# US 2021/0279616 A1 · Temporal Topic Machine Learning Operation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0279616 A1 - Document ID: US20210279616A1 - Status: Published application - Kind: A1 - Application number: 17/330,160 - Filed: 2021-05-25 - Published: 2021-09-09 - Earliest claimed: 2017-02-14 - Assignees: Cognitive Scale, Inc. - Applicants: Cognitive Scale, Inc. - Inventors: Ayan Acharya; Matthew Sanchez; Omar Eid - Web page: https://patentgrep.com/patent/US20210279616A1 ## CPC - G06N 5/043: Distributed expert systems; Blackboards - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method, system and computer readable medium for generating a cognitive insight comprising: receiving information regarding a temporal sequence of events; performing a temporal topic machine learning operation on the temporal sequence of events; generating a cognitive profile based upon the information generated by performing the temporal topic machine learning operation; and, generating a cognitive insight based upon the cognitive profile generated using the temporal topic machine learning operation. ## Claims ### Claim at position 1 1-20. (canceled) ### Claim 21 A computer-implementable method for generating a cognitive insight comprising: receiving data from a plurality of data sources, at least one of the plurality of data sources providing information regarding a temporal sequence of events; performing a cognitive learning operation via a cognitive inference and learning system, the cognitive learning operation processing data from at least some of the plurality of data sources, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; performing a temporal topic machine learning operation on the temporal sequence of events; generating a cognitive profile based upon the cognitive learning result and information generated by performing the temporal topic machine learning operation, the cognitive profile referencing personal data associated with a user; and, generating a cognitive insight based upon the cognitive profile generated based upon the cognitive learning result and the information generated by performing the temporal topic machine learning operation; and wherein the cognitive learning technique is one of a plurality of cognitive learning techniques, the plurality of cognitive learning techniques comprising a direct correlations cognitive learning technique, an explicit likes/dislikes cognitive learning technique, a patterns and concepts cognitive learning technique, a behavior cognitive learning technique, a concept entailment cognitive learning technique, and a contextual recommendation cognitive learning technique, the direct correlations cognitive learning technique being associated with a declared learning style and bounded by a data-based cognitive learning category, an explicit likes/dislikes cognitive learning technique being associated with the declared learning style and bounded by an interaction-based cognitive learning category, the patterns and concepts cognitive learning technique being associated with an observed learning style and bounded by the data-based cognitive learning category, the behavior cognitive learning technique being associated with the observed learning style and bounded by the interaction-based cognitive learning category, the concept entailment cognitive learning technique being associated with an inferred learning style and bounded by the data-based cognitive learning category, and a contextual recommendation cognitive learning technique being associated with the inferred learning style and bounded by the interaction-based cognitive learning category. ### Claim 22 (depends on 21) The method of claim 21, wherein: the temporal topic machine learning operation discovers a plurality of event topics contained within a corpus contained within the temporal sequence of events. ### Claim 23 (depends on 22) The method of claim 22, wherein: the temporal topic machine learning operation generates a temporal topic model using the plurality of event topics, the temporal topic model comprising a topic model having a temporal aspect, the topic model comprising a statistical model implemented to discover abstract event topics occurring within the corpus, the temporal topic model comprising clusters of event topics, the event topics comprising portions of corpora associated with a particular temporal event and data attributes associated with the particular temporal event, a cluster of event topics having an associated individual node in an augmented gamma belief network. ### Claim 24 (depends on 23) The method of claim 23, further comprising: iteratively processing the corpus over time to identify information regarding relative preeminence of event topics associated with various events; and, using the information regarding relative preeminence of event topics to populate the temporal topic model. ### Claim 25 (depends on 24) The method of claim 24, wherein: the temporal topic model comprises a plurality of events, an earlier event of the plurality of events being separated from a next later event by a time interval, each of the plurality of events comprising a respective plurality of event topics. ### Claim 26 (depends on 25) The method of claim 25, wherein: each of the plurality of event topics comprise a plurality of associated attributes; and, at least some of the associated attributes are used when determining the relative preeminence of event topics over time. ### Claim 27 A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources, at least one of the plurality of data sources providing information regarding a temporal sequence of events; performing a cognitive learning operation via a cognitive inference and learning system, the cognitive learning operation processing data from at least some of the plurality of data sources, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; performing a temporal topic machine learning operation on the temporal sequence of events; generating a cognitive profile based upon the cognitive learning result and information generated by performing the temporal topic machine learning operation, the cognitive profile referencing personal data associated with a user; and, generating a cognitive insight based upon the cognitive profile generated based upon the cognitive learning result and the information generated by performing the temporal topic machine learning operation; and wherein the cognitive learning technique is one of a plurality of cognitive learning techniques, the plurality of cognitive learning techniques comprising a direct correlations cognitive learning technique, an explicit likes/dislikes cognitive learning technique, a patterns and concepts cognitive learning technique, a behavior cognitive learning technique, a concept entailment cognitive learning technique, and a contextual recommendation cognitive learning technique, the direct correlations cognitive learning technique being associated with a declared learning style and bounded by a data-based cognitive learning category, an explicit likes/dislikes cognitive learning technique being associated with the declared learning style and bounded by an interaction-based cognitive learning category, the patterns and concepts cognitive learning technique being associated with an observed learning style and bounded by the data-based cognitive learning category, the behavior cognitive learning technique being associated with the observed learning style and bounded by the interaction-based cognitive learning category, the concept entailment cognitive learning technique being associated with an inferred learning style and bounded by the data-based cognitive learning category, and a contextual recommendation cognitive learning technique being associated with the inferred learning style and bounded by the interaction-based cognitive learning category. ### Claim 28 (depends on 27) The system of claim 27, wherein the instructions executable by the processor further comprise instructions for: the temporal topic machine learning operation discovers a plurality of event topics contained within a corpus contained within the temporal sequence of events. ### Claim 29 (depends on 28) The system of claim 28, wherein: the temporal topic machine learning operation generates a temporal topic model using the plurality of event topics, the temporal topic model comprising a topic model having a temporal aspect, the topic model comprising a statistical model implemented to discover abstract event topics occurring within the corpus, the temporal topic model comprising clusters of event topics, the event topics comprising portions of corpora associated with a particular temporal event and data attributes associated with the particular temporal event, a cluster of event topics having an associated individual node in an augmented gamma belief network. ### Claim 30 (depends on 29) The system of claim 29, wherein: iteratively processing the corpus over time to identify information regarding relative preeminence of event topics associated with various events; and, using the information regarding relative preeminence of event topics to populate the temporal topic model. ### Claim 31 (depends on 30) The system of claim 30, wherein: the temporal topic model comprises a plurality of events, an earlier event of the plurality of events being separated from a next later event by a time interval, each of the plurality of events comprising a respective plurality of event topics. ### Claim 32 (depends on 31) The system of claim 31, wherein: each of the plurality of event topics comprise a plurality of associated attributes; and, at least some of the associated attributes are used when determining the relative preeminence of event topics over time. ### Claim 33 A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for: receiving data from a plurality of data sources, at least one of the plurality of data sources providing information regarding a temporal sequence of events; performing a cognitive learning operation via a cognitive inference and learning system, the cognitive learning operation processing data from at least some of the plurality of data sources, the cognitive learning operation applying a cognitive learning technique to generate a cognitive learning result; performing a temporal topic machine learning operation on the temporal sequence of events; generating a cognitive profile based upon the cognitive learning result and information generated by performing the temporal topic machine learning operation, the cognitive profile referencing personal data associated with a user; and, generating a cognitive insight based upon the cognitive profile generated based upon the cognitive learning result and the information generated by performing the temporal topic machine learning operation; and wherein the cognitive learning technique is one of a plurality of cognitive learning techniques, the plurality of cognitive learning techniques comprising a direct correlations cognitive learning technique, an explicit likes/dislikes cognitive learning technique, a patterns and concepts cognitive learning technique, a behavior cognitive learning technique, a concept entailment cognitive learning technique, and a contextual recommendation cognitive learning technique, the direct correlations cognitive learning technique being associated with a declared learning style and bounded by a data-based cognitive learning category, an explicit likes/dislikes cognitive learning technique being associated with the declared learning style and bounded by an interaction-based cognitive learning category, the patterns and concepts cognitive learning technique being associated with an observed learning style and bounded by the data-based cognitive learning category, the behavior cognitive learning technique being associated with the observed learning style and bounded by the interaction-based cognitive learning category, the concept entailment cognitive learning technique being associated with an inferred learning style and bounded by the data-based cognitive learning category, and a contextual recommendation cognitive learning technique being associated with the inferred learning style and bounded by the interaction-based cognitive learning category. ### Claim 34 (depends on 33) The non-transitory, computer-readable storage medium of claim 33, wherein the instructions executable by the processor further comprise instructions for: the temporal topic machine learning operation discovers a plurality of event topics contained within a corpus contained within the temporal sequence of events. ### Claim 35 (depends on 34) The non-transitory, computer-readable storage medium of claim 34, wherein: the temporal topic machine learning operation generates a temporal topic model using the plurality of event topics, the temporal topic model comprising a topic model having a temporal aspect, the topic model comprising a statistical model implemented to discover abstract event topics occurring within the corpus, the temporal topic model comprising clusters of event topics, the event topics comprising portions of corpora associated with a particular temporal event and data attributes associated with the particular temporal event, a cluster of event topics having an associated individual node in an augmented gamma belief network. ### Claim 36 (depends on 35) The non-transitory, computer-readable storage medium of claim 35, wherein: iteratively processing the corpus over time to identify information regarding relative preeminence of event topics associated with various events; and, using the information regarding relative preeminence of event topics to populate the temporal topic model. ### Claim 37 (depends on 36) The non-transitory, computer-readable storage medium of claim 36, wherein: the temporal topic model comprises a plurality of events, an earlier event of the plurality of events being separated from a next later event by a time interval, each of the plurality of events comprising a respective plurality of event topics. ### Claim 38 (depends on 37) The non-transitory, computer-readable storage medium of claim 37, wherein: each of the plurality of event topics comprise a plurality of associated attributes; and, at least some of the associated attributes are used when determining the relative preeminence of event topics over time. ### Claim 39 (depends on 33) The non-transitory, computer-readable storage medium of claim 33, wherein the computer executable instructions are deployable to a client system from a server system at a remote location. ### Claim 40 (depends on 33) The non-transitory, computer-readable storage medium of claim 33, wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis.