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State Farm Mutual Automobile Insurance Company – InsuranceNewsNet


2022 SEP 23 (NewsRx) — By a News Reporter-Staff News Editor at Insurance Daily News — According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Christopulos, Nicholas U. (Bloomington, IL, US); Donahue, Erik (Normal, IL, US); Goldfarb, Meghan Sims (Bloomington, IL, US); Hayward, Gregory L. (Bloomington, IL, US), filed on May 24, 2022, was made available online on September 8, 2022.

The assignee for this patent application is State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “As computer and computer networking technology has become less expensive and more widespread, more and more devices have started to incorporate digital “smart” functionalities. For example, controls and sensors capable of interfacing with a network may now be incorporated into devices such as vehicles and/or traffic control systems. Furthermore, it is possible for one or more vehicle and/or central controllers to interface with the smart devices or sensors.

“However, conventional systems may not be able to automatically detect and characterize various conditions or damage associated with a vehicle or building. Additionally, conventional systems may not be able to detect or sufficiently identify and describe damage that is hidden from human view, and that typically has to be characterized by explicit human physical exploration, extent and range of electrical malfunctions, etc. Conventional systems further may not be able to formulate precise characterizations of loss without including unconscious biases, and may not be able to equally weight all historical data in determining loss mitigation factors.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “The present disclosure generally relates to systems and methods for detecting damage, loss, and/or other conditions associated with a vehicle using a computer system and/or a building, land, structure, or other real property using a property monitoring system. Embodiments of exemplary systems and computer-implemented methods are summarized below. The methods and systems summarized below may include additional, less, or alternate components, functionality, and/or actions, including those discussed elsewhere herein.

“In one aspect, the present embodiments may relate to determining an automobile-based risk level via one or more processors, training a neural network to identify risk factors that are predictive electronic claim features, receiving information corresponding to (i) an automobile, and/or (ii) an automobile operator, analyzing the information using the trained neural network to generate one or more risk indicators, determining, by analyzing the risk indicators, a risk level corresponding to the automobile, and/or displaying, to a user, an insurance quotation based upon analyzing the risk indicators. The automobile may be a smart, autonomous, or semi-autonomous vehicle, and have sensors, software, and electronic components that direct autonomous or semi-autonomous vehicle features or technologies – each of which may have a various levels of risk, or lack thereof, that may be analyzed and determined by the present embodiments. Systems and methods may automatically generate risk models for various types of vehicle insurance types and loss types, such as by the application of artificial intelligence and machine learning methods as disclosed herein, to provide more granular risk models, leading to more accurate commercial offerings, and more appropriate matching premium price to actual risk.

“In another aspect; a computer-implemented method of determining an automobile-based risk level via one or more processors may include training, via one or more processors, a neural network to identify risk factors that are predictive of electronic vehicle claim records. The neural network may include a plurality of layers, and an input layer from among the plurality of layers may include a plurality of input parameters-with each corresponding to a different claim attribute. The method may include, via one or more processors, receiving information corresponding to (i) an automobile, and/or (ii) an automobile operator; and analyzing the information using the trained neural network. Analyzing the information may include generating, within the plurality of layers, one or more risk indicators corresponding to the information. The method may also include determining a risk level corresponding to the vehicle. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In another aspect, a computing system may include one or more processors, and one or more memories storing instructions. When the instructions are executed by the one or more processors, they may cause the computing system to provide a first application to a user of a client computing device. The first application, when executing on the client computing device, may cause the client computing device to obtain a set of information from an input device of the client computing device, and transmit, via a communication network interface of the client computing device, the set of information to a remote computing system. The instructions may cause the computing system to receive, at the remote computing system, the set of information and process, at the remote computing system, the set of information. The instructions may cause the computing system to identify, by the remote computing system, one or more risk indications, at least in part, by applying the set of information to a trained neural network and generate, by the remote computing system analyzing the one or more risk indications, a quotation, such as quote for auto insurance. The instructions may cause the computing system to (i) display the quotation to the user, and (ii) provide the quotation as input to a second application. The system may include additional, less, or alternate functionality, including that discussed elsewhere herein.

“Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

“The Figures depict preferred embodiments for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the systems and methods illustrated herein may be employed without departing from the principles of the invention described herein.”

The claims supplied by the inventors are:

“1. A computer-implemented method of determining damage to personal property, the method comprising: inputting, via one or more processors, historical claim data into a machine learning algorithm to train the algorithm to identify an insured vehicle, a respective type of the insured vehicle, respective insured vehicle features or characteristics, a peril associated with the insured vehicle, and/or a repair or replacement cost associated with the insured vehicle; receiving, via the one or more processors and/or the one or more transceivers, a digital image depicting damage to the insured vehicle, the digital image submitted by an insured entity via a webpage, website, and/or mobile device; and inputting, via the one or more processors, the digital image of the damaged insured vehicle into a processor having the trained machine learning algorithm installed in a memory unit, the trained machine learning algorithm identifying a type of the damaged insured vehicle, a respective feature or characteristic of the damaged insured vehicle, a peril associated with the damaged insured vehicle, and/or a repair or replacement cost associated with the damaged insured vehicle to facilitate handling an insurance claim associated with the damaged insured vehicle or enhancing an online customer experience.

“2. The computer-implemented method of claim 1, wherein the respective features or characteristics of the damaged insured vehicle include one or more autonomous or semi-autonomous technologies or systems.

“3. The computer-implemented method of claim 2, wherein the peril associated with the damaged insured vehicle comprises collision, comprehensive, the or water.

“4. The computer-implemented method of claim 1, the method further comprising: retrieving, via the one or more processors and/or the one or more transceivers, an insurance policy associated with the damaged insured vehicle; and determining, via the one or more processors, whether or not the peril associated with the damaged insured vehicle is a covered peril under the insurance policy.

“5. The computer-implemented method of claim 1, wherein the peril associated with the damaged insured vehicle comprises fire, smoke, water, hail, wind, or storm surge.

“6.-17. (canceled)

“18. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: input historical claim data into a machine learning algorithm to operate the algorithm to identify a damaged insured vehicle, respective damaged insured vehicle type, respective damaged insured vehicle features or characteristics, a peril associated with the damaged insured vehicle, and/or a repair or replacement cost associated with the damaged insured vehicle; receive a digital image depicting damage to the insured vehicle, the digital image being submitted by an insured entity via a webpage, webpage, or mobile device: and input the image of the damaged insured vehicle into a processor having the trained machine learning algorithm installed in a memory unit, the trained machine learning algorithm identifying a type of the damaged insured vehicle, a feature or characteristic of the damaged insured vehicle, a peril associated with the damaged insured vehicle, and/or a repair or replacement cost associated with the damaged insured vehicle to facilitate handling an insurance claim associated with the damaged insured vehicle.

“19. The non-transitory computer readable medium of claim 18, wherein the features or characteristics of the damaged insured vehicle include geographical area, make, model, transmission, tire, engine, autonomous or semi-autonomous features, air conditioning, power brakes, power windows, and/or color of the vehicle.

“20. The non-transitory computer readable medium of claim 18, containing further program instructions that when executed, cause the computer to: retrieve an insurance policy associated with the damaged insured vehicle; and determine whether or not the peril associated with the damaged insured vehicle is a covered peril under the insurance policy.”

For more information, see this patent application: Christopulos, Nicholas U.; Donahue, Erik; Goldfarb, Meghan Sims; Hayward, Gregory L. Automobile Monitoring Systems and Methods for Detecting Damage and Other Conditions. Filed May 24, 2022 and posted September 8, 2022. Patent URL: https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220220284517%22.PGNR.&OS=DN/20220284517&RS=DN/20220284517

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