System and method for query authorization and response generation using machine learning

A system comprising:

• a processor;
• and
• a non-transitory computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising:
• identifying an identity of a user;
• receiving a text query of the user;
• identifying, by executing a classifier, at least one named entity within the text query;
• retrieving, from a personnel database, a plurality of identities associated with the at least one named entity;
• receiving, from the user, a disambiguation input identifying a selected entity from the plurality of identities;
• parsing, by executing a trained machine-learning classifier, the text query, resulting in at least one of an identified subject or an identified domain of the text query;
• authorizing the user, based on the identity of the user and the at least one of the identified subject or the identified domain, to access a restricted database containing restricted data associated with the at least one of the identified subject or the identified domain;
• retrieving a response template specific to the at least one of the identified subject or the identified domain;
• filling, by accessing the restricted database, the response template with at least a portion of the restricted data associated with the at least one of the identified subject or the identified domain, resulting in a filled response template;
• determining, by executing a machine comprehension model on the filled response template, a probable readability token of at least a portion of the filled response template;
• and
• upon identifying that the probable readability is above a threshold, replying to the text query with the at least a portion of the filled response template.

A non-transitory computer-readable storage medium having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising:

• identifying an identity of a user;
• receiving a text query of the user;
• identifying, by executing a classifier, at least one named entity within the text query;
• retrieving, from a personnel database, a plurality of identities associated with the at least one named entity;
• receiving, from the user, a disambiguation input identifying a selected entity from the plurality of identities;
• parsing, by executing a trained machine-learning classifier, the text query, resulting in at least one of an identified subject or an identified domain of the text query;
• authorizing the user, based on the identity of the user and the at least one of the identified subject or the identified domain, to access a restricted database containing restricted data associated with the at least one of the identified subject or the identified domain;
• retrieving a response template specific to the at least one of the identified subject or the identified domain;
• filling, by accessing the restricted database, the response template with at least a portion of the restricted data associated with the at least one of the identified subject or the identified domain, resulting in a filled response template;
• determining, by executing a machine comprehension model on the filled response template, a probable readability of at least a portion of the filled response template;
• and
• upon identifying that the probable readability is above a threshold, replying to the text query with the at least a portion of the filled response template.