Explanatory Integrity Evaluation Method and System

A computer-implemented system comprising one or more processor-based devices configured to:

• train one or more computer-implemented neural networks by providing instructions that are executed by one or more hardware processors that are specifically configured for performing cognitive computing, wherein the training is performed using content comprising a first plurality of syntactical elements and the training comprises generating learned correspondences among the first plurality of syntactical elements;
• generate automatically a representation of a second plurality of syntactical elements by applying the trained one or more computer-implemented neural networks, wherein the second plurality of syntactical elements comprises one or more potential facts;
• generate automatically a representation of a third plurality of syntactical elements by applying the trained one or more computer-implemented neural networks, wherein the third plurality of syntactical elements comprises one or more potential conclusions that are based on the one or more potential facts;
• apply automatically an explanatory integrity evaluation algorithm to evaluate the combination of the one or more potential facts and the one or more potential conclusions;
• determine automatically based on the explanatory integrity evaluation algorithm's evaluation an explanatory quality score of the combination of the one or more potential facts and the one or more potential conclusions;
• and
• provide a communication to a user comprising a fourth plurality of syntactical elements, wherein the communication is provided in accordance with the determined explanatory quality score.

A computer-implemented system comprising one or more processor-based devices configured to:

• train one or more computer-implemented neural networks by providing instructions that are executed by one or more hardware processors that are specifically configured for performing cognitive computing, wherein the training is performed using content comprising a first plurality of syntactical elements and the training comprises generating learned correspondences among the first plurality of syntactical elements;
• generate automatically a representation of a second plurality of syntactical elements by applying the trained one or more computer-implemented neural networks, wherein the second plurality of syntactical elements comprises one or more potential conclusions that are each based upon a plurality of potential facts;
• determine automatically a distinct probability that each of the one or more potential conclusions represents objective reality;
• apply automatically an explanatory integrity evaluation algorithm that is performed in accordance with each of the distinct probabilities;
• evaluate automatically the results of the performing of the explanatory integrity evaluation algorithm and update each of the distinct probabilities;
• generate, by applying the trained one or more computer-implemented neural networks, a representation of a third plurality of syntactical elements that is in accordance with the updated distinct probabilities;
• and
• provide a communication to a user that comprises the third plurality of syntactical elements.