State Farm Mutual Automobile Insurance Company

He Yang

Bradley A Sliz

Jennifer Malia Andrus

Marigona Bokshi-Drotar

Holly Lambert

Shane Tomlinson

Daniel J Green

Michael L Bernico

Carlee A Clymer

Derek Lawless

Holly Kay Sanderson

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 system and computer-implemented method for facilitating a user of a mobile device obtaining image data of damage to a vehicle for damage assessment includes capturing image data of a vehicle with the mobile device. The mobile device may include an orientation model for capturing the image data. The captured image data is analyzed, and a determination is made of the orientations of the images of the captured image data. In addition, a determination is made as to whether the captured image data can be used for the damage assessment. The captured image data may then be transmitted to a damage estimator computing device for estimating an amount of damage to the vehicle.

Methods and systems for obtaining image data of a vehicle for automatic damage assessment

A system and computer-implemented method for processing images of a damaged vehicle and estimating a repair cost of the damaged vehicle includes receiving image data of a vehicle from a user mobile device. The image data is processed to determine whether one or more parts of the damaged vehicle are damaged. In addition, one or more parts of the damaged vehicle are identified for repair and one or more parts of the damaged vehicle are identified for replacement. A cost associated for the repair of each of the one or more parts of the damaged vehicle for repair is estimated based on estimated repair cost data contained in a parts repair database. Moreover, a cost associated with the replacement of each of the one or more parts of the damaged vehicle for replacement is determined based on replacement cost data contained in a parts replacement database.

Methods and systems for automatic processing of images of a damaged vehicle and estimating a repair cost

A system for capturing images of damage to an object configured to (i) store an orientation model associated with an object; (ii) receive, from a user, a request to analyze damage to the object; (iii) instruct the user to position a camera at a first position relative to the object; (iv) receive an image of the object from the camera; (v) determine whether the received image is properly framed; (vi) if the received image is not properly framed, instruct the user to adjust the position of the camera; and (vii) if the received image is properly framed, instruct the user to position the camera at a second position relative to the object. As a result, obtaining images of sufficient quality for proper processor analysis may be facilitated.

Systems and methods for model-based analysis of damage to a vehicle

A system and computer-implemented method for automatically predicting the labor, hours, and parts costs for repair of a vehicle includes receiving one or more images of the vehicle from a policyholder. A damage assessment model is accessed. The damage assessment model corresponds to features of vehicle damage based on a plurality of damaged vehicle images contained in an image training database. The damage assessment model is compared to the images of the vehicle and vehicle damage is identified based on the images. In addition, in response to identifying the vehicle damage, total labor costs, total parts costs, and total hours for repair of the vehicle are predicted based on the associated total labor costs, total parts costs, and total hours for repair data contained in the historical claims database.

Growing community of inventors

The Colony, TX, United States of America

He Yang