Temporal topic machine learning operation

1. 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 plurality of cognitive learning techniques 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 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, the 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.

2. The method of claim 1 , wherein:

the temporal topic machine learning operation discovers a plurality of event topics contained within a corpus contained within the temporal sequence of events.

3. The method of claim 2 , 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.

4. The method of claim 3 , 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.

5. The method of claim 4 , 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.

6. The method of claim 5 , 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.

7. 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 plurality of cognitive learning techniques 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 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.

8. The system of claim 7 , 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.

9. The system of claim 8 , 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.

10. The system of claim 9 , 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.

11. The system of claim 10 , 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.

12. The system of claim 11 , 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.

13. 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 plurality of cognitive learning techniques 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 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.

14. The non-transitory, computer-readable storage medium of claim 13 , 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.

15. The non-transitory, computer-readable storage medium of claim 14 , 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.

16. The non-transitory, computer-readable storage medium of claim 15 , 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.

17. The non-transitory, computer-readable storage medium of claim 16 , 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.

18. The non-transitory, computer-readable storage medium of claim 17 , 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.

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.