Methods for improved delta velocity determination using machine learning and devices thereof
Methods, non-transitory computer readable media, and insurance claim analysis devices are disclosed that provide an improved, automated delta velocity determination. With this technology, one or more images of a damaged motor vehicle and contextual data, associated with an electronic insurance claim and a motor vehicle accident involving the damaged motor vehicle, are obtained. The obtained images and one or more portions of the contextual data are compared to historical sets of images of damaged motor vehicles and corresponding additional contextual data and actual delta velocity values. A delta velocity value is calculated based on the comparison. The calculated delta velocity value is provided to verify damage severity during automated processing of the electronic insurance claim.
1. A method for improved, automated delta velocity determination, the method comprising:
obtaining, by an insurance claim analysis device, one or more images of a damaged motor vehicle and contextual data associated with an electronic insurance claim and a motor vehicle accident involving the damaged motor vehicle;
calculating, by the insurance claim analysis device, a delta velocity value by applying a trained machine learning model to the obtained images and contextual data, wherein the trained machine learning model has been trained using historical sets of images of damaged motor vehicles and corresponding additional contextual data and actual delta velocity values, each of the historical sets associated with another motor vehicle accident or insurance claim; and
providing, by the insurance claim analysis device, the calculated delta velocity value to verify damage severity during automated processing of the electronic insurance claim.
2. The method of claim 1 , further comprising updating, by the insurance claim analysis device, the machine learning model based on another actual delta velocity value for the damaged motor vehicle associated with the motor vehicle accident.
3. The method of claim 1 , wherein the machine learning model comprises an artificial or convolutional neural network.
4. The method of claim 1 , further comprising obtaining, by the insurance claim analysis device, at least the historical sets of images of damaged motor vehicles and corresponding additional contextual data from a National Highway Traffic Safety Administration (NHTSA) National Automotive Sampling System (NASS) database.
5. The method of claim 1 , wherein the one or more images comprise a plurality of digital images and each of the digital images comprises a unique perspective view of the damaged motor vehicle.
6. An insurance claim analysis device, comprising memory comprising programmed instructions stored thereon and one or more processors configured to execute the stored programmed instructions to:
obtain one or more images of a damaged motor vehicle and contextual data associated with an electronic insurance claim and a motor vehicle accident involving the damaged motor vehicle;
calculate a delta velocity value by applying a trained machine learning model to the obtained images and contextual data, wherein the trained machine learning model has been trained using historical sets of images of damaged motor vehicles and corresponding additional contextual data and actual delta velocity values, each of the historical sets associated with another motor vehicle accident or insurance claim; and
provide the calculated delta velocity value to verify damage severity during automated processing of the electronic insurance claim.
7. The insurance claim analysis device of claim 6 , wherein the processors are further configured to execute the stored programmed instructions to update the machine learning model based on another actual delta velocity value for the damaged motor vehicle associated with the motor vehicle accident.
8. The insurance claim analysis device of claim 6 , wherein the machine learning model comprises an artificial or convolutional neural network and the processors are further configured to execute the stored programmed instructions to train the neural network using the historical sets of images of damaged motor vehicles and corresponding additional contextual data and actual delta velocity values, each of the historical sets associated with another motor vehicle accident or insurance claim.
9. The insurance claim analysis device of claim 6 , wherein the processors are further configured to execute the stored programmed instructions to obtain at least the historical sets of images of damaged motor vehicles and corresponding additional contextual data from a National Highway Traffic Safety Administration (NHTSA) National Automotive Sampling System (NASS) database.
10. The insurance claim analysis device of claim 6 , wherein the one or more images comprise a plurality of digital images and each of the digital images comprises a unique perspective view of the damaged motor vehicle.
11. A non-transitory machine readable medium having stored thereon instructions for improved, automated delta velocity determination comprising executable code that, when executed by one or more processors, causes the processors to:
obtain one or more images of a damaged motor vehicle and contextual data associated with an electronic insurance claim and a motor vehicle accident involving the damaged motor vehicle;
calculate a delta velocity value by applying a trained machine learning model to the obtained images and contextual data, wherein the trained machine learning model has been trained using historical sets of images of damaged motor vehicles and corresponding additional contextual data and actual delta velocity values, each of the historical sets associated with another motor vehicle accident or insurance claim; and
provide the calculated delta velocity value to verify damage severity during automated processing of the electronic insurance claim.
12. The non-transitory machine readable medium of claim 11 , wherein the executable code, when executed by the processors, further causes the processors to update the machine learning model based on another actual delta velocity value for the damaged motor vehicle associated with the motor vehicle accident.
13. The non-transitory machine readable medium of claim 11 , wherein the machine learning model comprises an artificial or convolutional neural network.
14. The non-transitory machine readable medium of claim 11 , wherein the executable code, when executed by the processors, further causes the processors to obtain at least the historical sets of images of damaged motor vehicles and corresponding additional contextual data from a National Highway Traffic Safety Administration (NHTSA) National Automotive Sampling System (NASS) database.
15. The non-transitory machine readable medium of claim 11 , wherein the one or more images comprise a plurality of digital images and each of the digital images comprises a unique perspective view of the damaged motor vehicle.