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.
Patent No.:
Date of Patent:
Dec. 10, 2024
Filed:
Oct. 12, 2020
Applicant:
Apple Inc., Cupertino, CA (US);
Inventors:
Anish Prabhu, Seattle, WA (US);
Sayyed Karen Khatamifard, Seattle, WA (US);
Hessam Bagherinezhad, Seattle, WA (US);
Assignee:
Apple Inc., Cupertino, CA (US);
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/20 (2017.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06F 18/25 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06T 7/254 (2017.01); G06T 7/73 (2017.01); G06V 10/28 (2022.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01); G06V 40/20 (2022.01);
U.S. Cl.
CPC ...
G06T 7/254 (2017.01); G06F 18/214 (2023.01); G06F 18/217 (2023.01); G06F 18/24 (2023.01); G06F 18/251 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06T 7/74 (2017.01); G06V 10/28 (2022.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/7788 (2022.01); G06V 10/82 (2022.01); G06V 20/52 (2022.01); G06V 40/20 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20224 (2013.01);
Abstract
Aspects of the subject technology relate to machine learning based object recognition using pixel difference information. A difference image generated by subtraction of a current image from one or more previous images can be provided, as input, to a machine-learning engine. The machine-learning may output a detected object or a detected action based, at least in part, on the difference image. In this way, temporal information about the object can be provided to, and used by, a machine-learning model that is structured to accept image input.