State Farm Mutual Automobile Insurance Company

Carlee A Clymer

Bradley A Sliz

He Yang

Jennifer Malia Andrus

Douglas Albert Graff

Ronald R Duehr

Justin Devore

Shane Tomlinson

Gary Foreman

Jason Beckman

John Westhues

Stephen E Wilson

Bradley J Walder

Yohan Santos

Robin A Rose

Michael Brown

Carrie A Read

Pavan Kumar Bhagavatula

Rajesh Panicker

Stephen Young

Alexis Danielle Cates

Michele Wittman

Himanshu Chhita

Michael Cicilio Fresquez

John G McConkey

Aaron Ames

Joshua T Monk

Erin E Miller

Tracey Leigh Knorr

David A Pluimer

Chad Mychal Hirst

Vincent M Hummel

Denson Smith

In a computer-implemented method and associated tangible non-transitory computer-readable medium, an image of a damaged vehicle may be analyzed to generate a repair estimate. A dataset populated with digital images of damaged vehicles and associated claim data may be used to train a deep learning neural network to learn damaged vehicle image characteristics that are predictive of claim data characteristics, and a predictive similarity model may be generated. Using the predictive similarity model, one or more similarity scores may be generated for a digital image of a newly damaged vehicle, indicating its similarity to one or more digital images of damaged vehicles with known damage level, repair time, and/or repair cost. A repair estimate may be generated for the newly damaged vehicle based on the claim data associated with images that are most similar to the image of the newly damaged vehicle.

Deep learning image processing method for determining vehicle damage

A method of identifying a vehicle total loss claim includes retrieving a plurality of historical vehicle records, labeling the records as repaired or total loss, calculating mean cost values, training a regression model, optimizing a probability threshold, analyzing a plurality of inputs to generate a prediction, and transmitting the prediction. A computing system includes a transceiver; a processor; and a memory storing instructions that, when executed by the processor, cause the computing system to receive answers, transmit the answers, receive a prediction, when the prediction is repairable, generate a repair suggestion, and when the prediction is total loss, generate a settlement offer. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to receive answers, transmit the answers, receive a prediction, when the prediction is repairable, generate a repair suggestion, and when prediction is total loss, generate a settlement offer.

Method and system for early identification and settlement of total loss claims

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Atlanta, GA, United States of America

Carlee A Clymer