Cognitive machine learning architecture

1. A computer-implementable method for generating a cognitive insight comprising:

receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system;

performing a cognitive learning operation via the cognitive inference and learning system using the training data, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning framework comprising a plurality of cognitive learning styles and a plurality of cognitive learning categories, each of the plurality of cognitive learning styles comprising a generalized learning approach implemented by the cognitive inference and learning system to perform the cognitive learning operation, each of the plurality of cognitive learning categories referring to a source of information used by the cognitive inference and learning system when performing the cognitive learning operation, an individual cognitive learning technique being associated with a primary cognitive learning style and bounded by an associated primary cognitive learning category, the cognitive learning operation applying the cognitive learning technique via a machine learning operation to generate a cognitive learning result;

performing a plurality of machine learning operations on the training data, the plurality of machine learning operations comprising the machine learning operation;

generating a cognitive profile based upon the information generated by performing the plurality of machine learning operations; and,

generating a cognitive insight based upon the profile generated using the plurality of machine learning operations.

2. The method of claim 1 , wherein:

the plurality of machine learning operations comprise a hierarchical topic model operation, the hierarchical topic model operation comprises a domain topic abstraction operation and a hierarchical topic operation, the domain topic abstraction operation providing a domain topic abstraction taxonomy, the domain topic abstraction taxonomy providing a classification of the training data as well as principles underlying the classification, the domain topic abstraction taxonomy providing a hierarchical taxonomy.

3. The method of claim 1 , wherein:

the plurality of machine learning operations comprise a temporal topic model operation, the temporal topic model operation comprises a temporal topic discover operation.

4. The method of claim 1 , wherein:

the plurality of machine learning operations comprise a ranked insight model operation, the ranked insight operation comprises a factor-needs operation.

5. The method of claim 1 , wherein:

at least one of the plurality of machine learning operations interacts with another of the plurality of machine learning operations when generating the cognitive profile.

6. The method of claim 1 , wherein:

the cognitive profile is continuously updated based upon at least one of a plurality of feedback information sources, the feedback information sources comprising information based upon feedback from interactions between the user and the cognitive insight and learning system, information from a query submitted by the user to the cognitive insight and learning system, information from external input data, information from a user navigating a hierarchical topic model and information received from a training system.

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 training data, the training data being based upon interactions between a user and a cognitive learning and inference system;

performing a cognitive learning operation via the cognitive inference and learning system using the training data, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning framework comprising a plurality of cognitive learning styles and a plurality of cognitive learning categories, each of the plurality of cognitive learning styles comprising a generalized learning approach implemented by the cognitive inference and learning system to perform the cognitive learning operation, each of the plurality of cognitive learning categories referring to a source of information used by the cognitive inference and learning system when performing the cognitive learning operation, an individual cognitive learning technique being associated with a primary cognitive learning style and bounded by an associated primary cognitive learning category, the cognitive learning operation applying the cognitive learning technique via a machine learning operation to generate a cognitive learning result;

performing a plurality of machine learning operations on the training data, the plurality of machine learning operations comprising the machine learning operation;

generating a cognitive profile based upon the information generated by performing the plurality of machine learning operations; and,

generating a cognitive insight based upon the profile generated using the plurality of machine learning operations.

8. The system of claim 7 , wherein:

the plurality of machine learning operations comprise a hierarchical topic model operation, the hierarchical topic model operation comprises a domain topic abstraction operation and a hierarchical topic operation, the domain topic abstraction operation providing a domain topic abstraction taxonomy, the domain topic abstraction taxonomy providing a classification of the training data as well as principles underlying the classification, the domain topic abstraction taxonomy providing a hierarchical taxonomy.

9. The system of claim 7 , wherein:

the plurality of machine learning operations comprise a temporal topic model operation, the temporal topic model operation comprises a temporal topic discover operation.

10. The system of claim 7 , wherein:

the plurality of machine learning operations comprise a ranked insight model operation, the ranked insight operation comprises a factor-needs operation.

11. The system of claim 7 , wherein:

at least one of the plurality of machine learning operations interacts with another of the plurality of machine learning operations when generating the cognitive profile.

12. The system of claim 7 , wherein:

the cognitive profile is continuously updated based upon at least one of a plurality of feedback information sources, the feedback information sources comprising information based upon feedback from interactions between the user and the cognitive insight and learning system, information from a query submitted by the user to the cognitive insight and learning system, information from external input data, information from a user navigating a hierarchical topic model and information received from a training system.

13. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:

receiving training data, the training data being based upon interactions between a user and a cognitive learning and inference system;

performing a cognitive learning operation via the cognitive inference and learning system using the training data, the cognitive learning operation implementing a cognitive learning technique according to a cognitive learning framework, the cognitive learning framework comprising a plurality of cognitive learning styles and a plurality of cognitive learning categories, each of the plurality of cognitive learning styles comprising a generalized learning approach implemented by the cognitive inference and learning system to perform the cognitive learning operation, each of the plurality of cognitive learning categories referring to a source of information used by the cognitive inference and learning system when performing the cognitive learning operation, an individual cognitive learning technique being associated with a primary cognitive learning style and bounded by an associated primary cognitive learning category, the cognitive learning operation applying the cognitive learning technique via a machine learning operation to generate a cognitive learning result;

performing a plurality of machine learning operations on the training data, the plurality of machine learning operations comprising the machine learning operation;

generating a cognitive profile based upon the information generated by performing the plurality of machine learning operations; and,

generating a cognitive insight based upon the profile generated using the plurality of machine learning operations.

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

the plurality of machine learning operations comprise a hierarchical topic model operation, the hierarchical topic model operation comprises a domain topic abstraction operation and a hierarchical topic operation, the domain topic abstraction operation providing a domain topic abstraction taxonomy, the domain topic abstraction taxonomy providing a classification of the training data as well as principles underlying the classification, the domain topic abstraction taxonomy providing a hierarchical taxonomy.

15. The non-transitory, computer-readable storage medium of claim 13 , wherein:

the plurality of machine learning operations comprise a temporal topic model operation, the temporal topic model operation comprises a temporal topic discover operation.

16. The non-transitory, computer-readable storage medium of claim 13 , wherein:

the plurality of machine learning operations comprise a ranked insight model operation, the ranked insight operation comprises a factor-needs operation.

17. The non-transitory, computer-readable storage medium of claim 13 , wherein:

at least one of the plurality of machine learning operations interacts with another of the plurality of machine learning operations when generating the cognitive profile.

18. The non-transitory, computer-readable storage medium of claim 13 , wherein:

the cognitive profile is continuously updated based upon at least one of a plurality of feedback information sources, the feedback information sources comprising information based upon feedback from interactions between the user and the cognitive insight and learning system, information from a query submitted by the user to the cognitive insight and learning system, information from external input data, information from a user navigating a hierarchical topic model and information received from a training system.

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.