claim 1, characterized in that it comprises the further step of providing the agent with means to traverse the semantic network by selecting data entities related to the one or more thoughts. 5. The computer implemented method of claim 1, characterized in that it comprises the further step of synthesizing the semantic network dynamically upon the agent providing the data representing the one or more thoughts. 6. The computer implemented method of claim 4, characterized in that it comprises the further step of storing one or more aspects of learning derived from the semantic network to a storage means. 7. The computer implemented method of claim 6, characterized in that it comprises the further step of basing the learning on selecting the data entities related to the one or more thoughts. 8. The computer implemented method of claim 6, characterized in that it comprises the further step of storing the one or more aspects of learning, thereby facilitating dynamic generation of one or more other semantic networks. 9. The computer implemented method of claim 6, characterized in that the concepts of the semantic network are stored to the storage means. 10. The computer implemented method of claim 9, characterized in that it comprises the further step of storing relationships between the concepts to the storage means. 11. The computer implemented method of claim 1, characterized in that it comprises the further step of generating the semantic network to include label-to-concept translation. 12. The computer implemented method of claim 11, characterized in that it comprises the further step of defining a label representing a string, and generating from the label a representation of a concept. 13. The computer implemented method of claim 12, characterized in that it comprises one or more of the further steps of: (a) defining the label by the agent; or (b) obtaining the label from another knowledge representation. 14-20. (canceled) 21. A computer system for generating a semantic network characterized in that it comprises: (a) one or more computers configured to provide, or provide access to, an information domain, wherein a data set is operable to represent the information domain, the data set being defined by data entities and one or more relationships between the data entities, and wherein an agent is operable to provide data representing one or more thoughts; and (b) a thought processor operable to synthesize, or facilitate the synthesis of, by one or more computer processors, a semantic network based on one or more interactions between the data entities and the one or more thoughts. 22. The computer system of claim 21, characterized in that the thought processor is operable to synthesize the semantic network dynamically upon the agent providing the data representing the one or more thoughts to facilitate new mappings. 23. The computer system of claim 22, characterized in that the new mappings provide one or more of the following: basic lookup functions; attribute hierarchy; or concept matching. 24-26. (canceled) 27. A computer implemented method for synthesizing media utilizing a semantic network characterized in that it comprises the steps of: (a) generating, or facilitating the generation of by one or more computer processors, a thought network based on one or more interactions between one or more data entities and one or more thoughts; and (b) transforming the thought network so as to generate and provide one or more forms of synthesized media to a consumer. 27. The computer implemented method of claim 26 characterized in that it comprises the further step of providing client-directed synthesized media based on a consumer-directed interaction whereby the consumer performs one of the following steps: (a) providing input to direct the generation of the synthesized media; or (b) selecting synthesized media from the one or more forms provided. 28. The computer implemented method of claim 26 characterized in that the synthesized media is web-integrable media. 29. The computer implemented method of claim 26 characterized in that it comprises the further step of storing the synthesized media as a content inventory. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network. The invention discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, such as a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a “thought network”. |
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G06N502 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network. The invention discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, such as a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a “thought network”. |
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Systems, methods, and non-transitory computer-readable media according to certain aspects can receive at least one message sent by a user of a social networking system to a page provided by the social networking system, where the page is associated with an entity. A training data set including a plurality of messages can be determined, and the training data set can indicate an intent classification for each of the plurality of messages. The intent classification can be indicative of an intent associated with a particular message. A machine learning model may be trained based at least in part on the training data set. A first intent classification for the at least one message can be determined, based at least in part on the machine learning model. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving, by a computing system, at least one message sent by a user of a social networking system to a page provided by the social networking system, the page associated with an entity; determining, by the computing system, a training data set including a plurality of messages, the training data set indicating an intent classification for each of the plurality of messages, the intent classification indicative of an intent associated with a particular message; training, by the computing system, a machine learning model based at least in part on the training data set; and determining, by the computing system, a first intent classification for the at least one message, based at least in part on the machine learning model. 2. The computer-implemented method of claim 1, wherein the machine learning model provides the first intent classification and a confidence score associated with the first intent classification. 3. The computer-implemented method of claim 2, wherein the first intent classification is displayed in a user interface associated with the page when the confidence score associated with the first intent classification is greater than or equal to a threshold value. 4. The computer-implemented method of claim 2, wherein the first intent classification is associated with the at least one message when the confidence score associated with the first intent classification is greater than or equal to a threshold value. 5. The computer-implemented method of claim 2, wherein the machine learning model provides one or more intent classifications for the at least one message and a confidence score associated with each of the intent classifications. 6. The computer-implemented method of claim 1, wherein the first intent classification is selected from intent classifications associated with the plurality of messages included in the training data set. 7. The computer-implemented method of claim 1, wherein the determining the training data set comprises performing a pattern search on one or more messages using one or more regular expressions. 8. The computer-implemented method of claim 7, wherein each of the one or more regular expressions is associated with a respective intent classification, and wherein a first message of the one or more messages that includes text matching a first regular expression of the one or more regular expressions is associated with the intent classification of the first regular expression. 9. The computer-implemented method of claim 1, wherein the determining the training data set comprises obtaining one or more messages for which the intent classification is designated based at least in part on human input. 10. The computer-implemented method of claim 1, further comprising receiving user input relating to whether the first intent classification is indicative of an intent associated with the at least one message. 11. A system comprising: at least one hardware processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to: receive at least one message sent by a user of a social networking system to a page provided by the social networking system, the page associated with an entity; determine a training data set including a plurality of messages, the training data set indicating an intent classification for each of the plurality of messages, the intent classification indicative of an intent associated with a particular message; train a machine learning model based at least in part on the training data set; and determine a first intent classification for the at least one message, based at least in part on the machine learning model. 12. The system of claim 11, wherein the machine learning model provides the first intent classification and a confidence score associated with the first intent classification. 13. The system of claim 12, wherein the first intent classification is displayed in a user interface associated with the page when the confidence score associated with the first intent classification is greater than or equal to a threshold value. 14. The system of claim 12, wherein the first intent classification is associated with the at least one message when the confidence score associated with the first intent classification is greater than or equal to a threshold value. 15. The system of claim 11, wherein the determination of the training data set comprises performing a pattern search on one or more messages using one or more regular expressions. 16. A non-transitory computer readable medium including instructions that, when executed by at least one hardware processor of a computing system, cause the computing system to: receive at least one message sent by a user of a social networking system to a page provided by the social networking system, the page associated with an entity; determine a training data set including a plurality of messages, the training data set indicating an intent classification for each of the plurality of messages, the intent classification indicative of an intent associated with a particular message; train a machine learning model based at least in part on the training data set; and determine a first intent classification for the at least one message, based at least in part on the machine learning model. 17. The non-transitory computer readable medium of claim 16, wherein the machine learning model provides the first intent classification and a confidence score associated with the first intent classification. 18. The non-transitory computer readable medium of claim 17, wherein the first intent classification is displayed in a user interface associated with the page when the confidence score associated with the first intent classification is greater than or equal to a threshold value. 19. The non-transitory computer readable medium of claim 17, wherein the first intent classification is associated with the at least one message when the confidence score associated with the first intent classification is greater than or equal to a threshold value. 20. The non-transitory computer readable medium of claim 16, wherein the determination of the training data set comprises performing a pattern search on one or more messages using one or more regular expressions. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems, methods, and non-transitory computer-readable media according to certain aspects can receive at least one message sent by a user of a social networking system to a page provided by the social networking system, where the page is associated with an entity. A training data set including a plurality of messages can be determined, and the training data set can indicate an intent classification for each of the plurality of messages. The intent classification can be indicative of an intent associated with a particular message. A machine learning model may be trained based at least in part on the training data set. A first intent classification for the at least one message can be determined, based at least in part on the machine learning model. |
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G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems, methods, and non-transitory computer-readable media according to certain aspects can receive at least one message sent by a user of a social networking system to a page provided by the social networking system, where the page is associated with an entity. A training data set including a plurality of messages can be determined, and the training data set can indicate an intent classification for each of the plurality of messages. The intent classification can be indicative of an intent associated with a particular message. A machine learning model may be trained based at least in part on the training data set. A first intent classification for the at least one message can be determined, based at least in part on the machine learning model. |
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Techniques for improving the performance of a quantum processor are described. Some techniques employ improving the processor topology through design and fabrication, reducing intrinsic/control errors, reducing thermally-assisted errors and methods of encoding problems in the quantum processor for error correction. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A hybrid computational system comprising: at least one quantum processor comprising a plurality of qubits and a plurality of couplers; a configuration subsystem communicatively coupled to configure the at least one quantum processor, the configuration subsystem including at least one digital processor, and at least one non-transitory computer-readable storage medium communicatively coupled to the at least one digital processor and that stores at least one of processor-executable instructions or data, where in use the at least one digital processor: receives a problem Hamiltonian defined over at least two of the qubits, the problem Hamiltonian having a ground state that encodes a solution to a computational problem; during a first iteration on the computational problem: determines a plurality of change values for the problem Hamiltonian; updates the problem Hamiltonian to a new problem Hamiltonian using the plurality of change values; sends the new problem Hamiltonian to the at least one quantum processor; receives a changed solution set from the at least one quantum processor; and transforms the changed solution set to a solution set. 2. The hybrid computational system of claim 1 wherein, in use, the at least one digital processor further: returns the solution set. 3. The hybrid computational of claim 1 wherein, in use, the at least one digital processor selects at random for each entry in the plurality of change values either a change value or a no-change value in order to determine the plurality of change values for the problem Hamiltonian. 4. The hybrid computational of claim 3 wherein the change value is negative, the no-change value is positive, the problem Hamiltonian includes a plurality of local bias terms, the problem Hamiltonian includes a plurality of coupling terms, and, where in use, the at least one digital processor further: creates a plurality of new local bias terms from the product of the plurality of changes and the plurality of local bias terms; and creates a plurality of new coupling terms where each new coupling term includes the product of: a first entry in the plurality of changes, a second entry in the plurality of changes, and a first entry in the plurality of coupling terms that correspond to both the first entry in the plurality of changes and the second entry in plurality of changes. 5. The hybrid computational of claim 1 wherein, in use, the at least one quantum processor-performs quantum annealing or adiabatic quantum computing. 6. The hybrid computational of claim 1 wherein, in use, the at least one digital processor creates a plurality of new qubit values from the product of the plurality of changes and the changed solution set in order to transform the changed solution set to the solution set. 7. The hybrid computational of claim 1 wherein, in use, the at least one digital processor further receives an integer M. 8. The hybrid computational of claim 7 wherein, in use, the at least one digital processor further: during an Mth iteration on the computational problem: determines an Mth plurality of change values for the problem Hamiltonian; updates the problem Hamiltonian to a new Mth problem Hamiltonian using the Mth plurality of change values; sends the new Mth problem Hamiltonian to the at least one quantum processor; receives an Mth changed solution set from the at least one quantum processor; and transforms the Mth changed solution set to an Mth solution set. 9. The hybrid computational of claim 8 wherein, in use, the at least one processor further: during the Mth iteration on the computational problem: records the Mth plurality of change values; and records the Mth solution set. 10. A method to configure at least one quantum processor which comprises a plurality of qubits and a plurality of couplers, the method comprising: a configuration subsystem communicatively coupled to configure the at least one quantum processor, the configuration subsystem including at least one digital processor, and at least one non-transitory computer-readable storage medium communicatively coupled to the at least one digital processor and that stores at least one of processor-executable instructions or data, where in use the at least one digital processor: receiving, via at least one digital processor, a problem Hamiltonian defined over at least two of the qubits, the problem Hamiltonian having a ground state that encodes a solution to a computational problem; during a first iteration on the computational problem: determining, via at least one digital processor, a plurality of change values for the problem Hamiltonian; updating, via at least one digital processor, the problem Hamiltonian to a new problem Hamiltonian using the plurality of change values; sending the new problem Hamiltonian to the at least one quantum processor; receiving, via at least one digital processor, a changed solution set from the at least one quantum processor; and transforming, via at least one digital processor, the changed solution set to a solution set. 11. The method of claim 10, further comprising: returning the solution set. 12. The method of claim 10 wherein determining the plurality of change values for the problem Hamiltonian includes selecting at random for each entry in the plurality of change values either a change value or a no-change value. 13. The method of claim 12 wherein the change value is negative, the no-change value is positive, the problem Hamiltonian includes a plurality of local bias terms, the problem Hamiltonian includes a plurality of coupling terms, and, further comprising: creating a plurality of new local bias terms from the product of the plurality of changes and the plurality of local bias terms; and creating a plurality of new coupling terms where each new coupling term includes the product of: a first entry in the plurality of changes, a second entry in the plurality of changes, and a first entry in the plurality of coupling terms that correspond to both the first entry in the plurality of changes and the second entry in plurality of changes. 14. The method of claim 10 wherein transforming the changed solution set to the solution set includes creating a plurality of new qubit values from the product of the plurality of changes and the changed solution set. 15. The method of claim 10, further comprising: receiving, via the at least one digital processor, an integer M. 16. The method of claim 15, further comprising: during an Mth iteration on the computational problem: determining, via the at least one digital processor, an Mth plurality of change values for the problem Hamiltonian; updating, via the at least one digital processor, the problem Hamiltonian to a new Mth problem Hamiltonian using the Mth plurality of change values; sending the new Mth problem Hamiltonian to the at least one quantum processor; receiving an Mth changed solution set from the at least one quantum processor; and transforming the Mth changed solution set to an Mth solution set. 17. The method of claim 16, further comprising: during the Mth iteration on the computational problem: recording the Mth plurality of change values; and recording the Mth solution set. 18. A non-transitory computer-readable storage medium containing processor-executable instructions, which when executed cause at least one processor to: receive a problem Hamiltonian defined over a plurality of qubits wherein the problem Hamiltonian has a ground state that encodes a solution to a computational problem; during a first iteration on the computational problem: determine a plurality of change values for the problem Hamiltonian; update the problem Hamiltonian to a new problem Hamiltonian using the plurality of change values; send the new problem Hamiltonian to a quantum processor; receive a changed solution set from the quantum processor; and transform the changed solution set to a solution set. 19. The computer-readable storage medium of claim 18 wherein the processor-executable instructions when executed further cause the at least one processor to: return the solution set. 20. The computer-readable storage medium of claim 18 wherein the processor-executable instructions to determine the plurality of change values for the problem Hamiltonian when executed further cause the at least one processor to: select at random for each entry in the plurality of change values either a change value or a no-change value. 21. The computer-readable storage medium of claim 20 wherein the change value is negative, the no-change value is positive, the problem Hamiltonian includes a plurality of local bias terms, the problem Hamiltonian includes a plurality of coupling terms, and the processor-executable instructions when executed further cause the at least one processor to: create a plurality of new local bias terms from the product of the plurality of changes and the plurality of local bias terms; and create a plurality of new coupling terms where each new coupling term includes the product of: a first entry in the plurality of changes, a second entry in the plurality of changes, and a first entry in the plurality of coupling terms corresponding to both the first entry in the plurality of changes and the second entry in plurality of changes. 22. The computer-readable storage medium of claim 18 wherein the processor-executable instructions to transform the changed solution set to the solution set when executed further cause the at least one processor to: create a plurality of new qubit values from the product of the plurality of changes and the changed solution set. 23. The computer-readable storage medium of claim 18 wherein the processor-executable instructions when executed further cause the at least one processor to: receive an integer, M; and during an Mth iteration on the computational problem: determine an Mth plurality of change values for the problem Hamiltonian; update the problem Hamiltonian to a new Mth problem Hamiltonian using the Mth plurality of change values; send the new Mth problem Hamiltonian to a quantum processor; receive an Mth changed solution set from the quantum processor; transform the Mth changed solution set to an Mth solution set; record the Mth plurality of change values; and record the Mth solution set. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: Techniques for improving the performance of a quantum processor are described. Some techniques employ improving the processor topology through design and fabrication, reducing intrinsic/control errors, reducing thermally-assisted errors and methods of encoding problems in the quantum processor for error correction. |
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G06N99002 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Techniques for improving the performance of a quantum processor are described. Some techniques employ improving the processor topology through design and fabrication, reducing intrinsic/control errors, reducing thermally-assisted errors and methods of encoding problems in the quantum processor for error correction. |
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An electronic neural core circuit is provided, comprising a processor, and a memory. The memory comprises a plurality of neural compartments, each compartment comprising a first state variable representing a first state of the neural compartment, and a second state variable representing a second state of the neural compartment. The processor is configured to, for a first neural compartment: receive a synaptic input, perform first and second state variable operations, join operations utilizing input from state variables from another compartment that has been previously processed, thereby producing a join operation results, and produce a state variable output. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. An electronic neural core circuit, comprising: a plurality of neural compartments that are collectively serviced over time to evolve respective compartment states, wherein each servicing corresponds to a neuromorphic time step, and each compartment comprises a state variable representing a state of the neural compartment; wherein the neural core circuit is configured to perform operations to, for a neural compartment during the neuromorphic time step: a) receive a synaptic input; b) perform a state variable operation utilizing: 1) a stored state variable that was stored in the neural compartment prior to receipt of the synaptic input, and 2) the synaptic input, thereby producing a state variable result; c) perform a join operation utilizing: 1) the state variable result, 2) input from a state variable from an other compartment that has been previously processed, and 3) a join operation configuration that is stored in or associated with the neural compartment, thereby producing a join operation result; and d) produce a state variable output based on the join operation. 2. The circuit of claim 1, wherein the neural core circuit is further configured to produce a spike-related output if the join operation result reaches a spiking threshold. 3. The circuit of claim 2, wherein the spike-related output is an actual spike event. 4. The circuit of claim 2, wherein the spike-related output is a spiking state value only, that is a part of the state variable output. 5. The circuit of claim 1, wherein: the neural core circuit is further configured to utilize a stack; and the join operations include stack operations to communicate state variables from one dendritic compartment to a different dendritic compartment. 6. The circuit of claim 5, wherein: the stack operations include push and pop; and the neural core circuit is further configured to pop input from the state variables from the other compartment from the stack, and to push the state variable output to the stack. 7. The circuit of claim 1, wherein the operations include stack operations, the join operations, threshold operations, backward action potential (bAP) operations, mathematical operations, and Boolean logic operations. 8. The circuit of claim 1, wherein the neural core circuit is further configured to, upon completion of operation (d) for a first neural compartment, perform operations (a)-(d) for a second neural compartment, wherein at least one variable output of the first neural compartment is at least one of the variables from the other compartment in the second neural compartment. 9. The circuit of claim 8, wherein the neural core circuit is further configured to execute through a hierarchical dendritic tree structure from the dendritic compartments it processes and produce a spiking event from only a highest dendritic compartment of the dendritic tree structure. 10. The circuit of claim 9, wherein the neural core circuit is further configured to generate a backward action potential (bAP) that executes through the hierarchical dendritic tree structure in a reverse order, based on the spiking event. 11. The circuit of claim 10, wherein the neural core circuit is further configured to communicate the bAP, including its implicit spike time or spike time dependent state variable, to all fan-in synapses of all dendritic compartments that receive synaptic input. 12. The circuit of claim 10, wherein the neural core circuit is further configured to change one or more parameters associated with a neuron model of a dendritic compartment itself in response to a backward action potential (bAP) or forward going spikes or spiking state values. 13. The circuit of claim 12, wherein one of the parameters is a spiking threshold. 14. The circuit of claim 13, wherein the one or more parameters include at least the spiking threshold, state variable exponential decay time constants, current bias constants, scaling constants applied to synaptic inputs, and scaling constants applied to join operation inputs. 15. The circuit of claim 8, wherein the neural core circuit is further configured to concurrently process a plurality of dendritic compartments. 16. The circuit of claim 1, wherein: the state variable is a first state variable; the neural compartment comprises a second state variable representing a second state of the neural compartment; the neural core circuit is further configured to perform operations to, for a neural compartment during the neuromorphic time step: e) perform a second state variable operation utilizing: 1) a stored second state variable that was stored in the memory prior to receipt of the synaptic input, and 2) the first join operation, thereby producing a second state variable result; and f) perform a second join operation utilizing: 1) the second state variable result, 2) input from a second state variable from the other compartment that has been previously processed, and 3) join operation configuration that is stored in the memory associated with the neural compartment, thereby producing a second join operation result; and wherein the producing of the state variable output is further based on the second join operation. 17. A method executed by a processor of an electronic neural core circuit, comprising: during a neuromorphic time step: a) receiving a synaptic input at a dendritic compartment; b) performing a state variable operation utilizing: 1) a stored state variable that was stored in the memory prior to receipt of the synaptic input, and 2) the synaptic input, thereby producing a state variable result; c) performing a join operation utilizing: 1) the first state variable result, 2) input from a first state variable from an other compartment that has been previously processed, and 3) join operation configuration that is stored in the memory associated with the neural compartment, thereby producing a first join operation result; and d) producing a state variable output based on the join operation. 18. The method of claim 17, further comprising operating using a stack, and communicating state variables from one dendritic compartment to a different dendritic compartment using stack operations in the join operations. 19. The method of claim 18, further comprising popping input from the state variables from the other compartment from the stack, and pushing the state variable output to the stack. 20. The method of claim 17, wherein the operations include stack operations, the join operations, threshold operations, backward action potential (bAP) operations, mathematical operations, and Boolean logic operations. 21. The method of claim 17, further comprising: executing through a hierarchical dendritic tree structure from the dendritic compartments being processed; and producing a spiking event from only the highest level value. 22. At least one machine-readable storage medium, comprising a plurality of instructions adapted for execution within an electronic neural core circuit, wherein the instructions, responsive to being executed with the neural core circuit of a computing machine, cause the computing machine to perform operations that: during a neuromorphic time step: a) receive a synaptic input at a dendritic compartment; b) perform a state variable operation utilizing: 1) a stored state variable that was stored in the memory prior to receipt of the synaptic input, and 2) the synaptic input, thereby producing a state variable result; c) perform a join operation utilizing: 1) the first state variable result, 2) input from a first state variable from an other compartment that has been previously processed, and 3) join operation configuration that is stored in the memory associated with the neural compartment, thereby producing a first join operation result; and d) produce a state variable output based on the join operation. 23. The at least one machine readable medium of claim 22, wherein the instructions are further operable to configure the circuit to utilize a stack and the join operations include stack operations to communicate state variables from one dendritic compartment to a different dendritic compartment, wherein the stack operations include push and pop, and the processor is further configured to pop input from the state variables from the other compartment from the stack, and to push the state variable output to the stack. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: An electronic neural core circuit is provided, comprising a processor, and a memory. The memory comprises a plurality of neural compartments, each compartment comprising a first state variable representing a first state of the neural compartment, and a second state variable representing a second state of the neural compartment. The processor is configured to, for a first neural compartment: receive a synaptic input, perform first and second state variable operations, join operations utilizing input from state variables from another compartment that has been previously processed, thereby producing a join operation results, and produce a state variable output. |
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G06N3063 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An electronic neural core circuit is provided, comprising a processor, and a memory. The memory comprises a plurality of neural compartments, each compartment comprising a first state variable representing a first state of the neural compartment, and a second state variable representing a second state of the neural compartment. The processor is configured to, for a first neural compartment: receive a synaptic input, perform first and second state variable operations, join operations utilizing input from state variables from another compartment that has been previously processed, thereby producing a join operation results, and produce a state variable output. |
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Mechanisms are provided for performing a cognitive operation. The mechanisms receive an original graph data structure comprising nodes and edges between nodes and activity log information for nodes of the original graph data structure. The mechanisms identify a set of nodes in the original graph data structure having a predetermined pattern of activity in the activity log information, and a set of edges between these nodes. The mechanisms calculate an importance weight for each edge in the set of edges and modify the original graph data structure based on the calculated importance weights for the edges in the set of edges, to thereby generate a modified graph data structure. The mechanisms then perform a cognitive operation based on the modified graph data structure. The set of edges may comprise actual edges between the nodes and/or potential edges between the nodes. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system comprising a processor and a memory, for performing a cognitive operation, the method comprising: receiving, by the data processing system, an original graph data structure comprising nodes and edges between nodes; receiving, by the data processing system, activity log information for nodes of the original graph data structure; identifying, by the data processing system, a set of nodes in the original graph data structure having a predetermined pattern of activity in the activity log information, and a set of edges between these nodes; calculating, by the data processing system, an importance weight for each edge in the set of edges; modifying, by the data processing system, the original graph data structure based on the calculated importance weights for the edges in the set of edges, to thereby generate a modified graph data structure; and performing, by the data processing system, a cognitive operation based on the modified graph data structure, wherein the set of edges comprises at least one of actual edges between the nodes and potential edges between the nodes. 2. The method of claim 1, wherein modifying the original graph data structure to generate the modified graph data structure comprises at least one of removing one or more of the edges in the set of edges from the original graph data structure or changing an importance weight of an edge in the set of edges. 3. The method of claim 1, wherein the subset of nodes of the graph are nodes determined to be associated with one or more popular nodes identified by the predetermined pattern of activity, wherein a node is a popular node when an activity metric of the node consistently exceeds a threshold level of activity. 4. The method of claim 3, wherein the identified set of edges are popular-to-popular edges that connect a first popular node to a second popular node. 5. The method of claim 4, further comprising calculating, for each popular-to-popular edge in the set of edges, a correlation metric that correlates an activity pattern in the activity log information for the first popular node with an activity pattern in the activity log information for the second popular node of the popular-to-popular edge, and wherein modifying the original graph data structure based on the calculated importance weights for the edges in the set of edges comprises modifying the original graph data structure based on the calculated correlation metrics of each of the popular-to-popular edges in the set of edges. 6. The method of claim 5, wherein modifying the original graph data structure based on the calculated correlation metrics of each of the popular-to-popular edges in the set of edges comprises, for each of the popular-to-popular edges: determining if a correlation metric for the popular-to-popular edge satisfies a predetermined relationship with a predetermined correlation threshold value; and in response to determining that the correlation metric for the popular-to-popular edge does not satisfy the predetermined relationship, removing the popular-to-popular edge from the original graph data structure when generating the modified graph data structure based on the original graph data structure. 7. The method of claim 1, wherein modifying the original graph data structure based on the calculated importance weights for the edges in the set of edges comprises: calculating, for each edge in the set of edges, a correlation metric that correlates activity information for the nodes connected by the edge, based on the importance weight associated with the edge; determining, for each edge in the set of edges, whether a corresponding correlation metric of the edge satisfies a predetermined relationship with a predetermined correlation threshold value; and in response to determining that the correlation metric for the edge does not satisfy the predetermined relationship, removing the edge from the original graph data structure when modifying the original graph data structure to generate the modified graph data structure. 8. The method of claim 1, wherein the cognitive operation is at least one of a natural language question answering operation utilizing the modified graph data structure to identify related concepts in a corpus of information, or an information retrieval operation that retrieves information and ranks the retrieved information based on the modified graph data structure. 9. The method of claim 1, wherein the data processing system implements a parallel architecture having a plurality of processors, and wherein calculating the importance weight for each edge in the set of edges comprises distributing the calculations across the plurality of processors, and wherein modifying the original graph data structure comprises inputting, to a trimming processor in the data processing system, the importance weights calculated by the plurality of processors and modifying the original graph data structure to generate the modified graph data structure by removing edges in the set of edges from the original graph data structure that have importance weights that are below a predetermined threshold value. 10. The method of claim 1, wherein the nodes represent web pages of one or more web sites, and wherein the activity log information stores information regarding a number of page views of one or more web pages represented by the nodes. 11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to: receive an original graph data structure comprising nodes and edges between nodes; receive activity log information for nodes of the original graph data structure; identify, by the data processing system, a set of nodes in the original graph data structure having a predetermined pattern of activity in the activity log information, and a set of edges between these nodes; calculate an importance weight for each edge in the set of edges; modify the original graph data structure based on the calculated importance weights for the edges in the set of edges, to thereby generate a modified graph data structure; and perform a cognitive operation based on the modified graph data structure, wherein the set of edges comprises at least one of actual edges between the nodes and potential edges between the nodes. 12. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to modify the original graph data structure to generate the modified graph data structure at least by one of removing one or more of the edges in the set of edges from the original graph data structure or changing an importance weight of an edge in the set of edges. 13. The computer program product of claim 11, wherein the subset of nodes of the graph are nodes determined to be associated with one or more popular nodes identified by the predetermined pattern of activity, wherein a node is a popular node when an activity metric of the node consistently exceeds a threshold level of activity. 14. The computer program product of claim 13, wherein the identified set of edges are popular-to-popular edges that connect a first popular node to a second popular node. 15. The computer program product of claim 14, wherein the computer readable program further causes the data processing system to calculate, for each popular-to-popular edge in the set of edges, a correlation metric that correlates an activity pattern in the activity log information for the first popular node with an activity pattern in the activity log information for the second popular node of the popular-to-popular edge, and wherein the computer readable program further causes the data processing system to modify the original graph data structure based on the calculated importance weights for the edges in the set of edges at least by modifying the original graph data structure based on the calculated correlation metrics of each of the popular-to-popular edges in the set of edges. 16. The computer program product of claim 15, wherein the computer readable program further causes the data processing system to modify the original graph data structure based on the calculated correlation metrics of each of the popular-to-popular edges in the set of edges at least by, for each of the popular-to-popular edges: determining if a correlation metric for the popular-to-popular edge satisfies a predetermined relationship with a predetermined correlation threshold value; and in response to determining that the correlation metric for the popular-to-popular edge does not satisfy the predetermined relationship, removing the popular-to-popular edge from the original graph data structure when generating the modified graph data structure based on the original graph data structure. 17. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to modify the original graph data structure based on the calculated importance weights for the edges in the set of edges at least by: calculating, for each edge in the set of edges, a correlation metric that correlates activity information for the nodes connected by the edge, based on the importance weight associated with the edge; determining, for each edge in the set of edges, whether a corresponding correlation metric of the edge satisfies a predetermined relationship with a predetermined correlation threshold value; and in response to determining that the correlation metric for the edge does not satisfy the predetermined relationship, removing the edge from the original graph data structure when modifying the original graph data structure to generate the modified graph data structure. 18. The computer program product of claim 11, wherein the cognitive operation is at least one of a natural language question answering operation utilizing the modified graph data structure to identify related concepts in a corpus of information, or an information retrieval operation that retrieves information and ranks the retrieved information based on the modified graph data structure. 19. The computer program product of claim 11, wherein the nodes represent web pages of one or more web sites, and wherein the activity log information stores information regarding a number of page views of one or more web pages represented by the nodes. 20. An apparatus comprising: one or more processors; and a memory coupled to the one or more processors, wherein the memory comprises instructions which, when executed by the one or more processors, cause the one or more processors to: receive an original graph data structure comprising nodes and edges between nodes; receive activity log information for nodes of the original graph data structure; identify, by the data processing system, a set of nodes in the original graph data structure having a predetermined pattern of activity in the activity log information, and a set of edges between these nodes; calculate an importance weight for each edge in the set of edges; modify the original graph data structure based on the calculated importance weights for the edges in the set of edges, to thereby generate a modified graph data structure; and perform a cognitive operation based on the modified graph data structure, wherein the set of edges comprises at least one of actual edges between the nodes and potential edges between the nodes. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Mechanisms are provided for performing a cognitive operation. The mechanisms receive an original graph data structure comprising nodes and edges between nodes and activity log information for nodes of the original graph data structure. The mechanisms identify a set of nodes in the original graph data structure having a predetermined pattern of activity in the activity log information, and a set of edges between these nodes. The mechanisms calculate an importance weight for each edge in the set of edges and modify the original graph data structure based on the calculated importance weights for the edges in the set of edges, to thereby generate a modified graph data structure. The mechanisms then perform a cognitive operation based on the modified graph data structure. The set of edges may comprise actual edges between the nodes and/or potential edges between the nodes. |
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G06N5022 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Mechanisms are provided for performing a cognitive operation. The mechanisms receive an original graph data structure comprising nodes and edges between nodes and activity log information for nodes of the original graph data structure. The mechanisms identify a set of nodes in the original graph data structure having a predetermined pattern of activity in the activity log information, and a set of edges between these nodes. The mechanisms calculate an importance weight for each edge in the set of edges and modify the original graph data structure based on the calculated importance weights for the edges in the set of edges, to thereby generate a modified graph data structure. The mechanisms then perform a cognitive operation based on the modified graph data structure. The set of edges may comprise actual edges between the nodes and/or potential edges between the nodes. |
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A data processing apparatus that processes a spectral data item which stores, for each of a plurality of spectral components, an intensity value, includes a spectral component selecting unit and a classifier generating unit. The spectral component selecting unit is configured to select, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among the plurality of spectral components of the plurality of spectral data items. The classifier generating unit is configured to perform machine learning by using the plurality of machine-learning spectral components selected by the spectral component selecting unit and generate a classifier that classifies a spectral data item. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data processing apparatus that processes a spectral data item which stores, for each of a plurality of spectral components, an intensity value, comprising: a spectral component selecting unit configured to select, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among the plurality of spectral components of the plurality of spectral data items; and a classifier generating unit configured to perform machine learning by using the plurality of machine-learning spectral components selected by the spectral component selecting unit and generate a classifier that classifies a spectral data item. 2. The data processing apparatus according to claim 1, wherein the spectral component selecting unit selects the plurality of machine-learning spectral components in order of decreasing Mahalanobis distance. 3. The data processing apparatus according to claim 1, wherein the spectral component selecting unit selects the machine-learning spectral components in order of decreasing Mahalanobis distance separately for each of a plurality of combinations of the groups to be distinguished by the classifier. 4. The data processing apparatus according to claim 1, wherein the spectral component selecting unit selects the plurality of machine-learning spectral components finely at a part where the Mahalanobis distance is large and coarsely at a part where the Mahalanobis distance is small. 5. (canceled) 6. The data processing apparatus according to claim 1, wherein the spectral data items are spectral data items stored for respective pixels in image data. 7. The data processing apparatus according to claim 1, wherein the classifier generating unit performs, for each of the plurality of machine-learning spectral components, an intensity value averaging process in accordance with magnitude of a within-group variance of the plurality of spectral data items and performs machine learning. 8. The data processing apparatus according to claim 1, wherein the spectral data items are spectral data items including any one of spectral data items obtained by ultraviolet, visible, or infrared spectroscopy, spectral data items obtained by Raman spectroscopy, and mass spectral data items. 9. The data processing apparatus according to claim 1, wherein the spectral components are represented by a wave number or a mass-to-charge ratio. 10. The data processing apparatus according to claim 1, further comprising: a classifying unit configured to classify a spectral data item by using the classifier generated by the classifier generating unit. 11. The data processing apparatus according to claim 10, wherein two-dimensional image data is generated based on a classification result obtained by the classifying unit, the two-dimensional image data being data for distinguishably displaying pixels for which respective spectral data items are stored. 12-13. (canceled) 14. A sample information obtaining system comprising: the data processing apparatus according to claim 1; and a measuring unit configured to perform measurement on a sample to obtain the spectral data items. 15. The sample information obtaining system according to claim 14, wherein the measuring unit performs measurement on the basis of the machine-learning spectral components selected by the spectral component selecting unit to obtain the spectral data items. 16. A data processing method for processing a spectral data item which stores, for each of a plurality of spectral components, an intensity value, comprising: selecting, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among the plurality of spectral components of the plurality of spectral data items; and performing machine learning by using the plurality of machine-learning spectral components selected in the selecting, and generating a classifier that classifies a spectral data item. 17. The data processing method according to claim 16, further comprising: classifying a spectral data item by using the generated classifier. 18. (canceled) 19. A computer-readable storage medium storing a program causing a computer to execute a process, the process comprising: selecting, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among a plurality of spectral components of the plurality of spectral data items each storing, for each of the plurality of spectral components, an intensity value; and performing machine learning by using the plurality of machine-learning spectral components selected in the selecting and generating a classifier that classifies a spectral data item. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A data processing apparatus that processes a spectral data item which stores, for each of a plurality of spectral components, an intensity value, includes a spectral component selecting unit and a classifier generating unit. The spectral component selecting unit is configured to select, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among the plurality of spectral components of the plurality of spectral data items. The classifier generating unit is configured to perform machine learning by using the plurality of machine-learning spectral components selected by the spectral component selecting unit and generate a classifier that classifies a spectral data item. |
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G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A data processing apparatus that processes a spectral data item which stores, for each of a plurality of spectral components, an intensity value, includes a spectral component selecting unit and a classifier generating unit. The spectral component selecting unit is configured to select, based on a Mahalanobis distance between groups each composed of a plurality of spectral data items or a spectral shape difference between groups each composed of a plurality of spectral data items, a plurality of machine-learning spectral components from among the plurality of spectral components of the plurality of spectral data items. The classifier generating unit is configured to perform machine learning by using the plurality of machine-learning spectral components selected by the spectral component selecting unit and generate a classifier that classifies a spectral data item. |
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A method and associated systems for real-time analysis of a musical performance using analytics. A performance-analysis system receives feedback from which may be inferred an audience's reaction to the performance. This feedback may be derived from sensors embedded in instruments or microphones, from video-input devices that visually represent the audience's body language and facial expressions, and from performance ratings and natural-language comments submitted by audience members to a social-media network or performance-rating application. An analytics engine of the performance-analysis system uses methods of artificial intelligence to infer the audience's emotional state from the received feedback and to determine whether certain characteristics of the performance are undesirable. The system represents these inferences as a value of a performance index and represents the index value to the performers. The system may also make specific recommendations deemed likely to reduce undesirable performance characteristics during the remainder of the performance. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A performance-analysis system comprising a processor, a memory coupled to the processor, one or more sensors coupled to the processor and embedded into musical instruments used by performers of a musical performance, a network interface that connects the processor to a computer network, and a computer-readable hardware storage device coupled to the processor, the storage device containing program code configured to be run by the processor via the memory to implement a method for analysis of a musical performance using analytics, the method comprising: the processor electronically receiving feedback characterizing an ongoing musical performance by one or more performers; the processor inferring a characteristic of the performance by using an artificially intelligent analytics procedure to analyze the feedback, where the analytics procedure is a function of a repository of archived information about past performances by the one or more performers and of a knowledgebase from which the feedback may be given semantic meaning, and where the inferred characteristic is considered undesirable by the one or more performers; and the processor communicating the inferred characteristic to the one or more performers for the purpose of allowing the one or more performers to alter their ongoing performance in order to mitigate undesirability of the inferred characteristic. 2. The system of claim 1, where the feedback comprises an electronic record of the ongoing performance recorded by the sensors during the ongoing performance. 3. The system of claim 1, where the feedback comprises a performance rating submitted by an audience member through a personal communications device to an Internet-based service, and where the performance rating is received by the processor through the network interface. 4. The system of claim 1, where the feedback comprises a visual identification of involuntary behavior of the audience, and where the inferring comprises performing an analytics-based sentiment analysis upon the visual identification. 5. The system of claim 4, where the involuntary behavior comprises at least one audience member's body language and facial expressions. 6. The system of claim 1, where the feedback comprises: an electronic record of the ongoing performance recorded by the sensors during the ongoing performance, a set of performance ratings submitted by at least one audience member through a personal communications device to an Internet-based service, and a visual identification of at least one audience member's body language and facial expressions, and where the inferring comprises: performing an analytics-based sentiment analysis upon the visually identified body language and facial expressions, and computing a numeric performance index as a function of: a percent of the set of performance ratings that indicate an undesirable audience reaction, a percent of sentiments identified by the sentiment analysis that indicate an undesirable audience reaction, and a percent of notes played incorrectly by a performer of the one or more performers. 7. The system of claim 1, where the performance is a practice exercise, where the feedback comprises an electronic record of the ongoing performance recorded by the sensors during the ongoing performance, and where the characteristic is an inaccurate playing of a note of a musical score. 8. The system of claim 1, where the inferring further comprises a determination that a modification to the performance of at least one performer of the one or more performers during the remainder of the ongoing performance is likely to mitigate the undesirability of the characteristic, and where the communicating comprises recommending the modification to the at least one performer. 9. A method for analysis of a musical performance using analytics, the method comprising: a processor of a performance-analysis system electronically receiving feedback characterizing an ongoing musical performance by one or more performers; the processor inferring a characteristic of the performance by using an artificially intelligent analytics procedure to analyze the feedback, where the analytics procedure is a function of a repository of archived information about past performances by the one or more performers and of a knowledgebase from which the feedback may be given semantic meaning, where the inferred characteristic is considered undesirable by the one or more performers, and where the inferring further comprises a determination that a modification to the performance of at least one performer of the one or more performers during the remainder of the ongoing performance is likely to mitigate the undesirability of the characteristic; and the processor communicating the inferred characteristic to the one or more performers for the purpose of allowing the one or more performers to alter their ongoing performance in order to mitigate undesirability of the inferred characteristic, where the communicating comprises recommending the modification to the at least one performer. 10. The method of claim 9, where the feedback comprises an electronic record of the ongoing performance recorded during the ongoing performance by one or more sensors coupled to the processor and embedded into musical instruments used by performers. 11. The method of claim 9, where the feedback comprises a performance rating submitted by an audience member through a personal communications device to an Internet-based service, and where the performance rating is received by the processor through a network interface. 12. The method of claim 9, where the feedback comprises a visual identification of at least one audience member's body language and facial expressions, and where the inferring comprises performing an analytics-based sentiment analysis upon the visual identification. 13. The method of claim 9, where the feedback comprises: an electronic record of the ongoing performance recorded during the ongoing performance by one or more sensors coupled to the processor and embedded into musical instruments used by performers, a set of performance ratings submitted by at least one audience member through a personal communications device to an Internet-based service, and a visual identification of at least one audience member's body language and facial expressions, and where the inferring comprises: performing an analytics-based sentiment analysis upon the visually identified body language and facial expressions, and computing a numeric performance index as a function of: a percent of the set of performance ratings that indicate an undesirable audience reaction, a percent of sentiments identified by the sentiment analysis that indicate an undesirable audience reaction, and a percent of notes played incorrectly by a performer of the one or more performers. 14. The method of claim 9, where the performance is a practice exercise, where the feedback comprises an electronic record of the ongoing performance recorded by the sensors during the ongoing performance, and where the characteristic is an inaccurate playing of a note of a musical score. 15. The method of claim 9, further comprising providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable program code in the computer system, wherein the computer-readable program code in combination with the computer system is configured to implement the receiving, the inferring, and the communication. 16. A computer program product, comprising a computer-readable hardware storage device having a computer-readable program code stored therein, the program code configured to be executed by a performance-analysis system comprising a processor, a memory coupled to the processor, one or more sensors coupled to the processor and embedded into musical instruments used by performers of a musical performance, a network interface that connects the processor to a computer network, and a computer-readable hardware storage device coupled to the processor, the storage device containing program code configured to be run by the processor via the memory to implement a method for analysis of a musical performance using analytics, the method comprising: the processor electronically receiving feedback characterizing an ongoing musical performance by one or more performers; the processor inferring a characteristic of the performance by using an artificially intelligent analytics procedure to analyze the feedback, where the analytics procedure is a function of a repository of archived information about past performances by the one or more performers and of a knowledgebase from which the feedback may be given semantic meaning, and where the inferred characteristic is considered undesirable by the one or more performers, and where the inferring further comprises a determination that a modification to the performance of at least one performer of the one or more performers during the remainder of the ongoing performance is likely to mitigate the undesirability of the characteristic; and the processor communicating the inferred characteristic to the one or more performers for the purpose of allowing the one or more performers to alter their ongoing performance in order to mitigate undesirability of the inferred characteristic, where the communicating comprises recommending the modification to the at least one performer. 17. The computer program product of claim 16, where the feedback comprises an electronic record of the ongoing performance recorded during the ongoing performance by one or more sensors coupled to the processor and embedded into musical instruments used by performers. 18. The computer program product of claim 16, where the feedback comprises a performance rating submitted by an audience member through a personal communications device to an Internet-based service, and where the performance rating is received by the processor through a network interface. 19. The computer program product of claim 16, where the feedback comprises a visual identification of at least one audience member's body language and facial expressions, and where the inferring comprises performing an analytics-based sentiment analysis upon the visual identification. 20. The computer program product of claim 16, where the feedback comprises: an electronic record of the ongoing performance recorded during the ongoing performance by one or more sensors coupled to the processor and embedded into musical instruments used by performers, a set of performance ratings submitted by at least one audience member through a personal communications device to an Internet-based service, and a visual identification of at least one audience member's body language and facial expressions, and where the inferring comprises: performing an analytics-based sentiment analysis upon the visually identified body language and facial expressions, and computing a numeric performance index as a function of: a percent of the set of performance ratings that indicate an undesirable audience reaction, a percent of sentiments identified by the sentiment analysis that indicate an undesirable audience reaction, and a percent of notes played incorrectly by a performer of the one or more performers. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A method and associated systems for real-time analysis of a musical performance using analytics. A performance-analysis system receives feedback from which may be inferred an audience's reaction to the performance. This feedback may be derived from sensors embedded in instruments or microphones, from video-input devices that visually represent the audience's body language and facial expressions, and from performance ratings and natural-language comments submitted by audience members to a social-media network or performance-rating application. An analytics engine of the performance-analysis system uses methods of artificial intelligence to infer the audience's emotional state from the received feedback and to determine whether certain characteristics of the performance are undesirable. The system represents these inferences as a value of a performance index and represents the index value to the performers. The system may also make specific recommendations deemed likely to reduce undesirable performance characteristics during the remainder of the performance. |
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G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method and associated systems for real-time analysis of a musical performance using analytics. A performance-analysis system receives feedback from which may be inferred an audience's reaction to the performance. This feedback may be derived from sensors embedded in instruments or microphones, from video-input devices that visually represent the audience's body language and facial expressions, and from performance ratings and natural-language comments submitted by audience members to a social-media network or performance-rating application. An analytics engine of the performance-analysis system uses methods of artificial intelligence to infer the audience's emotional state from the received feedback and to determine whether certain characteristics of the performance are undesirable. The system represents these inferences as a value of a performance index and represents the index value to the performers. The system may also make specific recommendations deemed likely to reduce undesirable performance characteristics during the remainder of the performance. |
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A method for enhancing association rules including: performing an association rule algorithm to establish a list of established association rules, wherein the list of established association rules includes at least one antecedent item set, at least one consequent item set and at least one original confidence; performing minimization of a cost function to obtain vector(s) of the at least one antecedent item set and vector(s) of the at least one consequent item set according to the list of established association rules, wherein the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set correspond to the at least one antecedent item set and the at least one consequent item set; and establishing an enhanced association list according to the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for enhancing association rules, comprising: performing an association rule algorithm to establish a list of established association rules, wherein the list of established association rules comprises at least one antecedent item set, at least one consequent item set and at least one original confidence; performing minimization of a cost function to establish vector(s) of the at least one antecedent item set and vector(s) of the at least one consequent item set according to the list of established association rules, wherein the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set correspond to the at least one antecedent item set and the at least one consequent item set, respectively; and establishing a list of enhanced association rules according to the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set. 2. The enhancing method according to claim 1, further comprising: combining the list of established association rules and the list of enhanced association rules to establish a combined list of association rules. 3. The enhancing method according to claim 2, further comprising: generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set; obtaining at least one consequent item set corresponding to the at least one subset from the combined list of association rules according to the at least one subset; and sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and then using at least one part of the at least one consequent item set corresponding to the at least one subset as at least one newly added consequent item set corresponding to the at least one antecedent item set according to a sort order of the at least one sorted consequent item set. 4. The enhancing method according to claim 2, further comprising: determining, according to a constraint of the at least one consequent item set, whether one of the list of established association rules, the list of enhanced association rules and the combined list of association rules comprises at least one consequent item set corresponding to the at least one antecedent item set and matching the constraint; if yes, accessing the at least one consequent item set corresponding to the at least one antecedent item set; if no, generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set, obtaining at least one consequent item set corresponding to the at least one subset from the combined list of association rules according to the at least one subset, and sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and using at least one part of the at least one consequent item set corresponding to the at least one subset as at least one newly added consequent item set corresponding to the at least one antecedent item set according to the sort order. 5. The enhancing method according to claim 1, wherein the step of performing the association rule algorithm to obtain the list of established association rules comprises: accessing a plurality of transactions from a source database to obtain a training set; analyzing the training set to obtain the at least one antecedent item set, the at least one consequent item set, and a corresponding original confidence of each of the at least one antecedent item set and its corresponding consequent item set; and establishing the list of established association rules according to the at least one antecedent item set, the at least one consequent item set and the at least one original confidence. 6. The enhancing method according to claim 1, wherein the step of obtaining the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set comprises: (a) performing vector initialization, and then performing a prediction function to obtain a predictive confidence according to at least one initialized vector(s) of antecedent item set and the at least one initialized vector(s) of the at least one consequent item set; (b) determining whether a sum of squared errors between a current predictive confidence and the original confidence is greater than a tolerance value; if no, performing step (c); if yes, performing step (d); (c) using a currently obtained vector(s) of the at least one antecedent item set and a currently obtained vector(s) of the at least one consequent item set as the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set in the list of enhanced association rules; (d) adjusting the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set to re-perform the prediction function to obtain an updated predictive confidence, calculating the sum of squared errors between the updated predictive confidence and a corresponding original confidence, and performing step (b). 7. The enhancing method according to claim 1, further comprising: generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set; obtaining at least one consequent item set corresponding to the at least one subset from at least one of the list of established association rules and the list of enhanced association rules according to the at least one subset; and sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and then using at least one part of the at least one consequent item set corresponding to the at least one subset as at least one newly added consequent item set corresponding to the at least one antecedent item set according to the sort order. 8. An apparatus for enhancing association rules, comprising: a module for generating established association rules used for establishing a list of established association rules, wherein the list of established association rules comprises at least one antecedent item set, at least one consequent item set and at least one original confidence; and a module for enhancing association rules used for performing minimization of a cost function to establish vector(s) of the at least one antecedent item set and vector(s) of the at least one consequent item set, wherein the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set correspond to the at least one antecedent item set and the at least one consequent item set, respectively. 9. The enhancing apparatus according to claim 8, further comprising: a module for combining association rules used for combining the list of established association rules and the list of enhanced association rules to establish a combined list of association rules. 10. The enhancing apparatus according to claim 9, further comprising: a module for generating subsets of antecedent item set used for generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set; a module for generating consequent item sets used for obtaining at least one consequent item set corresponding to the at least one subset from the combined list of association rules according to the at least one subset; and a module for sorting and integrating consequent item sets used for sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and then using at least one part of the at least one consequent item set corresponding to the at least one subset as at least one newly added consequent item set corresponding to the at least one antecedent item set according to a sort order of the at least one sorted consequent item set. 11. The enhancing apparatus according to claim 9, further comprising: determining, according to a constraint of the at least one consequent item set, whether one of the list of established association rules, the list of enhanced association rules and the combined list of association rules comprises at least one consequent item set corresponding to the at least one antecedent item set and matching the constraint; if yes, accessing the at least one consequent item set corresponding to the at least one antecedent item set; if no, generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set, obtaining at least one consequent item set corresponding to the at least one subset from the combined list of association rules according to the at least one subset, and sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and at least one part of the at least one consequent item set corresponding to the at least one subset is used as at least one newly added consequent item set corresponding to the at least one antecedent item set according to the sort order. 12. The enhancing apparatus according to claim 8, wherein the module for generating established association rules accesses a plurality of transactions from a source database to obtain a training set, analyzes the training set to obtain the at least one antecedent item set, the at least one consequent item set, and a corresponding original confidence of each of the at least one antecedent item set and its corresponding consequent item set, and establishes the list of established association rules according to the at least one antecedent item set, the at least one consequent item set and the at least one original confidence. 13. The enhancing apparatus according to claim 8, wherein the module for enhancing association rules further is used for generating the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set by: (a) performing vector initialization and then performing a prediction function to obtain a predictive confidence according to at least one initialized vector(s) of antecedent item set and the at least one initialized vector(s) of the at least one consequent item set; (b) determining whether a sum of squared errors between a current predictive confidence and the original confidence is greater than a tolerance value; if no, performing step (c); if yes, performing step (d); (c) using a currently obtained vector(s) of the at least one antecedent item set and a currently obtained vector(s) of the at least one consequent item set as the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set used in the list of enhanced association rules; (d) adjusting the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set to re-perform the prediction function to obtain an updated predictive confidence and calculating the sum of squared errors between the updated predictive confidence and a corresponding original confidence; and performing step (b). 14. The enhancing apparatus according to claim 8, further comprising: a module for generating subsets of antecedent item set used for generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set; a module for generating consequent item sets for obtaining at least one consequent item set corresponding to the at least one subset from at least one of the list of established association rules and the list of enhanced association rules according to the at least one subset; and a module for sorting and integrating consequent item sets used for sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and then using at least one part of the at least one consequent item set corresponding to the at least one subset as at least one newly added consequent item set corresponding to the at least one antecedent item set according to a sort order of the at least one sorted consequent item set. 15. A computer readable medium having a software program stored therein, wherein when the software program is performed, an electronic apparatus with a controller performs a method for enhancing association rules, the method comprising: performing an association rule algorithm to establish a list of established association rules, wherein the list of established association rules comprises at least one antecedent item set, at least one consequent item set and at least one original confidence; performing minimization of a cost function to establish vector(s) of the at least one antecedent item set and vector(s) of the at least one consequent item set according to the list of established association rules, wherein the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set correspond to the at least one antecedent item set and the at least one consequent item set, respectively; and generating a list of enhanced association rules according to the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set. 16. The computer readable medium according to claim 15, wherein the enhancing method further comprises: combining the list of established association rules and the list of enhanced association rules to establish a combined list of association rules. 17. The computer readable medium according to claim 16, the enhancing method further comprises: generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set; obtaining at least one consequent item set corresponding to the at least one subset from the combined list of association rules according to the at least one subset; and sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and then using at least one part of the at least one consequent item set corresponding to the at least one subset as at least one newly added consequent item set corresponding to the at least one antecedent item set according to a sort order of the at least one sorted consequent item set. 18. The computer readable medium according to claim 16, wherein the enhancing method further comprises: determining, according to a constraint of the at least one consequent item set, whether one of the list of established association rules, the list of enhanced association rules and the combined list of association rules comprises at least one consequent item set corresponding to the at least one antecedent item set and matching the constraint; if yes, accessing the at least one consequent item set corresponding to the at least one antecedent item set; if no, generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set, obtaining at least one consequent item set corresponding to the at least one subset from the combined list of association rules according to the at least one subset, and sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and at least one part of the at least one consequent item set corresponding to the at least one subset is used as at least one newly added consequent item set corresponding to the at least one antecedent item set according to the sort order. 19. The computer readable medium according to claim 15, wherein in the enhancing method, the step of performing the association rule algorithm to obtain the list of established association rules comprises: accessing a plurality of transactions from a source database to obtain a training set; analyzing the training set to obtain the at least one antecedent item set, the at least one consequent item set, and a corresponding original confidence of each of the at least one antecedent item set and its corresponding consequent item set; and establishing the list of established association rules according to the at least one antecedent item set, the at least one consequent item set and the at least one original confidence. 20. The computer readable medium according to claim 15, wherein in the enhancing method, the step of obtaining the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set comprises: (a) performing vector initialization and then performing a prediction function to obtain a predictive confidence according to at least one initialized vector(s) of antecedent item set and the at least one initialized vector(s) of the at least one consequent item set; (b) determining whether a sum of squared errors between a current predictive confidence and the original confidence is greater than a tolerance value; if no, performing step (c); if yes, performing step (d); (c) using a current obtained vector(s) of the at least one antecedent item set and a current obtained vector(s) of the at least one consequent item set as the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set used in the list of enhanced association rules; (d) adjusting the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set to re-perform the prediction function to obtain an updated predictive confidence and calculating the sum of squared errors between the updated predictive confidence and a corresponding original confidence; and performing step (b). 21. The computer readable medium according to claim 15, the enhancing method further comprises: generating at least one subset of the at least one antecedent item set according to the at least one antecedent item set; obtaining at least one consequent item set corresponding to the at least one subset from at least one of the list of established association rules and the list of enhanced association rules according to the at least one subset; and sorting the at least one consequent item set corresponding to the at least one subset according to a predetermined rule, and then using at least one part of the at least one consequent item set corresponding to the at least one subset as at least one newly added consequent item set corresponding to the at least one antecedent item set according to the sort order. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A method for enhancing association rules including: performing an association rule algorithm to establish a list of established association rules, wherein the list of established association rules includes at least one antecedent item set, at least one consequent item set and at least one original confidence; performing minimization of a cost function to obtain vector(s) of the at least one antecedent item set and vector(s) of the at least one consequent item set according to the list of established association rules, wherein the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set correspond to the at least one antecedent item set and the at least one consequent item set; and establishing an enhanced association list according to the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set. |
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G06N5046 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method for enhancing association rules including: performing an association rule algorithm to establish a list of established association rules, wherein the list of established association rules includes at least one antecedent item set, at least one consequent item set and at least one original confidence; performing minimization of a cost function to obtain vector(s) of the at least one antecedent item set and vector(s) of the at least one consequent item set according to the list of established association rules, wherein the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set correspond to the at least one antecedent item set and the at least one consequent item set; and establishing an enhanced association list according to the vector(s) of the at least one antecedent item set and the vector(s) of the at least one consequent item set. |
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Systems, devices, articles, methods, and techniques for advancing quantum computing by removing unwanted interactions in one or more quantum processor. One approach includes creating an updated plurality of programmable parameters based at least in part on a received value for the characteristic magnetic susceptibility of the qubit in the at least one quantum processor, and returning the updated plurality of programmable parameters. Examples programmable parameters include local biases, and coupling values characterizing the problem Hamilton. Also, for example, a quantum processor may be summarized as including a first loop of superconducting material, a first compound Josephson junction interrupting the first loop of superconducting material, a first coupler inductively coupled to the first loop of superconducting material, a second coupler inductively coupled to the first loop of superconducting material, and a second loop of superconducting material proximally placed to the first loop of superconducting material inductively coupled to the first coupler and the second coupler. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computational system comprising: at least one quantum processor comprising: a plurality of qubits; a plurality of couplers, wherein each coupler provides controllable communicative coupling between a respective pair qubits of the plurality of qubits; and a plurality of magnetic susceptibility compensators, wherein each magnetic susceptibility compensator is proximate to a respective qubit of the plurality of qubits; at least one processor-based device communicatively coupled to the at least one quantum processor; and at least one non-transitory computer-readable storage medium communicatively coupled to the at least one processor-based device and which stores processor-executable instructions, which when executed causes at least one processor-based device to: initialize the quantum processor to an initial state; cause the quantum processor to evolve from the initial state toward a final state; and cause the quantum processor to add a flux bias to the plurality of magnetic susceptibility compensators. 2. The system of claim 1 further comprising: a global signal line, wherein the global signal line communicably couples to the plurality of magnetic susceptibility compensators. 3. The system of claim 2 wherein the processor-executable instructions when executed further cause the at least one processor to: cause the quantum processor to add a signal on the global signal line. 4. The system of claim 1 further comprising: a plurality of inductance tuners, wherein each inductance tuner interrupts a circuit in each magnetic susceptibility compensator in the plurality of magnetic susceptibility compensators. 5. The system of claim 4 wherein the processor-executable instructions when executed further cause the at least one processor to: cause the quantum processor to tune each of the plurality of inductance tuners to a respective value such that each circuit in each magnetic susceptibility compensator in the plurality of magnetic susceptibility compensators has a respective magnetic susceptibility opposite to the magnetic susceptibility of each qubit associated with each magnetic susceptibility compensator in the plurality of magnetic susceptibility compensators. 6. A computational method for operating a hybrid computer that comprises both a quantum processor and at least one processor-based device communicatively coupled to one another, the quantum processor comprising a plurality of qubits, a plurality of coupling devices, wherein each coupling device provides controllable communicative coupling between two of the plurality of qubits, and a plurality of magnetic susceptibility compensators, wherein each magnetic susceptibility compensator is proximate to a respective qubit of the plurality of qubits, the method comprising: initializing the quantum processor to an initial state; causing the quantum processor to evolve from the initial state toward a final state; and causing the quantum processor to add a flux bias to the plurality of magnetic susceptibility compensators. 7. The method of claim 6 further comprising: reading out states for the qubits in plurality of qubits of the quantum processor. 8. The method of claim 6 wherein the hybrid computer further comprises a global signal line, wherein the global signal line communicably couples to the plurality of magnetic susceptibility compensators; and the method further comprising: adding a signal on the global signal line. 9. The method of claim 6 wherein the hybrid computer further comprises a plurality of inductance tuners, wherein each inductance tuner interrupts a circuit in each magnetic susceptibility compensator in the plurality of magnetic susceptibility compensators; and the method further comprising: tuning each of the plurality of inductance tuners to a respective value such that each circuit in each magnetic susceptibility compensator in the plurality of magnetic susceptibility compensators has a respective magnetic susceptibility opposite to the magnetic susceptibility of each qubit associated with each magnetic susceptibility compensator in the plurality of magnetic susceptibility compensators. 10. A system for use in quantum processing, comprising: at least one non-transitory processor-readable medium that stores at least one of processor executable instructions or data; and at least one processor communicatively coupled to the at least one non-transitory processor-readable medium, and which, in response to execution of the at least one of processor executable instructions or data: receive a plurality of programmable parameters for at least one quantum processor, the programmable parameters which characterize a problem Hamilton; receive a value for a characteristic mutual inductance of antiferromagnetic coupling for the at least one quantum processor receive a value for a characteristic magnetic susceptibility of a qubit in the at least one quantum processor; create an updated plurality of programmable parameters based at least in part on the received value for the characteristic magnetic susceptibility of the qubit in the at least one quantum processor; and return the updated plurality of programmable parameters. 11. The system of claim 10 wherein the plurality of programmable parameters for the at least one quantum processor comprises: a plurality of local biases and a plurality of coupling values characterizing the problem Hamilton. 12. The system of claim 10 wherein the plurality of programmable parameters for the at least quantum processor comprises: a plurality of local biases; and wherein the processor-executable instructions when executed further cause the at least one processor to: construct a correction matrix; solve a linear system including a first vector, corresponding to a plurality of local biases, equal to the correction matrix right multiplied by a second vector, corresponding to a plurality of updated local biases, for the second vector; and return the plurality of updated local biases. 13. The system of claim 12 wherein: the correction matrix is symmetric; the correction matrix includes a plurality of diagonal entries and the diagonal entries are one; and the correction matrix includes a plurality of off-diagonal entries and each off-diagonal entry corresponds to a respective coupling in the problem Hamiltonian and is a product of: a respective coupling value of a plurality of coupling values, the value for the characteristic mutual inductance of antiferromagnetic coupling, and the value for the characteristic magnetic susceptibility of a qubit. 14. The system of claim 10 wherein the plurality of programmable parameters for the at least quantum processor comprises: a plurality of coupling values; and wherein the processor-executable instructions when executed further cause the at least one processor to: receive a mapping of a plurality of logical qubits defined on the at least quantum processor, wherein each logical qubit in the plurality of logical qubits includes a plurality of physical qubits, and a plurality of intra-logical qubit coupler; update a coupling value for an extra-logical qubit coupler to a logical qubit in the plurality of logical qubits; and return the updated coupling value for the extra-logical qubit coupler. 15. The system of claim 10 further comprising: at least one quantum processor comprising: a plurality of qubits; a plurality of couplers, wherein each coupler provides controllable communicative coupling between a respective pair of the plurality of qubits; a programming sub-system; and an evolution sub-system. 16. The system of claim 15 wherein the processor-executable instructions when executed further cause the at least one processor to: initialize the quantum processor, via the programming sub-system, to an initial state; and cause, via the evolution sub-system, the quantum processor to evolve from the initial state toward a final state characterized by the problem Hamiltonian. 17. A computational method comprising: receiving a plurality of programmable parameters for at least one quantum processor, the programmable parameters which characterize a problem Hamilton; receiving a value for a characteristic mutual inductance of antiferromagnetic coupling for the at least one quantum processor receiving a value for a characteristic magnetic susceptibility of a qubit in the at least one quantum processor; creating an updated plurality of programmable parameters based at least in part on the received value for the characteristic magnetic susceptibility of the qubit in the at least one quantum processor; and returning the updated plurality of programmable parameters. 18. The method of claim 17 wherein the plurality of programmable parameters for the at least quantum processor comprises a plurality of local biases; and the method further comprising: constructing a correction matrix; solving a linear system where the linear system includes a first vector, corresponding to a plurality of local biases, equal to the correction matrix right multiplied by a second vector, corresponding to a plurality of updated local biases, for the second vector; and returning the plurality of updated local biases. 19. The method of claim 18 wherein: the correction matrix is symmetric; the correction matrix includes a plurality of diagonal entries and the diagonal entries are one; and the correction matrix includes a plurality of off-diagonal entries and each off-diagonal entry corresponds to a respective coupling in the problem Hamiltonian and is a product of: a respective coupling value of a plurality of coupling values, the value for the characteristic mutual inductance of antiferromagnetic coupling, and the value for the characteristic magnetic susceptibility of a qubit. 20. The method of claim 17 wherein the plurality of programmable parameters for the at least quantum processor comprises a plurality of coupling values, and the method further comprising: receiving a mapping of a plurality of logical qubits defined on the at least quantum processor, wherein each logical qubit in the plurality of logical qubits includes a plurality of physical qubits, and a plurality of intra-logical qubit coupler; updating a coupling value for an extra-logical qubit coupler to a logical qubit in the plurality of logical qubits; and returning the updated coupling value for the extra-logical qubit coupler. 21. The method of claim 20 further comprising forming the updated coupling value for the extra-logical qubit coupler from the coupling value minus a correction term, and forming the correction term from a product, wherein the product includes: the value for the characteristic mutual inductance of antiferromagnetic coupling, the value for the characteristic magnetic susceptibility of a qubit, and a sum over the product of the coupling value for the extra-logical qubit coupler, and a plurality of inter-logical qubit coupling values. 22. A quantum processor, comprising: a first loop of superconducting material, that superconducts below a critical temperature; a first compound Josephson junction interrupting the first loop of superconducting material; a first coupler inductively coupled to the first loop of superconducting material; a second coupler inductively coupled to the first loop of superconducting material; and a second loop of superconducting material, that superconducts below a critical temperature, proximally placed to the first loop of superconducting material inductively coupled to the first coupler, and inductively coupled to the second coupler. 23. The quantum processor of claim 22 further comprising: a tunable inductance interrupting the second loop of superconducting material. 24. The quantum processor of claim 22 further comprising: a global signal line inductively coupled to the second loop of superconducting material. 25. The quantum processor of claim 22 wherein the global signal line inductively coupled to the first loop of superconducting material. 26. The quantum processor of claim 25 wherein: the global signal line inductively coupled to the second loop of superconducting material has a first mutual inductance value; the global signal line inductively coupled to the second loop of superconducting material has a second mutual inductance value associated with; the second loop of superconducting material inductively coupled to the first coupler has a third mutual inductance value; the first coupler inductively coupled to the first loop of superconducting material has a fourth mutual inductance value; and wherein a first ratio of the first mutual inductance value to the second mutual inductance value equals a second ratio of the third mutual inductance value to the fourth mutual inductance value. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems, devices, articles, methods, and techniques for advancing quantum computing by removing unwanted interactions in one or more quantum processor. One approach includes creating an updated plurality of programmable parameters based at least in part on a received value for the characteristic magnetic susceptibility of the qubit in the at least one quantum processor, and returning the updated plurality of programmable parameters. Examples programmable parameters include local biases, and coupling values characterizing the problem Hamilton. Also, for example, a quantum processor may be summarized as including a first loop of superconducting material, a first compound Josephson junction interrupting the first loop of superconducting material, a first coupler inductively coupled to the first loop of superconducting material, a second coupler inductively coupled to the first loop of superconducting material, and a second loop of superconducting material proximally placed to the first loop of superconducting material inductively coupled to the first coupler and the second coupler. |
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G06N99002 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems, devices, articles, methods, and techniques for advancing quantum computing by removing unwanted interactions in one or more quantum processor. One approach includes creating an updated plurality of programmable parameters based at least in part on a received value for the characteristic magnetic susceptibility of the qubit in the at least one quantum processor, and returning the updated plurality of programmable parameters. Examples programmable parameters include local biases, and coupling values characterizing the problem Hamilton. Also, for example, a quantum processor may be summarized as including a first loop of superconducting material, a first compound Josephson junction interrupting the first loop of superconducting material, a first coupler inductively coupled to the first loop of superconducting material, a second coupler inductively coupled to the first loop of superconducting material, and a second loop of superconducting material proximally placed to the first loop of superconducting material inductively coupled to the first coupler and the second coupler. |
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A cognitive information processing system comprising: a cognitive inference and learning system coupled to receive data from a plurality of data sources and to provide insights to a destination, the cognitive inference and learning system comprising a first interface, the first interface providing the data from the plurality of data sources to the cognitive interface and learning system, and, the cognitive inference and learning system comprising a second interface, the second interface providing the cognitively processed insights to the destination. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A cognitive information processing system comprising: a cognitive inference and learning system coupled to receive data from a plurality of data sources and to provide insights to a destination, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; the cognitive inference and learning system comprising a first interface, the first interface providing the data from the plurality of data sources to the cognitive interface and learning system, and, the cognitive inference and learning system comprising a second interface, the second interface providing the cognitively processed insights to the destination. 2. The cognitive information processing system of claim 1, wherein: the first interface comprises a sourcing agent application program interface (API). 3. The cognitive information processing system of claim 1, wherein: the second interface comprises a destination agent application program interface (API). 4. The cognitive information processing system of claim 1, further comprising: a third interface, the third interface comprises a cognitive applications application program interface (API). 5. The cognitive information processing system of claim 4, wherein: the third interface comprises a project and dataset agent application program interface (API), the project and dataset agent application program interface (API) enabling management of data and metadata associated with a cognitive insight project and a user account. 6. The cognitive information processing system of claim 4, wherein: the third interface comprises a cognitive search API, the cognitive search API uses natural language processes to access predetermined outputs from a cognitive graph. 7. The cognitive information processing system of claim 4, wherein: the third interface comprises a cognitive insight API, the cognitive insight API enabling configuration of an insight/learning operation. 8. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: processing data from a plurality of data sources to provide cognitively processed insights via a cognitive inference and learning system, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; receiving the data from the plurality of data sources to the cognitive interface and learning system via a first interface, and, providing the cognitively processed insights to a destination via a second interface. 9. The non-transitory, computer-readable storage medium of claim 8, wherein: the first interface comprises a sourcing agent application program interface (API). 10. The non-transitory, computer-readable storage medium of claim 8, wherein: the second interface comprises a destination agent application program interface (API). 11. The non-transitory, computer-readable storage medium of claim 8, wherein the instructions executable by the processor further comprise instructions for: interacting with at least one cognitive application via a third interface, the third interface comprising a cognitive applications application program interface (API). 12. The non-transitory, computer-readable storage medium of claim 11, wherein the instructions executable by the processor further comprise instructions for: the third interface comprises a project and dataset agent application program interface (API), the project and dataset agent application program interface (API) enabling management of data and metadata associated with a cognitive insight project and a user account. 13. The non-transitory, computer-readable storage medium of claim 11, wherein: the third interface comprises a cognitive search API, the cognitive search API uses natural language processes to access predetermined outputs from a cognitive graph. 14. The non-transitory, computer-readable storage medium of claim 11, wherein: the third interface comprises a cognitive insight API, the cognitive insight API enabling configuration of an insight/learning operation. 15. The non-transitory, computer-readable storage medium of claim 8, wherein the computer executable instructions are deployable to a client system from a server system at a remote location. 16. The non-transitory, computer-readable storage medium of claim 7, wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: A cognitive information processing system comprising: a cognitive inference and learning system coupled to receive data from a plurality of data sources and to provide insights to a destination, the cognitive inference and learning system comprising a first interface, the first interface providing the data from the plurality of data sources to the cognitive interface and learning system, and, the cognitive inference and learning system comprising a second interface, the second interface providing the cognitively processed insights to the destination. |
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G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A cognitive information processing system comprising: a cognitive inference and learning system coupled to receive data from a plurality of data sources and to provide insights to a destination, the cognitive inference and learning system comprising a first interface, the first interface providing the data from the plurality of data sources to the cognitive interface and learning system, and, the cognitive inference and learning system comprising a second interface, the second interface providing the cognitively processed insights to the destination. |
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Mechanisms are provided for monitoring quality and correctness of content in communications handled by a vendor. The mechanisms sample a set of communications handled by the vendor to generate a sample set of communications and extract content from the sample set of communications. The mechanisms compare the extracted content with expected content of communications handled by the vendor and analyze the extracted content and the expected content to thereby identify differences between the extracted content and the expected content based on results of the analysis. In addition, the mechanisms determine a level of significance of the differences and generate a notification of whether or not to modify the communications, or an operation of the vendor, based on the determined level of significance of differences. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system comprising a processor and a memory, for monitoring quality and correctness of content in communications handled by a vendor, comprising: sampling, by the data processing system, a set of communications handled by the vendor to generate a sample set of communications; extracting, by the data processing system, content from the sample set of communications; comparing, by the data processing system, the extracted content with expected content of communications handled by the vendor; analyzing, by the data processing system, the extracted content and the expected content thereby identifying differences between the extracted content and the expected content based on results of the analysis; determining, by the data processing system, a level of significance of the differences; and generating, by the data processing system, a notification of whether or not to modify the communications, or an operation of the vendor, based on the determined level of significance of differences. 2. The method of claim 1, further comprising: extracting, by the data processing system, one or more quality characteristics of the sample set of communications; and determining, by the data processing system, a level of quality of the sample set of communications based on the extracted one or more quality characteristics, wherein generating the notification of whether or not to modify the communications, or an operation of the vendor, is further based on the level of quality. 3. The method of claim 2, wherein the one or more quality characteristics of the sample set of communications comprises at least one of a speed of transmission of the communications, dropped data packets associated with the communications, audio output quality features of the communications, a number of confirmations of receipt received in response to the communications, or a number of responses received to the communications. 4. The method of claim 2, wherein determining a level of significance of the differences comprises: identifying discrepancies between at least one of terms, phrases, tokens of text, or portions of graphics in communications of the sample set of communications in comparison to required terms, phrases, tokens of text, or portions of graphics specified in the expected content; generating, for each communication in the sample set of communications, an accuracy measure value based on a total number of discrepancies; and determining a level of significance of the differences based on the accuracy measure values for the communications in the sample set of communications. 5. The method of claim 4, wherein generating, for each communication in the sample set of communications, an accuracy measure value based on the total number of discrepancies comprises weighting discrepancies in accordance with a type of a portion of content where the discrepancies are found such that a different weight is applied to discrepancies in at least two different types of portions of content. 6. The method of claim 1, further comprising: analyzing, by the data processing system, patient electronic medical records of a plurality of patients to identify, for each patient in the plurality of patients, whether or not the patient is non-compliant with an associated patient care plan; determining, by the data processing system, a mode of communication to use to communicate with patients determined to be non-compliant with their associated patient care plan; and sending, by a vendor system associated with the vendor, the set of communications to patients in the plurality of patients based on the determined mode of communication. 7. The method of claim 6, wherein the data processing system is a patient care plan creation and monitoring (PCPCM) system provided by a first provider, the vendor system is a sub-system associated with the PCPCM system, and the vendor is a part of an organization associated with the first provider. 8. The method of claim 6, wherein the data processing system is a patient care plan creation and monitoring (PCPCM) system provided by a first provider, the vendor is a third party vendor different from the first provider, and the vendor system is a computing system specifically configured to conduct communications with patients using the determined mode of communication. 9. The method of claim 1, wherein the communications are at least one of textual communications, graphical communications, or audible communications. 10. The method of claim 1, wherein the notification indicates the level of significance of the discrepancies and an identification of a corrective action to take to correct the discrepancies. 11. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to: sample a set of communications handled by the vendor to generate a sample set of communications; extract content from the sample set of communications; compare the extracted content with expected content of communications handled by the vendor; analyze the extracted content and the expected content thereby identifying differences between the extracted content and the expected content based on results of the analysis; determine a level of significance of the differences; and generate a notification of whether or not to modify the communications, or an operation of the vendor, based on the determined level of significance of differences. 12. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to: extract one or more quality characteristics of the sample set of communications; and determine a level of quality of the sample set of communications based on the extracted one or more quality characteristics, wherein generating the notification of whether or not to modify the communications, or an operation of the vendor, is further based on the level of quality. 13. The computer program product of claim 12, wherein the one or more quality characteristics of the sample set of communications comprises at least one of a speed of transmission of the communications, dropped data packets associated with the communications, audio output quality features of the communications, a number of confirmations of receipt received in response to the communications, or a number of responses received to the communications. 14. The computer program product of claim 12, the computer readable program further causes the data processing system to determine a level of significance of the differences at least by: identifying discrepancies between at least one of terms, phrases, tokens of text, or portions of graphics in communications of the sample set of communications in comparison to required terms, phrases, tokens of text, or portions of graphics specified in the expected content; generating, for each communication in the sample set of communications, an accuracy measure value based on a total number of discrepancies; and determining a level of significance of the differences based on the accuracy measure values for the communications in the sample set of communications. 15. The computer program product of claim 14, wherein the computer readable program further causes the data processing system to generate, for each communication in the sample set of communications, an accuracy measure value based on the total number of discrepancies at least by weighting discrepancies in accordance with a type of a portion of content where the discrepancies are found such that a different weight is applied to discrepancies in at least two different types of portions of content. 16. The computer program product of claim 11, wherein the computer readable program further causes the data processing system to: analyze patient electronic medical records of a plurality of patients to identify, for each patient in the plurality of patients, whether or not the patient is non-compliant with an associated patient care plan; determine a mode of communication to use to communicate with patients determined to be non-compliant with their associated patient care plan; and send, by a vendor system associated with the vendor, the set of communications to patients in the plurality of patients based on the determined mode of communication. 17. The computer program product of claim 16, wherein the data processing system is a patient care plan creation and monitoring (PCPCM) system provided by a first provider, the vendor system is a sub-system associated with the PCPCM system, and the vendor is a part of an organization associated with the first provider. 18. The computer program product of claim 16, wherein the data processing system is a patient care plan creation and monitoring (PCPCM) system provided by a first provider, the vendor is a third party vendor different from the first provider, and the vendor system is a computing system specifically configured to conduct communications with patients using the determined mode of communication. 19. The computer program product of claim 11, wherein the communications are at least one of textual communications, graphical communications, or audible communications. 20. An apparatus comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: sample a set of communications handled by the vendor to generate a sample set of communications; extract content from the sample set of communications; compare the extracted content with expected content of communications handled by the vendor; analyze the extracted content and the expected content thereby identifying differences between the extracted content and the expected content based on results of the analysis; determine a level of significance of the differences; and generate a notification of whether or not to modify the communications, or an operation of the vendor, based on the determined level of significance of differences. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Mechanisms are provided for monitoring quality and correctness of content in communications handled by a vendor. The mechanisms sample a set of communications handled by the vendor to generate a sample set of communications and extract content from the sample set of communications. The mechanisms compare the extracted content with expected content of communications handled by the vendor and analyze the extracted content and the expected content to thereby identify differences between the extracted content and the expected content based on results of the analysis. In addition, the mechanisms determine a level of significance of the differences and generate a notification of whether or not to modify the communications, or an operation of the vendor, based on the determined level of significance of differences. |
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G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Mechanisms are provided for monitoring quality and correctness of content in communications handled by a vendor. The mechanisms sample a set of communications handled by the vendor to generate a sample set of communications and extract content from the sample set of communications. The mechanisms compare the extracted content with expected content of communications handled by the vendor and analyze the extracted content and the expected content to thereby identify differences between the extracted content and the expected content based on results of the analysis. In addition, the mechanisms determine a level of significance of the differences and generate a notification of whether or not to modify the communications, or an operation of the vendor, based on the determined level of significance of differences. |
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Identifying relevant content for data gathered from real time communications includes obtaining conversational data from a real time communication, identifying contextual data with at least one contextual data source relevant to the real time communication, and inferring a meaning of the conversational data based on the contextual data. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for identifying relevant content for data gathered from real time communications, the method comprising: obtaining conversational data from a real time communication; identifying contextual data with at least one contextual data source relevant to said real time communication; learning patterns and relationships from said conversational data based on said contextual data from an analytical engine; and inferring a meaning of said conversational data based on said contextual data. 2. The method of claim 1, wherein said conversational data includes information from text-based messaging sources, voice sources, graphic sources, video sources, or combinations thereof. 3. The method of claim 1, wherein said contextual data includes at least one contextual data source that contains information about participants, physical resources, a domain taxonomy, an association database, or combinations thereof. 4. The method of claim 1, further comprising: with the analytical engine, analyzing said conversational data based on said contextual data using an analytical technique. 5. The method of claim 4, wherein said analytical technique includes pattern recognition, data analysis, structured and unstructured data analysis, predictive analysis, or combinations thereof. 6. The method of claim 1, further includes referencing a completion data source to provide additional contextual data for said conversational data to further infer said meaning of said conversational data or to provide relevant results. 7. The method of claim 6, wherein said completion data source includes databases that contain information about corporate data warehouses, public internet sources, domain specific databases, or combinations thereof. 8. The method of claim 1, further includes presenting relevant data in an output device. 9. A system for identifying relevant content for data gathered from real time communications, the system comprising: an obtaining engine to obtain conversational data from a real time communication; an identification engine to identify contextual data with at least one contextual data source relevant to said real time communication; an analytical engine to analyze said conversational data based on said contextual data; a learning engine to learn patterns and relationships from said conversational data based on said contextual data from said analytical engine; and an inference engine to infer a meaning of said conversational data based on said contextual data. 10. The system of claim 9, further includes a reference engine to reference a completion data source to provide additional contextual data for said conversational data to further infer said meaning of said conversational data or to provide relevant results. 11. The system of claim 9, wherein said patterns and said relationships are stored in an association database for inferring said meaning of said data gathered from said real time communications in subsequent operations. 12. The system of claim 9, further comprising a presentation engine to present relevant data in an output device. 13. A computer program product for identifying relevant content for data gathered from real time communications, comprising: a tangible computer readable storage medium, said tangible computer readable storage medium comprising computer readable program code embodied therewith, said computer readable program code comprising program instructions that, when executed, causes a processor to: obtain conversational data from a real time communication; identify contextual data with at least one contextual data source relevant to said real time communication; analyze said conversational data based on said contextual data; learn patterns and relationships from said conversational data based on said contextual data based on said analysis; and infer a meaning of said conversational data based on said contextual data. 14. The product of claim 13, further comprising computer readable program code comprising program instructions that, when executed, causes said processor to reference a completion data source to provide additional contextual data for said conversational data to further infer said meaning of said conversational data or provide relevant results. 15. The product of claim 13, further comprising computer readable program code comprising program instructions that, when executed, causes said processor to present relevant data in an output device. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Identifying relevant content for data gathered from real time communications includes obtaining conversational data from a real time communication, identifying contextual data with at least one contextual data source relevant to the real time communication, and inferring a meaning of the conversational data based on the contextual data. |
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G06N5048 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Identifying relevant content for data gathered from real time communications includes obtaining conversational data from a real time communication, identifying contextual data with at least one contextual data source relevant to the real time communication, and inferring a meaning of the conversational data based on the contextual data. |
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Techniques can relate to generating inferences based on network devices' measuring of environmental data points and generating notifications or controlling devices based on the inferences. One or more environmental data points can be accessed. Each environmental data point in the one or more environmental data points can include one measured by a detector device and that characterizes a corresponding environmental stimulus. At least one of the environmental data points can be indicative of a light intensity or power usage measured by a first device. An inference can be generated based on the one or more environmental data points. A notification or device control can be identified based on the inference. A communication can be generated and transmitted to a second device. Receipt of the communication can cause the second device to present the notification or to be controlled in accordance with the device control. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: receiving, at a performing network device, a plurality of environmental data points from a plurality of other network devices, wherein: at least two of the plurality of environmental data points correspond to measurements of different types of environmental stimuli; and each of the performing network device and the plurality of other network devices is part of a same network; accessing, from a local storage at the performing network device, a rule that relates environmental data inputs to inference outputs, wherein a corresponding rule is also stored at each of at least one of the plurality of other network devices such that each of the at least one of the plurality of other network devices is enabled to generate an inference output based on environmental data inputs; generating an inference using the rule and the plurality of environmental data points; identifying a notification or device control based on the inference; and facilitating a presentation of the notification or a device operation in accordance with the device control. 2. The method as recited in claim 1, wherein the rule is adaptive such that it is modified in accordance with a learning technique that processes manual device operation controls. 3. The method as recited in claim 1, further comprising: facilitating a presentation identifying the inference, the presentation including an option to confirm or reject the inference; receiving an input indicating whether the inference was confirmed; and modifying the rule based on the input. 4. The method as recited in claim 1, wherein identifying the notification or device control includes identifying the notification, and wherein facilitating the presentation of the notification or a device operation in accordance with the device control includes facilitating the presentation of the notification. 5. The method as recited in claim 1, wherein facilitating the presentation of the notification or a device operation in accordance with the device control includes transmitting a communication to an affected device, wherein the affected device is part of the same network and is different than each of the plurality of other network devices. 6. The method as recited in claim 1, wherein facilitating the presentation of the notification or a device operation in accordance with the device control includes: presenting, at the performing network device, the notification; or effecting an operation, at the performing network device, in accordance with the device control. 7. The method as recited in claim 1, wherein each of two or more of the plurality of other network devices is located in a different area of a building. 8. The method as recited in claim 1, wherein generating the inference includes inferring that a person is asleep or going to sleep. 9. The method as recited in claim 1, wherein generating the inference includes inferring that a person is at a premise, is not at a premise, is leaving a premise or is arriving at a premise. 10. A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which when executed on the one or more data processors, cause the one or more data processors to perform actions including: receiving a plurality of environmental data points from a plurality of other network devices, wherein: at least two of the plurality of environmental data points correspond to measurements of different types of environmental stimuli; and each of the system and the plurality of other network devices is part of a same network; locally accessing a rule that relates environmental data inputs to inference outputs, wherein a corresponding rule is also stored at each of at least one of the plurality of other network devices such that each of the at least one of the plurality of other network devices is enabled to generate an inference output based on environmental data inputs; generating an inference using the rule and the plurality of environmental data points; identifying a notification or device control based on the inference; and facilitating a presentation of the notification or a device operation in accordance with the device control. 11. The system as recited in claim 10, wherein the rule is adaptive such that it is modified in accordance with a learning technique that processes manual device operation controls. 12. The system as recited in claim 10, wherein the actions further include: facilitating a presentation identifying the inference, the presentation including an option to confirm or reject the inference; receiving an input indicating whether the inference was confirmed; and modifying the rule based on the input. 13. The system as recited in claim 10, wherein identifying the notification or device control includes identifying the notification, and wherein facilitating the presentation of the notification or a device operation in accordance with the device control includes facilitating the presentation of the notification. 14. The system as recited in claim 10, wherein facilitating the presentation of the notification or a device operation in accordance with the device control includes transmitting a communication to an affected device, wherein the affected device is part of the same network and is different than each of the plurality of other network devices. 15. The system as recited in claim 10, wherein facilitating the presentation of the notification or a device operation in accordance with the device control includes: presenting, at the system, the notification; or effecting an operation, at the system, in accordance with the device control. 16. The system as recited in claim 10, wherein each of two or more of the plurality of other network devices is located in a different area of a building. 17. The system as recited in claim 10, wherein generating the inference includes inferring that a person is asleep or going to sleep. 18. The system as recited in claim 10, wherein generating the inference includes inferring that a person is at a premise, is not at a premise, is leaving a premise or is arriving at a premise. 19. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions, at a performing network device, including: receiving a plurality of environmental data points from a plurality of other network devices, wherein: at least two of the plurality of environmental data points correspond to measurements of different types of environmental stimuli; and each of the performing network device and the plurality of other network devices is part of a same network; locally accessing a rule that relates environmental data inputs to inference outputs, wherein a corresponding rule is also stored at each of at least one of the plurality of other network devices such that each of the at least one of the plurality of other network devices is enabled to generate an inference output based on environmental data inputs; generating an inference using the rule and the plurality of environmental data points; identifying a notification or device control based on the inference; and facilitating a presentation of the notification or a device operation in accordance with the device control. 20. The computer-program product as recited in claim 19, wherein the rule is adaptive such that it is modified in accordance with a learning technique that processes manual device operation controls. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: Techniques can relate to generating inferences based on network devices' measuring of environmental data points and generating notifications or controlling devices based on the inferences. One or more environmental data points can be accessed. Each environmental data point in the one or more environmental data points can include one measured by a detector device and that characterizes a corresponding environmental stimulus. At least one of the environmental data points can be indicative of a light intensity or power usage measured by a first device. An inference can be generated based on the one or more environmental data points. A notification or device control can be identified based on the inference. A communication can be generated and transmitted to a second device. Receipt of the communication can cause the second device to present the notification or to be controlled in accordance with the device control. |
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G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Techniques can relate to generating inferences based on network devices' measuring of environmental data points and generating notifications or controlling devices based on the inferences. One or more environmental data points can be accessed. Each environmental data point in the one or more environmental data points can include one measured by a detector device and that characterizes a corresponding environmental stimulus. At least one of the environmental data points can be indicative of a light intensity or power usage measured by a first device. An inference can be generated based on the one or more environmental data points. A notification or device control can be identified based on the inference. A communication can be generated and transmitted to a second device. Receipt of the communication can cause the second device to present the notification or to be controlled in accordance with the device control. |
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Measurement-only topological quantum computation using both projective and interferometrical measurement of topological charge is described. Various issues that would arise when realizing it in fractional quantum Hall systems are discussed. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A quantum computing method for performing anyonic quantum computation, the method comprising: arranging computational anyons in an array; situating entangled pairs of anyons between each of the computational anyons; and providing a braiding transformation of the computational anyons with at least one measurement. 2. The quantum computing method of claim 1, wherein the measurement is an interferometric measurement. 3. The quantum computing method of claim 1, wherein the measurement is a projective measurement. 4. The quantum computing method of claim 3, wherein the projective measurement is a projective topological charge measurement. 5. The quantum computing method of claim 1, wherein the array of computational anyons is a linear array. 6. The quantum computing method of claim 1, wherein the measurement is a forced measurement. 7. The quantum computing method of claim 6, wherein the forced measurement is a projective forced measurement. 8. The quantum computing method of claim 6, wherein the forced measurement is an interferometric forced measurement. 9. The quantum computing method of claim 1, further comprising selecting the braiding transformation to represent a series of quantum gates. 10. The quantum computing method of claim 9, wherein the at least one measurement is a projective topological charge measurement. 11. A quantum computer, comprising: an array of computational anyons; entangled pairs of anyons between each of the computational anyons; and a measurement apparatus that implements a braiding transformation with the computational anyons. 12. The quantum computer of claim 11, wherein the measurement apparatus is an interferometer. 13. The quantum computer of claim 11, wherein the measurement apparatus is associated with a projective measurement. 14. The quantum computer of claim 13, wherein the projective measurement is a projective topological charge measurement. 15. The quantum computing method of claim 11, wherein the array of computational anyons is a linear array. 16. The quantum computing method of claim 11, wherein the measurement is a forced measurement. 17. A quantum computing method, comprising: with a classical processor, selecting a series of quantum gates associated with a selected computation; determining a braiding transformation based on the selected computation; and in a quantum computer, implementing the braiding transformation based on at least one measurement. 18. The quantum computing method of claim 17, wherein the at least one measurement is a forced measurement. 19. The quantum computing method of claim 18, wherein forced measurement is an interferometric forced measurement. 20. The quantum computing method of claim 18, wherein forced measurement is a projective topological charge measurement. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Measurement-only topological quantum computation using both projective and interferometrical measurement of topological charge is described. Various issues that would arise when realizing it in fractional quantum Hall systems are discussed. |
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G06N99002 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Measurement-only topological quantum computation using both projective and interferometrical measurement of topological charge is described. Various issues that would arise when realizing it in fractional quantum Hall systems are discussed. |
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A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neural network circuit, comprising: plurality of digital electronic neurons; and an electronic synapse array comprising a plurality of digital synapses interconnecting the neurons; wherein each synapse has a corresponding multi-bit fine-grain value representing a synaptic weight of the synapse; wherein each neuron includes a learning module for updating a synaptic weight of a connected synapse based on one or more learning rules; wherein each learning module is independently reconfigurable; and wherein each learning module of each neuron includes one or more digital counters, each digital counter decays at a corresponding decay rate during each timestep, and each digital counter resets to a pre-determined value in response to the neuron generating a spike signal. 2. The network circuit of claim 1, wherein: each synapse maintains m bits representing a corresponding multi-bit fine-grain value of the synapse; each multi-bit fine-grained value is a value from 0 to 2m−1, thereby enabling the synapses to provide noise tolerance; and each synapse has m pairs of bit lines, such that a corresponding multi-bit fine-grain value is written at once using only one word line when a synaptic weight of the synapse is updated. 3. (canceled) 4. The network circuit of claim 1, wherein, for each digital counter, a decay rate corresponding to the digital counter specifies a learning rule. 5. The network circuit of claim 4, wherein, for each neuron, a synaptic weight of a connected synapse is updated based on a learning rule specified in a decay rate of a digital counter of a learning module of the neuron. 6. The network circuit of claim 4, wherein, for each neuron, a synaptic weight of a connected synapse is updated based on a learning rule specified in a decay rate of a digital counter of a learning module of the neuron and a constant value. 7. The network circuit of claim 6, wherein the constant value is added to the synaptic weight of the connected synapse. 8. The network circuit of claim 6, wherein the constant value is subtracted from the synaptic weight of the connected synapse. 9. The network circuit of claim 4, wherein, for each neuron, a learning module of the neuron generates a digital signal for updating a synaptic weight of a connected synapse. 10. The network circuit of claim 1, wherein the learning rules include at least one of the following: spike-timing dependent plasticity (STDP), anti-STDP, Hebbian and anti-Hebbian. 11. A method comprising: interconnecting a plurality of digital electronic neurons via an electronic synapse array comprising a plurality of digital synapses; and for at least one neuron, updating a synaptic weight of a connected synapse based on one or more learning rules using a learning module of the neuron; wherein each synapse has a corresponding multi-bit fine-grain value representing a synaptic weight of the synapse; wherein each learning module is independently reconfigurable; and wherein each learning module of each neuron includes one or more digital counters, each digital counter decays at a corresponding decay rate during each timestep, and each digital counter resets to a pre-determined value in response to the neuron generating a spike signal. 12. The method of claim 11, wherein: each synapse maintains m bits representing a corresponding multi-bit fine-grain value of the synapse; each multi-bit fine-grained value is a value from 0 to 2m−1, thereby enabling the synapses to provide noise tolerance; and each synapse has m pairs of bit lines, such that a corresponding multi-bit fine-grain value is written at once using only one word line when a synaptic weight of the synapse is updated. 13. (canceled) 14. The method of claim 11, wherein, for each digital counter, a decay rate corresponding to the digital counter specifies a learning rule. 15. The method of claim 14, further comprising: for each neuron, updating a synaptic weight of a connected synapse based on a learning rule specified in a decay rate of a digital counter of a learning module of the neuron. 16. The method of claim 14, further comprising: for each neuron, updating a synaptic weight of a connected synapse based on a learning rule specified in a decay rate of a digital counter of a learning module of the neuron and a constant value. 17. The method of claim 16, further comprising: adding the constant value to the synaptic weight of the connected synapse. 18. The method of claim 16, further comprising: subtracting the constant value from the synaptic weight of the connected synapse. 19. The method of claim 14, further comprising: for each neuron, a learning module of the neuron generating a digital signal for updating a synaptic weight of a connected synapse. 20. The method of claim 11, wherein the learning rules include at least one of the following: spike-timing dependent plasticity (STDP), anti-STDP, Hebbian and anti-Hebbian. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array. |
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G06N308 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A reconfigurable neural network circuit is provided. The reconfigurable neural network circuit comprises an electronic synapse array including multiple synapses interconnecting a plurality of digital electronic neurons. Each neuron comprises an integrator that integrates input spikes and generates a signal when the integrated inputs exceed a threshold. The circuit further comprises a control module for reconfiguring the synapse array. The control module comprises a global final state machine that controls timing for operation of the circuit, and a priority encoder that allows spiking neurons to sequentially access the synapse array. |
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A processing device and method of classifying data are provided. The method comprises the computer-implemented steps of selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets, generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers, testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier, and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of classifying data, comprising the computer-implemented steps of: selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets; generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers; testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier; and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers. 2. The method of claim 1, further comprising normalizing the weight values of the 3D array of classifiers. 3. The method of claim 1, wherein when operated on an inputted data set, the 3D array of classifiers returns a prediction. 4. The method of claim 1, wherein when operated on an inputted data set, the 3D array of classifiers returns a prediction as a probability distribution over categories. 5. The method of claim 1, wherein the 3D array of classifiers comprises a comparison of model sets in the M number of model sets. 6. The method of claim 1, wherein the 3D array of classifiers comprises a comparison of data representation sets in the R number of data representation sets. 7. The method of claim 1, wherein the 3D array of classifiers comprises a comparison of sampling sets in the T number of sampling sets. 8. The method of claim 1, with the generating the M*R*T number of classifiers comprising training and testing the M*R*T number of classifiers. 9. The method of claim 1, with the generating the M*R*T number of classifiers comprising training and testing the M*R*T number of classifiers, wherein the training and the testing are performed substantially in parallel. 10. The method of claim 1, wherein the models comprise one or more of a parameterized support vector machine, a logistic regression model, a decision tree, or a neural network. 11. The method of claim 1, wherein a particular accuracy score of a particular individual classifier is converted into a particular weight value for the particular individual classifier using a predetermined statistical analysis. 12. The method of claim 1, wherein the selecting and generating are performed using large-scale parallel computing. 13. The method of claim 1, further comprising employing the 3D array of classifiers for data representation optimization, including feature selection. 14. The method of claim 1, wherein the 3D array of classifiers reveals latent relationships between data representation selection and model selection. 15. A processing device, comprising: a non-transitory memory storage comprising instructions; and one or more processors in communication with the memory, wherein the one or more processors execute the instructions to: select a M number of model sets, a R number of data representation sets, and a T number of sampling sets; generate a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers; test each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier; and assign a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers. 16. The processing device of claim 15, further comprising normalizing the weight values of the 3D array of classifiers. 17. The processing device of claim 15, wherein when operated on an inputted data set, the 3D array of classifiers returns a prediction. 18. The processing device of claim 15, wherein when operated on an inputted data set, the 3D array of classifiers returns a prediction as a probability distribution over categories. 19. The processing device of claim 15, wherein the 3D array of classifiers comprises a comparison of model sets in the M number of model sets. 20. The processing device of claim 15, wherein the 3D array of classifiers comprises a comparison of data representation sets in the R number of data representation sets. 21. The processing device of claim 15, wherein the 3D array of classifiers comprises a comparison of sampling sets in the T number of sampling sets. 22. The processing device of claim 15, with the generating the M*R*T number of classifiers comprising training and testing the M*R*T number of classifiers. 23. The processing device of claim 15, with the generating the M*R*T number of classifiers comprising training and testing the M*R*T number of classifiers, wherein the training and the testing are performed substantially in parallel. 24. The processing device of claim 15, wherein the models comprise one or more of a parameterized support vector machine, a logistic regression model, a decision tree, or a neural network. 25. The processing device of claim 15, wherein a particular accuracy score of a particular individual classifier is converted into a particular weight value for the particular individual classifier using a predetermined statistical analysis. 26. The processing device of claim 15, wherein the selecting and generating are performed using large-scale parallel computing. 27. The processing device of claim 15, further comprising employing the 3D array of classifiers for data representation optimization, including feature selection. 28. The processing device of claim 15, wherein the 3D array of classifiers reveals latent relationships between data representation selection and model selection. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A processing device and method of classifying data are provided. The method comprises the computer-implemented steps of selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets, generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers, testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier, and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers. |
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G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A processing device and method of classifying data are provided. The method comprises the computer-implemented steps of selecting a M number of model sets, a R number of data representation sets, and a T number of sampling sets, generating a M*R*T number of classifiers comprising a three-dimensional (3D) array of classifiers, testing each individual classifier in the 3D array of classifiers on a testing set to obtain accuracy scores for the each individual classifier, and assigning a weight value to the each individual classifier corresponding to each accuracy score, wherein the 3D array of classifiers comprises a 3D array of weighted classifiers. |
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An information processing device includes a processor configured to: acquire information of first status that represents a state of a first detection target; specify state transition in which the first status is changed based on the acquired information of the first status; acquire information of second status that represents a state of a second detection target; specify state transition in which the second status is changed based on the acquired information of the second status; specify a time point earlier than a first time point at which the state transition of the second status is specified as a second time point at which the state transition of the second status occurs; determine whether a combination of the second status and the first status at least at the second time point satisfies a predetermined condition; and perform predetermined processing according to a determination result indicating the condition is satisfied. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing device comprising: a memory; and a processor coupled to the memory and configured to: acquire information of first status that represents a state of a first detection target detected by a first sensor; specify state transition in which the first status is changed based on the acquired information of the first status; acquire information of second status that represents a state of a second detection target detected by a second sensor; specify state transition in which the second status is changed based on the acquired information of the second status; specify a time point earlier than a first time point at which the state transition of the second status is specified as a second time point at which the state transition of the second status occurs; determine whether or not a combination of the second status and the first status at least at the second time point satisfies a predetermined condition; and perform predetermined processing according to a determination result indicating the condition is satisfied. 2. The information processing device according to claim 1, wherein the processor is configured to determine whether the condition is satisfied between the second time point and the first time point. 3. The information processing device according to claim 1, wherein a time interval between the second time point and the first time point is limited to a time equal to or less than a predetermined time. 4. The information processing device according to claim 1, wherein the processor is configured to specify the second time point based on change in the time-series data used in the detecting of the state transition of the second status. 5. The information processing device according to claim 1, further comprising: the first sensor; and the second sensor. 6. The information processing device according to claim 5, wherein the first sensor is configured to detect a wireless tag by receiving a wireless signal generated by a nearby device included in the first detection target. 7. The information processing device according to claim 5, wherein the second sensor is configured to measure acceleration of the information processing device. 8. A method comprising: acquiring, by a processor, information of first status that represents a state of a first detection target detected by a first sensor; specifying, by the processor, state transition in which the first status is changed based on the acquired information of the first status; acquiring, by the processor, information of second status that represents a state of a second detection target detected by a second sensor; specifying, by the processor, state transition in which the second status is changed based on the acquired information of the second status; specifying, by the processor, a time point earlier than a first time point at which the state transition of the second status is specified as a second time point at which the state transition of the second status occurs; determining, by the processor, whether or not a combination of the second status and the first status at least at the second time point satisfies a predetermined condition; and performing, by the processor, predetermined processing according to a determination result indicating the condition is satisfied. 9. The method according to claim 8, wherein in the determining, whether the condition is satisfied between the second time point and the first time point is determined. 10. The method according to claim 8, wherein a time interval between the second time point and the first time point is limited to a time equal to or less than a predetermined time. 11. The method according to claim 8, wherein in the specifying of the second time point, the second time point is specified based on change in the time-series data used in the detecting of the state transition of the second status. 12. A non-transitory computer readable medium having stored therein a program that causes a computer to execute a process, the process comprising: acquiring information of first status that represents a state of a first detection target detected by a first sensor; specifying state transition in which the first status is changed based on the acquired information of the first status; acquiring information of second status that represents a state of a second detection target detected by a second sensor; specifying state transition in which the second status is changed based on the acquired information of the second status; specifying a time point earlier than a first time point at which the state transition of the second status is specified as a second time point at which the state transition of the second status occurs; determining whether or not a combination of the second status and the first status at least at the second time point satisfies a predetermined condition; and performing predetermined processing according to a determination result indicating the condition is satisfied. 13. The non-transitory computer readable medium according to claim 12, wherein the process further comprising: wherein in the determining, whether the condition is satisfied between the second time point and the first time point is determined. 14. The non-transitory computer readable medium according to claim 12, wherein the process further comprising: wherein a time interval between the second time point and the first time point is limited to a time equal to or less than a predetermined time. 15. The non-transitory computer readable medium according to claim 12, wherein the process further comprising: wherein in the specifying of the second time point, the second time point is specified based on change in the time-series data used in the detecting of the state transition of the second status. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: An information processing device includes a processor configured to: acquire information of first status that represents a state of a first detection target; specify state transition in which the first status is changed based on the acquired information of the first status; acquire information of second status that represents a state of a second detection target; specify state transition in which the second status is changed based on the acquired information of the second status; specify a time point earlier than a first time point at which the state transition of the second status is specified as a second time point at which the state transition of the second status occurs; determine whether a combination of the second status and the first status at least at the second time point satisfies a predetermined condition; and perform predetermined processing according to a determination result indicating the condition is satisfied. |
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G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An information processing device includes a processor configured to: acquire information of first status that represents a state of a first detection target; specify state transition in which the first status is changed based on the acquired information of the first status; acquire information of second status that represents a state of a second detection target; specify state transition in which the second status is changed based on the acquired information of the second status; specify a time point earlier than a first time point at which the state transition of the second status is specified as a second time point at which the state transition of the second status occurs; determine whether a combination of the second status and the first status at least at the second time point satisfies a predetermined condition; and perform predetermined processing according to a determination result indicating the condition is satisfied. |
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Various embodiments of systems and methods for generating predictive models are described herein. A computer system deployed in a distributed may receive configuration data from multiple electronic devices. The system may select a set of configuration data with respect to a device category and a subcategory to generate a prediction model. The predictive model includes hypothesis, an average deviation and information pertaining to optimal configuration data for the given subcategory and the device category. The computer system may also receive monitoring requests from electronic devices and retrieve appropriate predictive model with respect to the device category and subcategory. The system may reconfigure the electronic device based on the retrieve predictive model. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for generating a predictive model to reconfigure an electronic device from a plurality of electronic devices, the method comprising: sampling, at a processor of a computer, a set of configuration data received from the plurality of electronic devices, with respect to a first device category from a plurality of device categories and a first subcategory from a plurality of subcategories, to generate sample data for a time interval; generating, at the processor of the computer, training data and validation data from the sample data for the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, for the time interval; based on the generated training data and the validation data, generating, at the processor of the computer, the predictive model for the set of configuration data received from the plurality of electronic devices with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, for the time interval; and reconfiguring, at the processor of the computer, the electronic device from the plurality of electronic devices based on the configuration data included in the generated predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories. 2. The computer implemented method according to claim 1, wherein the method comprises: receiving, at the processor of the computer, configuration data from the plurality of electronic devices located in a plurality of geographical locations; storing, at a memory of the computer, the received configuration data corresponding to the plurality of device categories and the plurality of subcategories; and prior to sampling, retrieving, at the processor of the computer, the set of configuration data received from the plurality of electronic devices with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, from the stored configuration data. 3. The computer implemented method according to claim 1, wherein generating the training data and the validation data further includes: selecting, at the processor of the computer, a data sample from the sample data until a first predetermined number of data samples are selected to generate the training data; and selecting, at the processor of the computer, unselected data samples from the sample data to generate the validation data. 4. The computer implemented method according to claim 1, wherein generating the predictive model further includes: computing, at the processor of the computer, a first hypothesis with respect to the generated training data, wherein the first hypothesis is computed by applying regression analysis on the generated training data; computing, at the processor of the computer, an average deviation based on the computed first hypothesis, comprises: computing, at the processor of the computer, a second hypothesis by computing average mean distances of the training data from the first hypothesis, wherein the average mean distances is computed with respect to the subcategory from the plurality of subcategories, of the training data; computing, at the processor of the computer, difference between the first hypothesis and the second hypothesis to obtain a difference value; computing, at the processor of the computer, multiplicative inverse of the computed difference value to obtain a reciprocal value; and computing, at the processor of the computer, a first threshold high and a first threshold low with respect to the first hypothesis, wherein the first threshold high is computed by applying regression analysis on differences between the reciprocal value and the subcategory from the plurality of subcategories, of the training data, wherein the first threshold low is computed by applying regression analysis on summation of the reciprocal value and the subcategory from the plurality of subcategories, of the training data. 5. The computer implemented method according to claim 1, wherein the method further comprises: validating, at the processor of the computer, the generated predictive model based on the validation data generated from the sample data, wherein: upon successful validation, storing, at the memory of the computer, the validated predictive model; and upon unsuccessful validation, generating, at the processor of the computer, another predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories for a non-regular time interval. 6. The computer implemented method according to claim 1, wherein the method further comprises: periodically generating, at the processor of the computer, the predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, in non-regular time intervals to refine the generated predictive model. 7. The method according to claim 1, wherein the method for reconfiguring the electronic device from the plurality of electronic devices further comprises: periodically receiving, at the processor of the computer, a monitoring request to determine health status of the electronic device from the plurality of electronic devices; extracting, at the processor of the computer, an existing configuration data from the received monitoring request; identifying, at the processor of the computer, a second device category from the plurality of the device categories and a second subcategory from the plurality of subcategories, from the extracted configuration data; retrieving, at the processor of the computer, the predictive model from the memory with respect to the identified second device category from the plurality of the device categories and the identified second subcategory from the plurality of subcategories, wherein the retrieved predictive model includes a third hypothesis, a second threshold high, a second threshold low, and a plurality of optimal configuration data; mapping, at the processor of the computer, the extracted configuration data with the retrieved predictive model to determine the health status of the electronic device from the plurality of electronic devices, wherein: when the extracted configuration data lies on the third hypothesis or near to the third hypothesis, categorizing, at the processor of the computer, the health status of the electronic device from the plurality of electronic devices as ideal; when the extracted configuration data lies between the third hypothesis and the second threshold high or between the third hypothesis and the second threshold low, categorizing, at the processor of the computer, the health status of the electronic device from the plurality of electronic devices as change to optimal configurations; selecting, at the processor of the computer, an optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model; reconfiguring, the processor of the computer, the electronic device from the plurality of electronic devices, by transmitting the selected optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model to the electronic device from the plurality of electronic devices; and when the extracted configuration data lies beyond the second threshold high or beyond the second threshold low, categorizing, at the processor of the computer, the health status of the electronic device from the plurality of electronic devices as non-ideal; and transmitting, at the processor of the computer, an alert notification to the electronic device from the plurality of devices, to indicate non-ideal health status. 8. The computer implemented method according to claim 7, wherein reconfiguring the electronic device from the plurality of electronic devices further includes: upon successful reconfiguration, receiving, at the processor of the computer, a reconfiguration successful feedback from the electronic device from the plurality of electronic devices; upon unsuccessful reconfiguration, receiving, at the processor of the computer, a reconfiguration unsuccessful feedback from the electronic device from the plurality of electronic devices, wherein: selecting, at the processor of the computer, another optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model; iterating, at the processor of the computer, selection of another optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model, for a second predetermined number of attempts; and upon unsuccessful reconfiguration for the second predetermined number of attempts, transmitting, at the processor of the computer, an error message to indicate unsuccessful reconfiguration attempts and service center information based on the geographical location of the electronic device from the plurality of electronic devices. 9. A computer system for generating a predictive model to reconfigure an electronic device from a plurality of electronic devices, the system comprising: a memory to store a program code; a processor communicatively coupled to the memory, the processor configured to execute the program code to: sample a set of configuration data received from the plurality of electronic devices with respect to a first device category from a plurality of device categories and a first subcategory from a plurality of subcategories, to generate sample data for a time interval; generate training data and validation data from the sample data, for the first device category from a plurality of device categories and with respect to a first subcategory from a plurality of subcategories, for the time interval; based on the generated training data and the validation data, generate the predictive model for the set of configuration data received from the plurality of electronic devices with respect to the device category from the plurality of device categories and the subcategory from the plurality of subcategories, for the time interval; and reconfigure the electronic devices from the plurality of electronic devices based on the configuration data included in the generated predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories. 10. The computer system according to claim 9, wherein the processor executes the program code to: receive configuration data from a plurality of electronic devices located in a plurality of geographical locations; store the received configuration data corresponding to the plurality of device categories and the plurality of subcategories; and prior to sampling, retrieve the set of configuration data received from the plurality of electronic devices with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, from the stored configuration data. 11. The computer system according to claim 9, wherein the processor executes the program code to: select data sample from the sample data until a first predetermined number of data samples are selected to generate the training data; and select unselected data samples from the sample data to generate the validation data. 12. The computer system according to claim 9, wherein the processor executes the program code to: compute a first hypothesis with respect to the generated training data, wherein the first hypothesis is computed by applying regression analysis on the generated training data; compute an average deviation based on the computed first hypothesis, wherein: compute a second hypothesis by computing average mean distances of the training data from the first hypothesis, wherein the average mean distances is computed with respect to the subcategory from the plurality of subcategories, of the training data; compute difference between the first hypothesis and the second hypothesis to obtain a difference value; compute multiplicative inverse of the computed difference value between the first hypothesis and the second hypothesis to obtain a reciprocal value; and compute first threshold high and a first threshold low with respect to the first hypothesis, wherein the first threshold high is computed by applying regression analysis on differences between the reciprocal value and the subcategory from the plurality of subcategories, of the training data, wherein the first threshold low is computed by applying regression analysis on summation of the reciprocal value and the subcategory from the plurality of subcategories, of the training data. 13. The computer system according to claim 9, wherein the processor executes the program code to: validate the generated predictive model based on the validation data generated from the sample data, wherein: upon successful validation, store the validated predictive model in the memory; and upon unsuccessful validation, generate another predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories for a non-regular time interval. 14. The computer system according to claim 9, wherein the processor executes the program code to reconfigure the electronic device from the plurality of electronic devices: periodically receive a monitoring request to determine health status of the electronic device from the plurality of electronic devices; extract an existing configuration data from the received monitoring request; identify a second device category from the plurality of the device categories and a second subcategory from the plurality of subcategories, from the extracted configuration data; retrieve the predictive model from the memory with respect to the identified second device category from the plurality of the device categories and the identified second subcategory from the plurality of subcategories, wherein the retrieved predictive model includes a third hypothesis, a second threshold high, a second threshold low, and a plurality of optimal configuration data; map the extracted configuration data with the retrieved predictive model to determine the health status of the electronic device from the plurality of electronic devices, wherein: when the extracted configuration data lies on the third hypothesis or near to the third hypothesis, categorize the health status of the electronic device from the plurality of electronic devices as ideal; when the extracted configuration data lies between the third hypothesis and the second threshold high or between the third hypothesis and the second threshold low, categorize the health status of the electronic device from the plurality of electronic devices as change to optimal configurations; select an optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model; reconfigure the electronic device from the plurality of electronic devices, by transmitting the selected optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model, to the electronic device from the plurality of electronic devices; and when the extracted configuration data lies beyond the second threshold high or beyond the second threshold low, categorize the health status of the electronic device from the plurality of electronic devices as non-ideal; and transmit an alert notification to the electronic device from the plurality of devices, to indicate non-ideal health status. 15. A non-transitory computer readable storage medium to tangibly store instructions, which when executed by a computer, cause the computer to: sample a set of configuration data received from a plurality of electronic devices with respect to a first device category from a plurality of device categories and a first subcategory from a plurality of subcategories, to generate sample data for a time interval; generate training data and validation data from the sample data for the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, for the time interval; based on the generated training data and validation data generate the predictive model for the set of configuration data with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, for the time interval; and reconfigure the electronic devices from the plurality of electronic devices based on the configuration data included in the generated predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories. 16. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: receive configuration data from the plurality of electronic devices located in a plurality of geographical locations; store the received configuration data corresponding to the plurality of device categories and the plurality of subcategories; and prior to sampling, retrieve the set of configuration data received from the plurality of electronic devices with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories, from the stored configuration data. 17. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: select data sample from the sample data until a first predetermined number of data samples are selected to generate the training data; and select unselected data samples from the sample data to generate the validation data. 18. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: compute a first hypothesis with respect to the generated training data, wherein the first hypothesis is computed by applying regression analysis on the generated training data; compute an average deviation based on the computed first hypothesis, wherein: compute a second hypothesis by computing average mean distances of the training data from the first hypothesis, wherein the average mean distances is computed with respect to the subcategory from the plurality of subcategories, of the training data; compute difference between the first hypothesis and the second hypothesis to obtain a difference value; compute multiplicative inverse of the computed difference value between the first hypothesis and the second hypothesis to obtain a reciprocal value; and compute first threshold high and a first threshold low with respect to the first hypothesis, wherein the first threshold high is computed by applying regression analysis on differences between the reciprocal value and the subcategory from the plurality of subcategories, of the training data, wherein the first threshold low is computed by applying regression analysis on summation of the reciprocal value and the subcategory from the plurality of subcategories, of the training data. 19. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: validate the generated predictive model based on the validation data generated from the sample data, wherein: upon successful validation, store the validated predictive model into the memory; and upon unsuccessful validation, generate another predictive model with respect to the first device category from the plurality of device categories and the first subcategory from the plurality of subcategories for a non-regular time interval. 20. The non-transitory computer readable medium according to claim 15, further comprising instructions which when executed by the computer further causes the computer to: periodically receive a monitoring request to determine health status of the electronic device from the plurality of electronic devices; extract an existing configuration data from the received monitoring request; identify a second device category from the plurality of the device categories and a second subcategory from the plurality of subcategories, from the extracted configuration data; retrieve a stored predictive model from the memory with respect to the identified second device category from the plurality of the device categories and the identified second subcategory from the plurality of subcategories, wherein the retrieved predictive model includes a third hypothesis, a second threshold high, a second threshold low, and a plurality of optimal configuration data; map the extracted configuration data with the retrieved predictive model to determine the health status of the electronic device from the plurality of electronic devices, wherein: when the extracted configuration date lies on the third hypothesis or near to the third hypothesis, categorize the health status of the electronic device from the plurality of electronic devices as ideal; when the extracted configuration data lies between the third hypothesis and the second threshold high or between the third hypothesis and the second threshold low, categorize the health status of the electronic device from the plurality of electronic devices as change to optimal configurations; select an optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model; reconfigure the electronic device from the plurality of electronic devices, by transmitting the selected optimal configuration data from the plurality of optimal configuration data included in the retrieved predictive model, to the electronic device from the plurality of electronic devices; and when the extracted configuration data lies beyond the second threshold high or beyond the second threshold low, categorize the health status of the electronic device from the plurality of electronic devices as non-ideal; and transmit an alert notification to the electronic device from the plurality of devices, to indicate non-ideal health status. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Various embodiments of systems and methods for generating predictive models are described herein. A computer system deployed in a distributed may receive configuration data from multiple electronic devices. The system may select a set of configuration data with respect to a device category and a subcategory to generate a prediction model. The predictive model includes hypothesis, an average deviation and information pertaining to optimal configuration data for the given subcategory and the device category. The computer system may also receive monitoring requests from electronic devices and retrieve appropriate predictive model with respect to the device category and subcategory. The system may reconfigure the electronic device based on the retrieve predictive model. |
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G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Various embodiments of systems and methods for generating predictive models are described herein. A computer system deployed in a distributed may receive configuration data from multiple electronic devices. The system may select a set of configuration data with respect to a device category and a subcategory to generate a prediction model. The predictive model includes hypothesis, an average deviation and information pertaining to optimal configuration data for the given subcategory and the device category. The computer system may also receive monitoring requests from electronic devices and retrieve appropriate predictive model with respect to the device category and subcategory. The system may reconfigure the electronic device based on the retrieve predictive model. |
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A generation device generating an evaluation function for calculating an evaluation value of an evaluation target, the generation device including an acquisition unit acquiring learning data including a qualitative evaluation of the evaluation target; a generation unit generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and a setting unit setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied, and the like are provided. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A device for generating an evaluation function for calculating an evaluation value of an evaluation target, the device comprising: an acquisition unit acquiring learning data including a qualitative evaluation of the evaluation target; a generation unit generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and a setting unit setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied. 2. The device according to claim 1, wherein the acquisition unit acquires the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing two or more evaluation targets. 3. The device according to claim 1, wherein the acquisition unit acquires the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing the evaluation target with a predetermined evaluation criterion. 4. The device according to claim 1, wherein the generation unit generates the constraints based on the evaluation function, which includes a term based on a weighted sum of a plurality of basis functions to which an attribute value is input for each attribute of the evaluation target, and wherein the setting unit sets the weight of each of the basis functions so that the constraints are satisfied. 5. The device according to claim 4, wherein the generation unit generates the constraint, which includes a variable indicating whether or not each of the plurality of basis functions is included, and wherein the setting unit optimizes the weights by using an objective function including a total a number of basis functions included in the evaluation function. 6. The device according to claim 5, wherein the generation unit generates the objective function, which includes error variables, and wherein the setting unit optimizes the weights by using the objective function including the error variables. 7. The device according to claim 2, wherein the generation unit generates the constraints, an inequality including a difference in evaluation value of the evaluation function between the two or more evaluation targets as comparison targets and an evaluation threshold as a criterion of the qualitative evaluation, and wherein the setting unit sets a value of the evaluation threshold so that the constraints are satisfied. 8. The device according to claim 7, wherein the acquisition unit acquires the learning data, which includes the qualitative evaluations made by a plurality of evaluators, and wherein the generation unit generates, as the constraints, an inequality including the evaluation threshold for each evaluator. 9. The device according to claim 8, further comprising: a determination unit determining whether or not the difference in evaluation value between the two or more evaluation targets according to the evaluation function based on the weight set by the setting unit falls within a predetermined reference range with respect to the evaluation threshold, wherein in accordance with a determination result that the difference in the evaluation value does not fall within the reference range with respect to the evaluation threshold, the acquisition unit acquires an additional qualitative evaluation and adds the acquired additional qualitative evaluation to the learning data. 10. The device according to claim 9, further comprising: a presentation unit presenting to the evaluator the two or more evaluation targets for which a difference in evaluation value between the two or more evaluation targets falls within a reference range, wherein the acquisition unit acquires an qualitative evaluation made by an evaluator for the presented two or more evaluation targets and adds the acquired qualitative evaluation to the learning data. 11. A computer-implemented method for generating an evaluation function for calculating an evaluation value of an evaluation target, the method comprising: acquiring learning data including a qualitative evaluation of the evaluation target; generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied. 12. The computer-implemented method according to claim 11, wherein the acquiring the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing two or more evaluation targets. 13. The computer-implemented method according to claim 11, wherein the acquiring the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing the evaluation target with a predetermined evaluation criterion. 14. The computer-implemented method according to claim 11, wherein the generating the constraints based on the evaluation function, which includes a term based on a weighted sum of a plurality of basis functions to which an attribute value is input for each attribute of the evaluation target, and wherein the setting unit sets the weight of each of the basis functions so that the constraints are satisfied. 15. The computer-implemented method according to claim 14, wherein the generating the constraint, which includes a variable indicating whether or not each of the plurality of basis functions is included, and wherein the setting unit optimizes the weights by using an objective function including a total a number of basis functions included in the evaluation function. 16. The computer-implemented method according to claim 15, wherein the generating the objective function, which includes error variables, and wherein the setting unit optimizes the weights by using the objective function including the error variables. 17. The computer-implemented method according to claim 12, wherein the generating the constraints, an inequality including a difference in evaluation value of the evaluation function between the two or more evaluation targets as comparison targets and an evaluation threshold as a criterion of the qualitative evaluation, and wherein the setting unit sets a value of the evaluation threshold so that the constraints are satisfied. 18. The computer-implemented method according to claim 17, wherein the generating the constraint, which includes a variable indicating whether or not each of a plurality of basis functions is included, and wherein the setting unit optimizes the weights by using an objective function including a total a number of basis functions included in the evaluation function. 19. A non-transitory computer program product for generating an evaluation function for calculating an evaluation value of an evaluation target comprising a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code configured to perform: acquiring learning data including a qualitative evaluation of the evaluation target; generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied. 20. The non-transitory computer program product according to claim 19, wherein the acquiring the learning data, which includes, as the qualitative evaluation, a comparison result obtained by qualitatively comparing two or more evaluation targets. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A generation device generating an evaluation function for calculating an evaluation value of an evaluation target, the generation device including an acquisition unit acquiring learning data including a qualitative evaluation of the evaluation target; a generation unit generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and a setting unit setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied, and the like are provided. |
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G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A generation device generating an evaluation function for calculating an evaluation value of an evaluation target, the generation device including an acquisition unit acquiring learning data including a qualitative evaluation of the evaluation target; a generation unit generating a constraint to be satisfied by a value of the evaluation function for the evaluation target, based on the learning data; and a setting unit setting weight for a plurality of attributes in the evaluation function so that the constraint is satisfied, and the like are provided. |
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A neuromorphic device having synapses may include: a top electrode; a bottom electrode; and a variable resistive layer disposed between the top electrode and the bottom electrode. The variable resistive layer may include a plurality of carrier traps distributed at multiple energy levels. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neuromorphic device having a synapse, the synapse comprising: a top electrode; a bottom electrode; and a variable resistive layer disposed between the top electrode and the bottom electrode, wherein the variable resistive layer comprises a plurality of carrier traps distributed at multiple energy levels. 2. The neuromorphic device of claim 1, further comprising an upper blocking layer disposed between the top electrode and the variable resistive layer. 3. The neuromorphic device of claim 2, wherein the upper blocking layer comprises a semiconductor material that substantially lacks carrier traps. 4. The neuromorphic device of claim 1, further comprising a lower blocking layer disposed between the bottom electrode and the variable resistive layer. 5. The neuromorphic device of claim 4, wherein the lower blocking layer and the variable resistive layer comprise two or more of the same materials. 6. The neuromorphic device of claim 1, further comprising an upper barrier layer disposed between the top electrode and the variable resistive layer. 7. The neuromorphic device of claim 6, wherein the upper barrier layer comprises one or more of titanium (Ti), tantalum (Ta), titanium nitride (TiN), tantalum nitride (TaN), tungsten nitride (WN), and another barrier material. 8. The neuromorphic device of claim 1, further comprising a lower barrier layer disposed between the bottom electrode and the variable resistive layer. 9. The neuromorphic device of claim 1, wherein the variable resistive layer comprises two or more of silicon (Si), germanium (Ge), gallium (Ga), indium (In), arsenic (As), stibium (Sb), hafnium (Hf), tantalum (Ta), titanium (Ti), zirconium (Zr), lanthanum (La), vanadium (V), chrome (Cr), manganese (Mn), rubidium (Ru), strontium (Sr), yttrium (Y), niobium (Nb), molybdenum (Mo), ruthenium (Ru), iridium (Ir), aluminum (Al), silicon-germanium (SixGey), and an oxide thereof. 10. The neuromorphic device of claim 1, wherein the plurality of carrier traps comprise charge traps or dangling bonds at an interface between two or more materials, or dangling bonds in an amorphous material. 11. The neuromorphic device of claim 1, wherein the plurality of carrier traps comprise low carrier traps and high carrier traps, the low carrier traps being distributed at relatively low energy levels among the multiple energy levels, the high carrier traps being distributed at relatively high energy levels among the multiple energy levels. 12. The neuromorphic device of claim 11, wherein a density of the high carrier traps is higher than a density of the low carrier traps. 13. The neuromorphic device of claim 1, wherein the plurality of carrier traps are distributed between the Fermi level and the valence band. 14. A neuromorphic device comprising: a row line; a column line; and a synapse disposed between the row line and the column line, wherein the synapse comprises a variable resistive layer and a blocking layer in direct contact with the variable resistive layer, and wherein the variable resistive layer comprises a semiconducting material having a plurality of carrier traps distributed at multiple energy levels. 15. The neuromorphic device of claim 14, wherein the plurality of carrier traps correspond to defects based on an atomic combination within the variable resistive layer. 16. The neuromorphic device of claim 14, wherein the blocking layer comprises two or more materials that are the same as materials of the variable resistive layer. 17. The neuromorphic device of claim 14, further comprising a barrier layer in direct contact with the blocking layer. 18. The neuromorphic device of claim 17, wherein each of the row line, the column line, and the barrier layer comprise a conductive material having at least one of metals or metal compounds. 19. The neuromorphic device of claim 14, wherein the plurality of carrier traps comprise low carrier traps and high carrier traps, the low carrier traps being distributed at relatively low energy levels among the multiple energy levels, the high carrier traps being distributed at relatively high energy levels among the multiple energy levels. 20. The neuromorphic device of claim 19, wherein a density of the high carrier traps is higher than a density of the low carrier traps. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: A neuromorphic device having synapses may include: a top electrode; a bottom electrode; and a variable resistive layer disposed between the top electrode and the bottom electrode. The variable resistive layer may include a plurality of carrier traps distributed at multiple energy levels. |
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G06N30635 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A neuromorphic device having synapses may include: a top electrode; a bottom electrode; and a variable resistive layer disposed between the top electrode and the bottom electrode. The variable resistive layer may include a plurality of carrier traps distributed at multiple energy levels. |
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A computer-based sleep improvement system applying system intelligence to up-to-date user-reported data and/or recorded sleep data from a connected tracking device for assisting a user in the proactive improvement of sleep and treatment of insomnia includes a central server maintaining a library of information related to treatment of insomnia. The central server includes a video database of session video material, an audio database of session material, a case file database of information regarding the user and sleep habits of the user, and a plurality of tools assisting the user in treating insomnia. The system includes a session coordinator creating sessions of customized presentations for the user based upon the library of information and a user interface linked to the central server. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-based sleep improvement system applying system intelligence to up-to-date user-reported data for assisting a user in the proactive improvement of sleep and treatment of insomnia, comprising: a central server maintaining a library of information related to treatment of insomnia, the central server including: a video database of session video material; an audio database of session audio material; a case file database of information regarding the user and sleep habits of the user; and a plurality of tools assisting the user in treating insomnia; a session coordinator creating sessions of customized presentations for the user based upon the library of information; and a user interface linked to the central server. 2. The computer-based sleep improvement system according to claim 1, wherein the sessions are composed of a plurality of dynamically-combined individual movie and audio elements appearing to the user as a single seamless interactive movie. 3. The computer-based sleep improvement system according to claim 1, wherein the case file database includes a data source employed to personalize the sessions to each user. 4. The computer-based sleep improvement system according to claim 3, wherein the data source includes an initial sleep test, a daily sleep diary data, user access patterns, and prompted user input. 5. The computer-based sleep improvement system according to claim 3, wherein the sessions are composed of topics, slides, interactive input sessions, movies and/or audio. 6. The computer-based sleep improvement system according to claim 5, wherein the session coordinator decides upon content for the sessions based on an analysis of the user's initial sleep test information and prompted user input. 7. The computer-based sleep improvement system according to claim 6, wherein the sessions are weekly interactive video-based customized presentation, delivered over the Internet, during which the user learns cognitive and behavioral techniques. 8. The computer-based sleep improvement system according to claim 7, wherein the plurality of tools are selected from the group consisting of a Sleep Diary, a To Do List, a Daily Schedule, a Library, a Thought Checker, a Planner tool, a Compare Sleep Tags tool, a Relaxation Audio(s), a Recommended reading tool, Stats, a Sleep Report, Reminders and a Community tool. 9. The computer-based sleep improvement system according to claim 1, wherein the user interface is a graphical user interface accessed via a computer connected to a global communication network. 10. The computer-based sleep improvement system according to claim 1, wherein the session coordinator includes functionality for providing email, SMS communications and/or social networking. 11. The computer-based sleep improvement system according to claim 1, wherein the session coordinator includes a program interface, application program interface, and/or web applications. 12. The computer-based sleep improvement system according to claim 1, wherein the plurality of tools are selected from the group consisting of a Sleep Diary, a Compare Sleep Tags tool, a Relaxation Audio(s), a Sleep report, Reminders and a Community tool. 13. The computer-based sleep improvement system according to claim 1, wherein the plurality of tools includes a Sleep Diary providing a user interface for recording a daily sleep pattern, a quality rating and lifestyle factors. 14. The computer-based sleep improvement system according to claim 16, wherein the plurality of tools includes a Compare Sleep Tags tool allowing the user to compare sleep on nights associated with any particular “tag” added in the Sleep Diary. 15. The computer-based sleep improvement system according to claim 1, wherein the plurality of tools includes a Library. 16. The computer-based sleep improvement system according to claim 1, wherein the plurality of tools includes a Sleep Report providing a representation of Sleep Test data of the user. 17. The computer-based sleep improvement system according to claim 1, wherein the plurality of tools includes a Relaxation Audio providing digital audio designed to allow the user to select MP3 audio to guide relaxation techniques. 18. The computer-based sleep improvement system according to claim 1, wherein the plurality of tools includes a Reminder tool assisting the user in managing and receiving email and SMS reminders of daily tasks and motivational messages. 19. The computer-based sleep improvement system according to claim 1, wherein the plurality of tools includes a Community tool providing a graphical user interface offering the user a peer support network. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: A computer-based sleep improvement system applying system intelligence to up-to-date user-reported data and/or recorded sleep data from a connected tracking device for assisting a user in the proactive improvement of sleep and treatment of insomnia includes a central server maintaining a library of information related to treatment of insomnia. The central server includes a video database of session video material, an audio database of session material, a case file database of information regarding the user and sleep habits of the user, and a plurality of tools assisting the user in treating insomnia. The system includes a session coordinator creating sessions of customized presentations for the user based upon the library of information and a user interface linked to the central server. |
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G06N502 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A computer-based sleep improvement system applying system intelligence to up-to-date user-reported data and/or recorded sleep data from a connected tracking device for assisting a user in the proactive improvement of sleep and treatment of insomnia includes a central server maintaining a library of information related to treatment of insomnia. The central server includes a video database of session video material, an audio database of session material, a case file database of information regarding the user and sleep habits of the user, and a plurality of tools assisting the user in treating insomnia. The system includes a session coordinator creating sessions of customized presentations for the user based upon the library of information and a user interface linked to the central server. |
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Method for calculating whether an actual location of a target device is on one side of a boundary zone includes: receiving an estimated location of the target device; receiving a desired confidence level; forming a first circle with radius D, centered at the estimated location, where D is the shortest distance from the estimated location to the boundary zone; forming a second circle with radius R′, centered at the estimated location, wherein R′ is determined in such a way so that a likelihood that the actual location is inside the second circle equals or exceeds the desired confidence level; forming an angle with an apex at the estimated location and rays passing through two closest points to the estimated location where the second circle intersects the boundary zone; and using a size of an annulus formed by the first circle, the second circle, and the rays to estimate whether the actual location lies on the same side of the boundary zone. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for calculating whether an actual location of a target device is on one side of a boundary zone, the method comprising: receiving an estimated location of the target device; receiving a desired confidence level; forming a first circle with radius D, centered at the estimated location, where D is the shortest distance from the estimated location to the boundary zone; forming a second circle with radius R′, centered at the estimated location, wherein R′ is determined in such a way so that a likelihood that the actual location is inside the second circle equals or exceeds the desired confidence level; forming an angle with an apex at the estimated location and rays passing through two closest points to the estimated location where the second circle intersects the boundary zone; and using a size of an annulus formed by the first circle, the second circle, and the rays to estimate whether the actual location lies on the same side of the boundary zone as the estimated location, wherein the method is performed by one or more processors. 2. The method of claim 1, wherein the desired confidence level is a desired estimated probability that the actual location of the target device is within an uncertainty zone. 3. The method of claim 2, wherein the desired confidence level is constant for estimated locations received from a particular source. 4. The method of claim 1, further comprising receiving a second estimated location of the target device; and selecting the received estimated or the second estimated location of the target device as the estimated location for further processing. 5. The method of claim 4, wherein said selection is made based on cost or quality of the source from where the estimated or the second estimated location of the target device is received. 6. The method of claim 1, further comprising receiving terrain or morphology information; and used the received terrain or morphology information, in addition to said size of the annulus, to estimate whether the actual location lies on the same side of the boundary zone as the estimated location. 7. A method for calculating whether an actual location of a target device is on one side of a boundary zone, the method comprising: receiving an estimated location L of the target device; receiving a desired confidence level; determining a distance D, where D is the shortest distance from the estimated location to the boundary zone; forming a circle with radius R′, centered at the estimated location, wherein R′ is determined in such a way so that a likelihood that the actual location is inside the circle equals or exceeds the desired confidence level; forming a secant, perpendicular to D, passing through the point where D intersects the boundary zone; and using a size of an area bounded by the circle and the secant to estimate whether the actual location lies on the same side of the boundary zone as the estimated location, wherein the method is performed by one or more processors. 8. The method of claim 7, wherein the desired confidence level is a desired estimated probability that the actual location of the target device is within an uncertainty zone. 9. The method of claim 8, wherein the desired confidence level is constant for estimated locations received from a particular source. 10. The method of claim 7, further comprising receiving a second estimated location of the target device; and selecting the received estimated or the second estimated location of the target device as the estimated location for further processing. 11. The method of claim 10, wherein said selection is made based on cost or quality of the source from where the estimated or the second estimated location of the target device is received. 12. The method of claim 7, further comprising receiving terrain or morphology information; and used the received terrain or morphology information, in addition to said size of the annulus, to estimate whether the actual location lies on the same side of the boundary zone as the estimated location. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: Method for calculating whether an actual location of a target device is on one side of a boundary zone includes: receiving an estimated location of the target device; receiving a desired confidence level; forming a first circle with radius D, centered at the estimated location, where D is the shortest distance from the estimated location to the boundary zone; forming a second circle with radius R′, centered at the estimated location, wherein R′ is determined in such a way so that a likelihood that the actual location is inside the second circle equals or exceeds the desired confidence level; forming an angle with an apex at the estimated location and rays passing through two closest points to the estimated location where the second circle intersects the boundary zone; and using a size of an annulus formed by the first circle, the second circle, and the rays to estimate whether the actual location lies on the same side of the boundary zone. |
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G06N7005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Method for calculating whether an actual location of a target device is on one side of a boundary zone includes: receiving an estimated location of the target device; receiving a desired confidence level; forming a first circle with radius D, centered at the estimated location, where D is the shortest distance from the estimated location to the boundary zone; forming a second circle with radius R′, centered at the estimated location, wherein R′ is determined in such a way so that a likelihood that the actual location is inside the second circle equals or exceeds the desired confidence level; forming an angle with an apex at the estimated location and rays passing through two closest points to the estimated location where the second circle intersects the boundary zone; and using a size of an annulus formed by the first circle, the second circle, and the rays to estimate whether the actual location lies on the same side of the boundary zone. |
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Disclosed herein are systems and methods for data learning and classification for rapidly processing extremely large volumes of input data using one or more computing devices, that are application and platform independent, participating in a distributed parallel processing environment. In one embodiments, a system may comprise a plurality of parallel Map Reduction Aggregation Processors operating on the one or more computing devices, and configured to receive different sets of input data for data aggregation. Each of the Map Reduction Aggregation Processors may comprise one or more parallel Mapping Operation Modules configured to consistently dissect the input data into individual intermediate units of mapping outputs comprising consistently mapped data keys, and any values related to mapped data keys, conducive to simultaneous parallel reduction processing; and one or more parallel Reduction Operation Modules configured to continually and simultaneously consume the mapping outputs by eliminating the matching keys and aggregating values consistent with a specified reduction operation for all matching keys that are encountered during consumption of the mapping outputs. The system may also include an application-specific Classification Metric Function Operations Module operating on the one or more computing devices and configured to receive reduction outputs from the Reduction Operations Modules to determine distance and/or similarity between each of the different sets of input data with respect to one or more data classification categories using one or more distance and/or similarity calculations. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data learning and classification system for rapidly processing extremely large volumes of input data using one or more computing devices, that are application and platform independent, participating in a distributed parallel processing environment, the system comprising: a plurality of parallel Map Reduction Aggregation Processors operating on the one or more computing devices, and configured to receive different sets of input data for data aggregation, each of the Map Reduction Aggregation Processors comprising: one or more parallel Mapping Operation Modules configured to consistently dissect the input data into individual intermediate units of mapping outputs comprising consistently mapped data keys, and any values related to mapped data keys, conducive to simultaneous parallel reduction processing, and one or more parallel Reduction Operation Modules configured to continually and simultaneously consume the mapping outputs by eliminating the matching keys and aggregating values consistent with a specified reduction operation for all matching keys that are encountered during consumption of the mapping outputs; and an application-specific Classification Metric Function Operations Module operating on the one or more computing devices and configured to receive reduction outputs from the Reduction Operations Modules to determine distance and/or similarity between each of the different sets of input data with respect to one or more data classification categories using one or more distance and/or similarity calculations. 2. A data learning and classification system in accordance with claim 1, wherein the one or data classification categories comprise one or more parallel Nested Categorical Key Value Pair Collections consolidated from Reduction Operations Module outputs to provide key-based identifying of sets of input data. 3. A data learning and classification system in accordance with claim 2, wherein the key-based identifying is based on a category name associated with a category ID or a category ID associated with a category name. 4. A data learning and classification system in accordance with claim 3, further comprising a Category Manager comprising at least one key value pair collection of unique category names and category IDs, wherein the Map Reduction Aggregation Processors are further configured to access the Category Manager to provide the key-based identifying of input data using known category names and category IDs. 5. A data learning and classification system in accordance with claim 4, wherein the collection of categorical key value pairs for each key provides a categorical association including an optional numerical description of that key across any number of categorical keys contained in a Nested Categorical Key Value Pair Collection. 6. A data learning and classification system in accordance with claim 4, wherein the one or more Nested Categorical Key Value Pair Collections comprise all outputs using similar map reduction methods in at least one collection of nested key value pairs where the key for each key value pair maps to a nested collection of categorical key value pairs as its value. 7. A data learning and classification system in accordance with claim 4, wherein the Category Manager further comprises a Default Frequencies Collection comprising default category associations which may also include default starting frequencies for input data when Reduction Operations Module outputs contain one or more key values for input into the Nested Categorical Key Value Pair Collection. 8. A data learning and classification system in accordance with claim 7, wherein when unknown key values are encountered in Reduction Operations Module outputs during map reduction aggregation consolidation, the Default Frequencies Collection is accessed and at least one default category which may also include a default frequency for the input data being processed is used as a starting nested categorical key value pair entry collection in the new nested key value pair entry. 9. A data learning and classification system in accordance with claim 4, wherein the Classification Metric Function Operations Module may be configured to use a Classification Total Collection, comprising totals for each matching category identified when the Reduction Operations Module outputs contain keys that currently exist in the Nested Categorical Key Value Pair Collection, and to use a Category Totals Collection, comprising representative totals for each category, to produce a collection of penetration totals representing a classification score for each category identified during classification processing by the Classification Metric Function Module. 10. A data learning and classification system in accordance with claim 1, further comprising a blocking mechanism configured to regulate Mapping Operations Module outputs when production capacity exceeds a pre-determined production capacity threshold. 11. A data learning and classification system in accordance with claim 1, wherein each Map Reduction Aggregation Module further comprises one or more parallel Locality Sensitive Hashing (LSH) Operations Modules, each configured to produce a collection of values representing unique characteristics of combined Reduction Operation Module outputs from a specific set of input data, wherein the number of values contained within LSH Operations Module outputs for each set of input data is based on a number of distinct hash functions performed by each LSH Operations Module. 12. A data learning and classification system in accordance with claim 11, wherein each Map Reduction Aggregation Module further comprises a Second Blocking Mechanism configured to regulate Reduction Operations Module outputs when production capacity exceeds a pre-determined production capacity threshold. 13. A data learning and classification system in accordance with claim 11, wherein the Classification Metric Function Operations Module is further configured to receive LSH Operations Module outputs for use in the determining of similarity and/or distance between the different sets of input data and categories. 14. A data learning and classification system in accordance with claim 11, further comprising one or more parallel Nested Categorical Key Value Pair Collections consolidated from Reduction Operations Module outputs to provide key-based identifying of input data by category name associated with a category ID or category ID associated with category name, wherein the one or more Nested Categorical Key Value Pair Collections are further configured to store LSH Operations Module outputs prior to receipt by the Classification Metric Function Operations Module. 15. A data learning and classification system in accordance with claim 11, wherein the one or more LSH Operations Modules are further configured to produce one or more resulting Minimum Hash (MinHash) Values for each of the distinct hash functions performed, wherein a Collection of MinHash Value represents the unique characteristics of the sets of input data. 16. A data learning and classification system in accordance with claim 15, wherein the Map Reduction Aggregation Processors are further configured to consolidate the MinHash Values by storing LSH Operations Module outputs in one or more collections of nested key value pairs where the key for each key-value pair maps to a Nested Collection of Categorical Key-Value Pairs as its value. 17. A data learning and classification system in accordance with claim 16, wherein the Nested Collection of Categorical Key-Value Pairs may be partitioned based on each distinct hash function by assigning each distinct hash function a distinct hash function ID. 18. A data learning and classification system in accordance with claim 1, wherein the plurality of Map Reduction Aggregation Processors is configured to receive parallel sets of input data simultaneously. 19. A data learning and classification system in accordance with claim 1, wherein the Classification Metric Function Operations Module determines distance and/or similarity between the different sets of input data and categories using a classification metric function specified by an application programmer. 20. A data aggregation system in accordance with claim 19, wherein the Classification Metric Function Operations Module determine distance and/or similarity between the different sets of input data by performing multiple distance and/or similarity metric calculations consistent with the specified classification metric function. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Disclosed herein are systems and methods for data learning and classification for rapidly processing extremely large volumes of input data using one or more computing devices, that are application and platform independent, participating in a distributed parallel processing environment. In one embodiments, a system may comprise a plurality of parallel Map Reduction Aggregation Processors operating on the one or more computing devices, and configured to receive different sets of input data for data aggregation. Each of the Map Reduction Aggregation Processors may comprise one or more parallel Mapping Operation Modules configured to consistently dissect the input data into individual intermediate units of mapping outputs comprising consistently mapped data keys, and any values related to mapped data keys, conducive to simultaneous parallel reduction processing; and one or more parallel Reduction Operation Modules configured to continually and simultaneously consume the mapping outputs by eliminating the matching keys and aggregating values consistent with a specified reduction operation for all matching keys that are encountered during consumption of the mapping outputs. The system may also include an application-specific Classification Metric Function Operations Module operating on the one or more computing devices and configured to receive reduction outputs from the Reduction Operations Modules to determine distance and/or similarity between each of the different sets of input data with respect to one or more data classification categories using one or more distance and/or similarity calculations. |
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G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Disclosed herein are systems and methods for data learning and classification for rapidly processing extremely large volumes of input data using one or more computing devices, that are application and platform independent, participating in a distributed parallel processing environment. In one embodiments, a system may comprise a plurality of parallel Map Reduction Aggregation Processors operating on the one or more computing devices, and configured to receive different sets of input data for data aggregation. Each of the Map Reduction Aggregation Processors may comprise one or more parallel Mapping Operation Modules configured to consistently dissect the input data into individual intermediate units of mapping outputs comprising consistently mapped data keys, and any values related to mapped data keys, conducive to simultaneous parallel reduction processing; and one or more parallel Reduction Operation Modules configured to continually and simultaneously consume the mapping outputs by eliminating the matching keys and aggregating values consistent with a specified reduction operation for all matching keys that are encountered during consumption of the mapping outputs. The system may also include an application-specific Classification Metric Function Operations Module operating on the one or more computing devices and configured to receive reduction outputs from the Reduction Operations Modules to determine distance and/or similarity between each of the different sets of input data with respect to one or more data classification categories using one or more distance and/or similarity calculations. |
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The present invention generally relates to an improved system and method for providing email classification. Specifically, the present invention relates to an email classification system and method for analyzing the signature of an email for proper classification. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for providing simplified end-to-end security for computing devices in standalone, LAN, WAN or Internet architectures; said system comprising: an email processing module, comprising computer-executable code stored in non-volatile memory, a machine learning module, comprising computer-executable code stored in non-volatile memory, a processor, and a communications means, wherein said email processing module, said machine learning module, said processor, and said communications means are operably connected and are configured to: receive an email; remove hypertext markup language (HTML) from said email; remove white space, new line, carriage returns (CR) and tabs from said email; convert all text contained in said email to lowercase characters; compare text to relationship terms stored in a relationship term database; tag text matching one or more of said relationship terms; tag text comprising dates, numbers, indicators of time, measurement units, and currency symbols; tag text comprising parts of speech; compare text to lemmatize terms stored in a lemmatize dictionary database; tag text matching one or more lemmatize terms; remove non-essential punctuation from said text; calculate and weigh term frequency in said text using term frequency inverse document frequency; eliminate one or more terms with the lowest calculated weight; and classify said email based on remaining tags and terms. 2. The system of claim 1, wherein the classification of said email is accomplished via a Naive Bayes classifier process. 3. The system of claim 1, wherein the system further comprises a NaïBayes Trainer module and a NaïBayes classifier module. 4. The system of claim 1, wherein the classification of said email is accomplished via a Support Vector Machines (SVM) or Support Vector Networks (SVN) classifier process. 5. The system of claim 1, wherein the system further comprises one or more of a Support Vector Machine trainer module, a Support Vector Network trainer module, a Support Vector Machine classifier module, and a Support Vector Network classifier module. 6. The system of claim 1, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to match remaining terms with categories stored in a category database. 7. The system of claim 6, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to replace one or more remaining terms with replacement tags. 8. The system of claim 7, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to move said email to a location based on said replacement tags. 9. The system of claim 6, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to replace one or more remaining terms with replacement categories. 10. The system of claim 9, wherein said email processing module, said machine learning module, said processor, and said communications means are further configured to move said email to a location based on said replacement categories. 11. A method for classifying emails, said method comprising the steps of: receiving an email at an email processing module, comprising computer-executable code stored in non-volatile memory; removing hypertext markup language (HTML) from said email; removing multiple white space, and tabs from said email; converting all text contained in said email to lowercase characters; comparing text to relationship terms stored in a relationship term database; tagging text matching one or more of said relationship terms; tagging text comprising dates, numbers, indicators of time, measurement units, and currency symbols; tagging text comprising parts of speech; comparing text to lemmatize terms stored in a lemmatize dictionary database; tagging text matching one or more lemmatize terms; removing non-essential punctuation from said text; calculating and weigh term frequency in said text using term frequency inverse document frequency; eliminating one or more terms with the lowest calculated weight; and classifying said email based on remaining tags and terms. 12. The method of claim 11, wherein the classification of said email is accomplished via a Naive Bayes classifier process. 13. The method of claim 11, wherein the classification of said email is accomplished via a Support Vector Machines (SVM) or Support Vector Networks (SVN) classifier process. 14. The method of claim 11, further comprising the step of matching remaining terms with categories stored in a category database. 15. The method of claim 11, further comprising the step of replacing one or more remaining terms with replacement tags. 16. The method of claim 15, further comprising the step of moving said email to a location based on said replacement tags. 17. The method of claim 11, further comprising the step of replacing one or more remaining terms with replacement categories. 18. The method of claim 17, further comprising the step of moving said email to a location based on said replacement categories. |
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REJECTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: The present invention generally relates to an improved system and method for providing email classification. Specifically, the present invention relates to an email classification system and method for analyzing the signature of an email for proper classification. |
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G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The present invention generally relates to an improved system and method for providing email classification. Specifically, the present invention relates to an email classification system and method for analyzing the signature of an email for proper classification. |
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A method, system, and non-transitory compute readable medium for hidden evidence correlation and causation linking including computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements, forecasting the hidden evidence into future forecasted cycle measurements, and computing a causation link based on the future forecasted cycle measurements. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A hidden evidence correlation and causation linking system, the system comprising: a correlation computing device configured to compute a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements; a forecasting device configured to forecast the hidden evidence into future forecasted cycle measurements; and a causation computing device configured to compute a causation link based on the future forecasted cycle measurements. 2. The system of claim 1, further comprising an extraction device configured to extract, for a given time domain, temporal relations with a topic based on the user input from a large corpus of data. 3. The system of claim 2, wherein the extraction device converts the temporal relations to a time versus frequency graph by using a Fast Fourier Transform (FFT). 4. The system of claim 2, further comprising an evidence finding device configured to find evidence based on the temporal relations from the extraction device. 5. The system of claim 2, further comprising an evidence finding device configured to find evidence based on the temporal relations from the extraction device, wherein after finding the evidence, the evidence finding device determines a principal time series component of the evidence. 6. The system of claim 5, further comprising a re-sampling device configured to re-sample the temporal relations found by the evidence finding device to find hidden evidence based on the principal time series component. 7. The system of claim 6, wherein the hidden evidence comprises a re-sampled piece of evidence from the principal time series component. 8. The system of claim 1, wherein the forecasted cycle measurements are transformed into an amplitude versus frequency histogram, and wherein each histogram is compared and the causation computing device computes a probability density function to produce a degree of causation between the hidden evidence. 9. The system of claim 1, further comprising a first filtering device configured to filter out false negatives of hidden evidence that are correlated to each other by the correlation computing device. 10. The system of claim 9, wherein the first filtering device includes a first answer key associated with a high correlation and a low correlation between the hidden evidence in which parameters are compared of the hidden evidence to calculate a correlation threshold value, and wherein the first filtering device determines if the correlations are the false negatives based on a threshold value compared to the correlation threshold value. 11. The system of claim 1, further comprising a filtering device configured to filter out false positives of the hidden evidence that is determined to have a causation to each other by the causation computing device. 12. The system of claim 11, wherein the filtering device includes a second answer key associated with a high correlation and a low correlation between the hidden evidence, and wherein based on a comparison with the second answer key, a causation score with training data is used to minimize false positives and includes semantic features. 13. The system of claim 1, further comprising a first filtering device configured to filter out false negatives of hidden evidence that are correlated to each other by the correlation computing device; and a second filtering device configured to filter out false positives of the hidden evidence that is determined to have a causation to each other by the causation computing device. 14. The system of claim 13, wherein the first filtering device includes a first answer key associated with a high correlation and a low correlation between the hidden evidence in which parameters are compared of the hidden evidence to calculate a correlation threshold value, wherein the first filtering device determines if the correlations comprise the false negatives based on a threshold value compared to the correlation threshold value, wherein the second filtering device includes a second answer key associated with a high correlation and a low correlation between the hidden evidence, and wherein based on a comparison with the second answer key, a causation score with training data is used to minimize false positives and includes semantic features. 15. The system of claim 5, further comprising a first filtering device configured to filter out false negatives of hidden evidence that are correlated to each other by the correlation computing device; and a second filtering device configured to filter out false positives of the hidden evidence that is determined to have a causation to each other by the causation computing device, wherein the first filtering device includes a first answer key associated with a high correlation and a low correlation between the hidden evidence in which parameters are compared of the hidden evidence to calculate a correlation threshold value, wherein the first filtering device determines if the correlations comprise the false negatives based on a threshold value compared to the correlation threshold value, wherein the second filtering device includes a second answer key associated with a high correlation and a low correlation between the hidden evidence, and wherein based on a comparison with the second answer key, a causation score with training data is used to minimize false positives and includes semantic features. 16. The system of claim 1, further comprising an extraction device configured to extract, for a given time domain, temporal relations with a topic based on the user input from a large corpus of data; an evidence finding device configured to find evidence based on the temporal relations from the extraction device; and a re-sampling device configured to re-sample the temporal relations found by the evidence finding device to find hidden evidence based on a principal time series component. 17. A non-transitory computer-readable recording medium recording a program for hidden evidence correlation and causation linking, the program causing a computer to perform: computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements; forecasting the hidden evidence into future forecasted cycle measurements; and computing a causation link based on the future forecasted cycle measurements. 18. The non-transitory computer-readable recording medium of claim 17, wherein the forecasted cycle measurements are transformed into an amplitude versus frequency histogram, and wherein each histogram is compared and the computing the causation link further applies a probability density function to produce a degree of causation between the hidden evidence. 19. A hidden evidence correlation and causation linking method, the method comprising: computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements; forecasting the hidden evidence into future forecasted cycle measurements; and computing a causation link based on the future forecasted cycle measurements. 20. The method of claim 19, wherein the forecasted cycle measurements are transformed into an amplitude versus frequency histogram, and wherein each histogram is compared and the computing the causation link further applies a probability density function to produce a degree of causation between the hidden evidence. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A method, system, and non-transitory compute readable medium for hidden evidence correlation and causation linking including computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements, forecasting the hidden evidence into future forecasted cycle measurements, and computing a causation link based on the future forecasted cycle measurements. |
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G06N5022 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method, system, and non-transitory compute readable medium for hidden evidence correlation and causation linking including computing a correlation link based on hidden evidence found in relation to a user input in hidden cycle measurements, forecasting the hidden evidence into future forecasted cycle measurements, and computing a causation link based on the future forecasted cycle measurements. |
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A method, system and product for predicting an outcome of a program based on input. The method comprising: obtaining an input to be used by a program prior to executing the program; predicting by, a machine learning module, a predicted outcome of the program based on the input; wherein the predicted outcome is selected from the group consisting of: a pass outcome and a fail outcome, wherein the pass outcome is the program executing without failing when using the input, and wherein the fail outcome is the program failing when using the input. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, comprising: obtaining an input to be used by a program prior to executing the program; predicting by, a machine learning module, a predicted outcome of the program based on the input, wherein said predicting is performed by a processor; wherein the predicted outcome is selected from the group consisting of: a pass outcome and a fail outcome, wherein the pass outcome is the program executing without failing when using the input, and wherein the fail outcome is the program failing when using the input. 2. The computer-implemented method of claim 1 further comprising: executing the program; determining an execution outcome of the program, wherein the execution outcome is selected from the group consisting of: the pass outcome and the fail outcome, wherein the execution outcome is different from the predicted outcome; and updating the machine learning module, wherein said updating is based on the input and the execution outcome. 3. The computer-implemented method of claim 2, wherein the machine learning module comprises a data repository configured to store inputs and an associated outcome for each input, wherein said updating the machine learning module comprises substituting the predicted outcome with the executed outcome in the data repository. 4. The computer-implemented method of claim 1, wherein the predicted outcome is the fail outcome, the method further comprises outputting the fail outcome. 5. The computer-implemented method of claim 4, wherein the input comprises a vector of attributes, wherein said outputting is outputting a subset of the attributes, wherein the subset is indicative of a cause of the fail outcome. 6. The computer-implemented method of claim 4, wherein said outputting is outputting to a developer of the program. 7. The computer-implemented method of claim 4, wherein said outputting is outputting to a user executing the program, the method further comprises executing the program in response to an instruction from the user. 8. The computer-implemented method of claim 1, wherein the predicted outcome is the fail outcome, the method further comprises avoiding executing the program in view of said predicting. 9. The computer-implemented method of claim 1, wherein the machine learning module is trained based on past execution outcomes. 10. A computerized apparatus having a processor, the processor being adapted to perform the steps of: obtaining an input to be used by a program prior to executing the program; predicting by, a machine learning module, a predicted outcome of the program based on the input, wherein the predicted outcome is selected from the group consisting of: a pass outcome and a fail outcome, wherein the pass outcome is the program executing without failing when using the input, and wherein the fail outcome is the program failing when using the input. 11. The computerized apparatus of claim 10, wherein the processor is further adapted to perform the steps of: executing the program: determining an execution outcome of the program, wherein the execution outcome is selected from the group consisting of: the pass outcome and the fail outcome, wherein the execution outcome is different from the predicted outcome; and updating the machine learning module, wherein said updating is based on the input and the execution outcome. 12. The computerized apparatus of claim 11, wherein the machine learning module comprises a data repository configured to store inputs and an associated outcome for each input, wherein said updating the machine learning module comprises substituting the predicted outcome with the executed outcome in the data repository. 13. The computerized apparatus of claim 10, wherein the predicted outcome is the fail outcome, wherein the processor is further adapted to perform the step of outputting the fail outcome. 14. The computerized apparatus of claim 13, wherein the input comprises a vector of attributes, wherein said outputting is outputting a subset of the attributes, wherein the subset is indicative of a cause of the fail outcome. 15. The computerized apparatus of claim 13, wherein said outputting is outputting to a developer of the program. 16. The computerized apparatus of claim 13, wherein said outputting is outputting to a user executing the program, wherein the processor is further adapted to perform the step of executing the program in response to an instruction from the user. 17. The computerized apparatus of claim 10, wherein the predicted outcome is the fail outcome, wherein the processor is further adapted to perform the step of avoiding executing the program in view of said predicting. 18. The computerized apparatus of claim 10, wherein the machine learning module is trained based on past execution outcomes. 19. A computer program product comprising a computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform a method comprising: obtaining an input to be used by a program prior to executing the program; predicting by, a machine learning module, a predicted outcome of the program based on the input, wherein the predicted outcome is selected from the group consisting of: a pass outcome and a fail outcome, wherein the pass outcome is the program executing without failing when using the input, and wherein the fail outcome is the program failing when using the input. |
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ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: A method, system and product for predicting an outcome of a program based on input. The method comprising: obtaining an input to be used by a program prior to executing the program; predicting by, a machine learning module, a predicted outcome of the program based on the input; wherein the predicted outcome is selected from the group consisting of: a pass outcome and a fail outcome, wherein the pass outcome is the program executing without failing when using the input, and wherein the fail outcome is the program failing when using the input. |
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G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method, system and product for predicting an outcome of a program based on input. The method comprising: obtaining an input to be used by a program prior to executing the program; predicting by, a machine learning module, a predicted outcome of the program based on the input; wherein the predicted outcome is selected from the group consisting of: a pass outcome and a fail outcome, wherein the pass outcome is the program executing without failing when using the input, and wherein the fail outcome is the program failing when using the input. |
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A technique for caching evidence for answering questions in a cache memory of a data processing system (that is configured to answer questions) includes receiving a first question. The first question is analyzed to identify a first set of characteristics of the first question. A first set of evidence for answering the first question is loaded into the cache memory. A second question is received. The second question is analyzed to identify a second set of characteristics of the second question. A portion of the first set of evidence, whose expected usage in answering the second question is below a determined threshold, is unloaded from the cache memory. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for caching evidence for answering questions in a cache memory of a data processing system that is configured to answer questions, comprising: receiving, by the data processing system, a first question; analyzing, by the data processing system, the first question to identify a first set of characteristics of the first question; loading, by the data processing system, a first set of evidence into the cache memory of the data processing system for answering the first question; receiving, by the data processing system, a second question; analyzing, by the data processing system, the second question to identify a second set of characteristics of the second question; and unloading from the cache memory, by the data processing system, a portion of the first set of evidence whose expected usage in answering the second question is below a determined threshold. 2. The method of claim 1, wherein the unloading, by the data processing system, a portion of the first set of evidence is performed at a rate that is based on a relevance of the first set of characteristics to the second set of characteristics. 3. The method of claim 1, wherein the first set of characteristics and the second set of characteristics each include a question type, an evidence size, primary search attributes, and an evidence score that are respectively associated with the first and second questions. 4. The method of claim 1, further comprising: loading additional evidence for a hypothesis related to one of the first and second questions into the cache memory when another question is not received. 5. The method of claim 1, further comprising comparing the first set of characteristics to the second set of characteristics to determine the expected usage for the first set of evidence. 6. The method of claim 1, wherein the cache memory is shared between processors of a node and the data processing system is a high performance computing system that includes multiple nodes. 7. The method of claim 1, wherein the unloading, by the data processing system, a portion of the first set of evidence is performed at a rate that is based on a relevance of the first set of characteristics to subsequent question characteristics. 8. A computer program product for managing cache memory of a question answering system, the computer program product comprising: a computer-readable storage device; and computer-readable program code embodied on the computer-readable storage device, wherein the computer-readable program code, when executed by a data processing system, causes the data processing system to: receive a first question; analyze the first question to identify a first set of characteristics of the first question; load a first set of evidence into the cache memory of the data processing system for answering the first question; receive a second question; analyze the second question to identify a second set of characteristics of the second question; and unload from the cache memory a portion of the first set of evidence whose expected usage in answering the second question is below a determined level. 9. The computer program product of claim 8, wherein the unloading a portion of the first set of evidence is performed at a rate that is based on a relevance of the first set of characteristics to the second set of characteristics. 10. The computer program product of claim 8, wherein the first set of characteristics and the second set of characteristics each include a question type, an evidence size, primary search attributes, and an evidence score that are respectively associated with the first and second questions. 11. The computer program product of claim 8, wherein the computer-readable program code, when executed by the data processing system, is further configured to cause the data processing system to load additional evidence for a hypothesis related to one of the first and second questions into the cache memory when another question is not received. 12. The computer program product of claim 8, further comprising comparing the first set of characteristics to the second set of characteristics to determine the expected usage for the first set of evidence. 13. The computer program product of claim 8, wherein the cache memory is shared between processors of a node of a high performance computing system that includes multiple nodes. 14. The computer program product of claim 8, wherein the unloading a portion of the first set of evidence is performed at a rate that is based on a relevance of the first set of characteristics to subsequent question characteristics. 15. A data processing system, comprising: a cache memory; and a processor coupled to the cache memory, wherein the processor is configured to: receive a first question; analyze the first question to identify a first set of characteristics of the first question; load a first set of evidence into the cache memory for answering the first question; receive a second question; analyze the second question to identify a second set of characteristics of the second question; and unload from the cache memory a portion of the first set of evidence whose expected usage in answering the second question is below a determined level. 16. The data processing system of claim 15, wherein the unloading a portion of the first set of evidence is performed at a rate that is based on a relevance of the first set of characteristics to the second set of characteristics. 17. The data processing system of claim 15, wherein the first set of characteristics and the second set of characteristics each include a question type, an evidence size, primary search attributes, and an evidence score that are respectively associated with the first and second questions. 18. The data processing system of claim 15, wherein the processor is further configured to load additional evidence for a hypothesis related to one of the first and second questions into the cache memory when another question is not received. 19. The data processing system of claim 15, wherein the processor is further configured to compare the first set of characteristics to the second set of characteristics to determine the expected usage for the first set of evidence. 20. The data processing system of claim 15, wherein the cache memory is shared between processors of a node and the data processing system is a high performance computing system that includes multiple nodes. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A technique for caching evidence for answering questions in a cache memory of a data processing system (that is configured to answer questions) includes receiving a first question. The first question is analyzed to identify a first set of characteristics of the first question. A first set of evidence for answering the first question is loaded into the cache memory. A second question is received. The second question is analyzed to identify a second set of characteristics of the second question. A portion of the first set of evidence, whose expected usage in answering the second question is below a determined threshold, is unloaded from the cache memory. |
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G06N5022 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A technique for caching evidence for answering questions in a cache memory of a data processing system (that is configured to answer questions) includes receiving a first question. The first question is analyzed to identify a first set of characteristics of the first question. A first set of evidence for answering the first question is loaded into the cache memory. A second question is received. The second question is analyzed to identify a second set of characteristics of the second question. A portion of the first set of evidence, whose expected usage in answering the second question is below a determined threshold, is unloaded from the cache memory. |
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An electronic neuron device that includes a thresholding unit which utilizes current-induced spin-orbit torque (SOT). A two-step switching scheme is implemented with the device. In the first step, a charge current through heavy metal (HM) places the magnetization of a nano-magnet along the hard-axis (i.e. an unstable point for the magnet). In the second step, the device receives a current (from an electronic synapse) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the net synaptic current determines the final orientation of the magnetization. A resistive crossbar array may also be provided which functions as the synapse generating a bipolar current that is a weighted sum of the inputs of the device. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A thresholding device for an electronic neuron, comprising: a) a heavy metal layer having a high spin orbit coupling; b) a perpendicular magnetic anisotropy free layer having a bottom surface in contact with a top surface of the heavy metal layer; c) a perpendicular magnetic anisotropy pinned layer; and d) an oxide tunnel barrier connected between the free layer and the pinned layer, wherein the pinned layer, the oxide tunnel barrier, and the free layer form a magnetic tunnel junction. 2. The thresholding device of claim 1, further comprising a current source, the current source configured to: a) supply a first charge current through the heavy metal layer, from a first end of the heavy metal layer to a second end of the heavy metal layer in a first direction along a first axis of the heavy metal layer to generate a torque which aligns the free layer magnetization in a direction along a second axis transverse to the first axis; and then b) supply a second charge current from the pinned layer, through the magnetic tunnel junction, to the second end of the heavy metal layer to exert a torque on the magnetization of the free layer to align the free layer to either one of two orientations along a third axis, the third axis transverse to the first and second axis, the two orientations anti-parallel to one another. 3. The thresholding device of claim 1, wherein the second supply charge current is substantially zero to allow the magnetic orientation of the free layer to randomly select between the two orientations along the third axis. 4. The thresholding device of claim 1, wherein the heavy metal comprises beta-Tantalum, Tungsten, or Platinum. 5. The thresholding device of claim 1, wherein the pinned layer and the free layer comprise a ferromagnetic material. 6. The thresholding device of claim 5, wherein the pinned layer and the free layer comprise CoFe or CoFeB. 7. The thresholding device according to claim 5, wherein the oxide tunnel barrier comprises MgO. 8. A random number generating device, comprising: a) a heavy metal layer having a high spin orbit coupling; b) a perpendicular magnetic anisotropy free layer having a bottom surface in contact with a top surface of the heavy metal layer; c) a perpendicular magnetic anisotropy pinned layer; d) an oxide tunnel barrier connected between the free layer and the pinned layer, wherein the pinned layer, the oxide tunnel barrier, and the free layer form a magnetic tunnel junction; and e) a current switching device; and f) a current source, the current source configured to supply a first charge current through the heavy metal layer, from a first end of the heavy metal layer to a second end of the heavy metal layer in a first direction along a first axis of the heavy metal layer to generate a torque which aligns the free layer magnetization in a direction along a second axis transverse to the first axis, and then turn off to allow a random thermal field to tilt the magnetization of the free layer 104 closer to one of two stable orientations. 9. An artificial neural network arrangement, comprising: a) a plurality of electrically conductive row crossbars, each row crossbar connect to a first terminal of a plurality of resistive memory elements; b) a plurality of electrically conductive column crossbars, each column crossbar connected to a second terminal of a plurality of resistive memory elements; c) a plurality of thresholding devices, each of the thresholding devices comprising: i) a heavy metal layer having a high spin orbit coupling; ii) a perpendicular magnetic anisotropy free layer having a bottom surface in contact with a top surface of the heavy metal layer; iii) a perpendicular magnetic anisotropy pinned layer; iv) an oxide tunnel barrier connected between the free layer and the pinned layer, wherein the pinned layer, the oxide tunnel barrier, and the free layer form a magnetic tunnel junction, wherein the pinned layer is connected to one of the column crossbars. 10. The neural network arrangement according to claim 9, further comprising: a) a plurality of electronic switching devices each connecting the row crossbars to a voltage source. 11. The neural network arrangement according to claim 10, wherein each of the electronic switching devices comprises a control input, and wherein each control input is connected to an input signal. 12. The neural network arrangement according to claim 11, wherein each input signal is connected to the control inputs of a pair of the electronic switching devices; and wherein one of said pair of electronic switching devices is connected to a positive voltage source and the other one of said pair of electronic switching devices is connected to a negative voltage source. 13. The neural network arrangement according to claim 12, wherein the electronic switching devices comprise a transistor. 14. The neural network arrangement according to claim 9, wherein resistive memory elements comprise GeSbTe memristors. 15. The neural network arrangement according to claim 9, wherein the resistive memory elements comprise Ag—Se memristors. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: An electronic neuron device that includes a thresholding unit which utilizes current-induced spin-orbit torque (SOT). A two-step switching scheme is implemented with the device. In the first step, a charge current through heavy metal (HM) places the magnetization of a nano-magnet along the hard-axis (i.e. an unstable point for the magnet). In the second step, the device receives a current (from an electronic synapse) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the net synaptic current determines the final orientation of the magnetization. A resistive crossbar array may also be provided which functions as the synapse generating a bipolar current that is a weighted sum of the inputs of the device. |
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G06N30635 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: An electronic neuron device that includes a thresholding unit which utilizes current-induced spin-orbit torque (SOT). A two-step switching scheme is implemented with the device. In the first step, a charge current through heavy metal (HM) places the magnetization of a nano-magnet along the hard-axis (i.e. an unstable point for the magnet). In the second step, the device receives a current (from an electronic synapse) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the net synaptic current determines the final orientation of the magnetization. A resistive crossbar array may also be provided which functions as the synapse generating a bipolar current that is a weighted sum of the inputs of the device. |
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Systems and techniques for predictive classification in action sports are described herein. A start point for an action may be identified in a data stream including a plurality of data sets corresponding to the action. The data stream may be collected from a sensor array. Action performance features may be extracted from the data stream subsequent to the start point. The action performance features may be compared in real-time to a set of statistical models. A label may be selected for the action based on the comparison. A likelihood of success may be generated for the action based on the comparison. The label for the action and the likelihood of success may be output for display on a display device. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for predictive action assessment in action sports, the system comprising: one or more processors; a memory including instructions that, when executed by the one or more processors, cause the one or more processors to perform operations to: identify a start point for an action in a data stream including a plurality of data sets corresponding to the action, the data stream collected from a sensor array; extract action performance features from the data stream subsequent to the start point; compare, in real time, the action performance features to a set of statistical models; select a label for the action based on the comparison; generate a likelihood of success for the action based on the comparison; and output the label for the action and the likelihood of success for display on a display device. 2. The system of claim 1, wherein the instructions to extract the action performance features include instructions to extract features from a time period before the start point of the action. 3. The system of claim 1, further comprising instructions that cause the one or more processors to perform operations to: select a map from a set of maps using geolocation data included in the data stream, the map including a plurality of data items corresponding to a location of the action; and wherein the instructions to generate the likelihood of success include instructions to use at least one data item of the plurality of data items corresponding to the location. 4. The system of claim 1, further comprising instructions that cause the one or more processors to perform operations to: collect a set of historical data corresponding to a set of actions for an action sport; extract past performance features for each action of the set of actions; generate a statistical model for each action using the past performance features; and create the set of statistical models using the statistical model for each action. 5. The system of claim 1, further comprising instructions that cause the one or more processors to perform operations to: collect a plurality of historical data sets corresponding to the action, each historical data set of the plurality of historical data sets including scoring data corresponding to the action; extract a set of score indicator features from the plurality of historical data sets; generate a score model for the action using the set of score indicator features; compare the action performance features to the score model; determine an action score based on the comparison; and output, for display on the display device, the action score. 6. The system of claim 5, further comprising instructions that cause the one or more processors to perform operations to: obtain a set of run scores for a run including the action; derive a total run score for the run using the set of run scores and the action score; and output, for display on the display device, the total run score. 7. The system of claim 5, wherein the set of score indicator features includes at least one of a difficulty of the action, a success rate of the action, a statistical measure of the action, and a frequency with which the action is performed. 8. At least one computer readable medium including instructions for predictive action assessment in action sports that, when executed by a machine, cause the machine to perform operations to: identify a start point for an action in a data stream including a plurality of data sets corresponding to the action, the data stream collected from a sensor array; extract action performance features from the data stream subsequent to the start point; compare, in real time, the action performance features to a set of statistical models; select a label for the action based on the comparison; generate a likelihood of success for the action based on the comparison; and output the label for the action and the likelihood of success for display on a display device. 9. The at least one computer readable medium of claim 8, wherein the instructions to extract the action performance features include instructions to extract features from a time period before the start point of the action. 10. The at least one computer readable medium of claim 8, further comprising instructions that cause the one or more processors to perform operations to: select a map from a set of maps using geolocation data included in the data stream, the map including a plurality of data items corresponding to a location of the action; and wherein the instructions to generate the likelihood of success include instructions to use at least one data item of the plurality of data items corresponding to the location. 11. The at least one computer readable medium of claim 8, further comprising instructions that cause the one or more processors to perform operations to: collect a set of historical data corresponding to a set of actions for an action sport; extract past performance features for each action of the set of actions; generate a statistical model for each action using the past performance features; and create the set of statistical models using the statistical model for each action. 12. The at least one computer readable medium of claim 8, further comprising instructions that cause the one or more processors to perform operations to: collect a plurality of historical data sets corresponding to the action, each historical data set of the plurality of historical data sets including scoring data corresponding to the action; extract a set of score indicator features from the plurality of historical data sets; generate a score model for the action using the set of score indicator features; compare the action performance features to the score model; determine an action score based on the comparison; and output, for display on the display device, the action score. 13. The at least one computer readable medium of claim 12, further comprising instructions that cause the one or more processors to perform operations to: obtain a set of run scores for a run including the action; derive a total run score for the run using the set of run scores and the action score; and output, for display on the display device, the total run score. 14. The at least one computer readable medium of claim 12, wherein the set of score indicator features includes at least one of a difficulty of the action, a success rate of the action, a statistical measure of the action, and a frequency with which the action is performed. 15. A method for predictive action assessment in action sports, the method comprising: identifying a start point for an action in a data stream including a plurality of data sets corresponding to the action, the data stream collected from a sensor array; extracting action performance features from the data stream subsequent to the start point; comparing, in real time, the action performance features to a set of statistical models; selecting a label for the action based on the comparison; generating a likelihood of success for the action based on the comparison; and outputting the label for the action and the likelihood of success for display on a display device. 16. The method of claim 15, wherein extracting the action performance features includes extracting features from a time period before the start point of the action. 17. The method of claim 15, further comprising: selecting a map from a set of maps using geolocation data included in the data stream, the map including a plurality of data items corresponding to a location of the action; and wherein generating the likelihood of success includes using at least one data item of the plurality of data items corresponding to the location. 18. The method of claim 15, further comprising: collecting a set of historical data corresponding to a set of actions for an action sport; extracting past performance features for each action of the set of actions; generating a statistical model for each action using the past performance features; and creating the set of statistical models using the statistical model for each action. 19. The method of claim 15, further comprising: collecting a plurality of historical data sets corresponding to the action, each historical data set of the plurality of historical data sets including scoring data corresponding to the action; extracting a set of score indicator features from the plurality of historical data sets; generating a score model for the action using the set of score indicator features; comparing the action performance features to the score model; determining an action score based on the comparison; and outputting, for display on the display device, the action score. 20. The method of claim 19, further comprising: obtaining a set of run scores for a run including the action; deriving a total run score for the run using the set of run scores and the action score; and outputting, for display on the display device, the total run score. 21. The method of claim 19, wherein the set of score indicator features includes at least one of a difficulty of the action, a success rate of the action, a statistical measure of the action, and a frequency with which the action is performed. |
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PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems and techniques for predictive classification in action sports are described herein. A start point for an action may be identified in a data stream including a plurality of data sets corresponding to the action. The data stream may be collected from a sensor array. Action performance features may be extracted from the data stream subsequent to the start point. The action performance features may be compared in real-time to a set of statistical models. A label may be selected for the action based on the comparison. A likelihood of success may be generated for the action based on the comparison. The label for the action and the likelihood of success may be output for display on a display device. |
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G06N7005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems and techniques for predictive classification in action sports are described herein. A start point for an action may be identified in a data stream including a plurality of data sets corresponding to the action. The data stream may be collected from a sensor array. Action performance features may be extracted from the data stream subsequent to the start point. The action performance features may be compared in real-time to a set of statistical models. A label may be selected for the action based on the comparison. A likelihood of success may be generated for the action based on the comparison. The label for the action and the likelihood of success may be output for display on a display device. |
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An aspect of the present disclosure aims to reduce problems associated with data acquisition of a rule set. Systems and methods enabling a semantic reasoner to stage acquisition of data objects necessary to bring each of the rules stored in the knowledge base to a conclusion are disclosed. To that end, a dependency chain is constructed, identifying whether and how each rule depends on other rules. Based on the dependency chain, the rules are assigned to difference epochs and reasoning engine is configured to perform machine reasoning over rules of each epoch sequentially. Moreover, when processing rules of each epoch, data objects referenced by the rules assigned to a currently processed epoch are acquired according to a certain order established based on criteria such as e.g. cost of acquisition of data objects. Such an approach provides automatic determination and just-in-time acquisition of data objects required for semantic reasoning. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method executed by a semantic reasoner in a system, the method comprising: constructing a dependency chain for a plurality of rules, the dependency chain identifying whether and how each rule of the plurality of rules depends on other rules of the plurality of rules; based on the dependency chain, assigning each rule of the plurality of rules to one epoch of a sequence of N epochs E1-EN, N being an integer equal to or greater than 2; and performing machine reasoning over the N epochs sequentially, wherein performing machine reasoning over each epoch comprises using one or more criteria to establish an order for acquiring one or more data objects referenced by one or more rules assigned to the epoch and acquiring the one or more data objects in the established order. 2. The method according to claim 1, wherein: each rule of one or more rules assigned to a first epoch E1 of the sequence does not depend on any rules of the plurality of rules, and each rule of one or more rules assigned to an epoch subsequent to the first epoch E1 depends on at least one rule of an epoch immediately preceding the epoch in the sequence of N epochs. 3. The method according to claim 2, wherein performing machine reasoning over each epoch Ei, i being an integer equal to or greater than 1 and equal to or smaller than N, comprises steps of: (a) identifying one or more data objects referenced by all rules of the one or more rules of the epoch Ei that have not reached a conclusion, (b) using the one or more criteria to establish the order for acquiring one or more data objects of the data objects identified in step (a) that have not yet been acquired in the epoch Ei, wherein establishing the order comprises assigning each data object of the one or more data objects of the data objects identified in step (a) that have not yet been acquired in the epoch Ei to one group of an ordered set of one or more groups, (c) acquiring one or more data objects assigned to a group that is first in the ordered set of the one or more groups, and (d) using a reasoning engine to perform machine reasoning over one or more data objects acquired in step (c). 4. The method according to claim 3, wherein performing machine reasoning over the each epoch Ei further comprises: (e) following the machine reasoning of step (d), identifying which zero or more rules of the one or more rules of the epoch Ei have not reached a conclusion, and (f) iterating steps (a)-(e) until all rules of the one or more rules of the epoch Ei have reached a conclusion. 5. The method according to claim 3, wherein step (b) comprises identifying one or more atoms comprised within the one or more rules assigned to the epoch Ei, the one or more atoms referencing the plurality of data objects and determining, for each atom of the one or more atoms, a data source for acquiring one or more data objects referenced by the atom. 6. The method according to claim 5, wherein determining the data source for each atom comprises determining an XPath of YANG object that maps to the atom. 7. The method according to claim 6, wherein determining the XPath of YANG object that maps to the atom comprises determining the XPath of YANG object based on one or more YANG models of one or more network elements referenced by the plurality of rules, base ontology of a knowledge base comprising the plurality of rules, and a set of predefined mapping rules. 8. The method according to claim 1, wherein constructing the dependency chain comprises determining, for each rule of the plurality of rules, whether the rule depends on one or more other rules of the plurality of rules. 9. The method according to claim 8, wherein a first rule of the plurality of rules is determined to depend on a second rule of the plurality of rules when execution of the first rule depends on execution of the second rule. 10. The method according to claim 1, wherein the one or more criteria comprise one or more of a location of a data object in a memory, the data object having been acquired in one or more preceding epochs of the sequence, and an estimated cost of acquiring the data object. 11. One or more computer readable storage media encoded with software comprising computer executable instructions for a semantic reasoner in a system and, when the software is executed, operable to: construct a dependency chain for a plurality of rules, the dependency chain identifying whether and how each rule of the plurality of rules depends on other rules of the plurality of rules; based on the dependency chain, assign each rule of the plurality of rules to one epoch of a sequence of N epochs E1-EN, N being an integer equal to or greater than 2; and perform machine reasoning over the N epochs sequentially, wherein performing machine reasoning over each epoch comprises using one or more criteria to establish an order for acquiring one or more data objects referenced by one or more rules assigned to the epoch and acquiring the one or more data objects in the established order. 12. The one or more computer readable storage media according to claim 11, wherein: each rule of one or more rules assigned to a first epoch E1 of the sequence does not depend on any rules of the plurality of rules, and each rule of one or more rules assigned to an epoch subsequent to the first epoch E1 depends on at least one rule of an epoch immediately preceding the epoch in the sequence of N epochs. 13. The one or more computer readable storage media according to claim 12, wherein performing machine reasoning over each epoch Ei, i being an integer equal to or greater than 1 and equal to or smaller than N, comprises steps of: (a) identifying one or more data objects referenced by all rules of the one or more rules of the epoch Ei that have not reached a conclusion, (b) using the one or more criteria to establish the order for acquiring one or more data objects of the data objects identified in step (a) that have not yet been acquired in the epoch Ei, wherein establishing the order comprises assigning each data object of the one or more data objects of the data objects identified in step (a) that have not yet been acquired in the epoch Ei to one group of an ordered set of one or more groups, (c) acquiring one or more data objects assigned to a group that is first in the ordered set of the one or more groups, and (d) using a reasoning engine to perform machine reasoning over one or more data objects acquired in step (c). 14. The one or more computer readable storage media according to claim 13, wherein performing machine reasoning over the each epoch Ei further comprises: (e) following the machine reasoning of step (d), identifying which zero or more rules of the one or more rules of the epoch Ei have not reached a conclusion, and (f) iterating steps (a)-(e) until all rules of the one or more rules of the epoch Ei have reached a conclusion. 15. The one or more computer readable storage media according to claim 13, wherein step (b) comprises identifying one or more atoms comprised within the one or more rules assigned to the epoch Ei, the one or more atoms referencing the plurality of data objects and determining, for each atom of the one or more atoms, a data source for acquiring one or more data objects referenced by the atom. 16. A system for enabling semantic reasoning, the system comprising: at least one memory configured to store computer executable instructions, and at least one processor coupled to the at least one memory and configured, when executing the instructions, to: construct a dependency chain for a plurality of rules, the dependency chain identifying whether and how each rule of the plurality of rules depends on other rules of the plurality of rules; based on the dependency chain, assign each rule of the plurality of rules to one epoch of a sequence of N epochs E1-EN, N being an integer equal to or greater than 2; and perform machine reasoning over the N epochs sequentially, wherein performing machine reasoning over each epoch comprises using one or more criteria to establish an order for acquiring one or more data objects referenced by one or more rules assigned to the epoch and acquiring the one or more data objects in the established order. 17. The system according to claim 16, wherein: each rule of one or more rules assigned to a first epoch E1 of the sequence does not depend on any rules of the plurality of rules, and each rule of one or more rules assigned to an epoch subsequent to the first epoch E1 depends on at least one rule of an epoch immediately preceding the epoch in the sequence of N epochs. 18. The system according to claim 17, wherein performing machine reasoning over each epoch Ei, i being an integer equal to or greater than 1 and equal to or smaller than N, comprises steps of: (a) identifying one or more data objects referenced by all rules of the one or more rules of the epoch Ei that have not reached a conclusion, (b) using the one or more criteria to establish the order for acquiring one or more data objects of the data objects identified in step (a) that have not yet been acquired in the epoch Ei, wherein establishing the order comprises assigning each data object of the one or more data objects of the data objects identified in step (a) that have not yet been acquired in the epoch Ei to one group of an ordered set of one or more groups, (c) acquiring one or more data objects assigned to a group that is first in the ordered set of the one or more groups, and (d) using a reasoning engine to perform machine reasoning over one or more data objects acquired in step (c). 19. The system according to claim 18, wherein performing machine reasoning over the each epoch Ei further comprises: (e) following the machine reasoning of step (d), identifying which zero or more rules of the one or more rules of the epoch Ei have not reached a conclusion, and (f) iterating steps (a)-(e) until all rules of the one or more rules of the epoch Ei have reached a conclusion. 20. The system according to claim 18, wherein step (b) comprises identifying one or more atoms comprised within the one or more rules assigned to the epoch Ei, the one or more atoms referencing the plurality of data objects and determining, for each atom of the one or more atoms, a data source for acquiring one or more data objects referenced by the atom. |
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