Patent Application Titled “Methods And Systems For Injury Segment Determination” Published Online (USPTO 20220366510): Patent Application

2022 DEC 02 (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 Dillard, John (Bloomington, IL, US); Dionesotes, Leann (Bloomington, IL, US); Ruby, David (Normal, IL, US); Westhues, John (Normal, IL, US), filed on August 1, 2022, was made available online on November 17, 2022.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “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.”

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 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 (i) receiving, via one or more processors, an auto accident loss report; (ii) analyzing the loss report using a machine learning model trained using historical claim data to determine a severity of an injury relating to the loss report; (iii) determining, based on the severity of the injury, an injury segment; and (iv) storing, via one or more processors, 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: (i) receive, via the one or more processors, an auto accident loss report; (ii) analyze the loss report using a machine learning model trained using historical claim data to determine a severity of an injury relating to the loss report; (iii) determine, based on the severity of the injury, an injury segment; and (iv) 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: (i) receive an auto accident loss report; (ii) analyze the loss report using a machine learning model trained using historical claim data to determine a severity of an injury relating to the loss report; (iii) determine, based on the severity of the injury, an injury segment; and (iv) store an indication of the injury segment.

“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 an injury segment, comprising: receiving, via one or more processors, an auto accident loss report; analyzing the loss report using a machine learning model trained using historical claim data to determine a severity of an injury relating to the loss report; determining, based on the severity of the injury, an injury segment; and storing, via one or more processors, 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 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: receive, via the one or more processors, an auto accident loss report; analyze the loss report using a machine learning model trained using historical claim data to determine a severity of an injury relating to the loss report; 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: receive an auto accident loss report; analyze the loss report using a machine learning model trained using historical claim data to determine a severity of an injury relating to the loss report; determine, based on the severity of the injury, an injury segment; and store an indication of the injury segment.

“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 more information, see this patent application: Dillard, John; Dionesotes, Leann; Ruby, David; Westhues, John. Methods And Systems For Injury Segment Determination. Filed August 1, 2022 and posted November 17, 2022.

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