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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 12067607 B1

PDFFull Text
Date of Patent:
Aug. 20, 2024

Filed:

Feb. 13, 2023

Neural contextual bandit based computational recommendation method and apparatus

Applicant:

Yahoo Assets Llc, New York, NY (US);

Inventors:

Suleyman Cetintas, Cupertino, CA (US);

Xian Wu, South Bend, IN (US);

Jian Yang, Palo Alto, CA (US);

Assignee:

YAHOO ASSETS LLC, New York, NY (US);

Attorneys:

James J. DeCarlo

Greenberg Traurig

Primary Examiner:

Mila Airapetian

Int. Cl.
CPC ...
G06Q 30/00 (2023.01); G06F 16/22 (2019.01); G06F 16/901 (2019.01); G06F 16/9535 (2019.01); G06N 20/00 (2019.01); G06Q 10/087 (2023.01); G06Q 30/0204 (2023.01); G06Q 30/0282 (2023.01); G06Q 30/0601 (2023.01); G06Q 50/00 (2012.01);
U.S. Cl.
CPC ...
G06Q 30/0631 (2013.01); G06F 16/2237 (2019.01); G06F 16/2264 (2019.01); G06F 16/9024 (2019.01); G06F 16/9535 (2019.01); G06N 20/00 (2019.01); G06Q 10/087 (2013.01); G06Q 30/0204 (2013.01); G06Q 30/0282 (2013.01); G06Q 30/0625 (2013.01); G06Q 50/01 (2013.01);
Abstract

Disclosed are systems and methods utilizing neural contextual bandit for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to make item recommendations using latent relations and latent representations, which can improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods use neural network modeling in automatic selection of a number of items for recommendation to a user and using feedback in connection with the recommendation for further training of the model(s).

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