Invention Grant
- Patent Title: Machine learning for digital image selection across object variations
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Application No.: US17729515Application Date: 2022-04-26
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Publication No.: US11921777B2Publication Date: 2024-03-05
- Inventor: Ajay Jain , Sanjeev Tagra , Sachin Soni , Ryan Timothy Rozich , Nikaash Puri , Jonathan Stephen Roeder
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Agency: FIG. 1 Patents
- Main IPC: G06F16/583
- IPC: G06F16/583 ; G06F16/535 ; G06F16/9535 ; G06F18/214 ; G06F18/22 ; G06F18/24 ; G06N3/045 ; G06N3/047 ; G06N3/08 ; G06N20/00 ; G06Q30/0251 ; G06Q30/0601 ; G06F16/957

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.
Public/Granted literature
- US20220253478A1 Machine Learning for Digital Image Selection Across Object Variations Public/Granted day:2022-08-11
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