output
stringlengths 7
3.46k
| input
stringclasses 1
value | instruction
stringlengths 129
114k
|
|---|---|---|
G06N502 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method for object classification in a decision tree based adaptive boosting (AdaBoost) classifier implemented on a single-instruction multiple-data (SIMD) processor is provided that includes receiving feature vectors extracted from N consecutive window positions in an image in a memory coupled to the SIMD processor and evaluating the N consecutive window positions concurrently by the AdaBoost classifier using the feature vectors and vector instructions of the SIMD processor, in which the AdaBoost classifier concurrently traverses decision trees for the N consecutive window positions until classification is complete for the N consecutive window positions. |
|
A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data. stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, in a data processing system, for data stream change detection and model swapping in a stream computing platform, the method comprising: building a model for each input data stream in a stream computing platform, wherein each tuple of each given input data stream is tagged with a key corresponding to the given input data stream; performing an operation on each input data stream using its corresponding model; detecting a misdirected input data stream, wherein the misdirected input data stream is tagged with a key that does not correspond to the misdirected input data stream; pausing the misdirected input data stream; and swapping a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream. 2. The method of claim 1, wherein building the model for each input data stream comprises performing machine learning on a predetermined number of initial input tuples to form a machine learning model. 3. The method of claim 2, wherein the machine learning model is a time series forecasting model. 4. The method of claim 3, wherein the time series forecasting model is an ARIMA model or a Holt-Winters model. 5. The method of claim 1, wherein performing an operation on each input data stream comprises performing a time series forecasting operation on each input data stream using its corresponding model. 6. The method of claim 1, wherein detecting the misdirected input data stream comprises: performing error detection on a result of the operation for a given input data stream; and responsive to determining that a number of errors in the results of the operation for the given input data stream exceeds a threshold, determining that the given input data stream is misdirected. 7. The method of claim 1, wherein detecting the misdirected input data stream comprises: responsive to determining the input data stream does not fit its corresponding model, determining that the given input data stream is misdirected. 8. The method of claim 1, further comprising: responsive to pausing the misdirected input data stream, entering the misdirected input data stream in a hash table that lists current paused data streams and their corresponding model parameters. 9. The method of claim 8, wherein swapping the model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream comprises: swapping the model corresponding to the misdirected input data stream with each other model corresponding to a paused input data stream in the hash table; determining which other model is a best fit for the misdirected input data stream; and swapping the model corresponding to the misdirected input data stream with the other model that is the best fit for the misdirected input data stream. 10-20. (canceled) |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data. stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A mechanism is provided in a stream computing platform for data stream change detection and model swapping. The mechanism builds a model for each input data stream in a stream computing platform. Each tuple of each given input data stream is tagged with a key corresponding to the given input data stream. The mechanism performs an operation on each input data stream using its corresponding model. The mechanism detects a misdirected input data stream, which is tagged with a key that does not correspond to the misdirected input data stream. The mechanism pauses the misdirected input data. stream swaps a model corresponding to the misdirected input data stream with another model corresponding to another paused input data stream. |
|
Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs). Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: accessing information that defines one or more time frames associated with a key performance indicator (KPI), each of the time frames having a set of one or more thresholds wherein each threshold represents the end of a range of values corresponding to a particular state of the KPI, and wherein the KPI is defined by a search query that derives a value indicative of the performance of a service at a point in time or during a period of time, the value derived from machine data pertaining to one or more entities that provide the service; assigning one or more threshold values to the thresholds, comprising, for each time frame: identifying training data for the time frame, and determining the one or more threshold values for the time frame in consideration of the identified training data; and wherein the method is performed by a computer system comprising one or more processors. 2. The method of claim 1, wherein assigning the one or more threshold values comprises assigning the one or more threshold values to the thresholds automatically based on a schedule, a frequency interval, or an event. 3. The method of claim 1, wherein the assigning the one or more threshold values comprises assigning a first threshold value to a threshold and subsequently assigning a second threshold value to the threshold, wherein the first threshold value and the second threshold value are based on training data from different time durations. 4. The method of claim 1, wherein determining the one or more threshold values further comprise: analyzing the KPI values to determine a statistical metric indicating changes in the training data; and updating the set of one or more thresholds for the one or more time frames. 5. The method of claim 1, wherein the training data comprises simulated data, historical data, or example data. 6. The method of claim 1, wherein the training data comprises simulated values, historical values, or example values of the KPI. 7. The method of claim 1, wherein the training data comprises training data that was generated by or about the one or more entities during a fixed duration of time. 8. The method of claim 1, wherein the training data is the most current historical data. 9. The method of claim 1, wherein the one or more time frames occur multiple times within a time cycle, wherein the time cycle is based on a daily time cycle, a weekly time cycle, or a monthly time cycle. 10. The method of claim 1, wherein determining one or more thresholds comprises determining a change to an existing threshold value, wherein the change is based on a delta value, a percentage value, or an absolute value. 11. The method of claim 1, further comprising causing for display a graphical user interface including a presentation schedule with one or more time slots corresponding to each of the time frames, the one or more time slots having a threshold marker for each of the one or more thresholds of the set. 12. The method of claim 1, further comprising causing for display a graphical user interface including a presentation schedule with a plurality of time slots, wherein one or more of the time slots correspond to a first time frame and have a unifying appearance to distinguish the one or more time slots from time slots corresponding to another time frame. 13. The method of claim 1, further comprising executing the search query defining the KPI to derive a KPI value and assigning the particular state of the KPI when the KPI value is within a range bounded by the one or more thresholds. 14. The method of claim 1, wherein the machine data is stored as time-stamped events. 15. The method of claim 1, wherein the machine data is stored as time-stamped events, where each time-stamped event includes a portion of raw machine data. 16. The method of claim 1, wherein the machine data is stored as time-stamped events including portions of raw machine data and is accessed using a late-binding schema. 17. The method of claim 1, wherein the search query uses a late-binding schema to extract values indicative of the performance of the service from time-stamped events after the search query is initiated. 18. The method of claim 1, wherein the machine data pertaining to the entity comprises heterogeneous machine data from multiple sources. 19. The method of claim 1, wherein the machine data pertaining to the entity comprises machine data from the entity and another entity. 20. A system comprising: a memory; and a processing device coupled with the memory to: access information that defines one or more time frames associated with a key performance indicator (KPI), each of the time frames having a set of one or more thresholds wherein each threshold represents the end of a range of values corresponding to a particular state of the KPI, and wherein the KPI is defined by a search query that derives a value indicative of the performance of a service at a point in time or during a period of time, the value derived from machine data pertaining to one or more entities that provide the service; assign one or more threshold values to the thresholds, comprising, for each time frame: identify training data for the time frame, and determine the one or more threshold values for the time frame in consideration of the identified training data; and wherein the method is performed by a computer system comprising one or more processors. 21. The system of claim 20, wherein assigning the one or more threshold values comprises assigning the one or more threshold values to the thresholds automatically based on a schedule, a frequency interval, or an event. 22. The system of claim 20, wherein the assigning the one or more threshold values comprises assigning a first threshold value to a threshold and subsequently assigning a second threshold value to the threshold, wherein the first threshold value and the second threshold value are based on training data from different time durations. 23. The system of claim 20, wherein determining the one or more threshold values further comprise: analyzing the KPI values to determine a statistical metric indicating changes in the training data; and updating the set of one or more thresholds for the one or more time frames. 24. The system of claim 20, wherein the training data comprises simulated data, historical data, or example data. 25. A non-transitory computer readable storage medium encoding instructions thereon that, in response to execution by one or more processing devices, causes the processing device to perform operations comprising: accessing information that defines one or more time frames associated with a key performance indicator (KPI), each of the time frames having a set of one or more thresholds wherein each threshold represents the end of a range of values corresponding to a particular state of the KPI, and wherein the KPI is defined by a search query that derives a value indicative of the performance of a service at a point in time or during a period of time, the value derived from machine data pertaining to one or more entities that provide the service; assigning one or more threshold values to the thresholds, comprising, for each time frame: identifying training data for the time frame, and determining the one or more threshold values for the time frame in consideration of the identified training data; and wherein the method is performed by a computer system comprising one or more processors. 26. The non-transitory computer readable storage medium of claim 25, wherein assigning the one or more threshold values comprises assigning the one or more threshold values to the thresholds automatically based on a schedule, a frequency interval, or an event. 27. The non-transitory computer readable storage medium of claim 25, wherein the assigning the one or more threshold values comprises assigning a first threshold value to a threshold and subsequently assigning a second threshold value to the threshold, wherein the first threshold value and the second threshold value are based on training data from different time durations. 28. The non-transitory computer readable storage medium of claim 25, wherein determining the one or more threshold values further comprise: analyzing the KPI values to determine a statistical metric indicating changes in the training data; and updating the set of one or more thresholds for the one or more time frames. 29. The non-transitory computer readable storage medium of claim 25, wherein the training data comprises simulated data, historical data, or example data. 30. The non-transitory computer readable storage medium of claim 25, wherein the training data comprises simulated values, historical values, or example values of the KPI. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs). Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Techniques are disclosed for providing adaptive thresholding technology for Key Performance Indicators (KPIs). Adaptive thresholding technology may automatically assign new values or adjust existing values for one or more thresholds of one or more time policies. Assigning threshold values using adaptive thresholding may involve identifying training data (e.g., historical data, simulated data, or example data) for the time frames and analyzing the training data to identify variations within the data (e.g., patterns, distributions, trends). A threshold value may be determined based on the variations and may be assigned to one or more of the thresholds without additional user intervention. |
|
Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of detecting an association between semantic entities, comprising: identifying semantic entities and associated semantic collections present in one or more knowledge bases, wherein the semantic entities include one or more of single words or multi-word phrases, and the semantic entities of a semantic collection share an entity type; determining a time period for analysis; dividing the time period into one or more time slices; generating, for each time slice, a set of word embeddings for the identified semantic entities based on one or more corpora; determining, for each time slice, a first semantic association strength between a first semantic entity input and a second semantic entity input; determining, for each time slice, a second semantic association strength between the first semantic entity input and a plurality of semantic entities in a semantic collection that is associated with the second semantic entity; and providing an output based on the first and second semantic association strengths for the one or more time slices. 2. The method of claim 1, wherein the one or more corpora comprise structured data and unstructured data. 3. The method of claim 1, wherein identifying semantic entities includes one or more of: (1) automatic methods of identifying one or more single words or multi-word phrases as semantic entities belonging to semantic collections and (2) selecting one or more single words or multi-word phrases forcibly from the one or more knowledge bases. 4. The method of claim 3, wherein the one or more single words or multi-word phrases are selected forcibly from information compiled from a structured database. 5. The method of claim 1, wherein identifying semantic entities is performed on all text in the one or more knowledge bases for the time period. 6. The method of claim 1, wherein the word embeddings are generated using one or more of Word2vec, AdaGram, fastText, and Doc2vec. 7. The method of claim 1, wherein the word embeddings are generated for each time slice independently of word embeddings generated for other time slices. 8. The method of claim 1, wherein the word embeddings for a time slice are generated by leveraging word embeddings from a previous time slice. 9. The method of claim 1, wherein the plurality of semantic entities associated with the semantic collection that is associated with the second semantic entity does not include the second semantic entity. 10. The method of claim 1, wherein the second semantic association strength is a mean, median, or a percentile of a set of semantic association strengths between the first semantic entity input and the plurality of semantic entities associated with a semantic collection that is associated with the second semantic entity. 11. The method of claim 1, further comprising: detecting an increase in the first semantic association strength of a first time slice relative to the first semantic association strength of a second, subsequent time slice; and determining whether the increase in the first semantic association strength is statistically significant relative to the corresponding second semantic association. 12. The method of claim 11, wherein the statistical significance of the increase is determined based on a p-value as a measure of statistical significance of the first semantic association strength relative to the corresponding second semantic association. 13. The method of claim 1, further comprising: selecting the first entity input and the second entity input based on a level of co-occurrence between the first entity and the second entity in the one or more knowledge bases. 14. The method of claim 13, wherein the level of co-occurrence between the first entity and the second entity is zero. 15. The method of claim 1, further comprising: receiving the first entity input and the second entity input from a user. 16. The method of claim 1, further comprising: determining, for each time slice, a count of documents present in the one or more corpora containing the first entity and the second entity; and determining a time difference between (1) a first date associated with an increase in the first semantic association strength for a first time slice relative to the first semantic association strength for a second, subsequent time slice and (2) a second date associated with an increase in a count of documents containing the first entity and the second entity for a third time slice relative to a count of documents containing the first entity and the second entity for a fourth time slice. 17. The method of claim 16, further comprising: detecting the increase in the count of documents containing the first entity and the second entity based on a slope of a curve in a fixed axis, wherein the curve is based on the time period on an x-axis of the curve and the count of documents on a y-axis of the curve. 18. The method of claim 16, further comprising: detecting the second increase in the count of documents containing the first entity and the second entity based on a document count threshold. 19. The method of claim 1, wherein each of the first entity and the second entity is one or more of the following entity types: bio-molecules, bio-entities, diseases, adverse events, phenotypes, companies, institutions, universities, hospitals, people, drugs, medical instruments, or medical procedures. 20. The method of claim 1, wherein the output enables a user device to display a graph line that is created by plotting each of the first semantic association strengths for each of the time slices over the time period. 21. The method of claim 1, wherein the output enables a user device to display a graph line that is created by plotting each of mean second semantic association strengths for each of the time slices over the time period. 22. The method of claim 1, wherein the output enables a user device to display a graph line that is created by plotting a count of documents present in the one or more corpora containing the first entity and the second entity for each of the time slices over the time period. 23. A system for detecting an association between semantic entities, comprising: a memory that stores a module; and a processor configured to run the module stored in the memory that is configured to cause the processor to: identify semantic entities and associated semantic collections present in one or more knowledge bases, wherein the semantic entities include one or more of single words or multi-word phrases, and the semantic entities of a semantic collection share an entity type; determine a time period for analysis; divide the time period into one or more time slices; generate, for each time slice, a set of word embeddings for the identified semantic entities based on one or more corpora; determine, for each time slice, a first semantic association strength between a first semantic entity input and a second semantic entity input; determine, for each time slice, a second semantic association strength between the first semantic entity input and a plurality of semantic entities in a semantic collection that is associated with the second semantic entity; and provide an output based on the first and second semantic association strengths for the one or more time slices. 24. The system of claim 23, wherein identifying semantic entities includes one or more of: (1) automatic methods of identifying one or more single words or multi-word phrases as semantic entities belonging to semantic collections and (2) selecting one or more single words or multi-word phrases forcibly from the one or more knowledge bases. 25. The system of claim 23, wherein the second semantic association strength is a mean, a median, or a percentile of a set of semantic association strengths between the first semantic entity input and the plurality of semantic entities associated with a semantic collection that is associated with the second semantic entity. 26. The system of claim 23, wherein the module stored in the memory is further configured to cause the processor to: detect an increase in the first semantic association strength of a first time slice relative to the first semantic association strength of a second, subsequent time slice; and determine whether the increase in the first semantic association strength is statistically significant relative to the corresponding second semantic association. 27. The system of claim 23, wherein the statistical significance of the increase is determined based on a p-value as a measure of statistical significance of the first semantic association strength relative to the corresponding second semantic association. 28. The system of claim 23, wherein the module stored in the memory is further configured to cause the processor to: select the first entity input and the second entity input based on a level of co-occurrence between the first entity and the second entity in the one or more knowledge bases. 29. The system of claim 23, wherein the module stored in the memory is further configured to cause the processor to: determine, for each time slice, a count of documents present in the one or more corpora containing the first entity and the second entity; and determine a time difference between (1) a first date associated with an increase in the first semantic association strength for a first time slice relative to the first semantic association strength for a second, subsequent time slice and (2) a second date associated with an increase in a count of documents containing the first entity and the second entity for a third time slice relative to a count of documents containing the first entity and the second entity for a fourth time slice. 30. The system of claim 29, wherein the module stored in the memory is further configured to cause the processor to: detect the increase in the count of documents containing the first entity and the second entity based on a slope of a curve in a fixed axis, wherein the curve is based on the time period on an x-axis of the curve and the count of documents on a y-axis of the curve. 31-126. (canceled) |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths. |
|
G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Disclosed systems, methods, and computer readable media can detect an association between semantic entities and generate semantic information between entities. For example, semantic entities and associated semantic collections present in knowledge bases can be identified. A time period can be determined and divided into time slices. For each time slice, word embeddings for the identified semantic entities can be generated; a first semantic association strength between a first semantic entity input and a second semantic entity input can be determined; and a second semantic association strength between the first semantic entity input and semantic entities associated with a semantic collection that is associated with the second semantic entity can be determined. An output can be provided based on the first and second semantic association strengths. |
|
The arithmetic processing circuit includes a first layer configured to dispose a learning neural network to compute a coefficient to be set in a recognition neural network, configured to recognize input data by using the coefficient computed on a basis of a recognition result of the recognition neural network with for the input data serving as a reference for computing the coefficient and a recognition result serving as a reference for the input data serving as the reference. The circuit further includes a second layer configured to dispose the recognition neural network to recognize the input data by the coefficient computed by the learning neural network. The circuit still further includes a third layer disposed between the first layer and the second layer, and configured to dispose a memory connected to both of the learning neural network and the recognition neural network. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. An arithmetic processing circuit comprising: a first layer configured to dispose a learning neural network to compute a coefficient to be set in a recognition neural network, wherein the recognition neural network is configured to recognize input data by using the coefficient computed on a basis of a recognition result of the recognition neural network with respect to the input data serving as a reference for computing the coefficient and a recognition result serving as a reference with respect to the input data serving as the reference; a second layer configured to dispose the recognition neural network to recognize the input data by the coefficient computed by the learning neural network; and a third layer disposed between the first layer and the second layer, and configured to dispose a memory connected to both of the learning neural network and the recognition neural network. 2. The arithmetic processing circuit according to claim 1, wherein the input data are data containing array data, the recognition neural network includes: a generation circuit to generate reduced array data from the array data, being configured by connecting, at a plurality of stages, one or more pairs of: a computing circuit to generate a first partial array by a sum-of-product computation of elements contained in part of the array data and the coefficient corresponding to the elements per part of the array data and by a predetermined function computation; and a subsampling circuit to generate a second partial array by subsampling the elements from the first partial array generated per part of the array data; and an output circuit to compute a sum of product of elements of the reduced array data and a coefficient corresponding to the elements of the reduced array data, and to output an output value with a predetermined function computation, and the learning neural network includes: a comparison circuit to compare the recognition result serving as the reference with respect to the input data serving as the reference with an output value from the output circuit; and a coefficient generation circuit configured by connecting, at a plurality of stages, one or more pairs of: a backward propagation computing circuit provided corresponding to the output circuit and respective stages of the generation circuits, and configured to generate a coefficient to be handed over to the output circuit and to each stage and a differential value of a first partial array on a basis of the differential value given by the comparison result of the comparison circuit; and a restoration circuit to restore the first partial array on a basis of associative relational information representing an associative relation of the subsampling for generating the second partial array from the partial array, and the generated differential values. 3. The arithmetic processing circuit according to claim 2, wherein each stage of the recognition neural network hands over the associative relational information, the first partial array generated by the computing circuit at each stage and the coefficient used in the computing circuit at each stage of the recognition neural network to the computing circuit at each stage of the learning neural network at predetermined operation timing, each stage of the learning neural network hands over the coefficient generated by the computing circuit at each stage of the learning neural network to the computing circuit at each stage of the recognition neural network at the predetermined operation timing, and the memory includes: a first FIFO (First In First Out) circuit to hand over the associative relational information, the first partial array and the coefficient at first operation timing to each stage of the learning neural network from each stage of the recognition neural network; and a second FIFO (First In First Out) circuit to hand over the coefficient at second operation timing to each stage of the recognition neural network from each stage of the learning neural network. 4. The arithmetic processing circuit according to claim 3, wherein each stage of the learning neural network includes: a variation generation circuit to generate a variation of the coefficient from the first partial array generated at each stage of the recognition neural network and the differential value in the first partial array; and an update circuit to generate a coefficient to be handed over to each stage of the recognition neural network by sequentially integrating generated variations. 5. An information processing apparatus comprising: a processor; a first memory; and an arithmetic processing circuit, the arithmetic processing circuit including: a first layer configured to dispose a learning neural network to compute a coefficient to be set in a recognition neural network, wherein the recognition neural network is configured to recognize input data from the first memory by using the coefficient computed on a basis of a recognition result of the recognition neural network with respect to the input data serving as a reference for computing the coefficient and a recognition result serving as a reference with respect to the input data serving as the reference in accordance with control of the processor; a second layer configured to dispose the recognition neural network to recognize the input data by the coefficient computed by the learning neural network; and a third layer disposed between the first layer and the second layer, and configured to dispose a second memory connected to both of the learning neural network and the recognition neural network. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: The arithmetic processing circuit includes a first layer configured to dispose a learning neural network to compute a coefficient to be set in a recognition neural network, configured to recognize input data by using the coefficient computed on a basis of a recognition result of the recognition neural network with for the input data serving as a reference for computing the coefficient and a recognition result serving as a reference for the input data serving as the reference. The circuit further includes a second layer configured to dispose the recognition neural network to recognize the input data by the coefficient computed by the learning neural network. The circuit still further includes a third layer disposed between the first layer and the second layer, and configured to dispose a memory connected to both of the learning neural network and the recognition neural network. |
|
G06N30454 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The arithmetic processing circuit includes a first layer configured to dispose a learning neural network to compute a coefficient to be set in a recognition neural network, configured to recognize input data by using the coefficient computed on a basis of a recognition result of the recognition neural network with for the input data serving as a reference for computing the coefficient and a recognition result serving as a reference for the input data serving as the reference. The circuit further includes a second layer configured to dispose the recognition neural network to recognize the input data by the coefficient computed by the learning neural network. The circuit still further includes a third layer disposed between the first layer and the second layer, and configured to dispose a memory connected to both of the learning neural network and the recognition neural network. |
|
In this invention, a property of a prediction target or analysis target can be predicted or analyzed with a high degree of precision during a transition from a stage in which there is extremely little or no known data about said prediction target or analysis target to a stage in which a sufficient amount of known data has been accumulated. This learning-model selection system comprises a model-evaluating means for evaluating learning models and a model-selecting means for selecting either a target learning model or a higher-order learning model on the basis of the result of the evaluation. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning model selection system comprising: a memory that stores a set of instructions; and at least one Central Processing Unit (CPU) configured to execute the set of instructions to: evaluate a learning model; and select one learning model from a target learning model and a higher-order learning model on a basis of a result of the evaluation, wherein the learning model is information representing regularity found between values of an objective variable and values of an explanation variable explaining the values of the objective variable, the target learning model is a learning model generated on a basis of a target data set which is a set of a plurality of pieces of target data, the higher-order learning model is a learning model generated on a basis of a higher-order data set which is a set of a plurality of pieces of the target data and a plurality of pieces of similar data, the target data is information in which values of an objective variable for a specific target are associated with values of an explanation variable explaining the values of the objective variable, and the similar data is information in which values of an objective variable for a target similar to the specific target are associated with values of an explanation variable explaining the values of the objective variable. 2. The learning model selection system according to claim 1, wherein the at least one CPU is further configured to: select the one target model from the target learning model and the higher-order learning model on a basis of the result of the evaluation as a learning model used when predicting the specific target, and the learning model is a function of predicting the value of the objective variable, the values of the explanation variable being input to the function. 3. The learning model selection system according to claim 1, wherein the at least one CPU is further configured to: update the target learning model on a basis of the target data set, and update the higher-order learning model on a basis of the higher-order data set in a process in which the target data is accumulated. 4. The learning model selection system according to claim 3, wherein the higher-order data set is a set including the target data and first to n-th pieces of similar data (n is a natural number), and the at least one CPU is further configured to: update the higher-order learning model on a basis of a higher-order data set in which an amount of the target data and an amount of each of the first to n-th pieces of the similar data are approximately equal to each other. 5. The learning model selection system according to claim 1, wherein the at least one CPU is further configured to: select the higher-order learning model in a stage in which the amount of the target data is small, and select the target learning model, instead of the higher-order learning model, at a timing at which the evaluation of the target learning model satisfies a predetermined criterion in the process in which the target data is accumulated. 6. The learning model selection system according to claim 1, wherein the at least one CPU is further configured to: select the higher-order learning model in a stage in which the amount of the target data is small, and select the target learning model, instead of the higher-order learning model, at a timing at which evaluation of the target learning model has exceeded evaluation of the higher-order learning model in the process in which the target data is accumulated. 7. The learning model selection system according to claim 3, wherein, in a semantic hierarchical model having at least three layers, a first node belonging to a certain layer in the semantic hierarchical model corresponds to the specific target and the target data set, a second node, which is a node including the first node, corresponds to the higher-order data set, a third node further including the second node corresponds to a second higher-order data set, and the at least one CPU is further configured to: receive input of the semantic hierarchical model, and generates the target learning model corresponding to the first node, the higher-order learning model corresponding to the second node, and a second higher-order learning model corresponding to the third node, in the semantic hierarchical model, update the target learning model, the higher-order learning model, and the second higher-order learning model in the process in which the target data is accumulated, and select a model whose evaluation is high rated from the target learning model, the higher-order learning model, and the second higher-order learning model in the process in which the target data is accumulated. 8. The learning model selection system according to claim 1, wherein the at least one CPU is further configured to: evaluate the learning model on a basis of an average value and a distribution value of values indicating errors, which are calculated using an N-fold cross-validation method. 9. The learning model selection system according to claim 1, wherein the at least one CPU is further configured to: evaluate the learning model with an evaluation index indicating how many layers a node corresponding to the learning model is separated from the first node in the semantic hierarchical model. 10. A learning model selection method comprising: evaluating a learning model; and selecting one target model from a target learning model and a higher-order learning model on a basis of a result of the evaluation, wherein the learning model is information representing regularity found between values of an objective variable and values of an explanation variable explaining the values of the objective variable, the target learning model is a learning model generated on a basis of a target data set which is a set of a plurality of pieces of target data, the higher-order learning model is a learning model generated on a basis of a higher-order data set which is a set of a plurality of pieces of the target data and a plurality of pieces of similar data, the target data is information in which values of an objective variable for a specific target are associated with values of an explanation variable explaining the values of the objective variable, and the similar data is information in which values of an objective variable for a target similar to the specific target are associated with values of an explanation variable explaining the values of the objective variable. 11. A non-transitory computer readable storage medium storing a program causing a computer to execute: first processing of evaluating a learning model; and second processing of selecting one learning model from a target learning model and a higher-order learning model on a basis of a result of the evaluation, wherein the learning model is information representing regularity found between values of an objective variable and values of an explanation variable explaining the values of the objective variable, the target learning model is a learning model generated on a basis of a target data set which is a set of a plurality of pieces of target data, the higher-order learning model is a learning model generated on a basis of a higher-order data set which is a set of a plurality of pieces of the target data and a plurality of pieces of similar data, the target data is information in which values of an objective variable for a specific target are associated with values of an explanation variable explaining the values of the objective variable, and the similar data is information in which values of an objective variable for a target similar to the specific target are associated with values of an explanation variable explaining the values of the objective variable. 12. A learning model selection system comprising: a memory that stores a set of instructions; and at least one Central Processing Unit (CPU) configured to execute the set of instructions to: evaluate a learning model; and select one learning model from a target learning model and a similar learning model on a basis of a result of the evaluation, wherein the learning model is information representing regularity found between values of an objective variable and values of an explanation variable explaining the values of the objective variable, the target learning model is a learning model generated on a basis of a target data set which is a set of a plurality of pieces of target data, the similar learning model is a learning model generated on a basis of a similar data set which is a set of one or a plurality of pieces of similar data, the target data is information in which values of an objective variable for a specific target are associated with values of an explanation variable explaining the values of the objective variable, and the similar data is information in which values of an objective variable for a target similar to the specific target are associated with values of an explanation variable explaining the values of the objective variable. 13. A learning model selection method comprising: evaluating a learning model; and selecting one learning model from a target learning model and a similar learning model on a basis of a result of the evaluation, wherein the learning model is information representing regularity found between values of an objective variable and values of an explanation variable explaining the values of the objective variable, the target learning model is a learning model generated on a basis of a target data set which is a set of a plurality of pieces of target data, the similar learning model is a learning model generated on a basis of a similar data set which is a set of one or a plurality of pieces of similar data, the target data is information in which values of an objective variable for a specific target are associated with values of an explanation variable explaining the values of the objective variable, and the similar data is information in which values of an objective variable for a target similar to the specific target are associated with values of an explanation variable explaining the values of the objective variable. 14. A non-transitory computer readable storage medium storing a program causing a computer to execute: first processing of evaluating a learning model; and second processing of selecting one learning model from a target learning model and a similar learning model on a basis of a result of the evaluation, wherein the learning model is information representing regularity found between values of an objective variable and values of an explanation variable explaining the values of the objective variable, the target learning model is a learning model generated on a basis of a target data set which is a set of a plurality of pieces of target data, the similar learning model is a learning model generated on a basis of a similar data set which is a set of one or a plurality of pieces of similar data, the target data is information in which values of an objective variable for a specific target are associated with values of an explanation variable explaining the values of the objective variable, and the similar data is information in which values of an objective variable for a target similar to the specific target are associated with values of an explanation variable explaining the values of the objective variable. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: In this invention, a property of a prediction target or analysis target can be predicted or analyzed with a high degree of precision during a transition from a stage in which there is extremely little or no known data about said prediction target or analysis target to a stage in which a sufficient amount of known data has been accumulated. This learning-model selection system comprises a model-evaluating means for evaluating learning models and a model-selecting means for selecting either a target learning model or a higher-order learning model on the basis of the result of the evaluation. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: In this invention, a property of a prediction target or analysis target can be predicted or analyzed with a high degree of precision during a transition from a stage in which there is extremely little or no known data about said prediction target or analysis target to a stage in which a sufficient amount of known data has been accumulated. This learning-model selection system comprises a model-evaluating means for evaluating learning models and a model-selecting means for selecting either a target learning model or a higher-order learning model on the basis of the result of the evaluation. |
|
A method, system and computer-usable medium are disclosed for using a context dependency graph to automate the generation of an incorrect answer to a question suitable for a multiple choice exam. A reference corpus is used to generate a concept dependency graph that contains reference keywords and concepts associated with the subject domain of an input corpus. Relationships between the reference keywords and concepts within the concept dependency graph are identified. Once identified, they are used to process a set of input keywords and concepts extracted from the input corpus, and the reference keywords and concepts, to generate a set of distractor words. The resulting set of distractor words is then processed with a set of QA pairs associated with the input corpus to generate a set of multiple choice question-answers that include various distractor answers. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for automating the generation of an incorrect answer to a question suitable for a multiple choice exam, comprising: receiving an input corpus associated with a subject domain; using a reference corpus to generate a concept dependency graph containing reference keywords and concepts associated with the subject domain; identifying relationships between reference keywords and concepts within the concept dependency graph; and using the identified relationships to process a set of input keywords and concepts, and the reference keywords and concepts, to generate a set of distractor words. 2. The method of claim 1, further comprising: processing the set of distractor words with a set of question-answer (QA) pairs associated with the input corpus to generate a set of multiple choice question-answers containing a set of distractor answers. 3. The method of claim 2, wherein: the set of input keywords and concepts are extracted from the input corpus and the set of associated question-answer (QA) pairs. 4. The method of claim 1, wherein: the distractor words are located in the concept dependency graph and not in the input corpus. 5. The method of claim 2, further comprising: eliminating individual multiple choice question-answers in the set of multiple choice question-answers according to a question quality criteria based upon aspects selected from a group consisting of specificity, context, and ambiguity. 6. The method of claim 5, wherein: a machine learning model and a natural language processing (NLP) library are used to determine the individual multiple choice question-answers to be eliminated. 7. A system comprising: a processor; a data bus coupled to the processor; and a computer-usable medium embodying computer program code, the computer-usable medium being coupled to the data bus, the computer program code used for automating the generation of an incorrect answer to a question suitable for a multiple choice exam and comprising instructions executable by the processor and configured for: receiving an input corpus associated with a subject domain; using a reference corpus to generate a concept dependency graph containing reference keywords and concepts associated with the subject domain; identifying relationships between reference keywords and concepts within the concept dependency graph; and using the identified relationships to process a set of input keywords and concepts, and the reference keywords and concepts, to generate a set of distractor words. 8. The system of claim 7, further comprising: processing the set of distractor words with a set of question-answer (QA) pairs associated with the input corpus to generate a set of multiple choice question-answers containing a set of distractor answers. 9. The system of claim 8, wherein: the set of input keywords and concepts are extracted from the input corpus and the set of associated question-answer (QA) pairs. 10. The system of claim 7, wherein: the distractor words are located in the concept dependency graph and not in the input corpus. 11. The system of claim 8, further comprising: eliminating individual multiple choice question-answers in the set of multiple choice question-answers according to a question quality criteria based upon aspects selected from a group consisting of specificity, context, and ambiguity. 12. The system of claim 11, wherein: a machine learning model and a natural language processing (NLP) library are used to determine the individual multiple choice question-answers to be eliminated. 13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: receiving an input corpus associated with a subject domain; using a reference corpus to generate a concept dependency graph containing reference keywords and concepts associated with the subject domain; identifying relationships between reference keywords and concepts within the concept dependency graph; and using the identified relationships to process a set of input keywords and concepts, and the reference keywords and concepts, to generate a set of distractor words. 14. The non-transitory, computer-readable storage medium of claim 13, further comprising: processing the set of distractor words with a set of question-answer (QA) pairs associated with the input corpus to generate a set of multiple choice question-answers containing a set of distractor answers. 15. The non-transitory, computer-readable storage medium of claim 14, wherein: the set of input keywords and concepts are extracted from the input corpus and the set of associated question-answer (QA) pairs. 16. The non-transitory, computer-readable storage medium of claim 13, wherein: the distractor words are located in the concept dependency graph and not in the input corpus. 17. The non-transitory, computer-readable storage medium of claim 14, further comprising: eliminating individual multiple choice question-answers in the set of multiple choice question-answers according to a question quality criteria based upon aspects selected from a group consisting of specificity, context, and ambiguity. 18. The non-transitory, computer-readable storage medium of claim 17, wherein: a machine learning model and a natural language processing (NLP) library are used to determine the individual multiple choice question-answers to be eliminated. 19. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are deployable to a client system from a server system at a remote location. 20. The non-transitory, computer-readable storage medium of claim 13, wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A method, system and computer-usable medium are disclosed for using a context dependency graph to automate the generation of an incorrect answer to a question suitable for a multiple choice exam. A reference corpus is used to generate a concept dependency graph that contains reference keywords and concepts associated with the subject domain of an input corpus. Relationships between the reference keywords and concepts within the concept dependency graph are identified. Once identified, they are used to process a set of input keywords and concepts extracted from the input corpus, and the reference keywords and concepts, to generate a set of distractor words. The resulting set of distractor words is then processed with a set of QA pairs associated with the input corpus to generate a set of multiple choice question-answers that include various distractor answers. |
|
G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method, system and computer-usable medium are disclosed for using a context dependency graph to automate the generation of an incorrect answer to a question suitable for a multiple choice exam. A reference corpus is used to generate a concept dependency graph that contains reference keywords and concepts associated with the subject domain of an input corpus. Relationships between the reference keywords and concepts within the concept dependency graph are identified. Once identified, they are used to process a set of input keywords and concepts extracted from the input corpus, and the reference keywords and concepts, to generate a set of distractor words. The resulting set of distractor words is then processed with a set of QA pairs associated with the input corpus to generate a set of multiple choice question-answers that include various distractor answers. |
|
In one embodiment, a method includes sending a request for a measure of affinity associated with a first user for a particular content associated with a second user, where the measure of affinity predicts a level of interest the first user has for the particular content; sending weighting information for computing the measure of affinity, where the weighting information includes information specifying a first weight to be attributed to a first predictor function that is based on the second user and a second weight to be attributed to a second predictor function that is based on concepts associated with the particular content; receiving the measure of affinity; and sending, to the first user, the particular content, based on the received measure of affinity. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising, by one or more computing devices associated with an application: sending, to a computer server machine, a request for a first measure of affinity associated with a first user for a particular content, wherein the first measure of affinity predicts a level of interest the first user has for the particular content, and wherein the particular content is associated with a second user; sending, to the computer server machine, a first weighting information that is to be used in computing the first measure of affinity, wherein the first weighting information comprises information specifying: a first weight to be attributed to a first predictor function that is based on the second user, and a second weight to be attributed to a second predictor function that is based on one or more concepts associated with the particular content; receiving, by one or more of the computing devices associated with the application, the first measure of affinity; and sending, to the first user, the particular content, based at least in part on the received first measure of affinity. 2. The method of claim 1, further comprising determining the first weight and the second weight based on the relative importance of their respective predictor functions to the first measure of affinity with respect to sending the particular content to the first user. 3. The method of claim 1, wherein: the first predictor function predicts a level of interest the first user has in viewing content posted by the second user; and the second predictor function predicts a level of interest the first user has for the particular content. 4. The method of claim 1, wherein the first weighting information further comprises information specifying a third weight to be attributed to a third predictor function that is based on one or more attributes of the first user. 5. The method of claim 1, further comprising: sending a request for a second measure of affinity associated with the first user for the particular content; determining one or more weights to be included in a second weighting information; sending the second weighting information that is to be used in computing the second measure of affinity; receiving the second measure of affinity; and wherein the particular content is sent to the first user based at least in part on the received first measure of affinity and the second measure of affinity. 6. The method of claim 5, wherein the second weighting information comprises information specifying: a fourth weight to be attributed to a fourth predictor function, the fourth predictor function being based on one or more attributes of the first user; a fifth weight to be attributed to a fifth predictor function, the fifth predictor function being based on a history of actions performed by the first user on a particular client system associated with the first user; and a sixth weight to be attributed to a sixth predictor function, the sixth predictor function predicting a level of interest the first user has for the second user. 7. The method of claim 1, wherein a delivery of the particular content is customized based on the received measure of affinity. 8. The method of claim 1, wherein the particular content comprises a social endorsement for a particular item. 9. The method of claim 1, wherein the particular content comprises a request to join a particular application. 10. The method of claim 1, wherein determining the subsequent action comprises determining a customization of the delivery of information to the first user. 11. The method of claim 1, wherein determining the subsequent action comprises determining to filter content that is sent to first user. 12. The method of claim 1, wherein the measure of affinity is further based on one or more actions taken by the first user with respect to a client system associated with the first user. 13. The method of claim 12, wherein the one or more of the actions taken by the first user with respect to the client system comprises actions using a telephone feature of the client system, accessing content through a network connection, actions using contact information stored on the client system, or actions affecting one or more applications on the client system. 14. The method of claim 1, wherein the computer server machine is associated with a social-networking system and the first user is a user of the social-networking system. 15. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: send a request for a measure of affinity associated with a first user for a particular content, wherein the measure of affinity predicts a level of interest the first user has for the particular content, and wherein the particular content is associated with-a second user; send weighting information that is to be used in computing the measure of affinity, wherein the weighting information comprises information specifying: a first weight to be attributed to a first predictor function that is based on the second user, and a second weight to be attributed to a second predictor function that is based on one or more concepts associated with the particular content; receive the measure of affinity; and send, to the first user, the particular content, based at least in part on the received measure of affinity. 16. The media of claim 15, wherein the software is further operable when executed to: determine the first weight and the second weight based on the relative importance of their respective predictor functions to the first measure of affinity with respect to sending the particular content to the first user. 17. The media of claim 15, wherein: the first predictor function predicts a level of interest the first user has in viewing content posted by the second user; and the second predictor function predicts a level of interest the first user has for the particular content. 18. The media of claim 15, wherein the first weighting information further comprises information specifying a third weight to be attributed to a third predictor function that is based on one or more attributes of the first user. 19. The media of claim 15, wherein the software is further operable when executed to: receive a second measure of affinity associated with the first user for the particular content; send second weighting information that is to be used in computing the second measure of affinity; receive the second measure of affinity; and wherein the particular content is sent to the first user based at least in part on the received first measure of affinity and the second measure of affinity. 20. A system comprising: one or more processors associated with one or more computer servers associated with a social-networking system; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: send a request for a measure of affinity associated with a first user for a particular content, wherein the measure of affinity predicts a level of interest the first user has for the particular content, and wherein the particular content is associated with a second user; send weighting information that is to be used in computing the measure of affinity, wherein the weighting information comprises information specifying: a first weight to be attributed to a first predictor function that is based on the second user, and a second weight to be attributed to a second predictor function that is based on one or more concepts associated with the particular content; receive the measure of affinity; and send, to the first user, the particular content, based at least in part on the received measure of affinity. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: In one embodiment, a method includes sending a request for a measure of affinity associated with a first user for a particular content associated with a second user, where the measure of affinity predicts a level of interest the first user has for the particular content; sending weighting information for computing the measure of affinity, where the weighting information includes information specifying a first weight to be attributed to a first predictor function that is based on the second user and a second weight to be attributed to a second predictor function that is based on concepts associated with the particular content; receiving the measure of affinity; and sending, to the first user, the particular content, based on the received measure of affinity. |
|
G06N7005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: In one embodiment, a method includes sending a request for a measure of affinity associated with a first user for a particular content associated with a second user, where the measure of affinity predicts a level of interest the first user has for the particular content; sending weighting information for computing the measure of affinity, where the weighting information includes information specifying a first weight to be attributed to a first predictor function that is based on the second user and a second weight to be attributed to a second predictor function that is based on concepts associated with the particular content; receiving the measure of affinity; and sending, to the first user, the particular content, based on the received measure of affinity. |
|
Contemplated systems and methods provide for machine learning and identification of regulatory interactions in biological pathways using a probabilistic graphical model, and especially for identification of interaction correlations among the regulatory parameters. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning engine, comprising: an omic input interface configured to receive a plurality of omic datasets; an omic processing module coupled with the interface and configured to: access a pathway model having a plurality of pathway elements in which at least two of the elements are coupled to each other via a path having a regulatory node that controls activity along the path as a function of a plurality of regulatory parameters; obtain, via the omic input interface, at least one of the omic datasets; infer, based on the at least one omic dataset and the pathway model, a set of interaction correlations among the plurality of regulatory parameters; and update the pathway model based on the interaction correlations. 2. The learning engine of claim 1 wherein the omic datasets comprise whole genome data, partial genome data, or differential sequence objects. 3. The learning engine of any of claims 1-2 further comprising a genomic database or sequencing device coupled to the omic input interface. 4. The learning engine of any one of claims 1-3 wherein the pathway elements comprise at least one of a DNA sequence, a RNA sequence, a protein, and a protein function. 5. The learning engine of any one of claims 1-4 wherein the pathway element comprises a DNA sequence and wherein the at least one of the plurality of regulatory parameters is selected from the group consisting of a transcription factor, a transcription activator, a RNA polymerase subunit, a cis-regulatory element, a trans-regulatory element, an acetylated histone, a methylated histone, and a repressor. 6. The learning engine of any one of claims 1-5 wherein the pathway element comprises a RNA sequence and wherein the at least one of the plurality of regulatory parameters is selected from the group consisting of an initiation factor, a translation factor, a RNA binding protein, a ribosomal protein, an siRNA, and a polyA binding protein. 7. The learning engine of any one of claims 1-6 wherein the pathway element comprises a protein and wherein the at least one of the plurality of regulatory parameters is a phosphorylation, an acylation, a proteolytic cleavage, and association with at least a second protein. 8. The learning engine of any one of claims 1-7 wherein the omics processing module is configured to infer the interaction correlation using a probabilistic model. 9. The learning engine of claim 8 wherein the probabilistic model uses a co-dependent regulation model. 10. The learning engine of claim 8 or 9 wherein the probabilistic model uses an independent regulation model. 11. The learning engine of claim 10 wherein the probabilistic model further determines a significance of dependence between the plurality of the regulatory parameters and the activity of the path and/or a significance of conditional dependence between the regulatory parameters given an activity of the path. 12. The learning engine of claim 11 wherein the probabilistic model further determines the sign of interaction for the regulatory parameters. 13. A method of generating a pathway model, comprising: obtaining, via an omic input interface, at least one omic dataset; accessing, via an omic processing module, a pathway model having a plurality of pathway elements in which at least two of the elements are coupled to each other via a path having a regulatory node that controls activity along the path as a function of a plurality of regulatory parameters; inferring, via the omic processing module, based on the at least one omic dataset and the pathway model, a set of interaction correlations among the plurality of regulatory parameters; and updating the pathway model based on the interaction correlations. 14. The method of claim 13 wherein the omic datasets comprise whole genome data, partial genome data, or differential sequence objects, and wherein the omic datasets are obtained from a genomic database, a BAM server, or a sequencing device. 15. The method of claim 13 or claim 14 wherein the step of inferring is based on a probabilistic model. 16. The method of claim 15 wherein the probabilistic model uses a co-dependent and/or independent regulation model. 17. The method of claim 16 further comprising a step of determining a significance of dependence between the plurality of the regulatory parameters and the activity of the path and/or a significance of conditional dependence between the regulatory parameters given an activity of the path. 18. The method of claim 17 further comprising a step of determining the sign of interaction for the regulatory parameters. 19. A method of identifying sub-type specific interaction correlations for regulatory parameters of a regulatory node in a pathway model, comprising: obtaining, via an omic input interface, at least one omic dataset representative of a sub-type tissue; accessing, via an omic processing module, the pathway model having a plurality of pathway elements in which at least two of the elements are coupled to each other via a path having the regulatory node that controls activity along the path as a function of the plurality of regulatory parameters; deriving the sub-type interaction correlations, via the omic processing module, from the at least one omic dataset representative of the sub-type tissue by probability analysis of interactions among the plurality of regulatory parameters; and presenting the derived sub-type interaction correlations in the pathway model. 20. The method of claim 19 wherein the sub-type tissue is a drug-resistant tissue, a metastatic tissue, a drug-treated tissue, or a clonal variant of a tissue. 21. The method of claim 19 further comprising a step of validating the derived sub-type interaction correlations using at least one of an in-vitro, in-silico, and in-vivo experiment. 22. A method of classifying an omic dataset representative of a tissue as belonging to a sub-type specific tissue, comprising: obtaining, via an omic input interface, the omic dataset representative of the tissue; deriving, for the omic dataset, a set of interaction correlations among a plurality of regulatory parameters of a regulatory node in a pathway model; matching the derived set of interaction correlations to an a priori known set of interaction correlations that is associated with a known sub-type specific tissue; and using the match to classify that the omic dataset representative of the tissue belongs to the known sub-type specific tissue. 23. The method of claim 22 wherein the step of obtaining comprises generating the omic dataset representative of the tissue from a tissue sample of a tissue with unknown regulatory characteristic. 24. The method of claim 22 or claim 23 wherein the tissue sample is a tumor tissue sample. 25. The method of any one of claims 22-24 wherein the known sub-type specific tissue is a drug-resistant tissue, a metastatic tissue, a drug-treated tissue, or a clonal variant of a tissue. 26. A method of identifying a druggable target in a pathway model having a plurality of pathway elements in which at least two of the elements are coupled to each other via a path having a regulatory node that controls activity along the path as a function of a plurality of regulatory parameters, the method comprising: obtaining, via an omic input interface, an omic dataset representative of a tissue; deriving, for the omic dataset, a set of interaction correlations among the plurality of regulatory parameters of the regulatory node in the pathway model; identifying a drug as affecting the activity of the path where the drug is predicted to interfere with the interaction correlations. 27. The method of claim 26 wherein the regulatory node affects at least one of transcription, translation, and post-translational modification of a protein. 28. The method of claim 26 wherein the drug is a commercially available drug and has a known mode of action. 29. A method of identifying a target pathway in a pathway model having a plurality of pathway elements in which at least two of the elements are coupled to each other via a path having a regulatory node that controls activity along the path as a function of a plurality of regulatory parameters, the method comprising: obtaining, via an omic input interface, an omic dataset representative of a tissue; deriving, for the omic dataset, a set of interaction correlations among the plurality of regulatory parameters of the regulatory node in the pathway model; identifying a pathway as the target pathway based on a known effect of a drug on the interaction correlation. 30. The method of claim 29 wherein the known effect is at least one of an inhibitory effect on a kinase, an inhibitory effect on a receptor, and an inhibitory effect on transcription. 31. The method of claim 29 wherein the target pathway is a calcium/calmodulin regulated pathway, a cytokine pathway, a chemokine pathway, a growth factor regulated pathway, a hormone regulated pathway, MAP kinase regulated pathway, a phosphatase regulated pathway, or a Ras regulated pathway. 32. The method of claim 29 further comprising a step of providing a treatment advice based on the identified pathway. 33. A method of in silico simulating a treatment effect of a drug, comprising: obtaining a pathway model having a plurality of pathway elements in which at least two of the elements are coupled to each other via a path having a regulatory node that controls activity along the path as a function of a plurality of regulatory parameters; identifying a drug that is known to affect at least one regulatory parameter; altering in silico, via an omic processing module and based on the known effect of the drug, at least one of the regulatory node, the activity, and at least of the regulatory parameters in the pathway model; and determining a secondary effect of the alteration in the pathway model. 34. The method of claim 33 wherein the secondary effect is in another regulatory node, another activity, and another regulatory parameter in the pathway model. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Contemplated systems and methods provide for machine learning and identification of regulatory interactions in biological pathways using a probabilistic graphical model, and especially for identification of interaction correlations among the regulatory parameters. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Contemplated systems and methods provide for machine learning and identification of regulatory interactions in biological pathways using a probabilistic graphical model, and especially for identification of interaction correlations among the regulatory parameters. |
|
Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, at a system comprising a processor, a search query submitted by a searcher, the search query having location ambiguity; identifying, by the system, using a location model, an unambiguous location for the search query to resolve the location ambiguity, wherein parameters of the location model used to identify the unambiguous location for the search query comprise at least one of a distance between the searcher and the unambiguous location, a search hit value associated with the unambiguous location, or a number of businesses within the unambiguous location; determining, by the system, a search result corresponding to the unambiguous location identified for the search query; and providing, by the system, the search result as a response to the search query having the location ambiguity. 2. The method of claim 1, further comprising generating the location model using a machine learning technique. 3. The method of claim 2, wherein the machine learning technique comprises a decision tree learning technique. 4. The method of claim 2, wherein the machine learning technique comprises a gradient boosted decision tree learning technique. 5. The method of claim 2, wherein generating the location model comprises generating a first location model for queries received from mobile devices and generating a second location model for queries received from fixed-location devices. 6. The method of claim 2, wherein generating the location model comprises: generating a training dataset; and applying the machine learning technique to the training dataset to generate the location model. 7. The method of claim 6, wherein generating the training dataset comprises: filtering a query log to identify first queries associated with locations without location ambiguity; generating, from the first queries, second queries having location ambiguity; identifying location candidates for the second queries; and computing values of training variables of the training dataset based on comparing the locations without location ambiguity of the first queries and the location candidates for the second queries. 8. A computer readable medium storing instructions which, when executed by a processor of a system, cause the processor to perform operations comprising: receiving a search query submitted by a searcher, the search query having location ambiguity; identifying, using a location model, an unambiguous location for the search query to resolve the location ambiguity, wherein parameters of the location model used to identify the unambiguous location for the search query comprise at least one of a distance between the searcher and the unambiguous location, a search hit value associated with the unambiguous location, or a number of businesses within the unambiguous location; determining a search result corresponding to the unambiguous location identified for the search query; and providing, by the system, the search result as a response to the search query having the location ambiguity. 9. The computer readable medium of claim 8, wherein the operations further comprise generating the location model using a machine learning technique. 10. The computer readable medium of claim 9, wherein the machine learning technique comprises a decision tree learning technique. 11. The computer readable medium of claim 9, wherein the machine learning technique comprises a gradient boosted decision tree learning technique. 12. The computer readable medium of claim 9, wherein generating the location model comprises generating a first location model for queries received from mobile devices and generating a second location model for queries received from fixed-location devices. 13. The computer readable medium of claim 9, wherein generating the location model comprises: generating a training dataset; and applying the machine learning technique to the training dataset to generate the location model. 14. The computer readable medium of claim 13, wherein generating the training dataset comprises: filtering a query log to identify first queries associated with locations without location ambiguity; generating, from the first queries, second queries having location ambiguity; identifying location candidates for the second queries; and computing values of training variables of the training dataset based on comparing the locations without location ambiguity of the first queries and the location candidates for the second queries. 15. A system comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to perform operations comprising receiving a search query submitted by a searcher, the search query having location ambiguity, identifying, using a location model, an unambiguous location for the search query to resolve the location ambiguity, wherein parameters of the location model used to identify the unambiguous location for the search query comprise at least one of a distance between the searcher and the unambiguous location, a search hit value associated with the unambiguous location, or a number of businesses within the unambiguous location, determining a search result corresponding to the unambiguous location identified for the search query, and providing, by the system, the search result as a response to the search query having the location ambiguity. 16. The system of claim 15, wherein the operations further comprise generating the location model using a machine learning technique. 17. The system of claim 16, wherein the machine learning technique comprises a decision tree learning technique. 18. The system of claim 16, wherein generating the location model comprises generating a first location model for queries received from mobile devices and generating a second location model for queries received from fixed-location devices. 19. The system of claim 18, wherein generating the location model comprises: generating a training dataset; and applying the machine learning technique to the training dataset to generate the location model. 20. The system of claim 19, wherein generating the training dataset comprises: filtering a query log to identify first queries associated with locations without location ambiguity; generating, from the first queries, second queries having location ambiguity; identifying location candidates for the second queries; and computing values of training variables of the training dataset based on comparing the locations without location ambiguity of the first queries and the location candidates for the second queries. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems and methods use machine learning techniques to resolve location ambiguity in search queries. In one aspect, a dataset generator generates a training dataset using query logs of a search engine. A training engine applies a machine learning technique to the training dataset to generate a location disambiguation model. A location disambiguation engine uses the location disambiguation model to resolve location ambiguity in subsequent search queries. |
|
A situational awareness system providing geographic, personal characteristic and subject matter of interest awareness using information obtained from location reporting mobile devices. The system displays personal characteristic information for target member segments defined with a great deal of specificity, which may be followed by online queries to the target member segment. Member participation in online queries may require interested members to “opt-in” to limit query distribution to members that have indicated an interest in responding to online queries on the particular subject matter of interest. This increases the efficiency and effectiveness of the online queries, while reducing the cost and imposition on members who are not be interested in participating. Obtaining geographic, personal characteristic and subject matter of interest awareness can be a free component provided as a benefit to registered members and customers, configured to occur prior to the payment screen required for online query distribution. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: defining, by a network device comprising a processor, target segment criteria comprising: receiving, by the network device, geographic data related to a geographic area of interest, receiving, by the network device, personal characteristic data related to a personal identity attribute data of interest, and receiving, by the network device, subject matter data related to a subject matter of interest; accessing, by the network device, stored personal identity attribute data related to members of an online response system comprising personal identity attribute data associated with the members of the online response system; identifying, by the network device, a target member segment among the members of the online response system by comparing the geographic data, the personal characteristic data, and the subject matter data to the personal identity attribute data; displaying, by the network device, data representing the target member segment by depicting the data on a graphical representation of the geographic area of interest, wherein the data representing the target member segment comprises a prompt to send text data to the target member segment. 2. The method of claim 1, wherein the stored personal identity attribute data comprises online social media data created by the members of the online response system. 3. The method of claim 1, further comprising: defining the target member segment based on traveler tracking parameters; transmitting an online message comprising traveler awareness data to the target member segment. 4. The method of claim 1, further comprising: receiving, by the network device, an online query through a menu-driven graphical user interface; receiving, by the network device, query parameters through the menu-driven graphical user interface; transmitting, by the network device, the online query to the target member segment; receiving, by the network device, query responses from the target member segment in accordance with the query parameters; and displaying, by the network device, data representing the query responses. 5. The method of claim 4, further comprising: defining, by the network device, an authorized member segment by receiving opt-in responses from a subset of the members comprising the target member segment indicating agreement to participate in the online query concerning the subject matter of interest; transmitting, by the network device, the online query to the authorized member segment; receiving, by the network device, query responses from the authorized member segment in accordance with the query parameters. 6. The method of claim 5, wherein receiving opt-in responses comprises transmitting an opt-in text message to the target member segment and receiving text messages comprising the opt-in responses. 7. The method of claim 4, wherein displaying the data representing the responses further comprises displaying data representing responses on the graphical representation of the geographic area of interest. 8. The method of claim 4, further comprising: calculating, by the network device, compensation data associated with compensation to a customer based on a number of the query responses received; and sending, by the network device, electronic compensation to each member of the target member segment that submitted the query responses that was accepted in accordance with a query parameter. 9. The method of claim 4, wherein the geographic data comprises a radius about a selected location, a political subdivision, or a type of establishment. 10. The method of claim 4, wherein displaying data representing the query responses comprises computing and displaying a male-to-female ratio of the members that submitted the query responses. 11. A computer readable storage medium storing non-transitory computer-executable instructions that, when executed by a computer system comprising a processor, cause the system to perform operations, comprising: defining target segment criteria comprising: receiving geographic data related to a geographic area of interest, receiving personal data related to personal identity attribute data of interest, and receiving subject matter data related to a subject matter of interest; accessing stored personal identity attribute data related to members of an online response system comprising personal identity attribute data associated with the members of the online response system; identifying a target member segment among the members of the online response system by comparing the geographic data, the personal data, and the subject matter data to the personal identity attribute data; displaying data representing the target member segment by depicting the data on a graphical representation of the geographic area of interest, wherein the data representing the target member segment comprises a prompt to send text data to the target member segment. 12. The computer readable storage medium of claim 11, further comprising: receiving an online query through a menu-driven graphical user interface; receiving query parameters through the menu-driven graphical user interface; transmitting the query question to the target member segment; receiving query responses from the target member segment in accordance with the query parameters; and displaying data representing the query responses. 13. The computer readable storage medium of claim 12, further comprising: defining an authorized member segment by receiving opt-in responses from a subset of the members comprising the target member segment indicating agreement to participate in the online query concerning the subject matter of interest; transmitting the online query to the authorized member segment; and receiving query responses from the authorized member segment in accordance with the query parameters. 14. The computer readable storage medium of claim 11, further comprising: generating compensation data associated with compensation charged to customers based on a number of query responses received; and initiating an electronic payment to each member of the target member segment that submitted a query response that was accepted in accordance with query parameters. 15. The computer readable storage medium of claim 12, wherein: the geographic data comprises a radius about a selected location, a political subdivision, or a type of establishment; and the data representing the query responses comprises a male-to-female ratio for the members that submitted query responses. 16. A system comprising: means for defining a target segment criteria comprising: means for receiving geographic data related to a geographic area of interest, means for receiving personal data related to personal identity attribute data of interest, and means for receiving subject matter data related to a subject matter of interest; means for accessing stored personal identity attribute data related to members of an online response system comprising personal identity attribute data associated with the members of the online response system; means for identifying a target member segment among the members of the online response system by comparing the geographic data, the personal data, and the subject matter data to the personal identity attribute data; means for displaying data representing the target member segment by depicting the data on a graphical representation of the geographic area of interest, wherein the data representing the target member segment comprises a prompt to send text data to the target member segment. 17. The system of claim 16, further comprising: means for receiving an online query through a menu-driven graphical user interface; means for receiving query parameters through the menu-driven graphical user interface; means for transmitting the query question to the target member segment; means for receiving query responses from the target member segment in accordance with the query parameters; and means for displaying data representing the query responses. 18. The system of claim 17, further comprising: means for defining an authorized member segment by receiving opt-in responses from a subset of the members comprising the target member segment indicating agreement to participate in the online query concerning the subject matter of interest; means for transmitting the online query to the authorized member segment; and means for receiving query responses from the authorized member segment in accordance with the query parameters. 19. The system of claim 16, further comprising: means for generating compensation data associated with compensation charged to customers based on a number of query responses received; and means for initiating an electronic payment to each member of the target member segment that submitted a query response that was accepted in accordance with query parameters. 20. The system of claim 19, wherein: the geographic data comprises a radius about a selected location, a political subdivision, or a type of establishment; and the data representing the query responses comprises a male-to-female ratio for the members that submitted query responses. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A situational awareness system providing geographic, personal characteristic and subject matter of interest awareness using information obtained from location reporting mobile devices. The system displays personal characteristic information for target member segments defined with a great deal of specificity, which may be followed by online queries to the target member segment. Member participation in online queries may require interested members to “opt-in” to limit query distribution to members that have indicated an interest in responding to online queries on the particular subject matter of interest. This increases the efficiency and effectiveness of the online queries, while reducing the cost and imposition on members who are not be interested in participating. Obtaining geographic, personal characteristic and subject matter of interest awareness can be a free component provided as a benefit to registered members and customers, configured to occur prior to the payment screen required for online query distribution. |
|
G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A situational awareness system providing geographic, personal characteristic and subject matter of interest awareness using information obtained from location reporting mobile devices. The system displays personal characteristic information for target member segments defined with a great deal of specificity, which may be followed by online queries to the target member segment. Member participation in online queries may require interested members to “opt-in” to limit query distribution to members that have indicated an interest in responding to online queries on the particular subject matter of interest. This increases the efficiency and effectiveness of the online queries, while reducing the cost and imposition on members who are not be interested in participating. Obtaining geographic, personal characteristic and subject matter of interest awareness can be a free component provided as a benefit to registered members and customers, configured to occur prior to the payment screen required for online query distribution. |
|
A method for obtaining user traits. In response to a first kind of data of a target user not being sufficient to obtain a trait of the target user, a second kind of data of the target user is collected, where the first kind of data and the second kind of data are different kinds of data. Based on the second kind of data, one or more reference users similar to the target user are determined. Based on the first kind of data of the reference users, the trait of the target user is determined. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of obtaining user traits, the method comprising: in response to determining, by a computer, that a first kind of data of a target user is not sufficient to obtain a trait of the target user, collecting, by the computer, a second kind of data of the target user, wherein the first kind of data and the second kind of data are different kinds of data; determining, by the computer, based on the second kind of data, one or more reference users similar to the target user; and obtaining, by the computer, the trait of the target user based on the first kind of data of the reference users. 2. A method in accordance with claim 1, wherein the first kind of data of the target user includes textual data that describes a text associated with the target user, and wherein collecting the second kind of data of the target user comprises: collecting, by the computer, behavior data of the target user, the behavior data describing historical behaviors of the target user. 3. A method in accordance with claim 2, wherein determining one or more reference users similar to the target user comprises: determining, by the computer, users having similar behaviors to the target user as the reference users based on the behavior data. 4. A method in accordance with claim 1, wherein determining one or more reference users similar to the target user comprises: determining, by the computer, the reference users from seed users, wherein each of the seed users is a user having the first kind of data sufficient to obtain the trait. 5. A method in accordance with claim 4, wherein obtaining the trait of the target user based on the first kind of data of the reference users comprises: determining, by the computer, based on at least one of the first kind of data and the second kind of data of seed users in the reference users, a deviation degree between a first seed user in the reference users and other seed users in the reference users; and in response to determining that the deviation degree exceeds a predetermined threshold, adjusting, by the computer, a contribution of the first kind of data of the first seed user to the obtaining of the trait. 6. A method in accordance with claim 1, wherein determining one or more reference users similar to the target user comprises: determining, by the computer, the reference users from non-seed users, wherein each of the non-seed users is a user with the first kind of data insufficient to obtain the trait. 7. A method in accordance with claim 6, wherein obtaining the trait of the target user based on the first kind of data of the reference users comprises: grouping, by the computer, non-seed users in the reference users based on the second kind of data of the non-seed users in the reference users; aggregating, by the computer, the first kind of data of the non-seed users in the reference users based on the grouping; and obtaining, by the computer, the trait based on the aggregated first kind of data. 8. A method in accordance with claim 1, further comprising: in response to determining, by the computer, that the first kind of data of the target user is sufficient to obtain the trait of the target user, storing, by the computer, the first kind of data of the target user for use in obtaining the trait of a further user. 9. A computer system for obtaining traits, the computer system comprising: one or more computer processors, one or more computer-readable storage media, and program instructions stored on one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions, in response to determining, by a computer, that a first kind of data of a target user is not sufficient to obtain a trait of the target user, to collect a second kind of data of the target user, wherein the first kind of data and the second kind of data are different kinds of data; program instructions to determine, based on the second kind of data, one or more reference users similar to the target user; and program instructions to obtain the trait of the target user based on the first kind of data of the reference users. 10. A computer system in accordance with claim 9, wherein the first kind of data of the target user includes textual data that describes a text associated with the target user, and wherein program instructions to collect the second kind of data of the target user comprise: program instructions to collect behavior data of the target user, the behavior data describing historical behaviors of the target user. 11. A computer system in accordance with claim 10, wherein program instructions to determine one or more reference users similar to the target user comprise: program instructions to determine users having similar behaviors to the target user as the reference users based on the behavior data. 12. A computer system in accordance with claim 9, wherein program instructions to determine one or more reference users similar to the target user comprise: program instructions to determine the reference users from seed users, wherein each of the seed users is a user having the first kind of data sufficient to obtain the trait. 13. A computer system in accordance with claim 12, wherein program instructions to obtain the trait of the target user based on the first kind of data of the reference users comprise: program instructions to determine, based on at least one of the first kind of data and the second kind of data of seed users in the reference users, a deviation degree between a first seed user in the reference users and other seed users in the reference users; and program instructions, in response to determining that the deviation degree exceeds a predetermined threshold, to adjust a contribution of the first kind of data of the first seed user to the obtaining of the trait. 14. A computer system in accordance with claim 9, wherein program instructions to determine one or more reference users similar to the target user comprises: program instructions to determine the reference users from non-seed users, wherein each of the non-seed users is a user with the first kind of data insufficient to obtain the trait. 15. A computer system in accordance with claim 14, wherein program instructions to obtain the trait of the target user based on the first kind of data of the reference users comprise: program instructions to group non-seed users in the reference users based on the second kind of data of the non-seed users in the reference users; program instructions to aggregate the first kind of data of the non-seed users in the reference users based on the grouping; and program instructions to obtain the trait based on the aggregated first kind of data. 16. A computer system in accordance with claim 9, further comprising: program instructions, in response to determining that the first kind of data of the target user is sufficient to obtain the trait of the target user, to store the first kind of data of the target user for use in obtaining the trait of a further user. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A method for obtaining user traits. In response to a first kind of data of a target user not being sufficient to obtain a trait of the target user, a second kind of data of the target user is collected, where the first kind of data and the second kind of data are different kinds of data. Based on the second kind of data, one or more reference users similar to the target user are determined. Based on the first kind of data of the reference users, the trait of the target user is determined. |
|
G06N5022 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method for obtaining user traits. In response to a first kind of data of a target user not being sufficient to obtain a trait of the target user, a second kind of data of the target user is collected, where the first kind of data and the second kind of data are different kinds of data. Based on the second kind of data, one or more reference users similar to the target user are determined. Based on the first kind of data of the reference users, the trait of the target user is determined. |
|
Predicting a malfunction of a component of a unit includes providing a transition matrix of a parameter of the component, wherein the transition matrix includes for a number of discrete value states of the parameter probabilities to switch from one discrete value state to another within a certain time period; providing the conditional probability distribution for the malfunction given the discrete value states; providing a current discrete value state of the parameter; determining a conditional probability distribution of the discrete value states given the current discrete value state for a future point in time based on the current discrete value state and on the transition matrix by use of a Markov chain; and determining a probability for the malfunction for the future point in time based on the conditional probability distribution of the discrete value states for the future point in time and the conditional probability distribution for the malfunction. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. Method for predicting a malfunction of a mechanical or electrical component of a unit comprising the steps of: measuring a current value of a parameter of the component; in an apparatus, determining a conditional probability distribution of the parameter for a future point in time given the current value of the parameter based on the current value of the parameter; in the apparatus, determining a conditional probability for a malfunction at the future point in time given the current value of the parameter based on the conditional probability distribution of the parameter for the future point in time given the current value of the parameter and on a conditional probability distribution for the malfunction given the parameter, and predicting the malfunction of the component on the basis of the conditional probability for a malfunction at the future point in time given the current value of the parameter. 2. Method according to claim 1 comprising transmitting the current value over a communication network to the apparatus. 3. Method according to claim 1, wherein the current value is measured by a sensor of the component. 4. Method according to claim 1, wherein the conditional probability distribution for the malfunction given the parameter is determined based on the conditional probability distribution of the parameter given the malfunction, the probability of the malfunction and the probability distribution of the parameter. 5. Method according to claim 1 comprising the further steps of: providing a transition matrix, wherein the transition matrix is based on probabilities to switch from one of a number of discrete value states to another of the discrete value states; providing the conditional probability distribution for the malfunction given the parameter being a conditional probability distribution for the malfunction given the discrete value states of said parameter; providing the current discrete value state of said parameter on the basis of the current value of the parameter; wherein the step of determining the conditional probability distribution of the parameter of the component for the future point in time given the current value of the parameter comprises the step of determining a conditional probability distribution of the discrete value states of said parameter for the future point in time given the current discrete value state of the parameter based on the current discrete value state of the parameter and on the transitional matrix; wherein the step of determining the conditional probability for the malfunction at the future point in time given the current value of the parameter comprises the step of determining a conditional probability for the malfunction at the future point in time given the current value state based on the conditional probability distribution of the discrete value states of said parameter for the future point in time given the current value state and on the conditional probability distribution for the malfunction given the discrete value states of said parameter. 6. Method according to claim 5, wherein intervals between the discrete value states are equidistant. 7. Method according claim 5, wherein intervals between the discrete value states are logarithmic or exponential. 8. Method according to claim 5, wherein the size of a value interval corresponding to a discrete value state of the discrete value states depends on the probability of the respective discrete value state. 9. Method according to claim 5, comprising the step of recording the values of the parameter and the step of determining the transition matrix on the basis of the recorded values of the parameter. 10. Method according to claim 5, comprising the step of determining the transition matrix on the basis of values of the parameter from the component and/or from other comparable components. 11. Method according to claim 1, wherein the statistical significance of the data underlying the probability for the malfunction at the future point in time given the current value of the parameter is calculated on the basis of the statistical significance of the data underlying the conditional probability distribution of the parameter given the current value of the parameter. 12. Method according to claim 1, wherein the probability for the malfunction given the current value of the parameter is determined for a number of future points in time. 13. Method according to claim 12, comprising the step of estimating a remaining useful life of the component on the basis of probabilities for the malfunction of the component given the number of future points in time. 14. Method according to claim 1, wherein the component is a gas turbine, and the malfunction of the transformer is one of a bearing defect, compressor defect, flow malfunction, turbine malfunction, or output malfunction, and the parameter for predicting the malfunction is temperature, lubricant condition in the bearings or in the oil tank, shaft or casing vibration, pressure of the gas in the turbine, electric output of a generator coupled with the turbine, ambient air condition or humidity, or a combination thereof; or the component is a transformer, and the malfunction of the component is one of an insulation defect or a cooling system defect of the transformer, and the parameter for predicting the malfunction is one or a combination of temperature of the coils, vibrations of the cooling fans, a condition of the oil surrounding the coils; or the component is a diesel engine, and the malfunction of the component is one of bearing defect or turbo charger defect, and the parameter for predicting the malfunction is one or a combination of temperature, vibrations, lubricant condition, outlet pressure of the compressor and fuel analysis. 15. Method according to claim 1, wherein the conditional probability for the malfunction at the future point in time given the current value of the parameter is determined on the basis of the integral over the parameter of the product of the conditional probability distribution of the parameter for the future point in time given the current value of the parameter with the conditional probability distribution for the malfunction given the parameter. 16. Method according to claim 15, wherein the current value of the parameter is associated to one of a plurality of discrete value states of said parameter which yields a current discrete value state, and the integral is determined on the basis of the sum of the products of the conditional probability distribution of the discrete value state of the parameter for the future point in time given the current discrete value state of the parameter with the conditional probability distribution for the malfunction given the discrete value state of the parameter over all discrete value states. 17. Method according to claim 1 comprising the steps: determining probabilities for N single malfunctions of the component at a future point in time given current parameters; and determining a total probability of a malfunction of the component at the future point in time given the current parameters on the basis of the probabilities for single malfunctions of the component at the future point in time given current parameters. 18. Method according to claim 1 comprising the steps of: determining a probability for a malfunction of a unit comprising the component on the basis of the probability of the malfunction of the of the component and on the basis of probabilities of malfunction of other components of the unit. 19. (canceled) 20. Non-transitory computer program with instructions configured to perform the steps of claim 1 when executed on a processor. 21. Apparatus for predicting a malfunction of a mechanical or electrical component of a unit comprising: a parameter value prediction section for determining a conditional probability distribution of a parameter of the component for a future point in time given the current value of the parameter based on the current value of the parameter; a component malfunction prediction section for determining a conditional probability for a malfunction at the future point in time given the current value of the parameter based on the conditional probability distribution of the parameter for the future point in time given the current value of the parameter and on a conditional probability distribution for the malfunction given the parameter. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Predicting a malfunction of a component of a unit includes providing a transition matrix of a parameter of the component, wherein the transition matrix includes for a number of discrete value states of the parameter probabilities to switch from one discrete value state to another within a certain time period; providing the conditional probability distribution for the malfunction given the discrete value states; providing a current discrete value state of the parameter; determining a conditional probability distribution of the discrete value states given the current discrete value state for a future point in time based on the current discrete value state and on the transition matrix by use of a Markov chain; and determining a probability for the malfunction for the future point in time based on the conditional probability distribution of the discrete value states for the future point in time and the conditional probability distribution for the malfunction. |
|
G06N7005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Predicting a malfunction of a component of a unit includes providing a transition matrix of a parameter of the component, wherein the transition matrix includes for a number of discrete value states of the parameter probabilities to switch from one discrete value state to another within a certain time period; providing the conditional probability distribution for the malfunction given the discrete value states; providing a current discrete value state of the parameter; determining a conditional probability distribution of the discrete value states given the current discrete value state for a future point in time based on the current discrete value state and on the transition matrix by use of a Markov chain; and determining a probability for the malfunction for the future point in time based on the conditional probability distribution of the discrete value states for the future point in time and the conditional probability distribution for the malfunction. |
|
The present invention provides an attribute estimation system capable of acquiring an image of a person whose attributes are to be estimated accurately and informing the person of an attribute estimation result. The attribute estimation system (1) includes: a mirror (11) equipped with an information display terminal (12); and an attribute estimation device (3). The information display terminal (12) includes an image acquisition unit. The information display terminal (12) is disposed on the mirror (11) in such a manner that the information display terminal (12) can display information on a surface of the mirror (11) and that the image acquisition unit can acquire an image of a user of the mirror (11). The information display terminal (12) and the attribute estimation device (3) can be connected to each other via a communication line network (2). The attribute estimation device (3) generates attribute information estimated from the acquired image of the user of the mirror (11). The information display terminal (12) displays the generated attribute information on the surface of the mirror (11). | Please help me write a proper abstract based on the patent claims. CLAIM: 1. An attribute estimation system comprising: a mirror equipped with an information display terminal; and an attribute estimation device, wherein the information display terminal comprises an image acquisition unit, the information display terminal is disposed on the mirror in such a manner that the information display terminal can display information on a surface of the mirror and that the image acquisition unit can acquire an image of a user of the mirror, the information display terminal and the attribute estimation device can be connected to each other via a communication line network, the attribute estimation device generates attribute information estimated from the acquired image of the user of the mirror, and the information display terminal displays the generated attribute information on the surface of the mirror. 2. The attribute estimation system according to claim 1, wherein the attribute to be generated is impression. 3. The attribute estimation system according to claim 1, wherein the attribute estimation device comprises a learning image information database, an attribute information database, an estimation image information database, a model learning unit, and an attribute estimation unit, the model learning unit extracts a feature of an image obtained from the learning image information database, and prepares an evaluation reference model from the feature with reference to the attribute information database, and the attribute estimation unit extracts a feature from the image of the user with reference to the estimation image information database, and generates attribute information estimated from the feature with reference to the evaluation reference model. 4. The attribute estimation system according to claim 1, wherein the attribute estimation device further comprises an ancillary information database and an ancillary information generation unit, and the ancillary information generation unit generates ancillary information relevant to the generated attribute information with reference to the ancillary information database. 5. A mirror equipped with an information display terminal, for use in the attribute estimation system according to claim 1, wherein the information display terminal comprises an image acquisition unit. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: The present invention provides an attribute estimation system capable of acquiring an image of a person whose attributes are to be estimated accurately and informing the person of an attribute estimation result. The attribute estimation system (1) includes: a mirror (11) equipped with an information display terminal (12); and an attribute estimation device (3). The information display terminal (12) includes an image acquisition unit. The information display terminal (12) is disposed on the mirror (11) in such a manner that the information display terminal (12) can display information on a surface of the mirror (11) and that the image acquisition unit can acquire an image of a user of the mirror (11). The information display terminal (12) and the attribute estimation device (3) can be connected to each other via a communication line network (2). The attribute estimation device (3) generates attribute information estimated from the acquired image of the user of the mirror (11). The information display terminal (12) displays the generated attribute information on the surface of the mirror (11). |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The present invention provides an attribute estimation system capable of acquiring an image of a person whose attributes are to be estimated accurately and informing the person of an attribute estimation result. The attribute estimation system (1) includes: a mirror (11) equipped with an information display terminal (12); and an attribute estimation device (3). The information display terminal (12) includes an image acquisition unit. The information display terminal (12) is disposed on the mirror (11) in such a manner that the information display terminal (12) can display information on a surface of the mirror (11) and that the image acquisition unit can acquire an image of a user of the mirror (11). The information display terminal (12) and the attribute estimation device (3) can be connected to each other via a communication line network (2). The attribute estimation device (3) generates attribute information estimated from the acquired image of the user of the mirror (11). The information display terminal (12) displays the generated attribute information on the surface of the mirror (11). |
|
The present disclosure provides a semantic analysis method and apparatus based on artificial intelligence. The method includes: matching input information to be processed with a preset semantic template, in which the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to a target semantic template successfully matched with the input information; normalizing the formative data and generating a semantic analysis result corresponding to the input information. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A semantic analysis method based on artificial intelligence, comprising: matching input information to be processed with a preset semantic template, wherein, the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to the semantic template; normalizing the formative data and generating a semantic analysis result corresponding to the input information. 2. The method according to claim 1, wherein, the semantic slot information corresponding to the application scenario comprises: common semantic slot information and special semantic slot information. 3. The method according to claim 1, further comprising: when the input information is not matched with the semantic template, matching non target equipment information in the input information with the semantic slot information, and processing successfully matched semantic slot information to obtain candidate semantic slot information; when the input information contains target equipment information, matching the target equipment information with the equipment information; when the target equipment information is successfully matched with the equipment information, selecting target semantic slot information from the candidate semantic slot information according to preset semantic slot information corresponding to the target equipment information; and converting the input information to the formative data according to the target equipment information and the target semantic slot information. 4. The method according to claim 3, further comprising: when the target equipment information is not matched with the equipment information, feeding back semantic analysis failure information. 5. The method according to claim 3, further-comprising: when the input information does not include the target equipment information, detecting whether candidate equipment information corresponding to the candidate semantic slot information is unique; when the candidate equipment information is unique, converting the input information to the formative data according to the candidate equipment information and the candidate semantic slot information. 6. The method according to claim 5, further comprising: when the candidate equipment information is not unique, calculating score data corresponding to each candidate equipment information according to preset weights corresponding to the candidate semantic slot information; acquiring the target equipment information and the corresponding target semantic slot information from the candidate equipment information and the candidate semantic slot information according to the score data; converting the input information to the formative data according to the target equipment information and the target semantic slot information. 7. A semantic analysis apparatus based on artificial intelligence, comprising: a processor; and a memory, configured to store one or more software modules executable by the processor, wherein the one or more software modules comprise: a matching module, configured to match input information to be processed with a preset semantic template, wherein, the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; a converting module, configured to convert the input information to formative data according to the preset semantic template, when the input information to be processed is successfully matched with the semantic template; a generating module, configured to normalize the formative data and generate a semantic analysis result corresponding to the input information. 8. The apparatus according to claim 7, wherein, the semantic slot information corresponding to the application scenario comprises: common semantic slot information and special semantic slot information. 9. The apparatus according to claim 7, wherein the one or more software modules further comprise an obtaining module and a selecting module, wherein the matching module is further configured to match non target equipment information in the input information with the semantic slot information, when the input information is not matched with the semantic template; the obtaining module is configured to process successfully matched semantic slot information to obtain candidate semantic slot information; the matching module is further configured to match target equipment information with the equipment information, when the input information contains the target equipment information; the selecting module is configured to select target semantic slot information from the candidate semantic slot information according to preset semantic slot information corresponding to the target equipment information, when the target equipment information is matched with the equipment information successfully; the converting module is configured to convert the input information to the formative data according to the target equipment information and the target semantic slot information. 10. The apparatus according to claim 9, wherein the one or more software modules further comprise: a feedback module, configured to feed back semantic analysis failure information when the target equipment information is not matched with the equipment information. 11. The apparatus according to claim 9, wherein the one or more software modules further comprise a detecting module, wherein the detecting module is configured to detect whether candidate equipment information corresponding to the candidate semantic slot information is unique when the input information does not include the target equipment information; the converting module is configured to convert the input information to the formative data according to the candidate equipment information and the candidate semantic slot information, when the candidate equipment information is unique. 12. The apparatus according to claim 11, wherein the one or more software modules further comprise a calculating module and a determining module, wherein the calculating module is configured to calculate score data corresponding to each candidate equipment information according to preset weights corresponding to the candidate semantic slot information, when the candidate equipment information is not unique; the determining module is configured to acquire the target equipment information and the corresponding target semantic slot information from the candidate equipment information and the candidate semantic slot information according to the score data; the converting module is configured to convert the input information to formative data according to the target equipment information and the target semantic slot information. 13. A non-transitory computer-readable storage medium, configured to store instructions that, when executed by a processor of a terminal, cause the terminal to perform a semantic analysis method based on artificial intelligence, the method comprising: matching input information to be processed with a preset semantic template, wherein, the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to the semantic template; normalizing the formative data and generating a semantic analysis result corresponding to the input information. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: The present disclosure provides a semantic analysis method and apparatus based on artificial intelligence. The method includes: matching input information to be processed with a preset semantic template, in which the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to a target semantic template successfully matched with the input information; normalizing the formative data and generating a semantic analysis result corresponding to the input information. |
|
G06N3006 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: The present disclosure provides a semantic analysis method and apparatus based on artificial intelligence. The method includes: matching input information to be processed with a preset semantic template, in which the preset semantic template is generated according to semantic slot information and equipment information corresponding to an application scenario; when the input information to be processed is successfully matched with the preset semantic template, converting the input information to formative data according to a target semantic template successfully matched with the input information; normalizing the formative data and generating a semantic analysis result corresponding to the input information. |
|
A system and method provide spectral forecasting using a time series data set, wherein the time series data set includes one or more seasonality patterns, the system comprising a data collection module, wherein the data collection module is configured to record one or more recordings. Further, the system includes a filter, wherein the filter is configured to clean the one or more recordings made by the data collection module. Furthermore, the system includes a time series historian configured to store the cleaned one or more recordings as a time series data set. In addition, the system includes a determination module, the determination module comprising one or more processors and a non-transitory memory containing instructions that, when executed by said one or more processors, cause said one or more processors to perform a set of steps. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for spectral forecasting using a time series data set, wherein the time series data set includes one or more seasonality patterns, the system comprising: a data collection module, wherein the data collection module is configured to record one or more recordings; a filter, wherein the filter is configured to clean the one or more recordings made by the data collection module; a time series historian configured to store the cleaned one or more recordings as a time series data set; and a determination module, wherein the determination module comprises: one or more processors; and a non-transitory memory containing instructions that, when executed by said one or more processors, cause said one or more processors to perform a set of steps comprising: subtracting the mean of the time series data set from each element of the time series data set for making the time series data set mean centric; detrending the mean centric time series data set; obtaining a power spectrum of the de-trended mean centric time series data set; selecting a set of frequencies from the power spectrum of the mean centric time series data set, wherein the selecting of the set of frequencies is done based on energy of the frequencies, the energy being the highest in the power spectrum; reconstructing the time series data set from selected set of frequencies; and determining the cycle of optimal periodicity from the reconstructed time series. 2. The system as claimed in claim 1, wherein the one or more processors are further configured to reconstruct the time series data set from the selected set of frequencies by applying inverse fast Fourier transform on the selected set of frequencies. 3. The system as claimed in claim 1, wherein the one or more processors obtain the power spectrum of the mean centric time series data sets by applying fast Fourier transform on the mean centric time series data set. 4. The system as claimed in claim 1, wherein the one or more processors determine the cycle of optimal periodicity using autocorrelation technique. 5. The system as claimed in claim 1, wherein the one or more processors are further configured to forecast a set of future points based on the determined optimal periodicity and reconstructed time series data set, wherein the forecasting is performed by replicating the determined optimal periodicity present in the reconstructed time series data set in the future horizon for obtaining a set of future points. 6. The system as claimed in claim 5, wherein the one or more processors are further configured to perform reverse differencing on the set of future points. 7. The system as claimed in claim 6, wherein the one or more processors are further configured to add the mean of the time series data set to the set of future points for obtaining the forecasted time series data set. 8. A method for spectral forecasting using a time series data set, wherein the time series data set includes one or more seasonality patterns, the method comprising: subtracting a mean of the time series data set from each element of the time series data set for making the time series data set mean centric; performing first order differencing on the mean centric time series data set for detrending the mean centric time series data set; obtaining the power spectrum of the de-trended mean centric time series data set; selecting a set of frequencies from the power spectrum of the mean centric time series data set, wherein the selecting of the set of frequencies is done based on energy of the frequencies, the energy being the highest in the power spectrum; and determining the cycle of optimal periodicity from the selected set of frequencies. 9. The method as claimed in claim 8, further comprising reconstructing the time series data set from the selected set of frequencies by applying inverse fast Fourier transform on the selected set of frequencies. 10. The method as claimed in claim 8, further comprising forecasting a set of future points based on the determined optimal periodicity and the reconstructed time series data set, wherein the forecasting is performed by replicating the determined optimal periodicity present in the reconstructed time series data set in the future horizon for obtaining a set of future points. 11. The method as claimed in claim 10 further comprising, performing reverse differencing on the set of future points. 12. The method as claimed in claim 10 further comprising, adding the mean of the time series data set to the set of future points for obtaining the forecasted time series data set. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A system and method provide spectral forecasting using a time series data set, wherein the time series data set includes one or more seasonality patterns, the system comprising a data collection module, wherein the data collection module is configured to record one or more recordings. Further, the system includes a filter, wherein the filter is configured to clean the one or more recordings made by the data collection module. Furthermore, the system includes a time series historian configured to store the cleaned one or more recordings as a time series data set. In addition, the system includes a determination module, the determination module comprising one or more processors and a non-transitory memory containing instructions that, when executed by said one or more processors, cause said one or more processors to perform a set of steps. |
|
G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A system and method provide spectral forecasting using a time series data set, wherein the time series data set includes one or more seasonality patterns, the system comprising a data collection module, wherein the data collection module is configured to record one or more recordings. Further, the system includes a filter, wherein the filter is configured to clean the one or more recordings made by the data collection module. Furthermore, the system includes a time series historian configured to store the cleaned one or more recordings as a time series data set. In addition, the system includes a determination module, the determination module comprising one or more processors and a non-transitory memory containing instructions that, when executed by said one or more processors, cause said one or more processors to perform a set of steps. |
|
Described herein are methods for training a predictor of a user's emotional response to stimuli (e.g., digital content). In order to more accurately learn the nature of the emotional response of the user to the stimuli, in some embodiments, the training of the predictor involves collection of attention level data that indicates to which objects the user paid attention. The attention level data may be utilized to weight token instances representing visual objects from the stimuli. Such a weighting can help to train the emotional response predictor to better determine which objects influence the user's affective response and/or the extent of their influence on the user's affective response. In different embodiments, attention level information may come from different sources, such as eye tracking data of the user, and a model for predicting an interest level of the user in various visual objects. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for training an emotional response predictor, comprising: receiving temporal windows of token instances to which a user was exposed; wherein each temporal window of token instances comprises at least two token instances that have overlapping instantiation periods, with each of the at least two token instances representing a different visual object; receiving, for each temporal window of token instances, corresponding attention levels indicative of an extent of attention of the user in each of the different visual objects represented by the at least two token instances comprised in the temporal window of token instances; generating samples corresponding to the temporal windows of token instances; wherein for each temporal window of token instances, values in its corresponding sample are indicative of weights of the token instances belonging to the temporal window of token instances; and wherein values in the sample that correspond to the at least two token instances of the temporal window of token instances, are further weighted according to their corresponding attention levels; receiving target values corresponding to the temporal windows of token instances; wherein each target value corresponding to the temporal window of token instances represents an affective response of the user to being exposed to the token instances in the temporal window of token instances; and training the emotional response predictor utilizing a machine learning-based training algorithm that uses the samples and the target values as training data. 2. The method of claim 1, further comprising receiving eye tracking data of the user, which is indicative of objects the user gazed at while viewing content represented by a temporal window of token instances, and determining attention levels in at least two token instances from the temporal window of token instances; wherein the at least two token instances have overlapping instantiation periods, and each of the at least two token instances represents a different visual object. 3. The method of claim 1, further comprising determining attention in at least two token instances from the temporal window of token instances utilizing a model for predicting an interest level of the user in various visual objects; wherein the at least two token instances have overlapping instantiation periods, and each of the at least two token instances represents a different visual object. 4. The method of claim 1, further comprising determining attention in at least two token instances from the temporal window of token instances utilizing analysis of previous observations of interest of the user in token instances; wherein the at least two token instances have overlapping instantiation periods, and each of the at least two token instances represents a different visual object. 5. The method of claim 1, further comprising receiving eye tracking data of other users indicative of objects the other users gazed at while viewing content represented by a temporal window of token instances, and determining attention levels in at least two token instances from the temporal window of token instances; wherein the at least two token instances have overlapping instantiation periods, and each of the at least two token instances represents a different visual object. 6. The method of claim 1, further comprising using the emotional response predictor for predicting emotional state of the user after being exposed to a new temporal window of token instances; wherein the emotional response predictor utilizes a sample derived from the new temporal window of token instances in which values corresponding to the at least two token instances of the new temporal window of token instances are weighted according to attention levels of the user in the at least two token instances. 7. The method of claim 1, wherein training the emotional response predictor involves training at least two different machine learning-based emotional response predictors on data collected over periods where the user was in different situations. 8. The method of claim 1, wherein the training involves at least one of the following actions: setting parameters of a regression model utilized by the emotional response predictor to make its predictions, and setting weights of a neural network utilized by the emotional response predictor to make its predictions, setting parameters of a support vector machine for regression utilized by the emotional response predictor to make its predictions. 9. The method of claim 1, further comprising receiving additional samples comprising temporal windows of token instances that do not have corresponding target values, and training the emotional response predictor utilizing a semi-supervised training method. 10. The method of claim 1, further comprising determining a target value representing an affective response of the user to being exposed to content represented by a temporal window of token instances utilizing a model trained to predict an emotional state based on values obtained from a user measurement channel of the user. 11. The method of claim 1, further comprising obtaining a value of a user measurement channel of the user by measuring the user with a sensor while the user is exposed to a temporal window of token instances, and utilizing the value of the user measurement channel of the user to determine a target value corresponding to the temporal window of token instances. 12. A method for training an emotional response predictor that utilizes eye tracking, comprising: receiving temporal windows of token instances to which a user was exposed; wherein each temporal window of token instances comprises at least two token instances that have overlapping instantiation periods, with each of the at least two token instances representing a different visual object; receiving, for each temporal window of token instances, eye tracking data of the user, which is indicative of objects the user gazed at while viewing content represented by the temporal window of token instances, and determining attention levels in at the least two token instances from the temporal window of token instances that have overlapping instantiation periods and represent different visual objects. generating samples corresponding to the temporal windows of token instances; wherein for each temporal window of token instances, values in its corresponding sample are indicative of weights of the token instances belonging to the temporal window of token instances; and wherein values in the sample that correspond to the at least two token instances of the temporal window of token instances, are further weighted according to their corresponding attention levels; receiving target values corresponding to the temporal windows of token instances; wherein each target value corresponding to the temporal window of token instances represents an affective response of the user to being exposed to the token instances in the temporal window of token instances; and training the emotional response predictor utilizing a machine learning-based training algorithm that uses the samples and the target values as training data. 13. The method of claim 12, further comprising using the emotional response predictor for predicting emotional state of the user after being exposed to a new temporal window of token instances; wherein the emotional response predictor utilizes a sample derived from the new temporal window of token instances in which values corresponding to the at least two token instances of the new temporal window of token instances are weighted according to attention levels of the user in the at least two token instances. 14. The method of claim 12, wherein the training involves at least one of the following actions: setting parameters of a regression model utilized by the emotional response predictor to make its predictions, and setting weights of a neural network utilized by the emotional response predictor to make its predictions, setting parameters of a support vector machine for regression utilized by the emotional response predictor to make its predictions. 15. The method of claim 12, further comprising determining a target value representing an affective response of the user to being exposed to content represented by a temporal window of token instances utilizing a model trained to predict an emotional state based on values obtained from a user measurement channel of the user. 16. The method of claim 12, further comprising obtaining a value of a user measurement channel of the user by measuring the user with a sensor while the user is exposed to a temporal window of token instances, and utilizing the value of the user measurement channel of the user to determine a target value corresponding to the temporal window of token instances. 17. A method for training an emotional response predictor for a user by utilizing eye tracking data of other users, comprising: receiving temporal windows of token instances to which a user was exposed; wherein each temporal window of token instances comprises at least two token instances that have overlapping instantiation periods, with each of the at least two token instances representing a different visual object; receiving, for each temporal window of token instances, eye tracking data of the other users, which is indicative of objects the other users gazed at while viewing content represented by the temporal window of token instances, and determining attention levels in at the least two token instances from the temporal window of token instances that have overlapping instantiation periods and represent different visual objects. generating samples corresponding to the temporal windows of token instances; wherein for each temporal window of token instances, values in its corresponding sample are indicative of weights of the token instances belonging to the temporal window of token instances; and wherein values in the sample that correspond to the at least two token instances of the temporal window of token instances, are further weighted according to their corresponding attention levels; receiving target values corresponding to the temporal windows of token instances; wherein each target value corresponding to the temporal window of token instances represents an affective response of the user to being exposed to the token instances in the temporal window of token instances; and training the emotional response predictor utilizing a machine learning-based training algorithm that uses the samples and the target values as training data. 18. The method of claim 17, further comprising using the emotional response predictor for predicting emotional state of the user after being exposed to a new temporal window of token instances; wherein the emotional response predictor utilizes a sample derived from the new temporal window of token instances in which values corresponding to the at least two token instances of the new temporal window of token instances are weighted according to attention levels of the user in the at least two token instances. 19. The method of claim 17, wherein the training involves at least one of the following actions: setting parameters of a regression model utilized by the emotional response predictor to make its predictions, and setting weights of a neural network utilized by the emotional response predictor to make its predictions, setting parameters of a support vector machine for regression utilized by the emotional response predictor to make its predictions. 20. The method of claim 17, further comprising determining a target value representing an affective response of the user to being exposed to content represented by a temporal window of token instances utilizing a model trained to predict an emotional state based on values obtained from a user measurement channel of the user. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Described herein are methods for training a predictor of a user's emotional response to stimuli (e.g., digital content). In order to more accurately learn the nature of the emotional response of the user to the stimuli, in some embodiments, the training of the predictor involves collection of attention level data that indicates to which objects the user paid attention. The attention level data may be utilized to weight token instances representing visual objects from the stimuli. Such a weighting can help to train the emotional response predictor to better determine which objects influence the user's affective response and/or the extent of their influence on the user's affective response. In different embodiments, attention level information may come from different sources, such as eye tracking data of the user, and a model for predicting an interest level of the user in various visual objects. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Described herein are methods for training a predictor of a user's emotional response to stimuli (e.g., digital content). In order to more accurately learn the nature of the emotional response of the user to the stimuli, in some embodiments, the training of the predictor involves collection of attention level data that indicates to which objects the user paid attention. The attention level data may be utilized to weight token instances representing visual objects from the stimuli. Such a weighting can help to train the emotional response predictor to better determine which objects influence the user's affective response and/or the extent of their influence on the user's affective response. In different embodiments, attention level information may come from different sources, such as eye tracking data of the user, and a model for predicting an interest level of the user in various visual objects. |
|
Systems, methods and apparatus for automatic signal detection with temporal feature extraction in an RF environment are disclosed. An apparatus learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. A knowledge map is formed based on the learning data. The apparatus automatically extracts temporal features of the RF environment from the knowledge map. A real-time spectral sweep is scrubbed against the knowledge map. The apparatus is operable to detect a signal in the RF environment, which has a low power level or is a narrowband signal buried in a wideband signal, and which cannot be identified otherwise. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for automatic signal detection in a radio-frequency (RF) environment, comprising: learning the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data; forming a knowledge map based on the learning data; automatically extracting at least one temporal feature of the RF environment from the knowledge map; scrubbing a real-time spectral sweep against the knowledge map; and detecting at least one signal in the RF environment. 2. The method of claim 1, wherein the knowledge map comprises an array of normal distributions, wherein each normal distribution corresponds to how often the power level at each frequency has been at a particular level. 3. The method of claim 1, further comprising creating a profile of the RF environment based on the knowledge map, wherein the profile comprises the highest power level at which each frequency has been seen during the predetermined period. 4. The method of claim 3, further comprising identifying areas of transition in the profile based on gradient detection. 5. The method of claim 4, further comprising detecting the at least one signal based on matched positive and negative gradients of the profile. 6. The method of claim 1, wherein the at least one signal is transmitted from a remote signal emitting device and has a low power level. 7. The method of claim 1, wherein the at least one signal is a narrowband signal hidden in a wideband signal. 8. The method of claim 7, wherein the narrowband signal having a bandwidth ranging from 1 kHz to 60 kHz is inside the wideband signal across a spectrum up to about 6 GHz. 9. The method of claim 1, further comprising automatically fine-tuning a threshold of power level on a segmented basis while extracting the at least one temporal feature from the knowledge map. 10. The method of claim 1, wherein the frequency resolution of the knowledge map is based on an Fast Fourier Transform (FFT) size setting. 11. The method of claim 1, further comprising periodically reevaluating the RF environment and updating the knowledge map. 12. The method of claim 1, further comprising automatically recording relevant information in high definition when the at least one signal is detected. 13. The method of claim 1, further comprising sending notification and/or alarms to an operator after detecting the at least one signal. 14. A system for automatic signal detection in a radio-frequency (RF) environment, comprising: at least one apparatus for detecting signals in the RF environment; wherein the at least one apparatus is operable to sweep and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data; wherein the at least one apparatus is operable to form a knowledge map based on the learning data; wherein the at least one apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map; wherein the at least one apparatus is operable to scrub a real-time spectral sweep against the knowledge map; and wherein the at least one apparatus is operable to detect at least one signal in the RF environment. 15. The system of claim 14, further comprising a remote device in network-based communication with the at least one apparatus, wherein the knowledge map and detecting results are displayed on a remote device in real time. 16. An apparatus for detecting at least one signal in a radio-frequency (RF) environment, comprising: at least one processor coupled with at least one memory, and at least one sensor; wherein the apparatus is operable to sweep and learn the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data; wherein the apparatus is operable to form a knowledge map based on the learning data; wherein the apparatus is operable to automatically extract at least one temporal feature of the RF environment from the knowledge map; wherein the apparatus is operable to scrub a real-time spectral sweep against the knowledge map; and wherein the apparatus is operable to detect at least one signal in the RF environment. 17. The apparatus of claim 16, wherein at least one knowledge map is stored in the apparatus. 18. The apparatus of claim 16, wherein the apparatus is operable to obtain a knowledge map by communicating with another apparatus. 19. The apparatus of claim 16, wherein the apparatus is automatic and unmanned. 20. The apparatus of claim 16, wherein the apparatus is water resistant. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems, methods and apparatus for automatic signal detection with temporal feature extraction in an RF environment are disclosed. An apparatus learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. A knowledge map is formed based on the learning data. The apparatus automatically extracts temporal features of the RF environment from the knowledge map. A real-time spectral sweep is scrubbed against the knowledge map. The apparatus is operable to detect a signal in the RF environment, which has a low power level or is a narrowband signal buried in a wideband signal, and which cannot be identified otherwise. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems, methods and apparatus for automatic signal detection with temporal feature extraction in an RF environment are disclosed. An apparatus learns the RF environment in a predetermined period based on statistical learning techniques, thereby creating learning data. A knowledge map is formed based on the learning data. The apparatus automatically extracts temporal features of the RF environment from the knowledge map. A real-time spectral sweep is scrubbed against the knowledge map. The apparatus is operable to detect a signal in the RF environment, which has a low power level or is a narrowband signal buried in a wideband signal, and which cannot be identified otherwise. |
|
Provided are an apparatus and method for compressing a deep neural network (DNN). The DNN compression method includes receiving a matrix of a hidden layer or an output layer of a DNN, calculating a matrix representing a nonlinear structure of the hidden layer or the output layer, and decomposing the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A deep neural network (DNN) compression method performed by at least one processor, the method comprising: receiving a matrix of a hidden layer or an output layer of a DNN; calculating a matrix representing a nonlinear structure of the hidden layer or the output layer; and decomposing the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure. 2. The DNN compression method of claim 1, wherein the calculating of the matrix includes expressing the nonlinear structure as a manifold structure and calculating the matrix. 3. The DNN compression method of claim 2, wherein the calculating of the matrix includes calculating the matrix representing the manifold structure using a Laplacian matrix. 4. The DNN compression method of claim 1, wherein the decomposing of the matrix includes decomposing the hidden layer or the output layer into matrices satisfying an expression below: minU,V(∥W−UV∥2+αTr(VBVT)) [Expression] (W: the hidden-layer or output-layer matrix, U and V: the matrices obtained by decomposing the hidden-layer or output-layer matrix, α: a Lagrange multiplier, and B: a Laplacian matrix representing a nonlinear structure of the DNN). 5. The DNN compression method of claim 4, wherein the decomposing of the hidden layer or the output layer into the matrices satisfying the above expression includes: calculating C according to C=(I+αB); decomposing C as C=DDT through a Cholesky decomposition; calculating W(DT)−1 with DT; decomposing WDT−1 as W(DT)−1≈EΣF; and calculating U=E, V=ETWC−1 using E. 6. A deep neural network (DNN) compression apparatus including at least one processor, wherein the processor comprises: an input portion configured to receive a matrix of a hidden layer or an output layer of a DNN; a calculator configured to calculate a matrix representing a nonlinear structure of the hidden layer or the output layer; and a decomposer configured to decompose the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure. 7. The DNN compression apparatus of claim 6, wherein the calculator expresses the nonlinear structure as a manifold structure and calculates the matrix. 8. The DNN compression apparatus of claim 7, wherein the calculator calculates the matrix representing the manifold structure using a Laplacian matrix. 9. The DNN compression apparatus of claim 6, wherein the decomposer decomposes the hidden layer or the output layer into matrices satisfying an expression below: minU,V(∥W−UV∥2+αTr(VBVT)) [Expression] (W: the hidden-layer or output-layer matrix, U and V: the matrices obtained by decomposing the hidden-layer or output-layer matrix, α: a Lagrange multiplier, and B: a Laplacian matrix representing a nonlinear structure of the DNN). 10. The DNN compression apparatus of claim 9, wherein the decomposer calculates the matrices U and V satisfying the above expression by calculating C according to C=(I+αB), decomposing C as C=DDT through a Cholesky decomposition, calculating W(DT)−1 with DT, decomposing WDT−1 as W(DT)−1≈EΣF, and calculating U=E, V=ETWC−1 using E. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Provided are an apparatus and method for compressing a deep neural network (DNN). The DNN compression method includes receiving a matrix of a hidden layer or an output layer of a DNN, calculating a matrix representing a nonlinear structure of the hidden layer or the output layer, and decomposing the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure. |
|
G06N308 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Provided are an apparatus and method for compressing a deep neural network (DNN). The DNN compression method includes receiving a matrix of a hidden layer or an output layer of a DNN, calculating a matrix representing a nonlinear structure of the hidden layer or the output layer, and decomposing the matrix of the hidden layer or the output layer using a constraint imposed by the matrix representing the nonlinear structure. |
|
A learning apparatus includes: an update unit which updates a dictionary used by a classifier; a calculation unit which calculates, by using a dictionary updated and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and a determination unit which determines whether to update the dictionary, by using the loss, wherein, when the determination unit determines to update the dictionary, the update unit updates the dictionary by using the samples with labeling added with a new sample with labeling, and wherein the determination unit determines whether to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A learning apparatus comprising: an update unit configured to update a dictionary used by a classifier; a calculation unit configured to calculate, by using a dictionary updated by the update unit and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and a determination unit configured to determine whether or not to update the dictionary, by using the loss, wherein, when the determination unit determines to update the dictionary, the update unit updates the dictionary by using the samples with labeling added with a new sample with labeling, and wherein the determination unit determines whether or not to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling. 2. The learning apparatus according to claim 1, wherein, when the loss calculated by using the updated dictionary decreases in inverse proportion to a number of the samples with labeling, the determination unit determines not to update the dictionary. 3. The learning apparatus according to claim 2, wherein, when the loss calculated by using the updated dictionary is less than a past loss calculated by the calculation unit when a number of the samples with labeling is smaller by one, the determination unit determines not to update the dictionary. 4. The learning apparatus according to claim 2, wherein, when an average of the loss calculated by using the updated dictionary and a predetermined number of past losses is less than an average of a predetermined number of losses calculated by the calculation unit before the predetermined number of past losses are calculated, the determination unit determines not to update the dictionary. 5. The learning apparatus according to claim 2, wherein the determination unit calculates a correlation function between a ratio of a number of the samples with labeling to a first number of samples being smaller than the number of the samples with labeling by a predetermined number, and a ratio of a loss when a number of the samples with labeling is the first number of samples to a loss with respect to all the samples with labeling, and, when the correlation function is greater than a predetermined threshold value, determines not to update the dictionary. 6. The learning apparatus according to claim 1, further comprising: a selection unit configured to select a sample that is likely to be discriminated as a class not being a correct-answer class, from one or more samples not assigned with a label, as a sample being a target of labeling; and an acquisition unit configured to acquire, when a label is assigned to the sample being a target of labeling selected by the selection unit, the samples with labeling including the sample being a target of labeling, wherein the update unit updates the dictionary by using the samples with labeling acquired by the acquisition unit. 7. The learning apparatus according to claim 1, further comprising an output unit configured to output the dictionary when the determination unit determines not to update the dictionary. 8. (canceled) 9. A learning method comprising: updating a dictionary used by a classifier; by using the updated dictionary and one or more samples with labeling being samples assigned with labels, calculating a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; determining whether or not to update the dictionary, by using the loss; when determining to update the dictionary, updating the dictionary by using samples with labeling added with a new piece of the samples with labeling; by using the updated dictionary and the samples with labeling added with the new sample with labeling, calculating a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and determining whether or not to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling. 10. A computer-readable non-transitory recording medium storing a program causing a computer to perform: processing of updating a dictionary used by a classifier; processing of calculating, by using the updated dictionary and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and processing of determining whether or not to update the dictionary, by using the loss, wherein, when determining to update the dictionary, the processing of updating the dictionary is processing of updating the dictionary by using the samples with labeling added with a new piece of the samples with labeling, and wherein the processing of determining whether or not to update the dictionary is processing of determining whether or not to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A learning apparatus includes: an update unit which updates a dictionary used by a classifier; a calculation unit which calculates, by using a dictionary updated and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and a determination unit which determines whether to update the dictionary, by using the loss, wherein, when the determination unit determines to update the dictionary, the update unit updates the dictionary by using the samples with labeling added with a new sample with labeling, and wherein the determination unit determines whether to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A learning apparatus includes: an update unit which updates a dictionary used by a classifier; a calculation unit which calculates, by using a dictionary updated and one or more samples with labeling being samples assigned with labels, a ratio to a number of the samples with labeling as a loss with respect to all the samples with labeling; and a determination unit which determines whether to update the dictionary, by using the loss, wherein, when the determination unit determines to update the dictionary, the update unit updates the dictionary by using the samples with labeling added with a new sample with labeling, and wherein the determination unit determines whether to update the dictionary, by using a loss calculated by using the updated dictionary and a loss calculated by using the dictionary before updating with respect to all the samples with labeling before adding the new sample with labeling. |
|
Disclosed herein is method and system for dynamically generating adaptive responses to user interactions. A response generating system receives user interactions including user queries from the user. Generic characteristics and user specific features associated with the user are extracted by integrating the user interactions with pre-stored conversation history and data from data sources associated with the user. Further, domain specific keywords from the user interactions are extracted for identifying the domain associated with the user queries. Personality of the user is detected based on the generic characteristics and the user specific features. Finally, adaptive responses to the user queries are dynamically generated based on the personality of the user and the domain associated with the user queries. The method aims at enhancing overall user experience and satisfaction in the conversation along with minimizing the total time required for addressing the user queries. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for dynamically generating adaptive response to user interactions, the method comprising: receiving, by a response generating device, user interactions including one or more user queries; extracting, by the response generating device, one or more generic characteristics associated with the user from the user interactions; extracting, by the response generating device, one or more user specific features associated with the user by integrating the user interactions with a pre-stored conversation history of the user and data related to the user from one or more data sources; extracting, by the response generating device, one or more domain specific keywords associated with the one or more user queries, from the user interactions for identifying domain associated with the one or more user queries; detecting, by the response generating device, personality of the user based on the one or more generic characteristics and the one or more user specific features; and generating, by the response generating device, dynamically, one or more adaptive responses to the one or more user queries based on the personality of the user and the domain associated with the one or more user queries. 2. The method as claimed in claim 1, wherein the user interactions are received in one or more data formats comprising at least one of text, audio or video. 3. The method as claimed in claim 1, wherein the personality of the user comprises at least one of one or more personal details of the user, mood of the user during initial conversation of the user with the response generating device or interests of the user. 4. The method as claimed in claim 1, wherein the one or more generic characteristics comprises at least one of emotions of the user, expression and actions of the user or one or more linguistic variations in the user interactions. 5. The method as claimed in claim 1 further comprises determining consistency in mood of the user for generating the one or more adaptive responses to the user queries. 6. The method as claimed in claim 5, wherein the consistency in the mood of the user is determined by comparing current mood of the user with the mood of the user during initial conversation of the user with the response generating device. 7. The method as claimed in claim 1 further comprises providing one or more suggestions to the user when the response generating device is unable to extract the one or more generic characteristics the one or more user specific features and the one or more domain specific keywords from the user interactions. 8. The method as claimed in claim 7, wherein the one or more suggestions comprises at least one of consult a domain expert or consult a support personnel, along with one or more predetermined information related to the one or more suggestions. 9. A response generating device that dynamically generates adaptive response to user interactions, the response generating device comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to: receive user interactions including one or more user queries; extract one or more generic characteristics associated with the user from the user interactions; extract one or more user specific features associated with the user, by integrating the user interactions with a pre-stored conversation history of the user and data related to the user from one or more data sources; extract one or more domain specific keywords associated with the one or more user queries, from the user interactions for identifying domain associated with the one or more user queries; detect personality of the user based on the one or more generic characteristics and the one or more user specific features; and generate, dynamically, one or more adaptive responses to the one or more user queries based on the personality of the user and the domain associated with the one or more user queries. 10. The response generating device as claimed in claim 9, wherein the processor receives the user interactions in one or more data formats comprising at least one of text, audio or video. 11. The response generating device as claimed in claim 9, wherein the personality of the user comprises at least one of one or more personal details of the user, mood of the user during initial conversation of the user with the response generating device or interests of the user. 12. The response generating device as claimed in claim 9, wherein the one or more generic characteristics comprises at least one of emotions of the user, expression and actions of the user or one or more linguistic variations in the user interactions. 13. The response generating device as claimed in claim 9, wherein the processor determines consistency in mood of the user to generate the one or more adaptive responses to the user queries. 14. The response generating device as claimed in claim 13, wherein the processor determines the consistency in the mood of the user by comparing current mood of the user with the mood of the user during initial conversation of the user with the response generating device. 15. The response generating device as claimed in claim 9, wherein the processor provides one or more suggestions to the user when the response generating device is unable to extract the one or more generic characteristics, the one or more user specific features and the one or more domain specific keywords from the user interactions. 16. The response generating device as claimed in claim 15, wherein the one or more suggestions comprises at least one of consult a domain expert and consult a support personnel along with one or more predetermined information related to the one or more suggestions. 17. A non-transitory computer readable medium having stored thereon instructions for dynamically generating adaptive response to user interactions comprising executable code which when executed by one or more processors, causes the one or more processors to perform steps comprising: receive user interactions including one or more user queries; extract one or more generic characteristics associated with the user from the user interactions; extract one or more user specific features associated with the user by integrating the user interactions with a pre-stored conversation history of the user and data related to the user from one or more data sources; extract one or more domain specific keywords associated with the one or more user queries, from the user interactions for identifying domain associated with the one or more user queries; detect a personality of the user based on the one or more generic characteristics and the one or more user specific features; and generate dynamically one or more adaptive responses to the one or more user queries based on the personality of the user and the domain associated with the one or more user queries. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Disclosed herein is method and system for dynamically generating adaptive responses to user interactions. A response generating system receives user interactions including user queries from the user. Generic characteristics and user specific features associated with the user are extracted by integrating the user interactions with pre-stored conversation history and data from data sources associated with the user. Further, domain specific keywords from the user interactions are extracted for identifying the domain associated with the user queries. Personality of the user is detected based on the generic characteristics and the user specific features. Finally, adaptive responses to the user queries are dynamically generated based on the personality of the user and the domain associated with the user queries. The method aims at enhancing overall user experience and satisfaction in the conversation along with minimizing the total time required for addressing the user queries. |
|
G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Disclosed herein is method and system for dynamically generating adaptive responses to user interactions. A response generating system receives user interactions including user queries from the user. Generic characteristics and user specific features associated with the user are extracted by integrating the user interactions with pre-stored conversation history and data from data sources associated with the user. Further, domain specific keywords from the user interactions are extracted for identifying the domain associated with the user queries. Personality of the user is detected based on the generic characteristics and the user specific features. Finally, adaptive responses to the user queries are dynamically generated based on the personality of the user and the domain associated with the user queries. The method aims at enhancing overall user experience and satisfaction in the conversation along with minimizing the total time required for addressing the user queries. |
|
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values using one or more neural network layers to generate an alternative representation of the features, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: a deep network implemented in one or more computers that defines a plurality of layers of non-linear operations, wherein the deep network comprises: an embedding function layer configured to: receive an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource, and process each of the features using a respective embedding function to generate one or more numeric values, and one or more neural network layers configured to: receive the numeric values, and process the numeric values to generate an alternative representation of the features of the resource, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and a classifier configured to: process the alternative representation of the input to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category. |
|
ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values using one or more neural network layers to generate an alternative representation of the features, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category. |
|
G06N304 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for scoring concept terms using a deep network. One of the methods includes receiving an input comprising a plurality of features of a resource, wherein each feature is a value of a respective attribute of the resource; processing each of the features using a respective embedding function to generate one or more numeric values; processing the numeric values using one or more neural network layers to generate an alternative representation of the features, wherein processing the floating point values comprises applying one or more non-linear transformations to the floating point values; and processing the alternative representation of the input using a classifier to generate a respective category score for each category in a pre-determined set of categories, wherein each of the respective category scores measure a predicted likelihood that the resource belongs to the corresponding category. |
|
Systems and methods are provided for improving communication by various computing systems in a network. Each computing system can be used to receive and process data. The data can be associated with a process represented by a chain of tasks. The computing systems can determine various parameters associated with the chain of tasks for determining a risk associated with the chain of tasks. The computing system can also determine a risk associated with multiple chains of tasks and aggregate the risks associated with the multiple chains of tasks. Determining the risk associated with each chain of tasks in the multiple chains of tasks can normalize a risk represented by the chains of tasks. Determining the risk associated with each chain of tasks or normalizing the risks represented by the chains can improve communication by the various computing systems in the network. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, by a processing device, a data set associated with a clinical trial, wherein the data set includes data about a plurality of tasks to be completed for planning or implementing the clinical trial and a predetermined date for completing the plurality of tasks; storing, by the processing device, the data set; generating, by the processing device, a chain of tasks based on the stored data by determining a relationship between tasks in the plurality of tasks and electronically converting the stored data into the chain of tasks based on the relationship; receiving, by the processing device, a first subset of data associated with the chain of tasks, wherein the first subset of data includes data for determining a progress of completing the chain of tasks; determining, by the processing device, a buffer index associated with the chain of tasks based on the first subset of data, the buffer index corresponding to a likelihood of completing the chain of tasks by the predetermined date, wherein the buffer index can be used to determine when the processing device receives or processes an additional data set; and generating, by the processing device, an interface for display that includes data associated with the chain of tasks, the first subset of data, or the buffer index. 2. The method of claim 1, further comprising: generating, by the processing device, a plurality of chains of tasks based at least in part on the stored data; receiving, by the processing device, a first subset of data associated with each of chain of tasks in the plurality of chains of tasks; determining, by the processing device, a buffer index associated with each chain of tasks; comparing, by the processing device, buffer indices associated with each chain of tasks; and determining, by the processing device, a rank of each chain of tasks based at least in part on the buffer index associated with the chain of tasks, wherein the rank of each chain of tasks can be used to determine a likelihood of completing the chain of tasks by the predetermined date. 3. The method of claim 1, wherein the first subset of data includes: a relationship indicator representing the relationship between tasks in the chain of tasks; a challenging time associated with a task in the chain of tasks, the challenging time corresponding to an amount of time for completing the task; an implementation indicator associated with the task in the chain of tasks, the implementation indicator representing an amount of time remaining before completing the task; and a buffer period associated with the chain of tasks, the buffer period representing an amount of time after a final task in the chain of tasks. 4. The method of claim 3, wherein determining the buffer index associated with the chain of tasks based on the first subset of data includes: determining, by the processing device, a chain duration based at least in part on the challenging time, the chain duration representing an amount of time for completing the chain of tasks; determining, by the processing device, an amount of time remaining before completing the chain of tasks based on the amount of time remaining before completing the task and a challenging time associated with another task in the chain of tasks; and determining, by the processing device, a chain percentage by comparing the chain duration and the amount of time remaining before completing the chain of tasks. 5. The method of claim 4, wherein determining the buffer index associated with the chain of tasks based on the first subset of data includes: determining, by the processing device, a remaining buffer period for the chain of tasks based on the implementation indicator and the buffer period by comparing the amount of time remaining before completing the task in the chain of tasks and the buffer period, wherein the remaining buffer period indicates an amount of the buffer period remaining; and determining, by the processing device, a buffer percentage by comparing the buffer period and the remaining buffer period. 6. The method of claim 5, wherein determining the buffer index associated with the chain of tasks based on the first subset of data further includes: determining, by the processing device, the buffer index by comparing the chain percentage and the buffer percentage; determining, by the processing device, whether the buffer index is above or below a risk threshold by comparing the buffer index to the risk threshold; outputting, by the processing device, data corresponding to the buffer index via the interface; and outputting, by the processing device, a risk level indicating the likelihood of completing the chain of tasks by the predetermined date in response to determining that the buffer index is above or below the risk threshold. 7. The method of claim 6, further comprising: receiving, by the processing device, a desired buffer index; and determining a change in the predetermined date for adjusting the buffer index based on: i) the desired buffer index; ii) the predetermined date; and iii) the buffer period for the chain of tasks, wherein adjusting the buffer index includes adjusting the buffer index such that the buffer index corresponds to the desired buffer index; and outputting data for adjusting the buffer index. 8. The method of claim 1, further comprising: receiving, by the processing device, a second subset of data; and determining, by the processing device, an updated buffer index associated with the chain of tasks based on the second subset of data, the updated buffer index corresponding to an updated likelihood of completing the chain of tasks by the predetermined date; and generating, by the processing device, an updated interface for display that includes data associated with the chain of tasks, the second subset of data, or the updated buffer index. 9. A system comprising: a processing device; and a non-transitory computer-readable medium communicatively coupled to the processing device, wherein the processing device is configured to perform operations comprising: receiving a data set associated with a clinical trial, wherein the data set includes data about a plurality of tasks to be completed for planning or implementing the clinical trial and a predetermined date for completing the plurality of tasks; storing the data set; generating a chain of tasks based on the stored data by determining a relationship between tasks in the plurality of tasks and electronically converting the stored data into the chain of tasks based on the relationship; receiving a first subset of data associated with the chain of tasks, wherein the first subset of data includes data for determining a progress of completing the chain of tasks; determining a buffer index associated with the chain of tasks based on the first subset of data, the buffer index corresponding to a likelihood of completing the chain of tasks by the predetermined date, wherein the buffer index can be used to determine when the processing device receives or processes an additional data set; and generating an interface for display that includes data associated with the chain of tasks, the first subset of data, or the buffer index. 10. The system of claim 9, wherein the processing device is further configured to: generate a plurality of chains of tasks based at least in part on the stored data; receive a first subset of data associated with each of chain of tasks in the plurality of chains of tasks; determine a buffer index associated with each chain of tasks in the plurality of chains of tasks; compare buffer indices associated with each chain of tasks in the plurality of chains of tasks; and determine a rank of each chain of tasks based at least in part on the buffer index associated with the chain of tasks, wherein the rank of each chain of tasks can be used to determine a likelihood of completing the chain of tasks by the predetermined date. 11. The system of claim 9, wherein the first subset of data includes: a relationship indicator representing the relationship between tasks in the chain of tasks; a challenging time associated with a task in the chain of tasks, the challenging time corresponding to an amount of time for completing the task; an implementation indicator associated with the task in the chain of tasks, the implementation indicator representing an amount of time remaining before completing the task; and a buffer period associated with the chain of tasks, the buffer period representing an amount of time after a final task in the chain of tasks. 12. The system of claim 11, wherein the processing device is further configured to determine the buffer index associated with the chain of tasks based on the first subset of data by: determining a chain duration based at least in part on the challenging time, the chain duration representing an amount of time for completing the chain of tasks; determining an amount of time remaining before completing the chain of tasks based on the amount of time remaining before completing the task and a challenging time associated with another task in the chain of tasks; and determining a chain percentage by comparing the chain duration and the amount of time remaining before completing the chain of tasks. 13. The system of claim 12, wherein the processing device is further configured to determine the buffer index associated with the chain of tasks based on the first subset of data by: determining a remaining buffer period for the chain of tasks based on the implementation indicator and the buffer period by comparing the amount of time remaining before completing the task in the chain of tasks and the buffer period, wherein the remaining buffer period indicates an amount of the buffer period remaining; and determining a buffer percentage by comparing the buffer period and the remaining buffer period. 14. The system of claim 13, wherein the processing device is further configured to determine the buffer index associated with the chain of tasks based on the first subset of data by: determining the buffer index by comparing the chain percentage and the buffer percentage; determining whether the buffer index is above or below a risk threshold by comparing the buffer index to the risk threshold; outputting data corresponding to the buffer index via the interface; and outputting a risk level indicating the likelihood of completing the chain of tasks by the predetermined date in response to determining that the buffer index is above or below the risk threshold. 15. The system of claim 14, wherein the processing device is further configured to: receive a desired buffer index; and determine a change in the predetermined date for adjusting the buffer index based on: i) the desired buffer index; ii) the predetermined date; and iii) the buffer period for the chain of tasks, wherein adjusting the buffer index includes adjusting the buffer index such that the buffer index corresponds to the desired buffer index; and outputting data for adjusting the buffer index. 16. A non-transitory computer-readable medium storing program code executable by a processor device to cause a computing device to perform operations, the operations comprising: receiving a data set associated with a clinical trial, wherein the data set includes data about a plurality of tasks to be completed for planning or implementing the clinical trial and a predetermined date for completing the plurality of tasks; storing the data set; generating a chain of tasks based on the stored data by determining a relationship between tasks in the plurality of tasks and electronically converting the stored data into the chain of tasks based on the relationship; receiving a first subset of data associated with the chain of tasks, wherein the first subset of data includes data for determining a progress of completing the chain of tasks; determining a buffer index associated with the chain of tasks based on the first subset of data, the buffer index corresponding to a likelihood of completing the chain of tasks by the predetermined date, wherein the buffer index can be used to determine when the computing device receives or processes an additional data set; and generating an interface for display that includes data associated with the chain of tasks, the first subset of data, or the buffer index. 17. The non-transitory computer-readable storage medium of claim 16, further comprising program code to cause the computing device to perform the operations of: generating a plurality of chains of tasks based at least in part on the stored data; receiving a first subset of data associated with each of chain of tasks in the plurality of chains of tasks; determining a buffer index associated with each chain of tasks in the plurality of chains of tasks; comparing buffer indices associated with each chain of tasks in the plurality of chains of tasks; and determining a rank of each chain of tasks based at least in part on the buffer index associated with the chain of tasks, wherein the rank of each chain of tasks can be used to determine a likelihood of completing the chain of tasks by the predetermined date 18. The non-transitory computer-readable storage medium of claim 16, wherein the first subset of data includes: a relationship indicator representing the relationship between tasks in the chain of tasks; a challenging time associated with a task in the chain of tasks, the challenging time corresponding to an amount of time for completing the task; an implementation indicator associated with the task in the chain of tasks, the implementation indicator representing an amount of time remaining before completing the task; and a buffer period associated with the chain of tasks, the buffer period representing an amount of time after a final task in the chain of tasks. 19. The non-transitory computer-readable storage medium of claim 18, wherein the operation of determining the buffer index associated with the chain of tasks based on the first subset of data includes: determining a chain duration based at least in part on the challenging time, the chain duration representing an amount of time for completing the chain of tasks; determining an amount of time remaining before completing the chain of tasks based on the amount of time remaining before completing the task and a challenging time associated with another task in the chain of tasks; and determining a chain percentage by comparing the chain duration and the amount of time remaining before completing the chain of tasks. 20. The non-transitory computer-readable storage medium of claim 19, wherein the operation of determining the buffer index associated with the chain of tasks based on the first subset of data includes: determining a remaining buffer period for the chain of tasks based on the implementation indicator and the buffer period by comparing the amount of time remaining before completing the task in the chain of tasks and the buffer period, wherein the remaining buffer period indicates an amount of the buffer period remaining; determining a buffer percentage by comparing the buffer period and the remaining buffer period; determining the buffer index by comparing the chain percentage and the buffer percentage; determining whether the buffer index is above or below a risk threshold by comparing the buffer index to the risk threshold; outputting data corresponding to the buffer index via the interface; outputting a risk level indicating the likelihood of completing the chain of tasks by the predetermined date in response to determining that the buffer index is above or below the risk threshold; receiving a desired buffer index; and determining a change in the predetermined date for adjusting the buffer index based on: i) the desired buffer index; ii) the predetermined date; and iii) the buffer period for the chain of tasks, wherein adjusting the buffer index includes adjusting the buffer index such that the buffer index corresponds to the desired buffer index; and outputting data for adjusting the buffer index. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems and methods are provided for improving communication by various computing systems in a network. Each computing system can be used to receive and process data. The data can be associated with a process represented by a chain of tasks. The computing systems can determine various parameters associated with the chain of tasks for determining a risk associated with the chain of tasks. The computing system can also determine a risk associated with multiple chains of tasks and aggregate the risks associated with the multiple chains of tasks. Determining the risk associated with each chain of tasks in the multiple chains of tasks can normalize a risk represented by the chains of tasks. Determining the risk associated with each chain of tasks or normalizing the risks represented by the chains can improve communication by the various computing systems in the network. |
|
G06N7005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems and methods are provided for improving communication by various computing systems in a network. Each computing system can be used to receive and process data. The data can be associated with a process represented by a chain of tasks. The computing systems can determine various parameters associated with the chain of tasks for determining a risk associated with the chain of tasks. The computing system can also determine a risk associated with multiple chains of tasks and aggregate the risks associated with the multiple chains of tasks. Determining the risk associated with each chain of tasks in the multiple chains of tasks can normalize a risk represented by the chains of tasks. Determining the risk associated with each chain of tasks or normalizing the risks represented by the chains can improve communication by the various computing systems in the network. |
|
A method generates a behavioral model of a data center when a machine learning algorithm is applied. A team of human modelers that partition the data center into a plurality of connected nodes is analyzed by a behavioral model. The behavioral model of the data center detects an anomaly in a system behavior center by recursively applying the behavioral model to each node and simple component. A compressed metric vector for the node is generated by reducing a dimension of an input metric vector. A root cause of a failure caused is determined by the anomaly and an action is automatically recommended to an operator to resolve a problem caused by the failure. The proactively actions are taken to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of a server, comprising: generating a behavioral model of a data center when a machine learning algorithm is applied using a processor and a memory, wherein the behavioral model is structured based on analysis of a team of human modelers that partition the data center into a plurality of connected nodes, wherein each node is further decomposed by the team of human modelers into a connected set comprising at least one of a child node and a simple component, wherein the child node is the node which is a subset of another node, and wherein the simple component is the node which has not been further decomposed; detecting an anomaly in a system behavior using the behavioral model of the data center by recursively applying the behavioral model to each node and the simple component by, for each node: generating a compressed metric vector for the node by reducing a dimension of an input metric vector, wherein the input metric vector comprises at least one of a metric for the node and the compressed metric vector from the child node; determining whether anomalous behavior is occurring in the node by comparing the compressed metric vector with a compressed model vector, wherein the compressed model vector of the node is the compressed metric vector generated using at least one of the metric associated with the node operating in a satisfactory manner, and the compressed model vector of at least one child node; determining a root cause of a failure caused by the anomaly; automatically recommending an action to an operator to resolve a problem caused by the failure; and proactively taking actions to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. 2. The method of claim 1: wherein the team of human modelers identify at least one characteristic comprising a label, a type, a category, and a connection of each of the nodes, and wherein the team of human modelers to manually define what constitutes a group each of the nodes having a similar characteristics in the server. 3. The method of claim 1: wherein the machine learning algorithm improves the behavioral model based on a human knowledge applied in real time as an input by the team of human modelers. 4. The method of claim 1: wherein the dimension of the input metric vector is reduced using at least one of a principal component analysis and a neural network. 5. The method of claim 4: wherein a full system model of the data center is automatically updated based on a dynamic change detected from at least one of a creation, a destruction, and a modification of at least one of an interconnection and a flow in the data center based on a reapplication of a human knowledge to further enhance the machine learning algorithm. 6. The method of claim 5: wherein the full system model of the data center is automatically updated based on the dynamic change detected when the node is at least one of added, deleted, and moved in the data center. 7. A method comprising: generating a behavioral model of a data center when a machine learning algorithm is applied using a processor and a memory, wherein the behavioral model is trained based on a human knowledge deconstruction of the data center into a set of connected simplified components; detecting an anomaly in a system behavior based on the behavioral model of the data center; proactively taking actions to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. 8. The method of claim 7: determining a root cause of a failure caused by the anomaly; automatically recommending an action to an operator to resolve a problem caused by the failure, and wherein the behavioral model is generated based on analysis of a team of human modelers that decompose a complex system of the data center into a connected system of smaller constituent subsystems, and wherein a smaller constituent subsystems are further decomposed by the team of human modelers into a set of connected simple components. 9. The method of claim 8: wherein the team of human modelers identify at least one characteristic comprising a label, a type, a category, a connection, and a metric of each of the smaller constituent subsystems, and wherein a team of human modelers group each of the smaller constituent subsystems having a similar characteristics. 10. The method of claim 9: wherein the machine learning algorithm continually improves the behavioral model based on a human knowledge applied in real time as an input by the team of human modelers. 11. The method of claim 10: wherein the metric of each of the smaller constituent subsystems is compressed in a recursive fashion to ultimately build a full system model of the data center at a point in time. 12. The method of claim 11: wherein the full system model of the data center is automatically updated based on a dynamic change detected from at least one of a creation, a destruction, and a modification of at least one of an interconnection and a flow in the data center based on a reapplication of the human knowledge to further enhance the machine learning algorithm. 13. The method of claim 12: wherein the full system model of the data center is automatically updated based on the dynamic change detected when a component is at least one of added, deleted, and moved in the data center. 14. A system of an machine learning environment comprising: a computer server of the machine learning environment: the computer server including one or more computers having instructions stored thereon that when executed cause the one or more computers: to generate a behavioral model of a data center when a machine learning algorithm is applied using a processor and a memory, wherein the behavioral model is trained based on a human knowledge deconstruction of the data center into a set of connected simplified components; to detect an anomaly in a system behavior based on the behavioral model of the data center; to determine a root cause of a failure caused by the anomaly; to automatically recommend an action to an operator to resolve a problem caused by the failure; and to proactively take actions to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. 15. The system of claim 14: wherein the behavioral model is generated based on analysis of a team of human modelers that decompose a complex system of the data center into a connected system of smaller constituent subsystems, and wherein a smaller constituent subsystems are further decomposed by the team of human modelers into a set of connected simple components. 16. The system of claim 15: wherein the team of human modelers identify at least one characteristic comprising a label, a type, a category, a connection, and a metric of each of the smaller constituent subsystems. 17. The system of claim 16 further comprising: wherein a team of human modelers group each of the smaller constituent subsystems having a similar characteristics. 18. The system of claim 17: wherein the machine learning algorithm continually improves the behavioral model based on a human knowledge applied in real time as an input by the team of human modelers. 19. The system of claim 18: wherein the metric of each of the smaller constituent subsystems is compressed in a recursive fashion to ultimately build a full system model of the data center at a point in time. 20. The system of claim 19: wherein the full system model of the data center is automatically updated based on a dynamic change detected from at least one of a creation, a destruction, and a modification of at least one of an interconnection and a flow in the data center based on a reapplication of the human knowledge to further enhance the machine learning algorithm. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: A method generates a behavioral model of a data center when a machine learning algorithm is applied. A team of human modelers that partition the data center into a plurality of connected nodes is analyzed by a behavioral model. The behavioral model of the data center detects an anomaly in a system behavior center by recursively applying the behavioral model to each node and simple component. A compressed metric vector for the node is generated by reducing a dimension of an input metric vector. A root cause of a failure caused is determined by the anomaly and an action is automatically recommended to an operator to resolve a problem caused by the failure. The proactively actions are taken to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. |
|
G06N99005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A method generates a behavioral model of a data center when a machine learning algorithm is applied. A team of human modelers that partition the data center into a plurality of connected nodes is analyzed by a behavioral model. The behavioral model of the data center detects an anomaly in a system behavior center by recursively applying the behavioral model to each node and simple component. A compressed metric vector for the node is generated by reducing a dimension of an input metric vector. A root cause of a failure caused is determined by the anomaly and an action is automatically recommended to an operator to resolve a problem caused by the failure. The proactively actions are taken to keep the data center in a normal state based on the behavioral model using the machine learning algorithm. |
|
Systems and methods are provided herein for generating personalized timeline-based feeds to a user. A computer-implemented method for generating feeds to a user may be provided. The method may include generating a timeline comprising a plurality of milestones and needs associated with an event, and providing the feeds based on community wisdom. The feeds may be provided for each milestone on the time-line specific to the user, and may be configured to address the user's needs at each milestone. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented system for assisting a plurality of users in navigating one or more life events, the system comprising: (1) a matching engine configured to: receive input data from a plurality of computing devices associated with a plurality of users, wherein the input data comprises queries and insights from different users relating to the one or more life events; and match the insights with the queries to a plurality of milestones located along a plurality of timelines for one or more users; and (2) a recommendation engine configured to: predict needs of the one or more users based on the matched insights and queries; and generate personalized recommendations to the one or more users based on the users' predicted needs, wherein the timelines, milestones, queries, insights, and/or personalized recommendations are configured to be displayed as a set of visual objects on graphical displays of the computing devices associated with the one or more users. 2. The system of claim 1, wherein the one or more life events are selected from the group consisting of (i) diagnosis with a terminal illness, (ii) death, (iii) marriage, (iv) divorce and (v) retirement. 3. The system of claim 1, wherein the matching engine is configured to (1) categorize the queries for different subjects, (2) tag the queries, (3) find similar queries or existing answers to those queries, and/or (4) match queries to one or more users who are able to answer those queries, for each of the one or more life events. 4. The system of claim 1, wherein the matching engine is configured to aggregate and categorize the insights into a plurality of classes comprised of the plurality of milestones or life events. 5. The system of claim 4, wherein the plurality of milestones comprises clinical milestones and non-clinical milestones associated with the one or more life events, and wherein the matching engine is configured to establish relationships between the clinical milestones and non-clinical milestones by analyzing the users' queries and insights using a natural language processing (NLP) algorithm. 6. The system of claim 1, wherein the matching engine is configured to identify the type of life event(s) and the milestones that the one or more users is currently experiencing, has experienced, or is likely to experience, by deriving meaning from the users' queries and insights using a natural language processing (NLP) algorithm. 7. The system of claim 1, wherein the matching engine comprises statistical models that are configured to determine probabilistic decisions based on attaching of real-valued weights to each of the one or more life events or milestones, and wherein the probabilistic decisions are usable to aid the one or more users in navigating the life events and milestones. 8. The system of claim 1, wherein the predicted needs and personalized recommendations of the one or more users are mapped onto the plurality of timelines and milestones in a chronological order. 9. The system of claim 1, wherein the matching engine is configured to analyze a user's previous query in addition to a present query, and extrapolate information from the user's previous and present queries to determine present milestones and generate future milestones on the user's timelines for one or more life events. 10. The system of claim 1, wherein the recommendation engine comprises a predictive model that is configured to predict one or more future milestones for one or more users for one or more life events, and wherein the predictive model is trained using input data that is collected as a plurality of users undergo the same or similar life event or different life events. 11. The system of claim 1, further comprising: a curation engine configured to: receive the matched insights and queries from the matching engine; determine a frequency at which the matched queries and insights appear or are accessed by the one or more users at each of the plurality of milestones; filter the matched queries and insights based on the determined frequency at each of the plurality of milestones; and provide the filtered queries and insights to a plurality of curators for editing, curation and scoring. 12. The system of claim 11, wherein the curation engine is further configured to filter the scored queries and insights from the plurality of curators, by comparing their scores to one or more predetermined thresholds and ranking the queries and insights based said comparison of the scores. 13. The system of claim 1, wherein the recommendation engine is further configured to predict the needs of the one or more users based on one or more of the following: (1) profiles of the one or more users, (2) information obtained directly or indirectly from the one or more users via social media or a social networking website, (3) action or inaction of the one or more users pertaining to the milestones and/or life events, and/or (4) online interaction between two or more users. 14. The system of claim 1, wherein the recommendation engine is configured to generate new personalized recommendations to the one or more users when the matching engine receives new queries or insights from the one or more users, or when new milestones have been added to the plurality of timelines. 15. A computer-implemented method for assisting a plurality of users in navigating one or more life events, the method comprising: receiving input data from a plurality of computing devices associated with a plurality of users, wherein the input data comprises queries and insights from different users relating to the one or more life events; matching the insights with the queries to a plurality of milestones located along a plurality of timelines for one or more users; predicting needs of the one or more users based on the matched insights and queries; and generating personalized recommendations to the one or more users based on the users' predicted needs, wherein the timelines, milestones, queries, insights, and/or personalized recommendations are configured to be displayed as a set of visual objects on graphical displays of the computing devices associated with the one or more users. 16. The method of claim 15, wherein the one or more life events are selected from the group consisting of (i) diagnosis with a terminal illness, (ii) death, (iii) marriage, (iv) divorce and (v) retirement. 17. The method of claim 15, further comprising, for each of the one or more life events: (1) categorizing the queries for different subjects, (2) tagging the queries, (3) finding similar queries or existing answers to those queries, and/or (4) matching queries to one or more users who are able to answer those queries. 18. The method of claim 15, further comprising: aggregating and categorizing the insights into a plurality of classes comprised of the plurality of milestones or life events. 19. The method of claim 18, wherein the plurality of milestones comprises clinical milestones and non-clinical milestones associated with the one or more life events, the method further comprising: establishing relationships between the clinical milestones and non-clinical milestones by analyzing the users' queries and insights using a natural language processing (NLP) algorithm. 20. The method of claim 15, further comprising: identifying the type of life event(s) and the milestones that the one or more users is currently experiencing, has experienced, or is likely to experience, by deriving meaning from the users' queries and insights using a natural language processing (NLP) algorithm. 21. The method of claim 15, comprising: using statistical models to determine probabilistic decisions based on attaching of real-valued weights to each of the one or more life events or milestones, wherein the probabilistic decisions are usable to aid the one or more users in navigating the life events and milestones. 22. The method of claim 15, comprising: mapping the predicted needs and personalized recommendations of the one or more users onto the plurality of timelines and milestones in a chronological order. 23. The method of claim 15, comprising: analyzing a user's previous query in addition to a present query, and extrapolating information from the user's previous and present queries to determine present milestones and generate future milestones on the user's timelines for one or more life events. 24. The method of claim 15, comprising: using a predictive model to predict one or more future milestones for the one or more users for one or more life events, wherein the predictive model is trained using input data that is collected as a plurality of users undergo the same or similar life event or different life events. 25. The method of claim 15, further comprising: determining a frequency at which the matched queries and insights appear or are accessed by the one or more users at each of the plurality of milestones; filtering the matched queries and insights based on the determined frequency at each of the plurality of milestones; and providing the filtered queries and insights to a plurality of curators for editing, curation and scoring. 26. The method of claim 25, further comprising: filtering the scored queries and insights from the plurality of curators, by comparing their scores to one or more predetermined thresholds and ranking the queries and insights based on said comparison of the scores. 27. The method of claim 15, comprising: further predicting the needs of the one or more users based on one or more of the following: (1) profiles of the one or more users, (2) information obtained directly or indirectly from the one or more users via social media or a social networking website, (3) action or inaction of the one or more users pertaining to the milestones and/or life events, and/or (4) online interaction between two or more users. 28. The method of claim 15, further comprising: generating new personalized recommendations to the one or more users when the matching engine receives new queries or insights from the one or more users, or when new milestones have been added to the plurality of timelines. 29. A tangible computer readable medium storing instructions that, when executed by one or more servers, causes the one or more servers to perform a computer-implemented method for assisting a plurality of users in navigating one or more life events selected from the group consisting of (i) diagnosis with a terminal illness, (ii) death, (iii) marriage, (iv) divorce and (v) retirement, the method comprising: with aid of a matching engine: receiving input data from a plurality of computing devices associated with a plurality of users, wherein the input data comprises queries and insights from different users relating to the one or more life events; and matching the insights with the queries to a plurality of milestones located along a plurality of timelines for one or more users; and with aid of a recommendation engine: predicting needs of the one or more users based on the matched insights and queries; and generating personalized recommendations to the one or more users based on the users' predicted needs. 30. The computer readable medium of claim 29, wherein the stored instructions further comprise computer code for displaying the timelines, milestones, queries, insights, and/or personalized recommendations as a set of visual objects on graphical displays of the computing devices associated with the one or more users. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems and methods are provided herein for generating personalized timeline-based feeds to a user. A computer-implemented method for generating feeds to a user may be provided. The method may include generating a timeline comprising a plurality of milestones and needs associated with an event, and providing the feeds based on community wisdom. The feeds may be provided for each milestone on the time-line specific to the user, and may be configured to address the user's needs at each milestone. |
|
G06N7005 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems and methods are provided herein for generating personalized timeline-based feeds to a user. A computer-implemented method for generating feeds to a user may be provided. The method may include generating a timeline comprising a plurality of milestones and needs associated with an event, and providing the feeds based on community wisdom. The feeds may be provided for each milestone on the time-line specific to the user, and may be configured to address the user's needs at each milestone. |
|
A computer-implemented method for determining elastic properties for a heterogeneous anisotropic geological formation is described herein. The method includes grouping sonic velocity data from a borehole section (or borehole sections) into a number of clusters (e.g., one or more clusters). The sonic velocity data is grouped into clusters using petrophysical log data from the borehole section. The method also includes inverting the sonic velocity data for the clusters to determine elastic properties for each cluster. In some cases, the elastic properties for the clusters are combined to determine a relationship between the elastic properties and formation heterogeneity. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for determining a plurality of elastic properties for an heterogeneous anisotropic geological formation, the method comprising: grouping sonic velocity data from at least one borehole section into at least one cluster, wherein the sonic velocity data is grouped using petrophysical log data from the at least one borehole section; and inverting the sonic velocity data for at least one cluster to determine a plurality of elastic properties for each cluster. 2. The method of claim 1, further comprising: combining the elastic properties for a plurality of the clusters to determine a relationship between the elastic properties and formation heterogeneity. 3. The method of claim 1, further comprising: determining uncertainty parameters for each of the elastic properties. 4. The method of claim 1, wherein the anisotropic formation is transversely isotropic. 5. The method of claim 4, wherein the plurality of elastic properties comprises five elastic properties. 6. The method of claim 5, wherein the five elastic properties comprise three Thomsen parameters and two velocities. 7. The method of claim 1, wherein the elastic properties are independent of each other. 8. The method of claim 1, wherein at least some of the elastic properties are dependent on each other. 9. The method of claim 1, wherein the plurality of elastic properties comprises components of elastic stiffness tensors. 10. The method of claim 1, wherein the plurality of elastic properties comprises components of elastic compliance tensors. 11. The method of claim 1, wherein the plurality of elastic properties comprises elastic Young's moduli and Poisson's ratios. 12. The method of claim 1, wherein at least some of the elastic properties are density normalized. 13. The method of claim 1, wherein the sonic velocity data is inverted using a misfit-norm-based iterative process. 14. The method of claim 3, wherein the uncertainty parameters are determined using a Bayesian probability method 15. The method of claim 3, wherein the uncertainty parameters comprise posterior probability distribution functions for each of the elastic properties. 16. The method of claim 3, wherein the uncertainty parameters comprise posterior probability distribution functions for combinations of elastic properties. 17. The method of claim 1, wherein the petrophysical log data comprise data selected from the group consisting of: gamma ray measurements, density measurements, clay volume measurements, porosity measurements, resistivity measurements, mineralogical measurements, and a combination thereof. 18. The method of claim 1, further comprising: acquiring sonic log data for the borehole section; analyzing the sonic log data to determine the sonic velocity data for the borehole section. 19. The method of claim 4, further comprising: determining a dip azimuth and a dip angle of a transverse isotropic plane for the transversely isotropic formation. 20. The method of claim 19, wherein the dip azimuth and dip angle are determined from borehole image logs for the borehole section. 21. The method of claim 19, further comprising: determining borehole azimuth and borehole deviation for the borehole section. 22. The method of claims 21, further comprising: determining a relative dip angle between an axis of the borehole section and the transverse isotropic plane. 23. The method of claim 1, wherein the at least one borehole section comprises a plurality of borehole sections from at least two different wells. 24. A non-transitory computer readable medium encoded with instructions, which, when loaded on a computer, establish processes for determining a plurality of elastic properties for a heterogeneous anisotropic geological formation, the processes comprising: grouping sonic velocity data from at least one borehole section into at least one cluster, wherein the sonic velocity data is grouped using petrophysical log data from the at least one borehole section; and inverting the sonic velocity data for at least one cluster to determine a plurality of elastic properties for each cluster. 25. The non-transitory computer readable medium according to claim 24, wherein the processes further comprise: combining the elastic properties for a plurality of the clusters to determine a relationship between the elastic properties and formation heterogeneity. 26. The non-transitory computer readable medium according to claim 24, wherein the processes further comprise: determining uncertainty parameters for each of the elastic properties. 27. The non-transitory computer readable medium according to claim 24, wherein the anisotropic formation is transversely isotropic. 28. The non-transitory computer readable medium according to claim 27, wherein the plurality of elastic properties comprises five elastic properties. 29. A system for determining a plurality of elastic properties for a heterogeneous anisotropic geological formation, the system comprising: a processor; and a memory storing instructions executable by the processor to perform processes that include: grouping sonic velocity data from at least one borehole section into at least one cluster, wherein the sonic velocity data is grouped using petrophysical log data from the at least one borehole section; and inverting the sonic velocity data for at least one cluster to determine a plurality of elastic properties for each cluster. 30. The system according to claim 29, wherein the processes further comprise: combining the elastic properties for a plurality of the clusters to determine a relationship between the elastic properties and formation heterogeneity. 31. The system according to claim 29, wherein the processes further comprise: determining uncertainty parameters for each of the elastic properties. 32. The system according to claim 29, wherein the anisotropic formation is transversely isotropic. |
|
ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: A computer-implemented method for determining elastic properties for a heterogeneous anisotropic geological formation is described herein. The method includes grouping sonic velocity data from a borehole section (or borehole sections) into a number of clusters (e.g., one or more clusters). The sonic velocity data is grouped into clusters using petrophysical log data from the borehole section. The method also includes inverting the sonic velocity data for the clusters to determine elastic properties for each cluster. In some cases, the elastic properties for the clusters are combined to determine a relationship between the elastic properties and formation heterogeneity. |
|
G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A computer-implemented method for determining elastic properties for a heterogeneous anisotropic geological formation is described herein. The method includes grouping sonic velocity data from a borehole section (or borehole sections) into a number of clusters (e.g., one or more clusters). The sonic velocity data is grouped into clusters using petrophysical log data from the borehole section. The method also includes inverting the sonic velocity data for the clusters to determine elastic properties for each cluster. In some cases, the elastic properties for the clusters are combined to determine a relationship between the elastic properties and formation heterogeneity. |
|
Systems and methods for event-driven learning with spike timing dependent plasticity in neuromorphic computers are disclosed. A neuromorphic processor includes a synapse coupled to a pre-synaptic neuron and coupled to a post-synaptic neuron, the synapse including a synapse memory to store a synapse weight and synapse spike timing dependent plasticity (STDP) circuit coupled to the synapse memory. The pre-synaptic neuron includes a pre-synaptic neuron memory to store a pre-synaptic neuron spike history and a pre-synaptic neuron STDP circuit coupled to the pre-synaptic neuron memory, the pre-synaptic neuron STDP circuit to, in response to the pre-synaptic neuron firing, initiate performing long term potentiation. The post-synaptic neuron includes a post-synaptic neuron memory storing a post-synaptic neuron spike history and a post-synaptic neuron STDP circuit coupled to the post-synaptic neuron memory to, in response to the post-synaptic neuron firing, initiate performing long term depression. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neuromorphic processor, comprising: a synapse coupled to a pre-synaptic neuron and coupled to a post-synaptic neuron, the synapse including: a synapse memory to store a synapse weight; a synapse spike timing dependent plasticity (STDP) circuit coupled to the synapse memory; the pre-synaptic neuron including: a pre-synaptic neuron memory to store a pre-synaptic neuron spike history; and a pre-synaptic neuron STDP circuit coupled to the pre-synaptic neuron memory, the pre-synaptic neuron STDP circuit to, in response to the pre-synaptic neuron firing, initiate performing long term depression; the post-synaptic neuron including: a post-synaptic neuron memory storing a post-synaptic neuron spike history; a post-synaptic neuron STDP circuit coupled to the post-synaptic neuron memory to, in response to the post-synaptic neuron firing, initiate performing long term potentiation. 2. The neuromorphic processor of claim 1, wherein, as part of performing long term potentiation, the post-synaptic neuron STDP circuit is further to: overwrite the post-synaptic neuron spike history with a maximum value; transmit a signal indicating that the post-synaptic neuron has fired to the pre-synaptic neuron STDP circuit; the pre-synaptic neuron STDP circuit is further to: retrieve the pre-synaptic neuron spike history from the pre-synaptic neuron memory; determine a post-synaptic neuron spike history based on receiving the signal indicating that the post-synaptic neuron has fired; calculate a synapse weight offset based on the pre-synaptic neuron spike history and the post-synaptic neuron spike history; transmit the synapse weight offset to the synapse STDP circuit; and the synapse STDP circuit is further to update the synapse weight based on the synapse weight offset. 3. The neuromorphic processor of claim 1, wherein, as part of performing long term depression: the pre-synaptic neuron STDP circuit is further to: overwrite the pre-synaptic neuron spike history with a maximum value; transmit a signal indicating that the pre-synaptic neuron has fired to the post-synaptic neuron STDP circuit; the post-synaptic neuron STDP circuit is further to: retrieve the post-synaptic neuron spike history from the post-synaptic neuron memory; determine a pre-synaptic neuron spike history based on receiving the signal indicating that the pre-synaptic neuron has fired; calculate a synapse weight offset based on the pre-synaptic neuron spike history and the post-synaptic neuron spike history; transmit the synapse weight offset to the synapse STDP circuit; and the synapse STDP circuit is further to update the synapse weight based on the synapse weight offset. 4. The neuromorphic processor of claim 3, wherein the synapse is to transmit the signal indicating that the pre-synaptic neuron has fired to the pre-synaptic neuron. 5. The neuromorphic processor of claim 4, wherein a network on chip is to transmit the signal indicating that the pre-synaptic neuron has fired to the pre-synaptic neuron. 6. The neuromorphic processor of claim 2, further comprising: a reward-based STDP circuit coupled to the post-synaptic neuron, the reward-based STDP circuit to transmit a neuron reward value to the post-synaptic neuron STDP circuit; wherein the post-synaptic neuron STDP circuit is further to, as part of performing long term potentiation, transmit the neuron reward value to the pre-synaptic neuron STDP circuit; and wherein the pre-synaptic neuron STDP circuit is further to, as part of performing long term potentiation, multiply the synapse weight offset by the neuron reward value prior to transmitting the synapse weight offset to the synapse STDP circuit. 7. The neuromorphic processor of claim 3, further comprising a reward-based STDP circuit coupled to the post-synaptic neuron, the reward-based STDP circuit to transmit a neuron reward value to the post-synaptic neuron STDP circuit; wherein the post-synaptic neuron STDP circuit is further to, as part of performing long term depression, multiply the synapse weight offset by the neuron reward value prior to transmission of the synapse weight offset to the synapse STDP circuit. 8. A neuromorphic processor logic unit, comprising: a synapse coupled to a pre-synaptic neuron and coupled to a post-synaptic neuron; the synapse including: a synapse memory to store a synapse weight; a synapse spike timing dependent plasticity (STDP) circuit coupled to the synapse memory; the pre-synaptic neuron including: a pre-synaptic neuron memory to store a pre-synaptic neuron spike history; and a pre-synaptic neuron STDP circuit coupled to the pre-synaptic neuron memory, the pre-synaptic neuron STDP circuit to, in response to the pre-synaptic neuron firing, initiate performing long term depression; the post-synaptic neuron including: a post-synaptic neuron memory storing a post-synaptic neuron spike history; a post-synaptic neuron STDP circuit coupled to the post-synaptic neuron memory to, in response to the post-synaptic neuron firing, initiate performing long term potentiation. 9. The neuromorphic processor logic unit of claim 8 wherein, as part of performing long term potentiation: the post-synaptic neuron STDP circuit is further to: overwrite the post-synaptic neuron spike history with a maximum value; transmit a signal indicating that the post-synaptic neuron has fired to the pre-synaptic neuron STDP circuit; the pre-synaptic neuron STDP circuit is further to: retrieve the pre-synaptic neuron spike history from the pre-synaptic neuron memory; determine a post-synaptic neuron spike history based on receiving the signal indicating that the post-synaptic neuron has fired; calculate a synapse weight offset based on the pre-synaptic neuron spike history and the post-synaptic neuron spike history; transmit the synapse weight offset to the synapse STDP circuit; and the synapse STDP circuit is further to update the synapse weight based on the synapse weight offset. 10. The neuromorphic processor logic unit of claim 8 wherein, as part of performing long term depression: the pre-synaptic neuron STDP circuit is further to: overwrite the pre-synaptic neuron spike history with a maximum value; transmit a signal indicating that the pre-synaptic neuron has fired to the post-synaptic neuron STDP circuit; the post-synaptic neuron STDP circuit is further to, retrieve the post-synaptic neuron spike history from the post-synaptic neuron memory; determine a pre-synaptic neuron spike history based on receiving the signal indicating that the pre-synaptic neuron has fired; calculate a synapse weight offset based on the pre-synaptic neuron spike history and the post-synaptic neuron spike history; transmit the synapse weight offset to the synapse STDP circuit; and the synapse STDP circuit is further to update the synapse weight based on the synapse weight offset. 11. The neuromorphic processor logic unit of claim 9 wherein the synapse is to transmit the signal indicating that the post-synaptic neuron has fired to the pre-synaptic neuron. 12. The neuromorphic processor logic unit of claim 10, wherein a network on chip is to transmit the signal indicating that the pre-synaptic neuron has fired to the pre-synaptic neuron. 13. The neuromorphic processor logic unit of claim 9, further comprising: a reward-based STDP circuit coupled to the post-synaptic neuron; the reward-based STDP circuit to transmit a neuron reward value to the post-synaptic neuron STDP circuit; wherein the post-synaptic neuron STDP circuit is further to, as part of performing long term potentiation, transmit the neuron reward value to the pre-synaptic neuron STDP circuit; and wherein the pre-synaptic neuron STDP circuit is further to, as part of performing long term potentiation, multiply the synapse weight offset by the neuron reward value prior to transmitting the synapse weight offset to the synapse STDP circuit. 14. The neuromorphic processor logic unit of claim 10, further comprising a reward-based STDP circuit coupled to the post-synaptic neuron; the reward-based STDP circuit to transmit a neuron reward value to the post-synaptic neuron STDP circuit; and wherein the post-synaptic neuron STDP circuit is further to, as part of performing long term depression, multiply the synapse weight offset by the neuron reward value prior to transmitting the synapse weight offset to the synapse STDP circuit. 15. A method, comprising, in a neuromorphic processor: receiving a plurality of neuron inputs with a plurality of neurons, the plurality of neurons including a plurality of pre-synaptic neurons and a plurality of post-synaptic neurons; processing the plurality of inputs with the plurality of input neurons; performing long term potentiation based on a determination that at least one of the plurality of post-synaptic neurons has fired; and performing long term depression based on a determination that at least one of the plurality of pre-synaptic neurons has fired. 16. The method of claim 15, wherein performing long term potentiation comprises: overwriting a post-synaptic neuron spike history with a maximum value; transmitting a signal indicating that the post-synaptic neuron has fired to a pre-synaptic neuron spike timing dependent plasticity (STDP) circuit in one of the plurality of pre-synaptic neurons; retrieving a pre-synaptic neuron spike history from a pre-synaptic neuron memory; determining a post-synaptic neuron spike history based on receiving the signal indicating that the post-synaptic neuron has fired; calculating a synapse weight offset based on the pre-synaptic neuron spike history and the post-synaptic neuron spike history; transmitting the synapse weight offset to a synapse STDP circuit; and updating the synapse weight based on the synapse weight offset. 17. The method of claim 15, wherein performing long term depression comprises: overwriting a pre-synaptic neuron spike history with a maximum value; transmitting a signal indicating that the pre-synaptic neuron has fired a post-synaptic neuron STDP circuit in one of the plurality of post-synaptic neurons; retrieving a post-synaptic neuron spike history from a post-synaptic neuron memory; determining a pre-synaptic neuron spike history based on receiving the signal indicating that the pre-synaptic neuron has fired; calculating a synapse weight offset based on the pre-synaptic neuron spike history and the post-synaptic neuron spike history; transmitting the synapse weight offset to the synapse STDP circuit; and updating the synapse weight based on the synapse weight offset. 18. The method of claim 16, further comprising: transmitting a neuron reward value to the pre-synaptic neuron STDP circuit; and multiplying the synapse weight offset by the neuron reward value prior to transmitting the synapse weight offset to the synapse STDP circuit. 19. The method of claim 17, further comprising: transmitting a neuron reward value to a post-synaptic neuron STDP circuit; and multiplying the synapse weight offset by the neuron reward value prior to transmitting the synapse weight offset to the synapse STDP circuit. 20. The method of claim 16, wherein the signal indicating that the post-synaptic neuron has fired is transmitted to the pre-synaptic neuron via a synapse. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Systems and methods for event-driven learning with spike timing dependent plasticity in neuromorphic computers are disclosed. A neuromorphic processor includes a synapse coupled to a pre-synaptic neuron and coupled to a post-synaptic neuron, the synapse including a synapse memory to store a synapse weight and synapse spike timing dependent plasticity (STDP) circuit coupled to the synapse memory. The pre-synaptic neuron includes a pre-synaptic neuron memory to store a pre-synaptic neuron spike history and a pre-synaptic neuron STDP circuit coupled to the pre-synaptic neuron memory, the pre-synaptic neuron STDP circuit to, in response to the pre-synaptic neuron firing, initiate performing long term potentiation. The post-synaptic neuron includes a post-synaptic neuron memory storing a post-synaptic neuron spike history and a post-synaptic neuron STDP circuit coupled to the post-synaptic neuron memory to, in response to the post-synaptic neuron firing, initiate performing long term depression. |
|
G06N308 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Systems and methods for event-driven learning with spike timing dependent plasticity in neuromorphic computers are disclosed. A neuromorphic processor includes a synapse coupled to a pre-synaptic neuron and coupled to a post-synaptic neuron, the synapse including a synapse memory to store a synapse weight and synapse spike timing dependent plasticity (STDP) circuit coupled to the synapse memory. The pre-synaptic neuron includes a pre-synaptic neuron memory to store a pre-synaptic neuron spike history and a pre-synaptic neuron STDP circuit coupled to the pre-synaptic neuron memory, the pre-synaptic neuron STDP circuit to, in response to the pre-synaptic neuron firing, initiate performing long term potentiation. The post-synaptic neuron includes a post-synaptic neuron memory storing a post-synaptic neuron spike history and a post-synaptic neuron STDP circuit coupled to the post-synaptic neuron memory to, in response to the post-synaptic neuron firing, initiate performing long term depression. |
|
According to the plant control device of this invention, based on a modified target value candidate of a control output, a reference governor uses a prediction model in which a closed loop system including a plant and a feedback controller are modeled to sequentially calculate, across a finite prediction horizon, a predicted value of state quantities of a plant including a specific state quantity on which a constraint is imposed. At such time, if a predicted value of a specific state quantity relating to a certain modified target value candidate conflicts with a constraint, the reference governor excludes the modified target value candidate from candidates for a final modified target value. Thus, the computational load required to modify a target value of a control output is decreased while ensuring the satisfiability of a constraint. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A plant control device, comprising: a feedback controller that determines a control input of a plant by feedback control so that a control output of the plant approaches a target value, and a reference governor that modifies the target value that is provided to the feedback controller, wherein: the reference governor is configured to execute: prediction model calculation processing for sequentially calculating, across a finite prediction horizon, predicted values of state quantities of the plant that include a specific state quantity on which a constraint is imposed, using a prediction model in which a closed loop system including the plant and the feedback controller is modelled, based on a modified target value candidate of the control output, evaluation function calculation processing for calculating an evaluation value of the modified target value candidate using a previously defined evaluation function based on a calculation result obtained by the prediction model calculation processing, and modified target value determination processing for executing the prediction model calculation processing and the evaluation function calculation processing with respect to a plurality of modified target value candidates, and determining a final modified target value based on respective evaluation values of the plurality of modified target value candidates; and in a case where a predicted value of the specific state quantity that is predicted by prediction model calculation processing that relates to a certain modified target value candidate conflicts with a constraint, the reference governor excludes the certain modified target value candidate from candidates for the final modified target value. 2. The plant control device according to claim 1, wherein, in a case where a predicted value of the specific state quantity conflicts with a constraint during performance of prediction model calculation processing relating to a certain modified target value candidate, the reference governor cancels remaining calculations of the prediction model calculation processing relating to the certain modified target value candidate. 3. The plant control device according to claim 2, wherein: in the prediction model calculation processing, the reference governor calculates a predicted value of the state quantity discretely at a prediction cycle that is previously set, and in a case where a predicted value of the specific state quantity conflicts with a constraint at a discrete time point during a period from an initial discrete time point until a final discrete time point in prediction model calculation processing relating to a certain modified target value candidate, the reference governor cancels calculations of a predicted value of the state quantity at remaining discrete time points. 4. The plant control device according to claim 3, wherein the reference governor changes a threshold value for determining whether or not a predicted value of the specific state quantity conflicts with a constraint to a stricter value as discrete time points relating to the prediction model calculation processing proceed. 5. The plant control device according to claim 3, wherein: in the evaluation function calculation processing, the reference governor uses an evaluation function that gives a progressively favorable evaluation value as a difference between a predicted value of the control output at respective discrete time points calculated by the prediction model calculation processing and an original target value of the control output decreases; and in the modified target value determination processing, the reference governor determines a modified target value candidate for which the evaluation value is a most favorable value to be the final modified target value. 6. The plant control device according to claim 1, wherein: in the modified target value determination processing, the reference governor updates the modified target value candidate in accordance with a previously defined updating rule; and according to the updating rule, a next modified target value candidate is determined by means of a combination of a direction of a change in an evaluation value of a current modified target value candidate relative to an evaluation value of a previous modified target value candidate and a direction of a change in the current modified target value candidate relative to the previous modified target value candidate. 7. The plant control device according to claim 6, wherein, in the modified target value determination processing, if the evaluation value of the current modified target value candidate is a more favorable value than the evaluation value of the previous modified target value candidate, the reference governor provisionally determines the current modified target value candidate to be a final modified target value, and if the evaluation value of the current modified target value candidate is not a more favorable value than the evaluation value of the previous modified target value candidate, the reference governor maintains a final modified target value that is provisionally determined at a previous time as it is. 8. The plant control device according to claim 6, wherein in a case where remaining calculations are cancelled during performance of the prediction model calculation processing due to a conflict with a constraint, in the modified target value determination processing the reference governor increases a number of update operations for the modified target value candidate in accordance with an amount of a decrease in a computational load that accompanies cancellation of the calculations. |
|
REJECTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: According to the plant control device of this invention, based on a modified target value candidate of a control output, a reference governor uses a prediction model in which a closed loop system including a plant and a feedback controller are modeled to sequentially calculate, across a finite prediction horizon, a predicted value of state quantities of a plant including a specific state quantity on which a constraint is imposed. At such time, if a predicted value of a specific state quantity relating to a certain modified target value candidate conflicts with a constraint, the reference governor excludes the modified target value candidate from candidates for a final modified target value. Thus, the computational load required to modify a target value of a control output is decreased while ensuring the satisfiability of a constraint. |
|
G06N502 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: According to the plant control device of this invention, based on a modified target value candidate of a control output, a reference governor uses a prediction model in which a closed loop system including a plant and a feedback controller are modeled to sequentially calculate, across a finite prediction horizon, a predicted value of state quantities of a plant including a specific state quantity on which a constraint is imposed. At such time, if a predicted value of a specific state quantity relating to a certain modified target value candidate conflicts with a constraint, the reference governor excludes the modified target value candidate from candidates for a final modified target value. Thus, the computational load required to modify a target value of a control output is decreased while ensuring the satisfiability of a constraint. |
|
There is provided a method comprising: receiving a graph comprising nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; calculating a current global value defined by a function of the variable weights of the directional edges; calculating a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement; determining when the mismatch is within a tolerance requirement representing desired global values; and one of: randomly adjusting the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and outputting determined values for the variable weights when the mismatch is within the tolerance requirement. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for identifying values for variable weight parameters of edges of a network according to a desired global value within a tolerance requirement, comprising: receiving a graph representation of a network comprising a plurality of nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; calculating a current global value defined by a function of the variable weights of the directional edges; calculating a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement; determining when the mismatch is within a tolerance requirement representing a plurality of desired global values; and one of: randomly adjusting the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and outputting determined values for the variable weights of the directional edges when the mismatch is within the tolerance requirement. 2. The method of claim 1, wherein randomly adjusting comprises randomly adjusting the variable weights of the directional edges within the respective range according to a function proportional to the amount of mismatch. 3. The method of claim 1, wherein an iterative reduction in the amount of mismatch reduces the amount of the random adjustment within the respective range. 4. The method of claim 1, wherein the network represents a chemical reaction, wherein each node represents a reactant, or an intermediate product, wherein each directed edge represents a reaction condition, wherein the desired global value represents a desired product, wherein the tolerance requirement represents a tolerance of the desired product. 5. The method of claim 1, wherein the network represents a gene regulatory network, wherein each directed edge represents the effect of one gene product on another gene through various mechanisms of regulation, wherein the desired global value represents a phenotype, the mismatch from the desired global value represents a global cellular stress signal, wherein the tolerance requirement represents a range of phenotypes compatible with a constraining demand. 6. The method of claim 1, wherein an initial set of the variable weights assigned to the directional edges are randomly determined within the respective range. 7. The method of claim 1, further comprising receiving an initial topological structure of the network, and converting the initial topological structure to an adapted topological structure of the graph including at least a scale-free out-degree distribution, wherein the initial topological structure and the adapted topological structure are statistically similar according to a graph similarity requirement. 8. The method of claim 7, wherein the in-degree distribution of the adapted topological structure of the graph is a member of the group consisting of: scale-free distribution, exponential distribution, and binomial distribution. 9. The method of claim 7, wherein converting comprises performing a graph transformation. 10. The method of claim 1, further comprising analyzing the topological structure of the graph representation of the network, and applying the acts of the computer implemented method when the topological structure is identified at least as including a scale-free out-degree distribution. 11. The method of claim 1, further comprising receiving an initial graph state of the network, analyzing the graph to identify a set of largest nodes with the largest number of edges, and transforming the initial graph state of the network to an adapted graph state of the network having an adapted set of the largest nodes with a reduced number of the largest number of edges. 12. The method of claim 1, wherein the number of nodes is at least 1000. 13. The method of claim 1, wherein the current global value is calculated by a linear or non-linear combination of the variable weights of the directional edges. 14. The method of claim 1, wherein the current global value is calculated as a linear or non-linear combination of coordinates corresponding to the variable weight parameters. 15. The method of claim 1, wherein the graph represents a dynamical system represented by at least one function that describes time dependence motion of at least one point in a geometrical space. 16. The method of claim 1, wherein the mismatch is calculated by a step function outside the tolerance requirement and a value of zero within the tolerance requirement. 17. The method of claim 1, wherein the determined values are outputted when at least one combination of variable weight parameters that give rise to at least one set of coordinates or which the global value falls stably within the tolerance requirement of the desired global value. 18. A system for identifying values for variable weight parameters of edges of a network according to a desired global value within a tolerance requirement, comprising: a program store storing code; and a processor coupled to the program store for implementing the stored code, the code comprising: code to receive a graph representation of a network comprising a plurality of nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; code to calculate a current global value defined by a function of the variable weights of the directional edges, calculate a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement, and determine when the mismatch is within a tolerance requirement representing a plurality of desired global values; and one of: randomly adjust the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and output determined values for the variable weights of the directional edges when the mismatch is within the tolerance requirement. 19. A computer program product comprising a non-transitory computer readable storage medium storing program code thereon for implementation by a processor of a system for identifying values for variable weight parameters of edges of a network according to a desired global value within a tolerance requirement, comprising: instructions to receive a graph representation of a network comprising a plurality of nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; instructions to calculate a current global value defined by a function of the variable weights of the directional edges; instructions to calculate a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement; instructions to determine when the mismatch is within a tolerance requirement representing a plurality of desired global values; and instructions to perform one of: randomly adjust the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and output determined values for the variable weights of the directional edges when the mismatch is within the tolerance requirement. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: There is provided a method comprising: receiving a graph comprising nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; calculating a current global value defined by a function of the variable weights of the directional edges; calculating a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement; determining when the mismatch is within a tolerance requirement representing desired global values; and one of: randomly adjusting the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and outputting determined values for the variable weights when the mismatch is within the tolerance requirement. |
|
G06N308 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: There is provided a method comprising: receiving a graph comprising nodes with directional edges each associated with a variable weight representing an interaction strength of the respective direction edge adjustable according to a respective range; calculating a current global value defined by a function of the variable weights of the directional edges; calculating a mismatch between the current global value and a desired global value, wherein multiple different combinations of assigned variable weights of the directional edges are associated with the desired global value within the tolerance requirement; determining when the mismatch is within a tolerance requirement representing desired global values; and one of: randomly adjusting the variable weights of the directional edges within the respective range, and iterating the calculating the mismatch and determining when the mismatch is not within the tolerance requirement, and outputting determined values for the variable weights when the mismatch is within the tolerance requirement. |
|
A health and fitness management system is provided that has a health and fitness application operating, e.g., on a smart phone, that can wirelessly communicate with an activity module worn on the user which has a motion sensor, e.g., an accelerometer. The application accepts food and weight inputs (e.g., from the smart phone) and user activity units (e.g., from the activity unit) and develops a user intrinsic metabolism. The application includes fitness arc and health quotient graphical indicators that guide the user on health and fitness activities. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for instantaneously and continuously assessing real time energy balance for fitness management comprising, in order: (a) collecting food intake information for actual or expected food intake of a user over a specified period of time and contemporaneously converting the food intake information into food intake energy units for the first period of time, wherein the food intake units are based on relative energy content of one food compared to another without relying on standard caloric values; (b) collecting by a device activity information for actual or expected activity by the user over the specified period of time and contemporaneously converting the activity information into energy units for the user for the first period of time, wherein the collecting for actual activity is achieved by wireless transmission by a motion sensor and the motion sensor is a programmable accelerometer, the functional status of which is altered by: (i) programming independent of a magnetic field; (ii) collecting deflections of the accelerometer during the activity in various positions inside the engineered environment such that it records motion associated with one type of activity while excluding movement characteristic of another form of activity; (iii) applying a multiplier to the accelerometer deflections collected in (ii) to assign a weighted value indicative of the level of effort exerted during the activity; (iv) saving deflections from each type of movement such that deflection counts are segregated by activity type and determining the amount of relative energy expended by the user during any given time period in the activity type; (v) transferring deflection counts and relative energy expended to a device; and (vi) processing the deflection counts and relative energy expended for display by the device; (c) instantaneously deriving, via a computing device, a calculated currently determined constant that reflects efficiency, which is a rate at which the user extracts energy from the food units that can be referenced against predicted and actual changes in weight, wherein the constant is a surrogate for intrinsic metabolic rate; (d) instantaneously calculating by an algorithm from the calculated currently determined constant in (c) a predicted energy balance for the user, by: 1. calculating a ratio of an amount of activity units expected divided by an amount of activity observed; 2. calculating a ratio of an amount of food units expected divided by an amount of food units observed; 3. weighting the ratio in (a) against the ratio in (b) according to goals of the user; and 4. modifying the weighted ratio in (3) by a rate at which the user performs the activity/work (e) instantaneously predicting a change in weight from the predicted energy balance; and (f) determining fitness level of the user based on the efficiency of energy consumption. 2. The method of claim 1, wherein the programming independent of a magnetic field is by wireless transmission from a device. 3. The method of claim 2, wherein the wireless transmission is a Bluetooth™ signal. 4. The method of claim 2, wherein the device is selected from the group consisting of a smart cell phone, a computer and a tablet. 5. The method of claim 4, wherein the device is a smart cell phone. 6. The method of claim 1, wherein the programming independent of a magnetic field is by buttons on an activity module comprising the programmable accelerometer. 7. The method of claim 1, wherein the change in weight is displayable either as a numeric weight or as a colored dot display system. 8. The method of claim 7, wherein the colored dot display system comprises: (a) a red dot representing weight gain other than muscle; (b) a green dot representing muscle growth or weight loss; and (c) a yellow dot representing no change in fat/muscle ratios. 9. The method of claim 1, wherein the deflection counts in (d) are segregated by activity type in a processor. 10. The method of claim 9, wherein the activity type is a general activity type. 11. The method of claim 10, wherein the activity type consists of activity monitoring modes selected from the group consisting of a travel activity mode and a sleep activity mode. 12. The method of claim 1, wherein the accelerometer is a triaxial accelerometer. 13. The method of claim 1, wherein when the type of activity is sleep, the method further comprises a method for measuring quality of sleep comprising: (i) assigning a time period in which the user is going to bed; (ii) collecting deflections of the accelerometer during the assigned time period of (i); (iii) transferring the deflection counts collected in (ii) corresponding to sleep activity to a device; (iv) ending the time period assigned in (i); and (v) processing the deflection counts for display by the device, wherein an increase in accelerometer deflections recorded compared to an average of accelerometer deflections recorded is indicative of a sleep disorder. 14. The method of claim 13, wherein the sleep disorder is selected from the group consisting of sleep apnea, insomnia and restless leg syndrome. 15. The method of claim 14, wherein the sleep disorder is sleep apnea. 16. The method of claim 7, wherein when the type of activity is sleep, the method further comprises a method for determining an increased physiological benefit during sleep comprising: (i) assigning a time period in which the user is going to bed; (ii) collecting deflections of the accelerometer during the assigned time period of (i); (iii) transferring the deflection counts collected in (ii) corresponding to sleep activity to a device; (iv) ending the time period assigned in (i); and (v) processing the deflection counts for display by the device, wherein no change or a decrease in accelerometer deflections recorded compared to an average of accelerometer deflections recorded is indicative of an increased physiological benefit during sleep. 17. The method of claim 16, wherein the increased physiological benefit is an increase in interstitial space in brain. 18. The method of claim 16, wherein the increased physiological benefit is an increase in convective exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) in brain. 19. The method of claim 16, wherein the increased physiological benefit is an increased rate of clearance from brain of a protein linked to neurodegenerative disease. 20. The method of claim 19, wherein the protein is selected from the group consisting of β-amyloid (Aβ), α-synuclein and tau. 21. The method of claim 1, wherein the information used to determine weight gain other than muscle, muscle growth or weight loss, or no change in fat/muscle ratios, is integrated into multiple parameters that form a health quotient displayed by the device as a single point on a scale ranging from fit to healthy to unhealthy to at risk. 22. A method for instantaneously and continuously assessing real time energy balance for fitness management comprising, in order: (a) collecting food intake information for actual or expected food intake of a user over a specified period of time and contemporaneously converting the food intake information into food intake energy units for the first period of time, wherein the food intake units are based on relative energy content of one food compared to another without relying on standard caloric values; (b) collecting by a device activity information for actual or expected activity by the user over the specified period of time and contemporaneously converting the activity information into energy units for the user for the first period of time, wherein the collecting for actual activity is achieved by wireless transmission by a motion sensor and the motion sensor is a programmable accelerometer, the functional status of which is altered by: (i) at least one moveable magnet encircling the programmable accelerometer; (ii) collecting deflections of the accelerometer during the activity in various positions inside the engineered environment such that it records motion associated with one type of activity while excluding movement characteristic of another form of activity; (iii) applying a multiplier to the accelerometer deflections collected in (ii) to assign a weighted value indicative of the level of effort exerted during the activity; (iv) saving deflections from each type of movement such that deflection counts are segregated by activity type and determining the amount of relative energy expended by the user during any given time period in the activity type; (v) transferring deflection counts and relative energy expended to a device; and (vi) processing the deflection counts and relative energy expended for display by the device; (c) instantaneously deriving, via a computing device, a calculated currently determined constant that reflects efficiency, which is a rate at which the user extracts energy from the food units that can be referenced against predicted and actual changes in weight, wherein the constant is a surrogate for intrinsic metabolic rate; (d) instantaneously calculating by an algorithm from the calculated currently determined constant in (c) a predicted energy balance for the user, by: 1. calculating a ratio of an amount of activity units expected divided by an amount of activity observed; 2. calculating a ratio of an amount of food units expected divided by an amount of food units observed; 3. weighting the ratio in (a) against the ratio in (b) according to goals of the user; and 4. modifying the weighted ratio in (3) by a rate at which the user performs the activity/work (e) instantaneously predicting a change in weight from the predicted energy balance; and (f) determining fitness level of the user based on the efficiency of energy consumption. 23. The method of claim 22, wherein the change in weight is displayable either as a numeric weight or as a colored dot display system. 24. The method of claim 23, wherein the colored dot display system comprises: (a) a red dot representing weight gain other than muscle; (b) a green dot representing muscle growth or weight loss; and (c) a yellow dot representing no change in fat/muscle ratios. 25. The method of claim 22, wherein the deflection counts in (iv) are segregated by activity type in a processor. 26. The method of claim 22, wherein movement of the at least one moveable magnet around the programmable accelerometer initiates a programming change to alter the functional status of the accelerometer into activity monitoring modes. 27. The method of claim 26, wherein the activity monitoring modes are selected from the group consisting of a standard mode (S), a running/jogging mode (A+), a bicycle mode (A), a weight lifting/resistance training/yoga mode (W+), an aerobic-based gym equipment mode (W) and a sleet activity mode. 28. The method of claim 27, wherein the standard mode (S) comprises routine daily activity. 29. The method of claim 22, wherein the type of activity is selected from the group consisting of aerobic and non-aerobic. 30. The method of claim 29, wherein the aerobic activity is selected from the group consisting of walking, jogging, running, biking, tennis, basketball, soccer, circuit training and elliptical training. 31. The method of claim 29, wherein the non-aerobic activity is selected from the group consisting of weight lifting, yoga, Pilates, and resistance training. 32. The method of claim 22, wherein the accelerometer is a triaxial accelerometer. 33. The method of claim 22, wherein the at least one moveable magnet is a sphere. 34. The method of claim 22, wherein the at least one moveable magnet is contained within at least one tube. 35. The method of claim 34, wherein the at least one tube is located within a means for attaching the accelerometer to a user. 36. The method of claim 34, wherein the at least one tube is located within a casing of an activity module. 37. The method of claim 22, wherein when the type of activity is sleep, the method further comprises a method for measuring quality of sleep comprising: (i) assigning a time period in which the user is going to bed; (ii) collecting deflections of the accelerometer during the assigned time period of (i); (iii) transferring the deflection counts collected in (ii) corresponding to sleep activity to a device; (iv) ending the time period assigned in (i); and (v) processing the deflection counts for display by the device, wherein an increase in accelerometer deflections recorded compared to an average of accelerometer deflections recorded is indicative of a sleep disorder. 38. The method of claim 37, wherein the sleep disorder is selected from the group consisting of sleep apnea, insomnia and restless leg syndrome. 39. The method of claim 38, wherein the sleep disorder is sleep apnea. 40. The method of claim 23, wherein when the type of activity is sleep, the method further comprises a method for determining an increased physiological benefit during sleep comprising: (i) assigning a time period in which the user is going to bed; (ii) collecting deflections of the accelerometer during the assigned time period of (i); (iii) transferring the deflection counts collected in (ii) corresponding to sleep activity to a device; (iv) ending the time period assigned in (i); and (v) processing the deflection counts for display by the device, wherein no change or a decrease in accelerometer deflections recorded compared to an average of accelerometer deflections recorded is indicative of an increased physiological benefit during sleep. 41. The method of claim 40, wherein the increased physiological benefit is an increase in interstitial space in brain. 42. The method of claim 40, wherein the increased physiological benefit is an increase in convective exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF) in brain. 43. The method of claim 40, wherein the increased physiological benefit is an increased rate of clearance from brain of a protein linked to neurodegenerative disease. 44. The method of claim 43, wherein the protein is selected from the group consisting of β-amyloid (Aβ), α-synuclein and tau. 45. The method of claim 22, wherein the information used to determine weight gain other than muscle, muscle growth or weight loss, or no change in fat/muscle ratios, is integrated into multiple parameters that form a health quotient displayed by the device as a single point on a scale ranging from fit to healthy to unhealthy to at risk. |
|
ACCEPTED | Please predict whether this patent is acceptable.PATENT ABSTRACT: A health and fitness management system is provided that has a health and fitness application operating, e.g., on a smart phone, that can wirelessly communicate with an activity module worn on the user which has a motion sensor, e.g., an accelerometer. The application accepts food and weight inputs (e.g., from the smart phone) and user activity units (e.g., from the activity unit) and develops a user intrinsic metabolism. The application includes fitness arc and health quotient graphical indicators that guide the user on health and fitness activities. |
|
G06N5048 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: A health and fitness management system is provided that has a health and fitness application operating, e.g., on a smart phone, that can wirelessly communicate with an activity module worn on the user which has a motion sensor, e.g., an accelerometer. The application accepts food and weight inputs (e.g., from the smart phone) and user activity units (e.g., from the activity unit) and develops a user intrinsic metabolism. The application includes fitness arc and health quotient graphical indicators that guide the user on health and fitness activities. |
|
According to one embodiment, there is provided herein a system and method for producing a well lifecycle lift plan that includes considerations of multiple types of lift, multiple lift configurations associated with each lift type, and can be used to provide a prediction of when or if it would be desirable to change the lift plan at some time in the future. Another embodiment utilizes a heuristic database with rules that might be used to limit the solution space in some instances by restricting the solution to feasible configurations. A further embodiment teaches how multiple individual well optimization results might be combined with a reservoir model to obtain an optimized lift schedule for an entire field. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of managing production in a hydrocarbon producing well, comprising the steps of: a. accessing a decline curve for the well, said decline curve predicting production from the well over a predetermined period of time at a plurality of different time points; b. selecting a plurality of lift types, each of said lift types being associated with two or more different lift configurations; c. selecting a time point from among said plurality of different time points; d. for each of said plurality of lift types and said associated two or more different lift configurations, calculating a performance lift model value at said selected time point; e. using said decline curve and any of said calculated performance model values at said selected time point to determine a set of feasible equipment configurations at said selected time point; f. for each of said determined set of feasible equipment configurations at said selected time point, determining an objective function value; g. performing steps (c) through (f) for each of said plurality of different time points, thereby producing a network of feasible objective function values; h. determining a minimum travel path through said network of feasible objective function values, thereby obtaining a lifecycle lift plan for the well; and, i. implementing at least a portion of said lifecycle lift plan for the well. 2. The method of managing production in a hydrocarbon producing well according to claim 1, wherein said objective function value is a cost objective function value. 3. The method of managing production in a hydrocarbon producing well according to claim 1, wherein said objective function value is one of a maximum production value, a minimum changeover cost value, and a minimum downtime value. 4. The method of managing production in a hydrocarbon producing well according to claim 1, wherein step (f) comprises the step of: for each of said determined set of feasible equipment configurations at said selected time point, determining an objective function value that includes a cost to operate said feasible equipment configuration for a predetermined period of time. 5. The method of managing production in a hydrocarbon producing well according to claim 3, wherein said predetermined period of time is five years. 6. The method of managing production in a hydrocarbon producing well according to claim 1, wherein said objective function is a cost objective function and step (h) comprises the steps of (h1) determining a minimum travel path through said network of feasible cost objective function values, thereby obtaining a lifecycle lift plan for the well, and, (h2) based on said lifecycle lift plan calculating a net present value of said minimum travel path through said network of feasible cost objective function. 7. The method of managing production in a hydrocarbon producing well according to claim 1, wherein is provided a rules engine, and wherein step (e) comprises the step of: e. using said decline curve, any of said calculated performance model values at said selected time point, and said rules engine to determine a set of feasible equipment configurations at said selected time point; 8. The method of managing production in a hydrocarbon producing well according to claim 1, wherein is provided a rules engine, and wherein step (e) comprises the step of: e. using said decline curve, any of said calculated performance model values at said selected time point, and said rules engine to determine a set of feasible equipment configurations at said selected time point; 9. The method of managing production in a hydrocarbon producing well according to claim 1, wherein is provided a rules engine containing a plurality of heuristic risk values, and wherein said objective function value comprises a cost objective function value weighted by one or more of said heuristic risk values. 10. The method according to claim 1, wherein said decline curve predicts a production of at least one of gas, oil, and water as a function of time. 11. The method according to claim 1, wherein said determined objective function value of step (f) comprises at least one of a cost to operate, a cost of a changeover, a cost of a work over, and a cost of a power usage. 12. A method of managing production in a hydrocarbon producing well, comprising the steps of: a. accessing a well lifecycle lift plan calculated according to the steps of: (1) accessing a decline curve for the well, said decline curve predicting production from the well over a predetermined period of time at a plurality of different time points; (2) selecting a plurality of lift types, each of said lift types being associated with two or more different lift configurations; (3) selecting a time point from among said plurality of different time points; (4) for each of said plurality of lift types and said associated two or more different lift configurations, calculating a performance lift model value at said selected time point; (5) using said decline curve and any of said calculated performance model values at said selected time point to determine a set of feasible equipment configurations at said selected time point; (6) for each of said determined set of feasible equipment configurations at said selected time point, determining an objective function value; (7) performing steps (3) through (6) for each of said plurality of different time points, thereby producing a network of feasible objective function values; (8) determining a minimum travel path through said network of feasible objective function values, thereby obtaining a lifecycle lift plan for the well; and, b. implementing at least a portion of said well lifecycle lift plan for the well. 13. A method of managing production in a hydrocarbon producing well, comprising the steps of: a. selecting a plurality of lift types, each of said lift types being associated with two or more different lift configurations; in a computer: (1) accessing a decline curve for the well, said decline curve predicting production from the well over a predetermined period of time at a plurality of different time points; (2) selecting a plurality of lift types, each of said lift types being associated with two or more different lift configurations; (3) selecting a time point from among said plurality of different time points; (4) for each of said plurality of lift types and said associated two or more different lift configurations, calculating a performance lift model value at said selected time point; (5) using said decline curve and any of said calculated performance model values at said selected time point to determine a set of feasible equipment configurations at said selected time point; (6) for each of said determined set of feasible equipment configurations at said selected time point, determining an objective function value; (7) performing steps (3) through (6) for each of said plurality of different time points, thereby producing a network of feasible objective function values; (8) determining a minimum travel path through said network of feasible objective function values, thereby obtaining a lifecycle lift plan for the well; and, b. making at least one lift decision for the well based on said lifecycle lift plan. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: According to one embodiment, there is provided herein a system and method for producing a well lifecycle lift plan that includes considerations of multiple types of lift, multiple lift configurations associated with each lift type, and can be used to provide a prediction of when or if it would be desirable to change the lift plan at some time in the future. Another embodiment utilizes a heuristic database with rules that might be used to limit the solution space in some instances by restricting the solution to feasible configurations. A further embodiment teaches how multiple individual well optimization results might be combined with a reservoir model to obtain an optimized lift schedule for an entire field. |
|
G06N504 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: According to one embodiment, there is provided herein a system and method for producing a well lifecycle lift plan that includes considerations of multiple types of lift, multiple lift configurations associated with each lift type, and can be used to provide a prediction of when or if it would be desirable to change the lift plan at some time in the future. Another embodiment utilizes a heuristic database with rules that might be used to limit the solution space in some instances by restricting the solution to feasible configurations. A further embodiment teaches how multiple individual well optimization results might be combined with a reservoir model to obtain an optimized lift schedule for an entire field. |
|
Examples of the disclosure enable an information processing system to automatically implement a user interaction diagnostic engine. In some examples, the information processing system analyzes documents to identify parameters associated with profile features and performance metrics. Based on the parameters, correlation values between the profile features and one or more performance metrics are determined. Based on the correlation values, at least one profile feature is identified to improve a parameter associated with the performance metrics. A predictive model associated with the profile features and the performance metrics is generated. One or more actionable items configured to modify the at least one profile feature are determined such that, based on the predictive model, a parameter associated with the performance metrics are predicted to improve upon execution of the one or more actionable items. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. An information processing system comprising: a memory area storing a plurality of documents and computer-executable instructions for implementing a user interaction diagnostic engine; and a processor configured to execute the computer-executable instructions to: analyze the plurality of documents to identify a plurality of parameters associated with a plurality of profile features and a plurality of performance metrics; based on the identified plurality of parameters, determine a plurality of correlation values between the plurality of profile features and one or more performance metrics of the plurality of performance metrics; based on the determined plurality of correlation values, identify at least one profile feature of the plurality of profile features configured to improve one or more parameters associated with the one or more performance metrics; generate a predictive model associated with the plurality of profile features and the one or more performance metrics; and determine one or more actionable items configured to modify a parameter associated with the at least one identified profile feature such that, based on the generated predictive model, the one or more parameters associated with the one or more performance metrics are predicted to improve upon execution of the one or more actionable items. 2. The information processing system of claim 1, wherein the processor is configured to execute the computer-executable instructions to identify a parameter of the plurality of parameters based on content of at least one document of the plurality of documents. 3. The information processing system of claim 1, wherein the processor is configured to execute the computer-executable instructions to identify a parameter of the plurality of parameters based on a property of at least one document of the plurality of documents. 4. The information processing system of claim 1, wherein the processor is configured to execute the computer-executable instructions to: identify a first set of parameters associated with a first target segment, the plurality of parameters including the first set of parameters; and determine a first actionable item associated with the first target segment, the one or more actionable items including the first actionable item. 5. The information processing system of claim 4, wherein the processor is configured to execute the computer-executable instructions to: identify a second set of parameters associated with a second target segment, the plurality of parameters including the second set of parameters; and compare the first target segment with the second target segment to determine a second actionable item associated with one of the first target segment and the second target segment, the one or more actionable items including the second actionable item. 6. The information processing system of claim 5, wherein the processor is configured to execute the computer-executable instructions to: identify a third set of parameters associated with a first profile, the plurality of parameters including the third set of parameters; identify a fourth set of parameters associated with a second profile, the plurality of parameters including the fourth set of parameters; and compare the first profile with the second profile to determine a third actionable item associated with one of the first profile and the second profile, the one or more actionable items including the third actionable item. 7. The information processing system of claim 1, wherein the processor is configured to execute the computer-executable instructions to: identify one set of parameters associated with one target segment, the plurality of parameters including the one set of parameters; identify one or more other sets of parameters associated with one or more other target segments, the plurality of parameters including the one or more other sets of parameters; and compare the one target segment with the one or more other target segments to determine an actionable item associated with one of the one target segment and the one or more other target segments, the one or more actionable items including the actionable item. 8. The information processing system of claim 1, wherein the processor is configured to execute the computer-executable instructions to: identify one set of parameters associated with a first profile, the plurality of parameters including the one set of parameters; identify one or more other sets of parameters associated with a second profile, the plurality of parameters including the one or more other sets of parameters; and compare the first profile with the second profile to determine an actionable item associated with one of the first profile and the second profile, the one or more actionable items including the actionable item. 9. The information processing system of claim 1, wherein the processor is configured to execute the computer-executable instructions to determine whether the at least one profile feature is statistically relevant. 10. A computer-implemented method for implementing a user interaction diagnostic engine, the method comprising: retrieving, using a document collection module, a plurality of documents from a plurality of sources; analyzing, using a parameter extraction module, the plurality of documents to identify a plurality of parameters, the plurality of parameters associated with a plurality of profile features and a plurality of performance metrics; based on the identified plurality of parameters, determining, using a model generation module, one or more correlations between the plurality of parameters; determining, using the model generation module, one or more correlation values between the plurality of profile features and one or more performance metrics of the plurality of performance metrics; based on the one or more determined correlation values, identifying one or more profile features of the plurality of profile features configured to improve a parameter associated with at least one performance metric of the plurality of performance metrics; generating, using the model generation module, a predictive model associated with the plurality of parameters; determining, using an actionable item generation module, one or more actionable items associated with one or more modifications to a parameter associated with the one or more profile features that, using the generated predictive model, is predicted to improve the parameter associated with the at least one performance metric upon execution of the one or more actionable items. 11. The computer-implemented method of claim 10, wherein analyzing the plurality of documents comprises identifying a parameter of the plurality of parameters based on content of at least one document of the plurality of documents. 12. The computer-implemented method of claim 10, wherein analyzing the plurality of documents comprises identifying a parameter of the plurality of parameters based on a property of at least one document of the plurality of documents. 13. The computer-implemented method of claim 10, wherein analyzing the plurality of documents comprises: identifying one set of parameters associated with one target segment, the plurality of parameters including the one set of parameters; and identifying one or more other sets of parameters associated with one or more other target segments, the plurality of parameters including the one or more other sets of parameters; and wherein determining one or more actionable items comprises comparing the one target segment with the one or more other target segments to determine an actionable item associated with one of the one target segment and the one or more other target segments, the one or more actionable items including the actionable item. 14. The computer-implemented method of claim 10, wherein analyzing the plurality of documents comprises: identifying one set of parameters associated with a first profile, the plurality of parameters including the one set of parameters; and identifying one or more other sets of parameters associated with a second profile, the plurality of parameters including the one or more other sets of parameters; and wherein determining one or more actionable items comprises comparing the first profile with the second profile to determine an actionable item associated with one of the first profile and the second profile, the one or more actionable items including the actionable item. 15. The computer-implemented method of claim 10, wherein identifying one or more profile features comprises determining whether the one or more profile features are statistically relevant. 16. A system comprising: a parameter extraction module configured to analyze a plurality of documents to identify a plurality of parameters associated with a plurality of profile features and a plurality of performance metrics; a model generation module configured to determine a plurality of correlation values between the plurality of profile features and one or more performance metrics of the plurality of performance metrics based on the identified plurality of parameters, identify at least one profile feature of the plurality of profile features configured to improve one or more parameters associated with the one or more performance metrics based on the determined plurality of correlation values, and generate a predictive model associated with the plurality of profile features and the one or more performance metrics; and an actionable item generation module configured to determine one or more actionable items configured to modify a parameter associated with the at least one identified profile feature such that, based on the generated predictive model, the one or more parameters associated with the one or more performance metrics are predicted to improve upon execution of the one or more actionable items. 17. The system of claim 16, wherein the parameter extraction module is configured to identify a parameter of the plurality of parameters based on content of at least one document of the plurality of documents. 18. The system of claim 16, wherein the parameter extraction module is configured to identify a parameter of the plurality of parameters based on a property of at least one document of the plurality of documents. 19. The system of claim 16, wherein the parameter extraction module is configured to identify a first set of parameters associated with a first target segment, and the actionable item generation module is configured to determine a first actionable item associated with the first target segment, wherein the plurality of parameters includes the first set of parameters, and the one or more actionable items includes the first actionable item. 20. The system of claim 16, wherein the parameter extraction module is configured to identify one set of parameters associated with one target segment, and identify one or more other sets of parameters associated with one or more other target segments, and the actionable item generation module is configured to compare the one target segment with the one or more other target segments to determine an actionable item associated with one of the one target segment and the one or more other target segments, wherein the plurality of parameters includes the one set of parameters and the one or more other sets of parameters, and the one or more actionable items includes the actionable item. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Examples of the disclosure enable an information processing system to automatically implement a user interaction diagnostic engine. In some examples, the information processing system analyzes documents to identify parameters associated with profile features and performance metrics. Based on the parameters, correlation values between the profile features and one or more performance metrics are determined. Based on the correlation values, at least one profile feature is identified to improve a parameter associated with the performance metrics. A predictive model associated with the profile features and the performance metrics is generated. One or more actionable items configured to modify the at least one profile feature are determined such that, based on the predictive model, a parameter associated with the performance metrics are predicted to improve upon execution of the one or more actionable items. |
|
G06N5022 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Examples of the disclosure enable an information processing system to automatically implement a user interaction diagnostic engine. In some examples, the information processing system analyzes documents to identify parameters associated with profile features and performance metrics. Based on the parameters, correlation values between the profile features and one or more performance metrics are determined. Based on the correlation values, at least one profile feature is identified to improve a parameter associated with the performance metrics. A predictive model associated with the profile features and the performance metrics is generated. One or more actionable items configured to modify the at least one profile feature are determined such that, based on the predictive model, a parameter associated with the performance metrics are predicted to improve upon execution of the one or more actionable items. |
|
Technologies are described herein for identifying artificial intelligence content. According to some examples, content received is analyzed to determine if the content was generated by an artificial intelligence source. Upon identifying that the content was generated by an artificial intelligence source, the content is displayed in a manner that indicates the determination. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, the method comprising: receiving content generated by one or more content providers; determining that the content is generated by an artificial intelligence source; and generating an output to change how the content is displayed in a content viewer. 2. The computer-implemented method of claim 1, wherein determining that the content is content generated by an artificial intelligence source comprises: accessing an artificial intelligence source list comprising a listing of artificial intelligence sources; determining a source of the content; comparing the source of the content against the artificial intelligence source list; and determining that the source of the content is from one of the artificial intelligence sources. 3. The computer-implemented method of claim 1, wherein determining that the content is content generated by an artificial intelligence source comprises: accessing an artificial intelligence pattern list comprising a listing of patterns determined to indicate an artificial intelligence source; analyzing the content to determine a pattern; comparing the pattern of the content against the artificial intelligence pattern list; and determining that the pattern of the content matches at least one of the patterns determined to indicate an artificial intelligence source. 4. The computer-implemented method of claim 1, wherein the content provider comprises an Internet website. 5. The computer-implemented method of claim 1, wherein the content provider comprises a source of data, a source of video, a source of text, or a source of audio. 6. The computer-implemented method of claim 1, wherein the content viewer comprises an Internet browser. 7. The computer-implemented method of claim 1, wherein the content viewer comprises a word processing program or an application executed by an Internet browser. 8. The computer-implemented method of claim 1, further comprising an output to indicate a percentage of the content that is artificial intelligence content. 9. A computer-readable storage medium having computer-executable instructions stored thereupon that, when executed by a computer, cause the computer to: receive content generated by one or more content providers; determine that the content is generated by an artificial intelligence source; and generate an output to change how the content is displayed in a content viewer. 10. The computer-readable storage medium of claim 9, wherein the computer-executable instructions to determine that the content is content generated by an artificial intelligence source comprises computer-executable instructions to: access an artificial intelligence source list comprising a listing of artificial intelligence sources; determine a source of the content; compare the source of the content against the artificial intelligence source list; and determine that the source of the content is from one of the artificial intelligence sources. 11. The computer-readable storage medium of claim 10, wherein the source comprises a uniform resource locator, an organization, or an internet protocol address. 12. The computer-readable storage medium of claim 9, wherein the computer-executable instructions to determine that the content is content generated by an artificial intelligence source comprises computer-executable instructions to: access an artificial intelligence pattern list comprising a listing of patterns determined to indicate an artificial intelligence source; analyze the content to determine a pattern; compare the pattern of the content against the artificial intelligence pattern list; and determine that the pattern of the content matches at least one of the patterns determined to indicate an artificial intelligence source. 13. The computer-readable storage medium of claim 9, wherein the content provider comprises an Internet website. 14. The computer-readable storage medium of claim 9, wherein the content provider comprises a source of data, a source of video, a source of text, or a source of audio. 15. The computer-readable storage medium of claim 9, wherein the content viewer comprises an Internet browser. 16. The computer-readable storage medium of claim 9, wherein the content viewer comprises a word processing program or an application executed by an Internet browser. 17. The computer-readable storage medium of claim 9, further comprising computer-executable instructions to generate an output to indicate a percentage of the content that is artificial intelligence content. 18. A system comprising: a processor; and a computer-readable storage medium in communication with the processor, the computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the processor to receive content generated by one or more content providers, determine that the content is generated by an artificial intelligence source, and generate an output to change how the content is displayed in a content viewer. 19. The system of claim 18, wherein the computer-executable instructions to determine that the content is content generated by an artificial intelligence source comprises computer-executable instructions to: access an artificial intelligence source list comprising a listing of artificial intelligence sources to determine that the source of the content is from one of the artificial intelligence source; and access an artificial intelligence pattern list comprising a listing of patterns determined to indicate an artificial intelligence source to determine that the pattern of the content matches at least one of the patterns determined to indicate an artificial intelligence source. 20. The system of claim 18, further comprising computer-executable instructions to generate an output to indicate a percentage of the content that is artificial intelligence content. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Technologies are described herein for identifying artificial intelligence content. According to some examples, content received is analyzed to determine if the content was generated by an artificial intelligence source. Upon identifying that the content was generated by an artificial intelligence source, the content is displayed in a manner that indicates the determination. |
|
G06N5022 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Technologies are described herein for identifying artificial intelligence content. According to some examples, content received is analyzed to determine if the content was generated by an artificial intelligence source. Upon identifying that the content was generated by an artificial intelligence source, the content is displayed in a manner that indicates the determination. |
|
Disclosed is a system and method for processing a transaction. The method comprises receiving an input data from a user. The input data is associated with the transaction. The method further comprises creating a decision tree or a decision table based upon the input data and pre-defined rules. The decision tree comprises a parent node and a child node. The parent node and the child node comprise a first question and a second question respectively presented to the user. The decision table comprises a plurality of criteria/conditions being presented to the user. For each criteria/condition, a conditional response is received from the user. The method further comprises processing the transaction by identifying a solution, stored in a database, based on the decision tree or the decision table. | Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for processing a transaction, the method comprising: receiving, by a processor, an input data from a user, wherein the input data is associated with a transaction; creating, by the processor, a decision tree or a decision table based upon the input data and pre-defined rules, wherein the decision tree comprises a parent node and a child node, wherein the parent node and the child node comprise a first question and a second question respectively presented to the user, wherein the second question is dependent on a response to the first question received from the user, and wherein the decision table comprises a plurality of criteria/conditions being presented to the user, wherein a conditional response is received from the user for each criteria/condition, and wherein the conditional response indicates a criteria/condition being satisfied; and processing, by the processor, the transaction by identifying a solution stored in a database based on the decision tree or the decision table. 2. The method of claim 1, wherein the transaction is at least one of credit card chargeback process, or a securities dispute. 3. The method of claim 1, wherein the input data comprises at least one of an unauthorized transaction, a merchandise transaction, or a service. 4. The method of claim 1, further comprising capturing a log of the response to the first question received from the user. 5. The method of claim 4, wherein the log comprises an amount time taken to create the child node based on the response to the first question received from the user. 6. The method of claim 1, wherein the solution comprises: a reason code/resultant for the decision tree, and a custom procedural code/key field values/resultant value of the transaction for the decision table. 7. The method of claim 1, further comprising identifying a number of the child node in the decision tree. 8. The method of claim 1, further comprising generating a report comprising the first question, the second question, and the response to the first question. 9. A system for processing a transaction, the system comprising: a processor; and a memory coupled to the processor, wherein the processor executes a plurality of modules stored in the memory, the plurality of modules comprising: a receiving module to receive an input data from a user, wherein the input data is associated with a transaction; a creating module to create a decision tree or a decision table based upon the input data and pre-defined rules, wherein the decision tree comprises a parent node and a child node, wherein the parent node and the child node comprise a first question and a second question respectively presented to the user, wherein the second question is dependent on a response to the first question received from the user, and wherein the decision table comprises a plurality of criteria/conditions being presented to the user, wherein a conditional response is received from the user for each criteria/condition, and wherein the conditional response indicates a criteria/condition being satisfied; and a processing module to process the transaction to identify a solution stored in a database based on the decision tree or the decision table. 10. The system of claim 9, wherein the transaction is at least one of a credit card chargeback process, or a securities dispute. 11. The system of claim 9, wherein the input data comprises at least one of an unauthorized transaction, a merchandise transaction, or a service. 12. The system of claim 9, wherein the creating module captures a log of the response to the first question received from the user. 13. The system of claim 12, wherein the log comprises an amount of time taken to create the child node based on the response to the first question received from the user. 14. The system of claim 9, wherein the solution comprises: a reason code/resultant value for the decision tree, and a custom procedural code/key field values/resultant value of the transaction for the decision table. 15. The system of claim 9, wherein the creating module identifies a number of the child node in the decision tree. 16. A non-transitory computer readable medium comprising a program executable by a computing device for processing a transaction, the program comprising: a program code for receiving an input data from a user, wherein the input data is associated with a transaction; a program code for creating a decision tree or a decision table based upon the input data and pre-defined rules, wherein the decision tree comprises a parent node and a child node, wherein the parent node and the child node comprise a first question and a second question respectively presented to the user, wherein the second question is dependent on a response to the first question received from the user, and wherein the decision table comprises a plurality of criteria/conditions being presented to the user, wherein a conditional response is received from the user for each criteria/condition, and wherein the conditional response indicates a criteria/condition being satisfied; and a program code for processing the transaction by identifying a solution, stored in a database, based on the decision tree or the decision table. 17. The medium of claim 16, wherein the solution comprises: a reason code/resultant value for the decision tree, and a custom procedural code/key field values/resultant value of the transaction for the decision table. 18. The medium of claim 16, wherein the transaction is at least one of a credit card chargeback process, or a securities dispute. 19. The medium of claim 16, wherein the input data comprises at least one of an unauthorized transaction, a merchandise transaction, or a service. 20. The medium of claim 16, wherein the program code for creating a decision tree or a decision table captures a log of the response to the first question received from the user. |
|
PENDING | Please predict whether this patent is acceptable.PATENT ABSTRACT: Disclosed is a system and method for processing a transaction. The method comprises receiving an input data from a user. The input data is associated with the transaction. The method further comprises creating a decision tree or a decision table based upon the input data and pre-defined rules. The decision tree comprises a parent node and a child node. The parent node and the child node comprise a first question and a second question respectively presented to the user. The decision table comprises a plurality of criteria/conditions being presented to the user. For each criteria/condition, a conditional response is received from the user. The method further comprises processing the transaction by identifying a solution, stored in a database, based on the decision tree or the decision table. |
|
G06N5045 | Please help me predict the CPC LABEL for this patentPATENT ABSTRACT: Disclosed is a system and method for processing a transaction. The method comprises receiving an input data from a user. The input data is associated with the transaction. The method further comprises creating a decision tree or a decision table based upon the input data and pre-defined rules. The decision tree comprises a parent node and a child node. The parent node and the child node comprise a first question and a second question respectively presented to the user. The decision table comprises a plurality of criteria/conditions being presented to the user. For each criteria/condition, a conditional response is received from the user. The method further comprises processing the transaction by identifying a solution, stored in a database, based on the decision tree or the decision table. |
Subsets and Splits