Patent Issued for Automatic assessment of damage and repair costs in vehicles (USPTO 11443288): American International Group Inc.

2022 OCT 05 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Dalal, Siddhartha (Bridgewater, NJ, US), Li, Kaigang (Brooklyn, NY, US), Sharma, Gaurav (Webster, NY, US), Taliwal, Vikas (Boston, MA, US), filed on May 11, 2020, was published online on September 13, 2022, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11443288 is assigned to American International Group Inc. (New York, New York, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Currently, after a vehicle has been damaged in a road accident or otherwise, the vehicle must be taken by the owner or a tow company to an auto repair shop for inspection. Inspection of the vehicle by a mechanic at the auto repair shop is required in order to assess which parts of the vehicle need to be repaired or replaced. An estimate is then generated based on the inspection. In some cases, when an insurance claim is filed, the estimate is forwarded to an insurance company to approve the repairs before the repairs are made to the vehicle.

“From end-to-end, the process of vehicle inspection, estimate generation, claim approval, and vehicle repair can be long and complex, involving several parties including at least a customer, an auto repair shop, and a claim adjustor.

“Accordingly, there is a need in the art for an improved system that overcomes some of the drawbacks and limitations of conventional approaches.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “One embodiment of the disclosure includes a method for automatically estimating a repair cost for a vehicle, comprising: receiving, at a server computing device over an electronic network, one or more images of a damaged vehicle from a client computing device; performing computerized image processing on each of the one or more images to detect damage to a set of parts of the vehicle; and, calculating an estimated repair cost for the vehicle based on the detected damage based on accessing a parts database that includes repair costs. Additionally, in some embodiments, the server computing device may classify the loss as a total, medium, or small loss.

“Another embodiment of the disclosure provides a method for automatically estimating a repair cost for a vehicle, comprising: receiving, at a server computing device over an electronic network, one or more images of a damaged vehicle from a client computing device; performing image processing operations on each of the one or more images to detect external damage to a first set of parts of the vehicle; inferring internal damage to a second set of parts of the vehicle based on the detected external damage; and, calculating an estimated repair cost for the vehicle based on the detected external damage and inferred internal damage based on accessing a parts database that includes repair and labor costs for each part in the first and second sets of parts. Additionally, in some embodiments, the server computing device may classify the loss as a total, medium, or small loss.

“Another embodiment of the disclosure provides a mobile device comprising a camera, a display device, a processor, and a memory. The memory stores instructions that, when executed by the processor, cause the mobile device to display prompts on the display device to capture damage to a vehicle with the camera, by performing the steps of: receiving, in a first user interface screen displayed on the display device, a selection to initiate a new vehicle claim; displaying, in a second user interface screen displayed on the display device, graphical elements for selection of a prompting interface for capture of images of damage to the vehicle; receiving selection of a graphical element corresponding to a prompting interface; displaying one or more prompts on the display device to capture a portion of the vehicle based on the selection of the graphical element corresponding to the prompting interface; causing the camera of the client device to capture an image of the vehicle based on displaying an outline of the portion of the vehicle; and, causing the image of the vehicle to be uploaded to a server for estimation of repair costs of the vehicle based on the image. Additionally, in some embodiments, the server computing device may classify the loss as a total, medium, or small loss.

“Another embodiment of the disclosure provides a system for estimating a repair cost for a vehicle. The system includes a client computing device, an electronic communications network, and a server computing device. The client computing device is configured to: display one or more prompts on a display device of the client computing device to capture a portion of the vehicle that has sustained damage, and capture an image of the vehicle based on displaying an outline of the portion of the vehicle. The electronic communications network is configured to transfer the image of the vehicle to a server computing device. The server computing device is configured to: receive the image over the electronic communications network, perform image processing operations on the image to identify one or more damaged parts of the vehicle, and calculate an estimated repair cost for the vehicle based on accessing a parts database that includes repair and labor costs for each part in the one or more damaged parts. Additionally, in some embodiments, the server computing device may classify the loss as a total, medium, or small loss.”

The claims supplied by the inventors are:

“1. A method, comprising: causing a displaying of an outline of a selected damaged part of a damaged vehicle to be captured with a camera of a client device; causing the camera of the client device to capture an image of the damaged vehicle based on the displaying of the outline of the selected damaged part; receiving, at a server computing device over an electronic network and from the client device, the image of the damaged vehicle; aligning the image to an undamaged version of the damaged vehicle; segmenting the image into vehicle parts; and detecting damage to a set of parts of the damaged vehicle by comparing portions of each vehicle part in the image to corresponding portions of each vehicle part in the undamaged version of the damaged vehicle, wherein detecting damage to the set of parts includes: comparing at least one of edge distribution, texture comparison, and spatial correlation of portions of each vehicle part in the image to corresponding portions of each vehicle part in the undamaged version of the damaged vehicle; determining whether at least one of the edge distribution, the texture comparison, and the spatial correlation exceeds a respective threshold difference value, wherein damage is detected in a portion of a vehicle part in the image if at least one of the edge distribution, the texture comparison, and the spatial correlation exceed the respective threshold difference value; detecting a pose of the damaged vehicle in the image; and determining which external vehicle parts are damaged in the image; and calculating an estimated repair cost for the damaged vehicle based on which external vehicle parts are damaged based on accessing a parts database that includes repair costs.

“2. The method of claim 1, wherein the parts database that includes repair costs includes estimates for parts and labor for individual parts.

“3. The method of claim 1, further comprising removing artifacts from the image by: removing background material from the image; and removing specular reflection due to incident light on the damaged vehicle shown in the image.

“4. The method of claim 1, wherein damage is detected in a portion of a vehicle part in the image if at least two of the edge distribution, the texture comparison, and the spatial correlation exceed the respective threshold difference value.

“5. The method of claim 1, wherein the detecting damage to the set of parts includes comparing each of edge distribution, texture comparison, and spatial correlation of portions of each vehicle part in the image to corresponding portions of each vehicle part in the undamaged version of the damaged vehicle.

“6. The method of claim 1, wherein the detecting the pose of the damaged vehicle in the image comprises: training a first Convolutional Neural Networks (CNN) of a plurality of CNNs to detect the pose of a damaged vehicle in the image; and training each of the plurality of CNNs to detect damage on a respective vehicle part of a plurality of external vehicle parts; and executing the first CNN to detect the pose of the damaged vehicle in the image.

“7. The method of claim 1, wherein detecting damage to the set of parts further includes inferring damage to internal parts of the damaged vehicle from the determined damaged external vehicle parts; and wherein calculating the estimated repair cost for the damaged vehicle is further based on which internal vehicle parts are inferred to be damaged.

“8. The method of claim 7, wherein inferring damage to internal parts of the damaged vehicle from the determined damaged external vehicle parts comprises executing a Markov Random Field (MRF) algorithm.

“9. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause a computing device to perform operations of: causing a displaying of an outline of a selected damaged part of a damaged vehicle to be captured with a camera of a client device; causing the camera of the client device to capture an image of the damaged vehicle based on the displaying of the outline of the selected damaged part; receiving, from the client device, the image of the damaged vehicle; aligning the image to an undamaged version of the damaged vehicle; segmenting the image into vehicle parts; and detecting damage to a set of parts of the damaged vehicle by comparing portions of each vehicle part in the image to corresponding portions of each vehicle part in the undamaged version of the damaged vehicle, wherein detecting damage to the set of parts includes: comparing at least one of edge distribution, texture comparison, and spatial correlation of portions of each vehicle part in the image to corresponding portions of each vehicle part in the undamaged version of the damaged vehicle; determining whether at least one of the edge distribution, the texture comparison, and the spatial correlation exceeds a respective threshold difference value, wherein damage is detected in a portion of a vehicle part in the image if at least one of the edge distribution, the texture comparison, and the spatial correlation exceed the respective threshold difference value; detecting a pose of the damaged vehicle in the image; and determining which external vehicle parts are damaged in the image; and calculating an estimated repair cost for the damaged vehicle based on which external vehicle parts are damaged based on accessing a parts database that includes repair costs.

“10. The computer-readable medium of claim 9, wherein the parts database that includes repair costs includes estimates for parts and labor for individual parts.

“11. The computer-readable medium of claim 9, further comprising removing artifacts from the image by: removing background material from the image; and removing specular reflection due to incident light on the damaged vehicle shown in the image.

“12. The computer-readable medium of claim 9, wherein damage is detected in a portion of a vehicle part in the image if at least two of the edge distribution, the texture comparison, and the spatial correlation exceed the respective threshold difference value.

“13. The computer-readable medium of claim 9, wherein the detecting damage to the set of parts includes comparing each of edge distribution, texture comparison, and spatial correlation of portions of each vehicle part in the image to corresponding portions of each vehicle part in the undamaged version of the damaged vehicle.

“14. The computer-readable medium of claim 9, wherein the detecting the pose of the damaged vehicle in the image comprises: training a first Convolutional Neural Networks (CNN) of a plurality of CNNs to detect the pose of a damaged vehicle in the image; and training each of the plurality of CNNs to detect damage on a respective vehicle part of a plurality of external vehicle parts; and executing the first CNN to detect the pose of the damaged vehicle in the image.

“15. The computer-readable medium of claim 9, wherein detecting damage to the set of parts further includes inferring damage to internal parts of the damaged vehicle from the determined damaged external vehicle parts; and wherein calculating the estimated repair cost for the damaged vehicle is further based on which internal vehicle parts are inferred to be damaged.

“16. The computer-readable medium of claim 15, wherein the inferring damage to internal parts of the damaged vehicle from the determined damaged external vehicle parts comprises executing a Markov Random Field (MRF) algorithm.

“17. A computing device, comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the computing device to perform operations of: causing a displaying of an outline of a selected damaged part of a damaged vehicle to be captured with a camera of a client device; causing the camera of the client device to capture an image of the damaged vehicle based on the displaying of the outline of the selected damaged part; receiving, from the client device, the image of the damaged vehicle; aligning the image to an undamaged version of the damaged vehicle; segmenting the image into vehicle parts; and detecting damage to a set of parts of the damaged vehicle by comparing portions of each vehicle part in the image to corresponding portions of each vehicle part in the undamaged version of the damaged vehicle, wherein detecting damage to the set of parts includes: comparing at least one of edge distribution, texture comparison, and spatial correlation of portions of each vehicle part in the image to corresponding portions of each vehicle part in the undamaged version of the damaged vehicle; determining whether at least one of the edge distribution, the texture comparison, and the spatial correlation exceeds a respective threshold difference value, wherein damage is detected in a portion of a vehicle part in the image if at least one of the edge distribution, the texture comparison, and the spatial correlation exceed the respective threshold difference value; detecting a pose of the damaged vehicle in the image; and determining which external vehicle parts are damaged in the image; and calculating an estimated repair cost for the damaged vehicle based on which external vehicle parts are damaged based on accessing a parts database that includes repair costs.

“18. The computing device of claim 17, wherein the detecting the pose of the damaged vehicle in the image comprises: training a first Convolutional Neural Networks (CNN) of a plurality of CNNs to detect the pose of a damaged vehicle in the image; and training each of the plurality of CNNs to detect damage on a respective vehicle part of a plurality of external vehicle parts; and executing the first CNN to detect the pose of the damaged vehicle in the image.”

There are additional claims. Please visit full patent to read further.

URL and more information on this patent, see: Dalal, Siddhartha. Automatic assessment of damage and repair costs in vehicles. U.S. Patent Number 11443288, filed May 11, 2020, and published online on September 13, 2022. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11443288.PN.&OS=PN/11443288RS=PN/11443288

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