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. 17, 2024

Filed:

Mar. 15, 2022
Applicant:

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

Inventors:

Fayokemi Ojo, Baltimore, MD (US);

Ryan Rossi, Santa Clara, CA (US);

Jane Hoffswell, Seattle, WA (US);

Shunan Guo, San Jose, CA (US);

Fan Du, Milpitas, CA (US);

Sungchul Kim, San Jose, CA (US);

Chang Xiao, Sunnyvale, CA (US);

Eunyee Koh, San Jose, CA (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 16/904 (2019.01); G06N 3/02 (2006.01);
U.S. Cl.
CPC ...
G06F 16/904 (2019.01); G06N 3/02 (2013.01);
Abstract

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.


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