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:
Jun. 09, 2026

Filed:

May. 10, 2022
Applicant:

Ccc Intelligent Solutions Inc., Chicago, IL (US);

Inventors:

Bhadresh Dhanani, Chicago, IL (US);

Sagar Bachwani, Chicago, IL (US);

Ranjini Vaidyanathan, Chicago, IL (US);

Assignee:

CCC INTELLIGENT SOLUTIONS INC., Chicago, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06T 3/18 (2024.01); G06T 7/00 (2017.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/50 (2017.01); G06T 7/70 (2017.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/774 (2022.01); G06V 10/94 (2022.01); G06V 20/70 (2022.01); G06F 3/0482 (2013.01); G06Q 10/20 (2023.01); G06Q 30/0283 (2023.01);
U.S. Cl.
CPC ...
G06V 20/70 (2022.01); G06T 3/18 (2024.01); G06T 7/0002 (2013.01); G06T 7/0004 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/50 (2017.01); G06T 7/70 (2017.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/766 (2022.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/945 (2022.01); G06F 3/0482 (2013.01); G06Q 10/20 (2013.01); G06Q 30/0283 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30252 (2013.01); G06V 2201/08 (2022.01);
Abstract

An image processing system analyzes each of a set of vehicle images to determine if there is any damage to the vehicle depicted in each or any of the images. The image processing system uses a characterization engine in the form of a neural network based image model to process each of the pixels of each of the selected tagged images to determine the particular pixels of the image (or of the object depicted within the image) that depict the presence of damage to the object, and the likelihood of the pixels depicting damage. The characterization engine or image model may be developed or trained using a training engine that analyzes a plurality of images of different vehicles damaged in various different manners which have been annotated, on a pixel by pixel basis, to indicate which pixels of each image represent damaged areas of the objects and which have also been annotated, on an image basis, to indicate the view and/or zoom level of the image.


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