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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.

Patent No.:

US 12657538 B1

PDFFull Text
Date of Patent:
Jun. 16, 2026

Filed:

Jan. 09, 2023

Audio signal processing and dynamic natural language understanding

Applicant:

Truist Bank, Charlotte, NC (US);

Inventor:

Bjorn Austraat, New York, NY (US);

Assignee:

TRUIST BANK, Charlotte, NC (US);

Attorneys:

Michael A. Springs, Esq.

Shumaker, Loop & Kendrick, LLP

Patrick B. Horne

Primary Examiner:

Richard Z Zhu

Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 40/40 (2020.01); G06Q 10/0635 (2023.01); G06Q 30/015 (2023.01); G10L 15/16 (2006.01); G10L 15/22 (2006.01); G06F 40/279 (2020.01); G06F 40/289 (2020.01); G06F 40/295 (2020.01); G06F 40/35 (2020.01); G10L 15/18 (2013.01);
U.S. Cl.
CPC ...
G06Q 10/0635 (2013.01); G06F 40/30 (2020.01); G06F 40/40 (2020.01); G06Q 30/015 (2023.01); G10L 15/16 (2013.01); G10L 15/22 (2013.01); G06F 40/279 (2020.01); G06F 40/289 (2020.01); G06F 40/295 (2020.01); G06F 40/35 (2020.01); G10L 15/1815 (2013.01); G10L 15/1822 (2013.01);
Abstract

Systems and methods receive, from a user device through a communication channel, and process, in real-time, a natural language input comprising unstructured data that is derived from an audio signal. The natural language input is dynamically interpreted, the interpreting including applying the unstructured data to trained AI models that (i) perform ASR to generate textual data and (ii) contextualize the textual data using a NLU model. Based thereon, a risk element from the natural language input is identified, and a risk score is assigned that ranks inherent risk of the natural language input. The risk score is dynamically adjusted based on identifying additional risk element(s) during the natural language input and is based on an aggregation of the risk element and the additional risk element(s). Risk analysis is performed on the natural language input and includes comparing the risk score to a threshold.

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