# US 12,002,305 B1 · Systems and methods for generating improved vehicle usage analytics based upon vehicle sensor and telematics data

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- Number: US 12,002,305 B1
- Document ID: US12002305B1
- Status: Granted
- Kind: B1
- Application number: 16/780,512
- Filed: 2020-02-03
- Granted: 2024-06-04
- Earliest claimed: 2019-08-28
- Assignees: State Farm Mutual Automobile Insurance Company
- Applicants: State Farm Mutual Automobile Insurance Company
- Inventors: Ryan Michael Gross; Joseph Robert Brannan; Brian N. Harvey
- Web page: https://patentgrep.com/patent/US12002305B1

## CPC

- G06Q 40/02: Banking, e.g. interest calculation or account maintenance (credit or loans G06Q40/03)
- G06Q 40/08: Insurance
- G06Q 10/20: Administration of product repair or maintenance
- G06Q 30/0206: {Price or cost determination based on market factors}
- G06Q 30/0215: {Including financial accounts}
- G06Q 30/0282: Rating or review of business operators or products
- G06Q 50/40: Business processes related to the transportation industry (shipping G06Q10/083)
- G07C 5/008: {communicating information to a remotely located station (transmission systems for measured values G08C)}
- G07C 5/0808: {Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005)}
- G07C 5/085: {using electronic data carriers}
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G07C: TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE

## Abstract

A vehicle analytics (VA) computing device may be configured to: (i) receive, from the connected vehicle, telematics data corresponding to the connected vehicle; (ii) identify a plurality of trips of the vehicle based upon the telematics data; (iii) store the telematics data corresponding to each trip of the plurality of trips in a database in association with a trip identifier corresponding to the trip; (iv) determine, for each of the plurality of trips, a number of passengers present in the vehicle; (v) store the determined number of passengers in the database in association with the trip identifier corresponding to the trip; and (vi) calculate an insurance premium associated with an insurance policy based upon the determined number of passengers in the vehicle.

## Claims

### Claim 1

A vehicle analytics (VA) computing device comprising a processor in communication with a memory device and a vehicle, the processor configured to:
receive, from telematics sensors associated with the vehicle, historical telematics data corresponding to the vehicle, the historical telematics data including historical sensor data collected from the vehicle;
generate, using the historical telematics data, a driver profile model for a user of the vehicle;
receive, from the telematics sensors, additional telematics data associated with a plurality of trips driven by the user using the vehicle;
train, using machine learning techniques and the additional telematics data, the driver profile model to identify patterns in driving behaviors of the user, the driving behaviors including a driving style of the user, routes driven by the user, and driving preferences of the user;
store, in a database, the trained driver profile model;
receive, from the telematics sensors, current telematics data associated with a trip taken by the vehicle;
identify the user as a driver of the vehicle during the trip by comparing the current telematics data to the trained driver profile model;
in response to identifying the user as the driver of the vehicle, output, from the trained driver profile model, (i) a driver profile of the user and (ii) driving recommendations for the user based upon the identified patterns for the user, the driving recommendations including recommendations for the user to improve driving behaviors associated with vehicle operation;
cause the driving recommendations to be displayed on a user device of the user; transmit a prompt to the user device to confirm that the user is the driver of the vehicle;
receive, from the user device, a confirmation indicating that the user is the driver;
reinforce the trained driver profile model for the user based upon the confirmation, the confirmation causing the VA computing device to add weight to the identified patterns when identifying the user as the driver; and
determine, based upon subsequent telematics data received from the telematics sensors, that the user followed the driving recommendations transmitted to the user device.

### Claim 2 (depends on 1)

The VA computing device of claim 1, wherein the processor is configured to determine a number of passengers based upon the historical sensor data received from the vehicle.

### Claim 3 (depends on 1)

The VA computing device of claim 1, wherein the telematics sensors include at least one of one or more cameras, seat sensors, active suspension systems sensors, or door sensors.

### Claim 4 (depends on 1)

The VA computing device of claim 1, wherein the processor is further configured to:
identify, for each trip of the plurality of trips, a vehicle driver of the vehicle based upon the historical telematics data;
store, in association with each trip identifier, a user identifier corresponding to the identified vehicle driver for that trip identifier;
parse, for each user identifier, the database to aggregate historical telematics data associated with that user identifier; and
generate, for each user identifier, a usage-based insurance (UBI) policy associated with that user identifier based upon the aggregated historical telematics data corresponding to that user identifier, wherein each UBI policy includes a corresponding insurance premium.

### Claim 5 (depends on 4)

The VA computing device of claim 4, wherein to identify the vehicle driver of the vehicle, the processor is configured to:
receive, from the vehicle, current sensor data;
compare the received current sensor data to the historical sensor data stored in the database in association with a candidate user identifier; and
identify the candidate user identifier as the vehicle driver of the vehicle.

### Claim 6 (depends on 5)

The VA computing device of claim 5, wherein the processor is configured to:
generate, based upon the aggregated historical telematics data associated with the candidate user identifier, a prediction of an attribute that corresponds to the historical telematics data;
verify the prediction based upon an indicator input from a vehicle user to determine that the aggregated historical telematics data actually corresponds to the attribute; and
build, based upon the determination, a driver profile model for the vehicle user by storing the attribute in association with the aggregated historical telematics data and the candidate user identifier in the database.

### Claim 7 (depends on 1)

The VA computing device of claim 1, wherein the historical sensor data includes at least one of seat position settings, radio settings, camera data, or key fob data.

### Claim 8 (depends on 1)

The VA computing device of claim 1, wherein the processor is further configured to:
cause an application running on the user device to display the driving recommendations to the user.

### Claim 9 (depends on 1)

The VA computing device of claim 1, wherein the processor is further configured to:
receive funds from the user;
transmit the funds to a financial institution to be deposited into a hybrid savings account; and
transfer, from an insurance provider to the hybrid savings account, a reward when the user follows the driving recommendations.

### Claim 10 (depends on 1)

The VA computing device of claim 1, wherein the processor is further configured to:
store the historical telematics data corresponding to each trip of the plurality of trips in the database in association with a trip identifier corresponding to the trip; and
store the identified patterns in the database in association with the trip identifier corresponding to each of the plurality of trips.

### Claim 11

A computer-implemented method performed by a vehicle analytics (VA) computing device including a processor in communication with a memory device and a vehicle, the computer-implemented method comprising:
receiving, by the VA computing device, from telematics sensors associated with the vehicle, historical telematics data corresponding to the vehicle, the historical telematics data including historical sensor data collected from the vehicle;
generating, by the VA computing device using the historical telematics data, a driver profile model for a user of the vehicle;
receiving, by the VA computing device from the telematics sensors, additional telematics data associated with a plurality of trips driven by the user using the vehicle;
training, by the VA computing device using machine learning techniques and the additional telematics data, the driver profile model to identify, by the VA computing device, patterns in driving behaviors of the user, the driving behaviors including a driving style of the user, routes driven by the user, and driving preferences of the user;
storing, by the VA computing device, in a database, the trained driver profile model;
receiving, by the VA computing device from the telematics sensors, current telematics data associated with a trip taken by the vehicle;
identifying, by the VA computing device, the user as a driver of the vehicle during the trip by comparing the current telematics data to the trained driver profile model;
in response to identifying the user as the driver of the vehicle, outputting, by the VA computing device from the trained driver profile model, (i) a driver profile of the user and (ii) driving recommendations for the user based upon the identified patterns for the user, the driving recommendations including recommendations for the user to improve driving behaviors associated with vehicle operation;
causing, by the VA computing device, the driving recommendations to be displayed on a user device of the user;
transmitting, by the VA computing device, a prompt to the user device to confirm that the user is the driver of the vehicle;
receiving, by the VA computing device from the user device, a confirmation indicating that the user is the driver;
reinforcing, by the VA computing device, the trained driver profile model for the user based upon the confirmation, the confirmation causing the VA computing device to add weight to the identified patterns when identifying the user as the driver; and
determining, by the VA computing device based upon subsequent telematics data received from the telematics sensors, that the user followed the driving recommendations transmitted to the user device.

### Claim 12 (depends on 11)

The computer-implemented method of claim 11, wherein the method further comprises determining, by the VA computing device, a number of passengers based upon the historical sensor data received from the vehicle.

### Claim 13 (depends on 11)

The computer-implemented method of claim 11, wherein the telematics sensors include at least one of one or more cameras, seat sensors, active suspension systems sensors, or door sensors.

### Claim 14 (depends on 11)

The computer-implemented method of claim 11, wherein the method further comprises:
identifying, for each trip of the plurality of trips, a vehicle driver of the vehicle based upon the historical telematics data;
storing, in association with each trip identifier, a user identifier corresponding to the identified vehicle driver for that trip identifier;
parsing, for each user identifier, the database to aggregate historical telematics data associated with that user identifier; and
generating, for each user identifier, a usage-based insurance (UBI) policy associated with that user identifier based upon the aggregated historical telematics data corresponding to that user identifier, wherein each UBI policy includes a corresponding insurance premium.

### Claim 15 (depends on 14)

The computer-implemented method of claim 14, wherein identifying the vehicle driver of the vehicle comprises:
receiving, by the VA computing device, from the vehicle, current sensor data;
comparing, by the VA computing device, the received current sensor data to the historical sensor data stored in the database in association with a candidate user identifier; and
identifying, by the VA computing device, the candidate user identifier as the vehicle driver of the vehicle.

### Claim 16 (depends on 15)

The computer-implemented method of claim 15, wherein the method further comprises:
generating, by the VA computing device, based upon the aggregated historical telematics data associated with the candidate user identifier, a prediction of an attribute that corresponds to the historical telematics data;
verifying, by the VA computing device, the prediction based upon an indicator input from a vehicle user to determine that the aggregated historical telematics data actually corresponds to the attribute; and
building, by the VA computing device based upon the determination, a driver profile model for the vehicle user by storing the attribute in association with the aggregated historical telematics data and the candidate user identifier in the database.

### Claim 17 (depends on 11)

The computer-implemented method of claim 11, wherein the historical sensor data includes at least one of seat position settings, radio settings, camera data, or key fob data.

### Claim 18 (depends on 11)

The computer-implemented method of claim 11, wherein the method further comprises:
causing, by the VA computing device, an application running on the user device to display the driving recommendations to the user.

### Claim 19 (depends on 11)

The computer-implemented method of claim 11, wherein the method further comprises:
receiving, by the VA computing device funds from the user;
transmitting, by the VA computing device, the funds to a financial institution to be deposited into a hybrid savings account; and
transferring, by the VA computing device, from an insurance provider to the hybrid savings account, a reward when the user follows the driving recommendations.

### Claim 20 (depends on 11)

The computer-implemented method of claim 11, wherein the method further comprises:
store the historical telematics data corresponding to each trip of the plurality of trips in the database in association with a trip identifier corresponding to the trip; and
store the identified patterns in the database in association with the trip identifier corresponding to each of the plurality of trips.

### Claim 21

At least one non-transitory computer-readable medium having computer-executable instructions embodied thereon, wherein when executed by a vehicle analytics (VA) computing device including a processor in communication with a memory device and a vehicle, the computer-executable instructions cause the processor to:
receive, from telematics sensors associated with the vehicle, historical telematics data corresponding to the vehicle, the historical telematics data including historical sensor data collected from the vehicle;
generate, using the historical telematics data, a driver profile model for a user of the vehicle;
receive, from the telematics sensors, additional telematics data associated with a plurality of trips driven by the user using the vehicle;
train, using machine learning techniques and the additional telematics data, the driver profile model to identify patterns in driving behaviors of the user, the driving behaviors including a driving style of the user, routes driven by the user, and driving preferences of the user;
store, in a database, the trained driver profile model;
receive, from the telematics sensors, current telematics data associated with a trip taken by the vehicle;
identify the user as a driver of the vehicle during the trip by comparing the current telematics data to the trained driver profile model;
in response to identifying the user as the driver of the vehicle, output, from the trained driver profile model, (i) a driver profile of the user and (ii) driving recommendations for the user based upon the identified patterns for the user, the driving recommendations including recommendations for the user to improve driving behaviors associated with vehicle operation;
cause the driving recommendations to be displayed on a user device of the user;
transmit a prompt to the user device to confirm that the user is the driver;
receive, from the user device, a confirmation indicating that the user is the driver of the vehicle;
reinforce the trained driver profile model for the user based upon the confirmation, the confirmation causing the VA computing device to add weight to the identified patterns when identifying the user as the driver; and
determine, based upon subsequent telematics data received from the telematics sensors, that the user followed the driving recommendations transmitted to the user device.