Systems and methods for predicting the outcome of a business entity are presented. In embodiments, a system may receive explicit data reporting or indicating activities of a business entity, and other data from which information regarding the activities or level of operations of the entity may be inferred. Using one or more data processors, the system may generate inferred data regarding the business entity from a selected portion of the other data, and use at least some of the explicit data and the inferred data to determine which one of a series of defined sequential active states of development the entity currently is in. The system may further, using the result of the determination as the current state of the business, predict a final stage of the business entity, and a probability of evolving to that final stage from the current state. Other embodiments may be disclosed or claimed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of adaptive and reusable processing of retroactive sequences for automated predictions, comprising: receiving explicit data reporting or indicating activities of a plurality of business entities; receiving other data from which information regarding the activities or level of operations of the business entities may be inferred; using one or more data processors: generating inferred data regarding the business entities from a selected portion of the other data; processing the explicit data and the inferred data to generate a database of at least some of the business entities, entries in the database comprising at least one of partial timelines or sets of sequences of events of the business entities; processing the database to infer lifecycles for the business entities, a lifecycle comprising a final state, and a sequence of active states a business entity passes through prior to the final state; outputting the inferred lifecycles to a user. 2. The method of claim 1, wherein at least one of: the final state is one of closed, acquired, initial public offering and acquired to obtain employees; or the final state of the business entities is observed. 3. The method of claim 1, wherein at least one of: the sequences of active states include one or more of concept, seed, product development, market development and steady status; or the sequences of active states include one or more of concept, seed, product development, market development and steady status, and the sequences of active states conform to a defined series of hidden active states that a business entity may go through, and wherein in the defined series of active states, multiple actual events of the companies are subsumed by at least one of the active states. 4-5. (canceled) 6. The method of claim 1, wherein at least one of: lifecycles are generated for groups of companies and for individual companies; processing the database to infer lifecycles includes determining, for each of the sequence of active states in the lifecycle, expected values for a set of scores indicative of that active state; or processing the database to infer lifecycles includes determining, for each of the sequence of active states in the lifecycle, expected values for a set of scores indicative of that active state, and further comprising determining a vector that expresses a probability of transitioning from that active state to the final state of the lifecycle. 7. (canceled) 8. The method of claim 1, wherein processing the database to infer lifecycles includes at least one of: associating a Complex Hidden Markov Model (CHMM) with each company; or reconstructing sequences of potential active states in multiple passes, the passes separated in time from each other, and using different sources of information. 9-10. (canceled) 11. The method of claim 1, wherein at least one of: data corresponding to each business entity is extracted and processed to generate two types of dimensions: Slowly Changing Dimensions (SCDs) and Fast Changing Dimensions (FCDs), and wherein each dimension is represented by sequences of complex events; data corresponding to each business entity is extracted and processed to generate two types of dimensions: SCDs and FCDs, and wherein each dimension is represented by sequences of complex events, and a lifecycle of a business entity is inferred, at least in part, based on a comparison of its SCDs to those of other business entities; or data corresponding to each business entity is extracted and processed to generate two types of dimensions: SCDs and FCDs, and wherein each dimension is represented by sequences of complex events, and a lifecycle of a business entity is inferred, at least in part, based on a comparison of its SCDs to those of other business entities, and further comprising computing a distance of the business entity from other business entities in a cluster using a combination of the company's SCDs and a centroid of the entities in the cluster. 12-13. (canceled) 14. The method of claim 1, further comprising: receiving explicit data reporting or indicating activities of a business entity that has not yet reached a final state; receiving other data from which information regarding the activities or level of operations of the business entity may be inferred; and, using a data processor: generating inferred data regarding the business entity from a selected portion of the other data; using at least some of the explicit data and some of the inferred data, determining which one of a series of defined sequential active states of development that the business entity is currently in; using the result of the determination as the current state of the business, predicting a final stage of the business entity, and a probability of evolving to that final stage from the current state, by operating on at least some of the explicit data and some of the inferred data; and outputting the determined active state of the business, the predicted final stage, and the probability, to a user. 15. A method for predicting the outcome of a business entity, comprising: receiving explicit data reporting or indicating activities of a business entity; receiving other data from which information regarding the activities or level of operations of the business entity may be inferred; using a data processor: generating inferred data regarding the business entity from a selected portion of the other data; using at least some of the explicit data and some of the inferred data, determining which one of a series of defined sequential active states of development that the business entity is currently in; using the result of the determination as the current state of the business, predicting a final stage of the business entity, and a probability of evolving to that final stage from the current state, by operating on at least some of the explicit data and some of the inferred data; and outputting the determined active state of the business, the predicted final stage, and the probability, to a user. 16. The method of claim 15, wherein at least one of: the series of defined active states of development are inferred using Complex Hidden Markov Models; or the series of defined active states of development are inferred using Complex Hidden Markov Models, and the series of defined active states are specific to the business entity. 17. (canceled) 18. The method of claim 15, further comprising: calculating a confidence score as to the determined active state of development, and if the score is below a define threshold: repeating the determination to choose a second possible state for the business entity; predicting a second final state and a second probability of evolving to the second final state; and outputting the second final state and the second probability to the user. 19. The method of any of claim 15, wherein determining which one of a series of defined sequential active states of development that the business entity is currently in, and predicting a final stage of the business entity includes comparing the explicit data and at least some of the inferred data to analogous explicit data and inferred data in a database comprising a set of inferred lifecycles for business entities. 20. The method of claim 19, wherein at least one of: the explicit data and the other data is extracted and processed to generate two types of dimensions: Slowly Changing Dimensions (SCDs) and Fast Changing Dimensions (FCDs), and wherein each dimension is represented by sequences of complex events; or the explicit data and the other data is extracted and processed to generate two types of dimensions: SCDs and FCDs, and each dimension is represented by sequences of complex events, and a lifecycle of the business entity is inferred, at least in part, based on a comparison of its SCDs to those of other business entities in the database. 21. (canceled) 22. One or more computer-readable storage media comprising a plurality of instructions that in response to being executed cause a computing device to: receive explicit data reporting or indicating activities of a plurality of business entities; receive other data from which information regarding the activities or level of operations of the business entities may be inferred; generate inferred data regarding the business entities from a selected portion of the other data; process the explicit data and the inferred data to generate a database of at least some of the business entities, entries in the database comprising at least one of partial timelines or sets of sequences of events of the business entities; process the database to infer lifecycles for the business entities, a lifecycle comprising a final state, and a sequence of active states a business entity passes through prior to the final state; and output the inferred lifecycles to a user. 23. The one or more computer-readable storage media of claim 22, wherein at least one of: the final state is one of closed, acquired, initial public offering and acquired to obtain employees; the sequences of active states include one or more of concept, seed, product development, market development and steady status; or the sequences of active states include one or more of concept, seed, product development, market development and steady status and conform to a defined series of hidden active states that a business entity may go through, and wherein in the defined series of active states, multiple actual events of the companies are subsumed by at least one of the active states. 24-25. (canceled) 26. The one or more computer-readable storage media of claim 22, further comprising at least one of: a plurality of instructions that in response to being executed cause the computing device to determine, for each of the sequence of active states in the lifecycle, expected values for a set of scores indicative of that active state; or a plurality of instructions that in response to being executed cause the computing device to determine a vector that expresses a probability of transitioning from that active state to the final state of the lifecycle. 27. (canceled) 28. The one or more computer-readable storage media of claim 22, wherein processing the database to infer lifecycles includes at least one of: associating a Complex Hidden Markov Model (CHMM) with each company; or reconstructing sequences of potential active states in multiple passes, the passes separated in time from each other, and using different sources of information. 29. (canceled) 30. One or more computer-readable storage media comprising a plurality of instructions that in response to being executed cause a computing device to: receive explicit data reporting or indicating activities of a business entity; receive other data from which information regarding the activities or level of operations of the business entity may be inferred; generate inferred data regarding the business entity from a selected portion of the other data; using at least some of the explicit data and some of the inferred data, determine which one of a series of defined sequential active states of development that the business entity is currently in; using the result of the determination as the current state of the business, predict a final stage of the business entity, and a probability of evolving to that final stage from the current state, by operating on at least some of the explicit data and some of the inferred data; and output the determined active state of the business, the predicted final stage, and the probability, to a user. 31. The one or more computer-readable storage media of claim 30, wherein at least one of: the series of defined active states of development are inferred using Complex Hidden Markov Models; or the series of defined active states are specific to the business entity. 32. (canceled) 33. The one or more computer-readable storage media of claim 30, further comprising a plurality of instructions that in response to being executed cause the computing device to: calculate a confidence score as to the determined active state of development, and if the score is below a define threshold: repeat the determination to choose a second possible state for the business entity; predict a second final state and a second probability of evolving to the second final state; and output the second final state and the second probability to the user. 34. The one or more computer-readable storage media of claim 30, wherein at least one of: determining which one of a series of defined sequential active states of development that the business entity is currently in, and predicting a final stage of the business entity includes comparing the explicit data and at least some of the inferred data to analogous explicit data and inferred data in a database comprising a set of inferred lifecycles for business entities; or determining which one of a series of defined sequential active states of development that the business entity is currently in, and predicting a final stage of the business entity includes comparing the explicit data and at least some of the inferred data to analogous explicit data and inferred data in a database comprising a set of inferred lifecycles for business entities, and the explicit data and the other data is extracted and processed to generate two types of dimensions: Slowly Changing Dimensions (SCDs) and Fast Changing Dimensions (FCDs), wherein each dimension is represented by sequences of complex events. 35. (canceled)