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:
Oct. 07, 2025

Filed:

May. 10, 2022
Applicant:

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

Inventors:

Mohan Liu, Chicago, IL (US);

Neda Hantehzadeh, Chicago, IL (US);

Assignee:

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

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06V 20/70 (2022.01); G06F 3/0482 (2013.01); G06Q 10/20 (2023.01); G06Q 30/0283 (2023.01); 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 10/82 (2022.01); G06V 10/94 (2022.01);
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

A tool used in an image processing system assists a user to train one or more statistical image models or classification engines (e.g., the CNN models) that are used to detect damaged areas on a vehicle, to detect damage types and/or to detect segments of the depiction of the vehicle. The tool enables a user to select and annotate various different training images to be used to train the models, wherein each of the training images depicts damage of one or more damage types to various different vehicles or automobiles (including automobiles of different years/makes/models). The tool displays each of a set of selected training images and enables a user to indicate, on the displayed selected training image, using an electronic pen, a touch screen or any other type of selector device, one or more sets of pixels within the displayed image that are associated with or that depict damage to the vehicle, one or more sets of pixels within the displayed image that are associated with or that depict a particular type of vehicle damage and/or one or more sets of pixels within the displayed image that are associated with or included in a particular segment of the depiction of the vehicle within the image. The marked or tagged images are then used to train an image model to detect vehicle damage, types of vehicle damage and/or vehicle segments in new vehicle images.


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