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:
Mar. 05, 2024

Filed:

Apr. 26, 2022
Applicant:

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

Inventors:

Ajay Jain, Ghaziabad, IN;

Sanjeev Tagra, Panipat, IN;

Sachin Soni, New Delhi, IN;

Ryan Timothy Rozich, Austin, TX (US);

Nikaash Puri, New Delhi, IN;

Jonathan Stephen Roeder, Round Rock, TX (US);

Assignee:

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

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/583 (2019.01); G06F 16/535 (2019.01); G06F 16/9535 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06Q 30/0251 (2023.01); G06Q 30/0601 (2023.01); G06F 16/957 (2019.01);
U.S. Cl.
CPC ...
G06F 16/583 (2019.01); G06F 16/535 (2019.01); G06F 16/9535 (2019.01); G06F 18/214 (2023.01); G06F 18/22 (2023.01); G06F 18/24 (2023.01); G06N 3/045 (2023.01); G06N 3/047 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 30/0254 (2013.01); G06Q 30/0255 (2013.01); G06Q 30/0261 (2013.01); G06Q 30/0269 (2013.01); G06Q 30/0621 (2013.01); G06Q 30/0641 (2013.01); G06F 16/9577 (2019.01); G06V 2201/10 (2022.01);
Abstract

Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.


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