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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.:

US 11710158 B1

PDFFull Text
Date of Patent:
Jul. 25, 2023

Filed:

Jan. 23, 2020

Vehicle damage estimation

Applicant:

Ford Global Technologies, Llc, Dearborn, MI (US);

Inventors:

Pankaj Kumar, Canton, MI (US);

Avisek Naug, Nashville, TN (US);

Imad Makki, Dearborn Heights, MI (US);

Steven Joseph Szwabowski, Northville, MI (US);

Assignee:

FORD GLOBAL TECHNOLOGIES, LLC, Dearborn, MI (US);

Attorneys:

Frank A. MacKenzie

Bejin Bieneman PLC

Primary Examiner:

David Perlman

Int. Cl.
CPC ...
G06Q 30/0283 (2023.01); G06N 3/088 (2023.01); G06V 10/44 (2022.01); G06F 18/214 (2023.01); G06F 18/2132 (2023.01); G06F 18/21 (2023.01); G06N 3/045 (2023.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/778 (2022.01); G06V 10/82 (2022.01); G06V 10/20 (2022.01); G06V 20/20 (2022.01);
U.S. Cl.
CPC ...
G06Q 30/0283 (2013.01); G06F 18/214 (2023.01); G06F 18/2132 (2023.01); G06F 18/2193 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01); G06V 10/255 (2022.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/7796 (2022.01); G06V 10/82 (2022.01); G06V 20/20 (2022.01); G06V 2201/08 (2022.01);
Abstract

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to train a generative adversarial network (GAN) to reconstruct a missing portion of an image by determining a reconstructed portion of the image based on data from portions of the image surrounding the missing portion and compare an acquired image with the reconstructed portion of the image to determine a damaged portion. The instructions further include instructions to determine estimated damage based on the damaged portion.

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