US-20240420249-A1 - USING HISTORICAL DATA FOR SUBROGATION ON A DISTRIBUTED LEDGER

US20240420249A1US 20240420249 A1US20240420249 A1US 20240420249A1US-20240420249-A1

Abstract

Systems and methods are disclosed with respect to using a blockchain for managing the subrogation claim process related to a vehicle accident, in particular, utilizing historical data related to a vehicle or vehicle collisions as part of the subrogation process. An exemplary embodiment may include receiving historical sensor data, such as image, audio, telematics, and/or autonomous vehicle data, associated with a past vehicle collision; inputting the historical sensor data into a machine learning program to determine data relevant to a past vehicle collision; receiving current sensor data associated with a current vehicle collision; inputting the current sensor data into the machine learning program to determine data relevant to the current vehicle collision; and determining a percentage of fault of the vehicle collision for one or more autonomous vehicles, autonomous vehicle systems, and/or drivers based upon, at least in part, analysis of the historical sensor data and the current sensor data.

Inventors

LEISE WILLIAM J
RUNGE TRAVIS CHARLES
GRAFF DOUGLAS A
MCCOY ANTHONY
SKAGGS JAIME
CALL SHAWN M
MCCULLOUGH STACIE A
CLAYTON WENDY H
MAGERKURTH MELINDA TERESA
FLESHER KIM E

Assignees

STATE FARM MUTUAL AUTOMOBILE INSURANCE CO

Dates

Publication Date: 20241219
Application Date: 20240830
Priority Date: 20170906

Claims (20)

1 . A computer-implemented method of improved vehicle collision analysis, the method comprising: receiving, via one or more processors, historical sensor data associated with a past vehicle collision, wherein the historical sensor data includes data generated by smart infrastructure; inputting, via the one or more processors, the historical sensor data into an algorithm, the algorithm being a machine learning algorithm that is trained by the historical sensor data to determine a percentage of fault for human drivers or self-driving vehicles; receiving, via the one or more processors, current sensor data associated with a current vehicle collision, wherein the current sensor data includes data generated by smart infrastructure; and inputting, via the one or more processors, the current sensor data into the machine learning algorithm to determine a percentage of fault of the current vehicle collision for a human driver or a self-driving vehicle.
2 . The computer-implemented method of claim 1 , further comprising: generating, via the one or more processors, a new block including the determined percentage of fault or a link thereto; and adding, via the one or more processors, the new block to a blockchain.
3 . The computer-implemented method of claim 1 , wherein the current sensor data further includes data generated by a vehicle not involved in the current vehicle collision.
4 . The computer-implemented method of claim 1 , wherein the current sensor data further includes telematics data collected by another vehicle in a vicinity of the current vehicle collision.
5 . The computer-implemented method of claim 1 , further comprising: receiving, via the one or more processors, an electronic notification of the current vehicle collision generated by the vehicle from analysis of sensor data generated by one or more vehicle-mounted sensors.
6 . The computer-implemented method of claim 1 , further comprising: receiving, via the one or more processors, an electronic notification of the current vehicle collision generated by a vehicle from analysis of image data generated by one or more vehicle-mounted sensors or cameras.
7 . The computer-implemented method of claim 1 , further comprising: receiving, via the one or more processors, an electronic notification of the current vehicle collision generated by a vehicle from analysis of telematics data generated by one or more vehicle-mounted sensors.
8 . A computer-implemented method of improved vehicle collision analysis, the method comprising: receiving, via one or more processors, historical sensor data associated with a past vehicle collision, wherein the historical sensor data includes data generated by smart infrastructure; inputting, via the one or more processors, the historical sensor data into an algorithm, the algorithm being a machine learning algorithm that is trained by the historical sensor data to: (i) determine a percentage of fault for human drivers or self-driving vehicles, and (ii) determine data relevant to a past vehicle collision; receiving, via the one or more processors, current sensor data associated with a current vehicle collision, wherein the current sensor data includes data generated by smart infrastructure; and inputting, via the one or more processors, the current sensor data into the machine learning algorithm to determine: (i) that a vehicle was under autonomous control before, during, and/or after the current vehicle collision, and (ii) a percentage of fault for the vehicle determined to be under autonomous control.
9 . The computer-implemented method of claim 8 , further comprising: generating, via the one or more processors, a new block including the determined percentage of fault or a link thereto; and adding, via the one or more processors, the new block to a blockchain.
10 . The computer-implemented method of claim 8 , further comprising: creating, via the one or more processors, a new blockchain corresponding to the current vehicle collision, wherein the new blockchain includes the determined percentage of fault.
11 . The computer-implemented method of claim 8 , wherein the current sensor data further includes data generated by a vehicle not involved in the current vehicle collision.
12 . The computer-implemented method of claim 8 , wherein the current sensor data further includes telematics data collected by the vehicle, a mobile device traveling within the vehicle, another vehicle in a vicinity of the current vehicle collision, or combinations thereof.
13 . The computer-implemented method of claim 8 , further comprising: receiving, via the one or more processors, an electronic notification of the current vehicle collision generated by the vehicle from analysis of telematics data generated by one or more vehicle-mounted sensors.
14 . The computer-implemented method of claim 8 , wherein: the historical sensor data further includes data of control decisions implemented by autonomous vehicles; and the current sensor data includes data of a control decision implemented by the vehicle.
15 . A computer system for improved vehicle collision analysis, the system comprising: a network interface configured to interface with one or more processors; a first smart infrastructure component; a second smart infrastructure component; a memory configured to store non-transitory computer executable instructions and configured to interface with the one or more processors; and the one or more processors configured to interface with the memory, wherein the one or more processors are configured to execute the non-transitory computer executable instructions to cause the one or more processors to: receive, from the first smart infrastructure component, historical sensor data associated with a past vehicle collision; input the historical sensor data into a machine learning algorithm to train the machine learning algorithm to determine a percentage of fault for human drivers or self-driving vehicles; receive, from the second smart infrastructure component, current sensor data associated with a current vehicle collision; and input the current sensor data into the machine learning algorithm to determine a percentage of fault of the current vehicle collision for a human driver or a self-driving vehicle.
16 . The computer system of claim 15 , wherein the one or more processors are further configured to execute the non-transitory computer executable instructions to cause the one or more processors to: generate a new block including the determined percentage of fault or a link thereto; and add the new block to a blockchain.
17 . The computer system of claim 15 , wherein the one or more processors are further configured to execute the non-transitory computer executable instructions to cause the one or more processors to: create a new blockchain corresponding to the current vehicle collision, wherein the new blockchain includes the determined percentage of fault.
18 . The computer system of claim 15 , wherein the current sensor data further includes data generated by a vehicle not involved in the current vehicle collision.
19 . The computer system of claim 15 , wherein the one or more processors are further configured to execute the non-transitory computer executable instructions to cause the one or more processors to: receive an electronic notification of the current vehicle collision generated by the vehicle from analysis of the current sensor data generated by one or more vehicle-mounted sensors.
20 . The computer system of claim 15 , further including one or more vehicle-mounted cameras, and wherein the one or more processors are further configured to execute the non-transitory computer executable instructions to cause the one or more processors to: receive an electronic notification of the current vehicle collision generated based upon analysis of image data generated by the one or more vehicle-mounted cameras.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application is a continuation of U.S. patent application Ser. No. 18/130,153, filed Apr. 3, 2023, entitled “Using Historical Data for Subrogation on a Distributed Ledger,” which is a continuation of U.S. patent application Ser. No. 17/892,040, filed Aug. 19, 2022, entitled “Using Historical Data for Subrogation on a Distributed Ledger,” which is a continuation of U.S. patent application Ser. No. 16/999,260, filed Aug. 21, 2020, entitled “Using Historical Data for Subrogation on a Distributed Ledger,” which is a continuation of U.S. patent application Ser. No. 15/957,438, filed Apr. 19, 2018, entitled “Using Historical Data for Subrogation on a Distributed Ledger,” which claims priority to: (1) U.S. Provisional Application No. 62/555,030, entitled “Using a Blockchain for the Subrogation Claim Process,” and filed Sep. 6, 2017; (2) U.S. Provisional Application No. 62/554,907, entitled “Blockchain-Based Claim Handling,” and filed Sep. 6, 2017; (3) U.S. Provisional Application No. 62/555,358, entitled “Using a Blockchain for the Subrogation Claim Process,” and filed Sep. 7, 2017; and (4) U.S. Provisional Application No. 62/609,644, entitled “Using Historical Data for Subrogation on a Distributed Ledger,” and filed Dec. 22, 2017, each of which is hereby incorporated herein by reference in its entirety. TECHNICAL FIELD Systems and methods are disclosed with respect to using a blockchain for managing the subrogation claim process related to a vehicle accident, in particular, utilizing historical data related to a vehicle or vehicle collisions as part of the subrogation process. BACKGROUND The insurance claim process may involve a tremendous number of communications and interactions between parties involved in the process. Potential parties to the claim process may be insurance companies, repair shops, lawyers, arbitrators, government agencies, hospitals, drivers, and collection/collections agency. Sometimes the costs of repairs may be disputed and parties may pursue subrogation for particular charges. As an example, when an insured person suffers a covered loss, an insurer may pay costs to the insured person and pursue subrogation from another party involved in the loss. If an insured vehicle is involved in a collision and suffers a loss, the insurer may compensate the vehicle owner according to an insurance agreement. If, for example, the vehicle owner was not at fault in the collision, the insurer may pursue damages from another party, such as the insurer of the party who was at fault in the collision. An insurance agreement may include an obligation of an insured to assign the insured's claim against a party at fault to the insurer, who may then collect on the claim on the insured's behalf. Settling a subrogation payment may be a lengthy, complicated process. The various parties (e.g., parties at fault in a vehicle collision, owners of the vehicles, insurers, etc.) may need to exchange information relating to the collision to determine which party was at fault. Sources of information relevant to a fault information and/or subrogation payment may include information regarding parties involved in a loss, forensic data regarding the loss, vehicle data regarding a loss, etc. The various parties may verify and share information from a variety of sources, including information held by parties involved in a loss and their insurers, and information obtained from third parties (e.g., governmental entities, independent contractors, etc.). The parties to a subrogation payment (e.g., insurers) may make proposals to one another to settle the subrogation claim. A proposal may include an accounting of damages, such as the costs to a vehicle owner whose vehicle was damaged. If an insured person suffered an injury in a collision, the injured person's health care costs may be included in the accounting of damages. One or both of the parties to a subrogation claim may rely on independent third parties to assess costs, such as a repair cost estimate by an authorized automotive repair services provider for damage incurred in a collision. To settle the subrogation claim, the parties may indicate acceptance or approval of damages calculations, and a payment amount agreed upon between the parties to settle the claim. Parties may rely on a third-party intermediary to handle subrogation negotiations and resolution (e.g., validate information relating to a loss and facilitating communications between the insurers) at added expense. BRIEF SUMMARY Systems and methods are disclosed for utilizing a distributed ledger, or blockchain, to manage an insurance claim process, in particular, a subrogation claim process. The systems and methods disclose using evidence oracles for inputting information into the blockchain, utilizing machine learning to suggest amounts for the subrogation process, a line item dispute mechanism, and creating/managing a distributed ledger in response to a vehicle being in an c