Temporal topic machine learning operation

Patent metadata

Field	Value
Publication number	US-11748641-B2
Application number	US-202117330160-A
Country	US
Kind code	B2
Filing date	May 25, 2021
Priority date	Feb 14, 2017
Publication date	Sep 5, 2023
Grant date	Sep 5, 2023

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.

First claim

What is claimed is:

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 at

Assignees

• Cognitive Scale Inc
• Tecnotree Tech Inc

Inventors

• Acharya Ayan
• Sanchez Matthew
• Eid Omar

Classifications

• G06N5/043 Primary

  Distributed expert systems; Blackboards · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N20/00

  Machine learning · CPC title