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:
Jun. 16, 2026

Filed:

Mar. 30, 2022
Applicant:

Visa International Service Association, San Francisco, CA (US);

Inventors:

Sunipa Dev, Los Angeles, CA (US);

Yan Zheng, Los Gatos, CA (US);

Michael Yeh, Newark, CA (US);

Junpeng Wang, Santa Clara, CA (US);

Wei Zhang, Fremont, CA (US);

Archit Rathore, Salt Lake City, UT (US);

Assignee:

Visa International Service Association, San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/40 (2020.01);
U.S. Cl.
CPC ...
G06F 40/40 (2020.01);
Abstract

Described are a system, method, and computer program product for debiasing embedding vectors of machine learning models. The method includes receiving embedding vectors and generating two clusters thereof. The method includes determining a first mean vector of the first cluster and a second mean vector of the second cluster. The method includes determining a bias associated with each of a plurality of first candidate vectors and replacing the first mean vector with a first candidate vector based on the bias. The method includes determining a bias associated with each of a plurality of second candidate vectors and replacing the second mean vector with a second candidate vector based on the bias. The method includes repeatedly replacing the first and second mean vectors until an extremum of the bias score is reached, and debiasing the embedding vectors by linear projection using a direction defined by the first and second mean vectors.


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