The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 23, 2026

Filed:

Sep. 06, 2023
Applicant:

Bank of America Corporation, Charlotte, NC (US);

Inventors:

Shailendra Singh, Maharashtra, IN;

Sneha Padiyar, East Mumbai, IN;

Krithika Viswanathan, Chennai, IN;

Amit Mishra, Chennai, IN;

Assignee:

Bank of America Corporation, Charlotte, NC (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/015 (2023.01); G06N 20/20 (2019.01); G10L 15/06 (2013.01); G10L 15/22 (2006.01); G10L 25/18 (2013.01); G10L 25/63 (2013.01);
U.S. Cl.
CPC ...
G06Q 30/015 (2023.01); G06N 20/20 (2019.01); G10L 15/063 (2013.01); G10L 15/22 (2013.01); G10L 25/18 (2013.01); G10L 25/63 (2013.01); G10L 2015/0635 (2013.01);
Abstract

A computing platform may detect a live interaction between an agent and a customer. The computing platform may input information of the live interaction into a first machine learning model and a second machine learning model to produce a first solution deviation score and a first emotional deviation score, respectively. The computing platform may compare the first solution deviation score to a solution deviation threshold and the first emotional deviation score to an emotional deviation threshold. Based on detecting that one or more of the solution deviation threshold or the emotional deviation threshold are met or exceeded, the computing platform may initiate one or more corrective actions. The computing platform may update, based on the first solution deviation score and the first emotional deviation score, the first machine learning model and the second machine learning model.


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