IP Library Granted Patent US 12,100,054
Granted Patent B2
US 12,100,054 · App. 18/130,153 · Granted Sep 24, 2024

Using historical data for subrogation on a distributed ledger

Inventors: William J. Leise (Normal, IL); Douglas A. Graff (Mountain View, MO); Anthony McCoy (Normal, IL); Jaime Skaggs (Chenoa, IL); Shawn M. Call (Bloomington, IL); Stacie A. McCullough (Bloomington, IL); Wendy H. Clayton (Franklin, TN); Melinda Teresa Magerkurth (Utica, IL); Kim E. Flesher (Normal, IL); Travis Charles Runge (Heyworth, IL)
Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
G06Q40/08G06F16/27G06N20/00
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Quick Facts
Patent No.
US 12,100,054
App. No.
18/130,153
Granted
Sep 24, 2024
Kind
B2
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.

Claims (58)

1. A computer-implemented method of improved electronic insurance arbitration, the method comprising:

receiving, via one or more processors, historical sensor data associated with a past vehicle collision;

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;

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;

receiving, via the one or more processors, an electronic arbitration demand associated with the current vehicle collision; and

generating, via the one or more processors, a recommendation based upon, at least in part, analysis of the percentage of fault and the electronic arbitration demand.

2. The computer-implemented method of claim 1 , further comprising:

generating, via the one or more processors, a new block including the recommendation 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 is generated by smart infrastructure or by a vehicle not involved in the current vehicle collision.

4. The computer-implemented method of claim 1 , wherein the current sensor data includes telematics data collected by another vehicle in the 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. 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 the current sensor data generated by one or more vehicle-mounted sensors, and sensor or other data, such as telematics data, received from the vehicle and one or more nearby vehicles in the vicinity of the current vehicle collision location.

9. A computer-implemented method of improved electronic insurance arbitration, the method comprising:

receiving, via one or more processors, historical sensor data associated with a past vehicle collision;

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;

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;

receiving, via the one or more processors, an electronic arbitration demand associated with the current vehicle collision; and

generating, via the one or more processors, a recommendation based upon, at least in part, analysis of the percentage of fault and the electronic arbitration demand.

10. The computer-implemented method of claim 9 , further comprising:

generating, at the one or more processors, a new block including the recommendation or a link thereto; and

adding, at the one or more processors, the new block to a blockchain.

11. The computer-implemented method of claim 9 , wherein the current sensor data is generated by smart infrastructure or by a vehicle not involved in the current vehicle collision.

12. The computer-implemented method of claim 9 , wherein the current sensor data includes telematics data collected by the vehicle, a mobile device traveling within the vehicle, another vehicle in the vicinity of the current vehicle collision, or combinations thereof.

13. The computer-implemented method of claim 9 , 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 9 , further comprising:

receiving, via the one or more processors, 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, and sensor or other data, such as telematics data, received from the vehicle and one or more nearby vehicles in the vicinity of the current vehicle collision location.

15. A computer system for improved electronic insurance arbitration, the system comprising:

a network interface configured to interface with one or more processors;

one or more sensors;

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 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 current sensor data associated with a current vehicle collision;

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;

receive an electronic arbitration demand associated with the current vehicle collision; and

generate a recommendation based upon, at least in part, analysis of the percentage of fault and the electronic arbitration demand.

16. The 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 recommendation or a link thereto; and

add the new block to a blockchain.

17. The system of claim 15 , wherein the current sensor data is generated by smart infrastructure or by a vehicle not involved in the current vehicle collision.

18. The 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.

19. The system of claim 15 , wherein:

the processor is further configured to execute the non-transitory computer executable instructions to cause the processor to add, to a blockchain, an additional block including an electronic subrogation demand; and

the electronic subrogation demand is based on the determined percentage of fault, and wherein the electronic subrogation demand includes one or more line items directed to medical expenses.

20. The system of claim 19 , wherein:

the processor is further configured to execute the non-transitory computer executable instructions to cause the processor to add, to the blockchain, a block including a hash of the electronic subrogation demand.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 3, 2023
From: LEISE, WILLIAM J.; GRAFF, DOUGLAS A.; MCCOY, ANTHONY; SKAGGS, JAIME; CALL, SHAWN M.; MCCULLOUGH, STACIE A.; CLAYTON, WENDY H.; MAGERKURTH, MELINDA TERESA; FLESHER, KIM E.; RUNGE, TRAVIS CHARLES
To: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Reel/Frame 063206/0891 →
Continuity (8)
Continuation 17892040 · Aug 19, 2022
Continuation 16999260 · Aug 21, 2020
Continuation 15957438 · Apr 19, 2018
Provisional Application 62609644 · Dec 22, 2017
Provisional Application 62555358 · Sep 7, 2017
Provisional Application 62555030 · Sep 6, 2017
Provisional Application 62554907 · Sep 6, 2017
Related Publication 20230252577A1 · Aug 10, 2023