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:
Dec. 12, 2023

Filed:

Jul. 01, 2020
Applicant:

Cha-lin Simmons, Moraga, CA (US);

Inventors:

Chia-Lin Simmons, Moraga, CA (US);

Rafael Antonio Saavedra, Sunnyvale, CA (US);

Assignee:

Other;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06F 16/55 (2019.01); G06F 16/538 (2019.01); G06T 7/90 (2017.01); G06V 10/94 (2022.01); G06F 21/31 (2013.01); G06T 7/11 (2017.01); G06F 16/535 (2019.01); G06F 18/24 (2023.01); G06F 18/214 (2023.01); G06V 10/778 (2022.01); G06V 20/30 (2022.01); G06V 40/10 (2022.01);
U.S. Cl.
CPC ...
G06V 10/95 (2022.01); G06F 16/535 (2019.01); G06F 16/538 (2019.01); G06F 16/55 (2019.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06F 21/31 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06V 10/7788 (2022.01); G06V 20/30 (2022.01); G06V 40/103 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01);
Abstract

The disclosed techniques in artificial intelligence include at least a system and a computer-implemented method for performing a predictive search that compensates for a misclassification. For example, the system can set a user-defined classification of a physical characteristic of the user and retrieve an image captured by a camera device. The system can aggregate binary feedback data submitted by authorized users about the image, predict a classification for the physical characteristic by processing the image with a machine learning (ML) process, and search a database based on a query, which has criteria that includes an indication of the aggregate binary feedback data, the user-defined classification, the predicted classification, and/or data indicative of the user's feedback. The system can then identify a search result that satisfies the query and cause a user device to display a recommendation that includes the search result.


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