IP Library › Granted Patent US 11,787,422
Granted Patent B2
US 11,787,422 · App. 17/941,096 · Granted Oct 17, 2023

Systems and methods of determining effectiveness of vehicle safety features

Inventors: Jaime Skaggs (Chenoa, IL); Jody Thoele (Bloomington, IL); Angela Glusick (Bloomington, IL)
Assignee: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
B60W50/0098G06N20/00G06Q40/08G07C5/02B60W2050/0083B60W2556/55
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Quick Facts
Patent No.
US 11,787,422
App. No.
17/941,096
Granted
Oct 17, 2023
Kind
B2
Abstract

The following relates generally to determining effectiveness of an update to a vehicle feature. In some embodiments, information indicating an update to a vehicle feature, and accident record information may be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets.

Claims (60)

1. A computer-implemented method for use in determining effectiveness of an update to a vehicle feature, the method comprising:

obtaining, by one or more processors, vehicle data from a vehicle data repository, the vehicle data comprising a vehicle feature, and the vehicle feature being stored in an original equipment manufacturer (OEM)-agnostic terminology;

receiving, by the one or more processors, information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;

obtaining, by the one or more processors, vehicle accident record information for the vehicles having the vehicle feature, wherein the vehicle accident record information includes one or more of a number of accidents, a frequency of accidents, or a severity of accidents associated with the vehicles having the vehicle feature;

constructing, by the one or more processors, a first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;

constructing, by the one or more processors, a second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature; and

calculating, by the one or more processors, an effectiveness score of the update based upon both the first data set and the second dataset, wherein the calculating the effectiveness score comprises inputting the first dataset and the second dataset into a machine learning algorithm trained to calculate effectiveness scores of updates.

2. The computer-implemented method of claim 1 , wherein the vehicle feature is a vehicle safety feature.

3. The computer-implemented method of claim 1 , wherein:

the vehicle feature is a forward collision warning system; and

the update to the vehicle feature makes a sensitivity metric of the forward collision warning system more sensitive.

4. The computer-implemented method of claim 1 , wherein:

the vehicle accident record information further includes information of a weather condition occurring during accidents; and

the calculation of the effectiveness score is further based on the information of the weather condition occurring during the accidents.

5. The computer-implemented method of claim 4 , wherein the weather condition comprises a rain condition, a wind condition, and/or a sunlight condition.

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

determining, by the one or more processors, vehicles that the update has been implemented in; and

wherein the first dataset is constructed from the data from the vehicles having the vehicle feature from before the update was implemented; and

wherein the second dataset is constructed from the data from the vehicles having the vehicle feature from after the update was implemented.

7. The computer-implemented method of claim 1 , wherein the vehicle accident record information further includes subscription information including information of a start time of a subscription to the vehicle feature, and the method further comprises:

determining, by the one or more processors, that the vehicle feature is implemented at the start time of the subscription to the vehicle feature;

wherein the first dataset is constructed from the data from the vehicles having the vehicle feature from before the update was implemented; and

wherein the second dataset is constructed from the data from the vehicles having the vehicle feature from after the update was implemented.

8. The computer-implemented method of claim 7 , wherein the subscription information comprises information of a subscription to a hands-free driving feature.

9. The computer-implemented method of claim 1 , wherein the update to the vehicle feature comprises changes to button placement on a vehicle infotainment system.

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

receiving, by the one or more processors, insurance claims data; and

calculating, by the one or more processors, an increase or decrease in insurance premiums for vehicles having implemented the update, wherein the calculation is based upon: (i) the insurance claims data, and (ii) the effectiveness score of the update.

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

receiving, by the one or more processors, insurance claims data; and

calculating, by the one or more processors, an impact on: (i) cost of insurance claims, or (ii) amount of insurance claims for vehicles having implemented the update; and

wherein the calculation is based upon: (i) the insurance claims data, and (ii) the effectiveness score of the update.

12. The computer-implemented method of claim 1 , wherein the information indicating the update to the vehicle is received from a vehicle manufacturer, a third party aggregator, an application of a computing device of vehicle operators, or the vehicle data repository.

13. The computer-implemented method of claim 1 , wherein the calculating the effectiveness score comprises comparing the first dataset to the second dataset.

14. A computer system for use in determining effectiveness of an update to a vehicle feature, the computer system comprising one or more processors configured to:

obtain vehicle data from a vehicle data repository, the vehicle data comprising a vehicle feature, and the vehicle feature being stored in an original equipment manufacturer (OEM)-agnostic terminology;

receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;

obtain vehicle accident record information for the vehicles having the vehicle feature, wherein the vehicle accident record information includes one or more of a number of accidents, a frequency of accidents, or a severity of accidents associated with the vehicles having the vehicle feature;

construct a first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;

construct a second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature; and

calculate an effectiveness score of the update by inputting both the first data set and the second dataset into a machine learning algorithm trained to calculate effectiveness scores of updates.

15. The computer system of claim 14 , wherein the vehicle feature is a vehicle safety feature.

16. The computer system of claim 14 , wherein the one or more processors are further configured to:

determine vehicles that the update has been implemented in; and

wherein the first dataset is constructed from: (i) the data from the vehicles having the vehicle feature from before the update was implemented, and (ii) the accident record information; and

wherein the second dataset is constructed from: (i) the data from the vehicles having the vehicle feature from after the update was implemented, and (ii) the accident record information.

17. A computer system for use in determining effectiveness of an update to a vehicle feature, the computer system comprising:

one or more processors; and

a non-transitory program memory communicatively coupled to the one or more processors and storing executable instructions that, when executed by the one or more processors, cause the computer system to:

obtain vehicle data from a vehicle data repository, the vehicle data comprising a vehicle feature, and the vehicle feature being stored in an original equipment manufacturer (OEM)-agnostic terminology;

receive information indicating an update to the vehicle feature was sent to vehicles having the vehicle feature;

obtain vehicle accident record information for the vehicles having the vehicle feature, wherein the vehicle accident record information includes one or more of a number of accidents, a frequency of accidents, or a severity of accidents associated with the vehicles having the vehicle feature;

construct a first dataset with data from before the update was sent to or implemented in the vehicles having the vehicle feature;

construct a second dataset with data from after the update was sent to or implemented in the vehicles having the vehicle feature; and

calculate an effectiveness score of the update by inputting both the first data set and the second dataset into a machine learning algorithm trained to calculate effectiveness scores of updates.

18. The computer system of claim 17 , wherein the vehicle feature is a vehicle safety feature.

19. The computer system of claim 17 , wherein the instructions, when executed by the one or more processors, cause the computer system to:

determine vehicles that the update has been implemented in; and

wherein the first dataset is constructed from: (i) the data from the vehicles having the vehicle feature from before the update was implemented, and (ii) the accident record information; and

wherein the second dataset is constructed from: (i) the data from the vehicles having the vehicle feature from after the update was implemented, and (ii) the accident record information.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 3, 2023
From: SKAGGS, JAIME; THOELE, JODY ANN; GLUSICK, ANGELA
To: STATE FARM MUTUAL AUTOMOBILE INSURANCE COMPANY
Reel/Frame 065110/0256 →
Continuity (7)
Continuation 16928793 · Jul 14, 2020
Provisional Application 63349912 · Jun 7, 2022
Provisional Application 62935890 · Nov 15, 2019
Provisional Application 62905742 · Sep 25, 2019
Provisional Application 62879130 · Jul 26, 2019
Provisional Application 62874749 · Jul 16, 2019
Related Publication 20230001936A1 · Jan 5, 2023
Cited By (1)
US 12,394,253