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. 09, 2025

Filed:

Apr. 19, 2023
Applicant:

Ebay Inc., San Jose, CA (US);

Inventors:

Farah Abdallah, Seattle, WA (US);

Joshua Benjamin Tanner, Seattle, WA (US);

Jessica Erin Bullock, San Francisco, CA (US);

Joel Joseph Chengottusseriyil, San Jose, CA (US);

Jeff Steven White, San Jose, CA (US);

Assignee:

eBay Inc., San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 51/02 (2022.01); G06F 16/583 (2019.01); G06F 16/9035 (2019.01); G06F 16/9038 (2019.01); G06Q 10/10 (2023.01); G06Q 30/0201 (2023.01); G06Q 30/0202 (2023.01); G06Q 30/0251 (2023.01); G06Q 30/0282 (2023.01); G06Q 30/0283 (2023.01); G10L 15/22 (2006.01); H04L 65/40 (2022.01); H04L 67/306 (2022.01); H04L 67/50 (2022.01);
U.S. Cl.
CPC ...
H04L 51/02 (2013.01); G06F 16/583 (2019.01); G06F 16/9035 (2019.01); G06F 16/9038 (2019.01); G06Q 10/10 (2013.01); G06Q 30/0201 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0251 (2013.01); G06Q 30/0282 (2013.01); G06Q 30/0283 (2013.01); G10L 15/22 (2013.01); H04L 65/40 (2013.01); H04L 67/306 (2013.01); H04L 67/535 (2022.05); G10L 2015/225 (2013.01);
Abstract

Artificial assistant system notification techniques are described that overcome the challenges of conventional search techniques. In one example, a user profile is generated to describe aspects of products or services learned through natural language conversations between a user and an artificial assistant system. These aspects may include price as well as non-price aspects such as color, texture, material, and so forth. To learn the aspects, the artificial assistant system may leverage spoken utterances and text initiated by the user as well as learn the aspects from digital images output as part of the conversation. Once generated, the user profile is then usable by the artificial assistant system to assist in subsequent searches.


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