Visualization of damage on images

Patent metadata

Field	Value
Publication number	US-2025252408-A1
Application number	US-202519185996-A
Country	US
Kind code	A1
Filing date	Apr 22, 2025
Priority date	Jan 13, 2020
Publication date	Aug 7, 2025
Grant date	—

Abstract

A device may receive images of an object and information identifying the object, process the images using an artificial intelligence technique to identify parts of the object that are depicted in the images, and receive information identifying a location of damage on the object and information regarding the damage on the object. The device may process the information identifying the location of damage to identify a damaged part of the object, identify images depicting the damaged part, and identify, in the images, a location of the damaged part. The device may generate a first content item for display at the location of the damaged part in the images and generate a second content item for display with the images based on user interaction with the first content item, where the second content item includes information based on the information regarding the damage on the object.

First claim

What is claimed is:

1 . A method, comprising:
processing, by a device using an artificial intelligence technique, a plurality of images of a vehicle to identify one or more parts of the vehicle; processing, by the device, information identifying a location associated with damage on the vehicle to identify a damaged part of the vehicle; identifying, by the device and from the plurality of images, one or more images depicting the damaged part of the vehicle; and transmitting, by the device and to a server device, a content item with the one or more images depicting the damaged part of the vehicle,
wherein the content item includes a close-up image of the damaged part of the vehicle displayed at a location, in the one or more images, and
wherein the content item includes a shape based on a type of the damage on the vehicle.

2 . The method of claim 1 , wherein the content item varies by one or more of:
color, size, or pattern.

3 . The method of claim 1 , wherein the close-up image of the damaged part of the vehicle is displayed at a location adjacent to the location of the damage on the vehicle.

4 . The method of claim 1 , wherein the plurality of images are processed to form a standardized plurality of images.

5 . The method of claim 1 , wherein the content item is smaller than the depicted damaged part of the vehicle.

6 . The method of claim 1 , further comprising:
transmitting, to another device, the one or more images depicting the damaged part of the vehicle without transmitting one or more other images, of the plurality of images, that do not depict the damaged part of the vehicle.

7 . The method of claim 1 , further comprising:
processing information to identify a number of defects associated with the vehicle.

8 . A device, comprising:
one or more memories; and one or more processors, coupled to the one or more memories, configured to:
process, using an artificial intelligence technique, a plurality of images of a vehicle to identify one or more parts of the vehicle;
process information identifying a location associated with damage on the vehicle to identify a damaged part of the vehicle;
identify, from the plurality of images, one or more images depicting the damaged part of the vehicle;
store information identifying the damaged part of the vehicle; and
transmit, to a server device, a content item with the one or more images depicting the damaged part of the vehicle,
wherein the content item includes a close-up image of the damaged part displayed at a location, in the one or more images, and
wherein the content item includes a geometric shape based on a type of the damage on the vehicle.

9 . The device of claim 8 , wherein the one or more processors, to store information identifying the damaged part of the vehicle, are configured to:
store information identifying the damaged part of the vehicle in a data structure associated with one or more vehicle dealers.

10 . The device of claim 8 , wherein the content item is adjacent to the location of the damaged part of the vehicle.

11 . The device of claim 8 , wherein the plurality of images are processed to form a standardized plurality of images.

12 . The device of claim 8 , wherein the content item is smaller than the depicted damaged part of the vehicle.

13 . The device of claim 8 , wherein the one or more processors are further configured to:
transmit, to another device, the one or more images depicting the damaged part of the vehicle without transmitting one or more other images, of the plurality of images, that do not depict the damaged part of the vehicle.

14 . The device of claim 8 , wherein the information identifying the vehicle comprises one or more of:
a make of the vehicle, a model of the vehicle, a year of the vehicle, a vehicle identification number (VIN) of the vehicle, or an image of a license plate of the vehicle.

15 . A non-transitory computer-readable medium storing instructions, the instructions comprising:
one or more instructions that, when executed by one or more processors, cause the one or more processors to:
process, using an artificial intelligence technique, a plurality of images of a vehicle to identify one or more parts of the vehicle;
process information identifying a location associated with damage on the vehicle to identify a damaged part of the vehicle;
identify, from the plurality of images, one or more images depicting the damaged part of the vehicle; and
transmit, to a server device, a content item with the one or more images depicting the damaged part of the vehicle,
wherein the content item includes a close-up image of the damaged part of the vehicle displayed at a location, in the one or more images, and
wherein the content item includes a geometric shape based on a type of the damage on the vehicle.

16 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
generate the content item for display with the one or more images,
wherein the content item includes information based on information regarding the damage on the vehicle.

17 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, when executed by the one or more processors to process the plurality of images of the vehicle, cause the one or more processors to:
process information to identify a number of defects of the vehicle.

18 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, when executed by the one or more processors to process the plurality of images of the vehicle, cause the one or more processors to:
process the plurality of images of the vehicle to form a standardized plurality of images.

19 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
transmit the one or more images depicting the damaged part of the vehicle without transmitting one or more other images, of the plurality of images, that do not depict the damaged part of the vehicle.

20 . The non-transitory computer-readable medium of claim 15 , wherein the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:
receive the plurality of images from a vehicle dealer.

Assignees

• Capital One Services Llc

Inventors

• Price Micah
• Dagley Geoffrey
• Ghamsari Avid
• Tang Qiaochu
• Duckworth Staevan

Classifications

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06V20/20

  in augmented reality scenes · CPC title

• G06V10/235

  based on user input or interaction · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06V10/25

  Determination of region of interest [ROI] or a volume of interest [VOI] · CPC title