State Farm Mutual Automobile Insurance Company

Bradley A Sliz

He Yang

Jennifer Malia Andrus

Lucas Allen

Marigona Bokshi-Drotar

Holly Lambert

Shane Tomlinson

Daniel J Green

Ryan Knuffman

Michael L Bernico

Carlee A Clymer

Jeremy Cunningham

Derek Lawless

Holly Kay Sanderson

Gary Foreman

Sumit Patel

Dajun Xu

Albert Man

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 remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified. The identified damage or defect may be compared to a claimed damage or defect to determine whether the claimed damage or defect occurred.

Systems and methods for utilizing machine-assisted vehicle inspection to identify insurance buildup or fraud

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.

Methods and systems for automatically predicting the repair costs of a damaged vehicle from images

A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed based upon data indicative of at least one of vehicle damage or a vehicle defect being shown in the one or more sets of imaging data. Based upon the analyzing of the one or more sets of imaging data, damage to the vehicle or a defect of the vehicle may be identified and corrected.

Systems and methods for machine-assisted vehicle inspection

One or more processing elements may be trained to identify vehicle damages or vehicle damages based upon training data. A remotely-controlled (RC) and/or autonomously operated inspection device, such as a ground vehicle or drone, may capture one or more sets of imaging data indicative of at least a portion of an automotive vehicle, such as all or a portion of the undercarriage. The one or more sets of imaging data may be analyzed using the trained processing elements to identify a damage to the vehicle or defect of the vehicle.

Systems and methods for analyzing image data to automatically ensure image capture consistency are described. According to certain aspects, a server may access aerial and other image data and identify a set of parameters associated with the capture of the image data. The server may access a corresponding set of acceptable image capture parameters, and may compare the set of parameters to the set of acceptable parameters to determine whether image data is consistent with the set of acceptable parameters. In some embodiments, if the image data is not consistent, the server may generate a notification or instruction to cause an image capture component to recapture additional image data.

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Normal, IL, United States of America

Bradley A Sliz