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:
Nov. 11, 2025

Filed:

Dec. 29, 2023
Applicant:

Snap Inc., Santa Monica, CA (US);

Inventors:

Pavlo Chemerys, Amsterdam, NL;

Colin Eles, Marina del Rey, CA (US);

Ju Hu, Los Angeles, CA (US);

Qing Jin, Palo Alto, CA (US);

Yanyu Li, Malden, MA (US);

Ergeta Muca, Long Island City, NY (US);

Jian Ren, Marina Del Ray, CA (US);

Dhritiman Sagar, Marina del Rey, CA (US);

Aleksei Stoliar, Marina del Rey, CA (US);

Sergey Tulyakov, Santa Monica, CA (US);

Huan Wang, Somerville, MA (US);

Assignee:

Snap Inc., Santa Monica, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); G06N 3/0455 (2023.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G06T 11/00 (2006.01); G06V 10/82 (2022.01); G10L 15/18 (2013.01); G10L 15/22 (2006.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06N 3/0455 (2023.01); G06N 20/00 (2019.01); G06T 5/60 (2024.01); G06T 5/70 (2024.01); G06T 11/00 (2013.01); G10L 15/1815 (2013.01); G10L 15/22 (2013.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

Described is a system for improving machine learning models. In some cases, the system improves such models by identifying a performance characteristic for machine learning model blocks in an iterative denoising process of a machine learning model, connecting a prior machine learning model block with a subsequent machine learning model block of the machine learning model blocks within the machine learning model based on the identified performance characteristic, identifying a prompt of a user, the prompt indicative of an intent of the user for generative images, and analyzing data corresponding to the prompt using the machine learning model to generate one or more images, the machine learning model trained to generate images based on data corresponding to prompts.


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