Cognitive Machine Learning Architecture

Abstract

A method, system and computer readable medium 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 plurality of machine learning operations on the training data; 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.

1 - 20 . (canceled) 
     
     
         21 . 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; and,   generating a cognitive insight based upon the plurality of machine learning operations.   
     
     
         22 . The method of  claim 21 , 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.   
     
     
         23 . The method of  claim 21 , wherein:
 the plurality of machine learning operations comprise a temporal topic model operation, the temporal topic model operation comprises a temporal topic discover operation.   
     
     
         24 . The method of  claim 21 , wherein:
 the plurality of machine learning operations comprise a ranked insight model operation, the ranked insight operation comprises a factor-needs operation.   
     
     
         25 . The method of  claim 21 , 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.   
     
     
         26 . The method of  claim 21 , 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.   
     
     
         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 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; and, 
 generating a cognitive insight based upon the plurality of machine learning operations. 
   
     
     
         28 . The system of  claim 27 , 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.   
     
     
         29 . The system of  claim 27 , wherein:
 the plurality of machine learning operations comprise a temporal topic model operation, the temporal topic model operation comprises a temporal topic discover operation.   
     
     
         30 . The system of  claim 27 , wherein:
 the plurality of machine learning operations comprise a ranked insight model operation, the ranked insight operation comprises a factor-needs operation.   
     
     
         31 . The system of  claim 27 , 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.   
     
     
         32 . The system of  claim 27 , 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.   
     
     
         33 . 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; and,   generating a cognitive insight based upon the plurality of machine learning operations.   
     
     
         34 . The non-transitory, computer-readable storage medium of  claim 33 , 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.   
     
     
         35 . The non-transitory, computer-readable storage medium of  claim 33 , wherein:
 the plurality of machine learning operations comprise a temporal topic model operation, the temporal topic model operation comprises a temporal topic discover operation.   
     
     
         36 . The non-transitory, computer-readable storage medium of  claim 33 , wherein:
 the plurality of machine learning operations comprise a ranked insight model operation, the ranked insight operation comprises a factor-needs operation.   
     
     
         37 . The non-transitory, computer-readable storage medium of  claim 33 , 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.   
     
     
         38 . The non-transitory, computer-readable storage medium of  claim 33 , 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.   
     
     
         39 . 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. 
     
     
         40 . 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.