Patent ID: 11914844
Assignee: TRUIST BANK
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 10:
11. A system for training at least one neural network for dynamic filtering and display of content data, the system comprising a provider computing device that comprises one or more integrated software applications that perform the operations comprising:
(a) collecting content data by a Content Application Programming Interface (API), wherein the Content API collects the content data by interfacing with a remote database, and wherein the content data comprises audio data generated by a plurality of content data sources;
(b) processing the content data utilizing an audio-to-text software services that generates transcribed content data;
(c) receiving the transcribed content data by a content aggregation and reduction service, wherein
(i) the content aggregation and reduction service comprises one or more neural networks that each have at least one hidden layer,
(ii) the at least one hidden layer comprises a plurality of nodes with each node comprising at least one weighting coefficient, and wherein
(iii) the content aggregation and reduction service performs operations comprising
(A) processing the transcribed content data by performing an interrogative analysis that generates a first interrogative data set comprising one or more interrogatories, and source identifier data for each of the interrogatories, and
(B) processing the interrogative data by performing a subject classification analysis that generates a first subject classification data set comprising subject identifier data;

(d) performing a labelling analysis on the received transcribed content data to generate (i) annotated interrogative data comprising one or more known interrogatories, and a known source identifier data for each of the known interrogatories, and (ii) annotated subject classification data comprising known subject identifier data;
(e) generating an interrogative error data and subject classification error data, by performing operations comprising
(i) applying the first interrogative data set against the annotated interrogative data to generate the interrogative error data, and
(ii) applying the first subject classification data set against the annotated subject classification data to generate the subject classification error data;

(f) refining the one or more neural networks by performing operations comprising
(i) feeding the interrogative error data and the subject classification error data through the one or more neural networks, and
(ii) adjusting the weighting coefficients to minimize the interrogative error data and the subject classification error data.