Patent Issued for Methods and systems for injury segment determination (USPTO 11403712): State Farm Mutual Automobile Insurance Company

2022 AUG 23 (NewsRx) -- By a News Reporter-Staff News Editor at Hospital & Nursing Home Daily -- State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States) has been issued patent number 11403712, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Dillard, John (Bloomington, IL, US), Dionesotes, Leann (Bloomington, IL, US), Ruby, David (Normal, IL, US), Westhues, John (Normal, IL, US).

This patent was filed on October 13, 2020 and was published online on August 2, 2022.

From the background information supplied by the inventors, news correspondents obtained the following quote: “In various applications a need exists to quickly and accurately assign and/or route vehicle insurance and/or injury claims to one or more claim handlers who are situated in one or more tiers. Traditionally, auto and/or injury claims are assigned to a default, or “catch all”, pool of claims, wherein the claims may remain idle until such time that a claim handler reviews the claim and assigns it to an appropriate claim handling tier. Efforts to organize claims into proper filing categories have been attempted, but rely on customer input and self-categorization. Customers may be incentivized to assign higher-than-warranted severity to claims in order to result in faster processing times. Furthermore, no humans, regardless of skill level, are able to analyze the entirety of all claims filed historically in an insurer’s course of business to facilitate assignment and/or routing of claims. No humans, regardless of skill level, are able to analyze all ancillary documents filed with claims (e.g., electronic medical records) in a tractable period of time.

“Claim handlers are lacking in experience and may improperly assign or improperly route a loss report. The varied experience of claim handlers, and limited tooling, are problems in the prior art. For example, a policyholder may be involved in an accident, and may be injured. The policyholder may inform the insurer that the injury occurred, and provide the insurer with written and/or verbal documentation relating to the incident (e.g., the vehicle make and model, year, mileage, hospital name, general nature of injury, length of hospital stay, etc.). Next, an auto claim handler may manually review the provided information, and make a determination of severity based on the claim handler’s experience. However, conventional systems are unable to sufficiently identify/formulate precise characterizations of loss without resort to unconscious biases, and are unable to properly weight all historical data in determining loss mitigation factors in order to produce assignments and routes of loss reports that may be quantified, repeated, and whose accuracy can thus be improved.

“Furthermore, claim handlers lack the technical ability to analyze all past claims in a very short time (e.g., in microseconds). Claim handlers’ lack of experience, cognitive bias, fatigue, etc. may lead them to make errors in judgment as to whether an injury is severe or not. Important facts, such as the presence of electronic medical records including unfamiliar jargon (e.g., “ischemia”) may not be understood by claim handlers who lack a background in medicine. Arcane legal pleadings, such as complaints wherein the party identified in the loss report as the injured party is listed as a complainant or plaintiff, may not be understood by claim handlers. As such, a need exists for computerized methods and systems of automatically assigning and/or routing claims related to vehicle insurance and personal injury, wherein the methods and systems can be continuously trained on new data, operate around the clock, and predict results that are repeatable and quantifiable.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “The present disclosure generally relates to systems and methods for smart claim routing and smart claim assignment. Embodiments of exemplary systems and computer-implemented methods are summarized below. The methods and systems summarized below may include additional, fewer, or alternate components, functionality, and/or actions, including those discussed elsewhere herein.

“In one aspect, a computer-implemented method of determining an injury segment includes training, via a processor, a machine learning model using historical claim data to determine an injury claim severity; receiving, via a processor, an auto accident loss report; analyzing the loss report using the trained machine learning model to determine a severity of an injury; determining, based on the severity of the injury, an injury segment; and storing, via a processor, an indication of the injury segment.

“In another aspect, a computer system configured to determine an injury segment includes one or more processors configured to train, via the one or more processors, a machine learning model using historical claim data to determine an injury claim severity; receive, via the one or more processors, an auto accident loss report; analyze the loss report using the trained machine learning model to determine a severity of an injury; determine, based on the severity of the injury, an injury segment; and store, via the one or more processors, an indication of the injury segment.

“In yet another aspect, a non-transitory computer readable medium includes program instructions that when executed, cause a computer to train, via the one or more processors, a machine learning model using historical claim data to determine an injury claim severity; receive, via the one or more processors, an auto accident loss report; analyze the loss report using the trained machine learning model to determine a severity of an injury; determine, based on the severity of the injury, an injury segment; and store, via the one or more processors, an indication of the injury segment in association with the loss report.

“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 claims supplied by the inventors are:

“1. A computer-implemented method of determining an injury segment, comprising: training, via a processor, a machine learning model using historical claim data to determine an injury claim severity, receiving, via a processor, an auto accident loss report, analyzing the loss report using the trained machine learning model to determine a severity of an injury, determining, based on the severity of the injury, an injury segment, and storing, via a processor, an indication of the injury segment.

“2. The computer-implemented method of claim 1, wherein determining, based on the severity of the injury, the injury segment includes assigning the loss report to one or more tiers.

“3. The computer-implemented method of claim 2, wherein the one or more tiers are hierarchically related.

“4. The computer-implemented method of claim 1, wherein determining, based on the severity of the injury, the injury segment includes assigning the loss report to one or more ordered tiers, to create a routing.

“5. The computer-implemented method of claim 4, wherein the one or more ordered tiers are hierarchically related.

“6. The computer-implemented method of claim 1, wherein the severity is expressed by a numeric severity level.

“7. The computer-implemented method of claim 1, wherein the machine learning model is an artificial neural network.

“8. The computer-implemented method of claim 1, wherein the loss report includes one or both of (i) a photograph corresponding to the accident, and (ii) a textual description corresponding to the accident.

“9. The computer-implemented method of claim 1, wherein analyzing the loss report using the trained machine learning model to determine the severity of the injury includes analyzing electronic claim records corresponding to the accident.

“10. The computer-implemented method of claim 9, further comprising: analyzing vehicle telematics information.

“11. A computer system configured to determine an injury segment, the system comprising one or more processors configured to: train, via the one or more processors, a machine learning model using historical claim data to determine an injury claim severity, receive, via the one or more processors, an auto accident loss report, analyze the loss report using the trained machine learning model to determine a severity of an injury, determine, based on the severity of the injury, an injury segment, and store, via the one or more processors, an indication of the injury segment.

“12. The computer system of claim 11, further configured to: assign the loss report to one or more tiers.

“13. The computer system of claim 11, further configured to: determine a routing and route the injury claim via the routing.

“14. The computer system of claim 11, wherein the machine learning model is an artificial neural network.

“15. The computer system of claim 11, further configured to: analyze vehicle telematics information.

“16. A non-transitory computer readable medium containing program instructions that when executed, cause a computer to: train, via the one or more processors, a machine learning model using historical claim data to determine an injury claim severity, receive, via the one or more processors, an auto accident loss report, analyze the loss report using the trained machine learning model to determine a severity of an injury, determine, based on the severity of the injury, an injury segment, and store, via the one or more processors, an indication of the injury segment in association with the loss report.

“17. The non-transitory computer readable medium of claim 16, containing further program instructions that when executed, cause a computer to: assign the loss report to one or more tiers.

“18. The non-transitory computer readable medium of claim 16, containing further program instructions that when executed, cause a computer to: determine a routing and route the injury claim via the routing.

“19. The non-transitory computer readable medium of claim 16, containing further program instructions that when executed, cause a computer to: analyze vehicle telematics information.

“20. The non-transitory computer readable medium of claim 16, wherein the machine learning model is an artificial neural network.”

For the URL and additional information on this patent, see: Dillard, John. Methods and systems for injury segment determination. U.S. Patent Number 11403712, filed October 13, 2020, and published online on August 2, 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=11403712.PN.&OS=PN/11403712RS=PN/11403712

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