Granted Patent B2

US 12,632,904 · App. 18/816,233 · Granted May 19, 2026

Inventors: Ryan Michael Gross (Normal, IL); Joseph Robert Brannan (Bloomington, IL); Brian N. Harvey (Bloomington, IL)

Assignee: State Farm Mutual Automobile Insurance Company

G06Q40/08G06Q10/20G06Q30/0206G06Q30/0282G06Q50/40G07C5/008G07C5/0808G07C5/085

Abstract

Claims (41)

1 . A data analytics computing device in communication with a transportation network company (“TNC”) computing device that is associated with a TNC, and a vehicle computing device associated with a vehicle that at least periodically operates as part of the TNC, the data analytics computing device having at least one processor in communication with at least one memory, the at least one processor configured to:

train, using one or more machine learning programs, a driving model of a driver of the vehicle by applying to the driver model (i) telematics data collected while the driver is operating the vehicle, and (ii) ratings of the driver while operating the vehicle as part of the TNC;

receive additional telematics data and environment data indicative of current environmental conditions of an area where the vehicle is being operated;

receive, in real-time from the TNC computing device, vehicle usage data indicative of a current demand of vehicles as compared to a current supply of vehicles operating as part of the TNC;

further train, using the one or more machine learning programs, the driving model by applying the additional telematics data, the environment data, and the vehicle usage data to the driving model;

determine, in real-time, personalized coverage for the driver currently operating the vehicle as part of the TNC by executing, in real-time, the further trained driving model; and

transmit, to a user computing device associated with the driver, the personalized coverage.

2 . The data analytics computing device of claim 1 , wherein the at least one processor is further configured to receive the telematics data from the vehicle computing device, the telematics data associated with operation of the vehicle and collected by a plurality of sensors associated with the vehicle.

3 . The data analytics computing device of claim 1 , wherein the at least one processor is further configured to generate, using the one or more machine learning programs, the driving model.

4 . The data analytics computing device of claim 1 , wherein the at least one processor is further configured to:

receive additional ratings of the driver for a more recent operation of the vehicle; and

further train the driving model by applying the additional ratings to the driving model.

5 . The data analytics computing device of claim 1 , wherein rating of the driver and additional ratings of the driver are associated with a condition of the vehicle.

6 . The data analytics computing device of claim 1 , wherein the at least one processor is further configured to generate the driving model by overlaying ratings of the driver with data associated with a geographic region including a route for a ride provided to a passenger of the vehicle.

7 . A computer-implemented method using a data analytics computing device in communication with a transportation network company (“TNC”) computing device that is associated with a TNC, and a vehicle computing device associated with a vehicle that at least periodically operates as part of the TNC, the data analytics computing device having at least one processor in communication with at least one memory, the method comprising:

training, using one or more machine learning programs, a driving model of a driver of the vehicle by applying to the driver model (i) telematics data collected while the driver is operating the vehicle, and (ii) ratings of the driver while operating the vehicle as part of the TNC;

receiving additional telematics data and environment data indicative of current environmental conditions of an area where the vehicle is being operated;

receiving, in real-time from the TNC computing device, vehicle usage data indicative of a current demand of vehicles as compared to a current supply of vehicles operating as part of the TNC;

further training, using the one or more machine learning programs, the driving model by applying the additional telematics data, the environment data, and the vehicle usage data to the driving model;

determining, in real-time, personalized coverage for the driver currently operating the vehicle as part of the TNC by executing, in real-time, the further trained driving model; and

transmitting, to a user computing device associated with the driver, the personalized coverage.

8 . The computer-implemented method of claim 7 further comprising receiving the telematics data from the vehicle computing device, the telematics data associated with operation of the vehicle and collected by a plurality of sensors associated with the vehicle.

9 . The computer-implemented method of claim 7 further comprising generating, using the one or more machine learning programs, the driving model.

10 . The computer-implemented method of claim 7 further comprising:

receiving additional ratings of the driver for a more recent operation of the vehicle; and

further training the driving model by applying the additional ratings to the driving model.

11 . The computer-implemented method of claim 7 , wherein rating of the driver and additional ratings of the driver are associated with a condition of the vehicle.

12 . The computer-implemented method of claim 7 further comprising generating the driving model by overlaying ratings of the driver with data associated with a geographic region including a route for a ride provided to a passenger of the vehicle.

13 . At least one non-transitory computer-readable storage medium having computer-executable instructions embodied thereon, when executed by at least one processor of a data analytics computing device in communication with a transportation network company (“TNC”) computing device that is associated with a TNC, and a vehicle computing device associated with a vehicle that at least periodically operates as part of the TNC, the at least one processor in communication with at least one memory, the computer-executable instructions cause the at least one processor to:

train, using one or more machine learning programs, a driving model of a driver of the vehicle by applying to the driver model (i) telematics data collected while the driver is operating the vehicle, and (ii) ratings of the driver while operating the vehicle as part of the TNC;

receive additional telematics data and environment data indicative of current environmental conditions of an area where the vehicle is being operated;

receive, in real-time from the TNC computing device, vehicle usage data indicative of a current demand of vehicles as compared to a current supply of vehicles operating as part of the TNC;

further train, using the one or more machine learning programs, the driving model by applying the additional telematics data, the environment data, and the vehicle usage data to the driving model;

determine, in real-time, personalized coverage for the driver currently operating the vehicle as part of the TNC by executing, in real-time, the further trained driving model; and

transmit, to a user computing device associated with the driver, the personalized coverage.

14 . The at least one non-transitory computer-readable storage medium of claim 13 , wherein the computer-executable instructions further cause the at least one processor to receive the telematics data from the vehicle computing device, the telematics data associated with operation of the vehicle and collected by a plurality of sensors associated with the vehicle.

15 . The at least one non-transitory computer-readable storage medium of claim 13 , wherein the computer-executable instructions further cause the at least one processor to generate, using the one or more machine learning programs, the driving model.

16 . The at least one non-transitory computer-readable storage medium of claim 13 , wherein the computer-executable instructions further cause the at least one processor to:

receive additional ratings of the driver for a more recent operation of the vehicle; and

further train the driving model by applying the additional ratings to the driving model.

17 . The at least one non-transitory computer-readable storage medium of claim 13 , wherein the computer-executable instructions further cause the at least one processor to generate the driving model by overlaying ratings of the driver with data associated with a geographic region including a route for a ride provided to a passenger of the vehicle.

Assignments (1)

Continuity (7)

References Cited (136)