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:
Sep. 29, 2026

Filed:

May. 02, 2024
Applicant:

Dell Products L.p., Round Rock, TX (US);

Inventors:

Michael David Shepherd, Leander, TX (US);

Jonathan M. Whitson, Mountain Grove, MO (US);

Assignee:

Dell Products L.P., Round Rock, TX (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 25/63 (2013.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06V 40/16 (2022.01); G10L 13/08 (2013.01); G10L 15/18 (2013.01); G10L 15/22 (2006.01);
U.S. Cl.
CPC ...
G10L 13/08 (2013.01); G06V 40/174 (2022.01); G10L 15/1807 (2013.01); G10L 15/22 (2013.01); G10L 2015/223 (2013.01); G10L 2015/227 (2013.01);
Abstract

Techniques are provided for sentiment-based adaptation of digital human responses. One method comprises determining a sentiment of a user by analyzing a vocal sentiment, a text sentiment and/or a facial sentiment of the user; applying the determined sentiment of the user to a language model that determines a sentiment-tagged response to an input of the user based on the determined sentiment, wherein the sentiment-tagged response comprises a predicted sentiment label identifying a sentiment to be employed by a digital human when delivering the sentiment-tagged response to the user; and providing the sentiment-tagged response to the digital human for delivery to the user, wherein the digital human transforms at least a portion of the sentiment-tagged response into a spoken format using the predicted sentiment label and a text-to-speech model. A vocal tone, a facial expression and/or a body positioning of the digital human may be adjusted based on the determined sentiment.


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