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. 01, 2024

Filed:

Apr. 01, 2021
Applicant:

Allstate Insurance Company, Northbrook, IL (US);

Inventors:

Deborah-Anna Reznek, Redwood City, CA (US);

Adam Sturt, Chicago, IL (US);

Jeremy Werner, Oak Park, IL (US);

Adam Austin, Wheaton, IL (US);

Amber Parsons, Bothell, WA (US);

Xiaolan Wu, Sunnyvale, CA (US);

Ryan Rosenberg, Palo Alto, CA (US);

Lizette Lemus Gonzalez, Bothell, WA (US);

Weizhou Wang, Redwood City, CA (US);

Stephanie Wong, Chicago, IL (US);

Charles Cox, Seattle, WA (US);

Jean Utke, Lisle, IL (US);

Yusuf Mansour, Bothell, WA (US);

Tia Miceli, Aurora, IL (US);

Lakshmi Prabha Nattamai Sekar, Aurora, IL (US);

Meg G. Walters, Chicago, IL (US);

Dylan Stark, Arlington Heights, IL (US);

Emily Pavey, Chicago, IL (US);

Assignee:

Allstate Insurance Company, Northbrook, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 30/0283 (2023.01); G06V 20/10 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0004 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06Q 30/0283 (2013.01); G06V 20/176 (2022.01); G06V 20/188 (2022.01); G06T 2200/24 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/30161 (2013.01);
Abstract

Aspects of the disclosure relate to using computer vision methods for asset evaluation. A computing platform may receive historical images of a plurality of properties and corresponding historical inspection results. Using the historical images and historical inspection results, the computing platform may train a roof waiver model (which may be a computer vision model) to output inspection prediction information directly from an image. The computing platform may receive a new image corresponding to a particular residential property. Using the roof waiver model, the computing platform may analyze the new image to output of a likelihood of passing inspection. The computing platform may send, to a user device and based on the likelihood of passing inspection, inspection information indicating whether or not a physical inspection should be performed and directing the user device to display the inspection information, which may cause the user device to display the inspection information.


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