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:
Jul. 25, 2023

Filed:

Sep. 29, 2020
Applicant:

Pricewaterhousecoopers Llp, New York, NY (US);

Inventors:

Timothy Marco, Chicago, IL (US);

Joseph Voyles, Louisville, KY (US);

Kyungha Lim, Naperville, IL (US);

Kevin Paul, Chicago, IL (US);

Vasudeva Sankaranarayanan, Chicago, IL (US);

Assignee:

PricewaterhouseCoopers LLP, New York, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06V 20/20 (2022.01); G06F 18/214 (2023.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/40 (2022.01); G06V 20/64 (2022.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); G06F 18/214 (2023.01); G06V 10/40 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 20/20 (2022.01); G06V 20/64 (2022.01);
Abstract

Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.


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