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:
Mar. 19, 2024

Filed:

Apr. 08, 2021
Applicant:

Allstate Insurance Company, Northbrook, IL (US);

Inventors:

Stephen Cole, Northbrook, IL (US);

Keith Matera, Northbrook, IL (US);

Tia Miceli, Northbrook, IL (US);

Jean Utke, Northbrook, IL (US);

Assignee:

Allstate Insurance Company, Northbrook, IL (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2023.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06N 3/04 (2023.01); G06Q 10/10 (2023.01); G06Q 10/20 (2023.01); G06Q 30/02 (2023.01); G06Q 40/08 (2012.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06T 7/0002 (2013.01); G06F 18/214 (2023.01); G06F 18/2431 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06Q 10/10 (2013.01); G06Q 10/20 (2013.01); G06Q 30/0278 (2013.01); G06Q 40/08 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

Intelligent prediction systems and methods of use to train a neural network model to analyze images of property damage to detect and predict property damage of a property, the neural network model during training configured to (1) switch between one or more synthetic images comprising pixel-based masked annotations of damaged property from a synthetic engine and one or more real images comprising bounding box annotations of damaged property from a real database, and (2) freeze inactive class training to prevent learning on one or more inactive classes comprising one or more pre-determined missing annotated labels in the one or more synthetic images and/or the one or more real images.


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