Risk evaluation based on vehicle operator behavior

• US 10,121,205 B1
• Filed: 01/08/2015
• Issued: 11/06/2018
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Sanchez, Kenneth J., Chan, Aaron Scott, Baker, Matthew R., Zettinger, Matthew, Nepomuceno, John A., Fields, Brian Mark
• Primary Examiner(s): Niquette, Robert R
• Application Number:
  US14/592,265
• Time in Patent Office: 1,398 Days
• Field of Search: None
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

A method includes retrieving data about vehicle operator behavior via a computer network, wherein the data about vehicle operator behavior is generated by a motion sensing device adapted to monitor movements of a vehicle operator, and clustering the data about vehicle operator behavior into a plurality of groups of data. The method further includes determining a numerical level of risk corresponding to each of the plurality of groups of data and generating an insurance rate for an insurance product, wherein the insurance rate is consistent with the numerical levels of risk corresponding to each of the plurality of groups of data.

Claims

1. A method comprising:

generating, in a motion sensing device disposed in a vehicle and adapted to monitor movements of a vehicle operator, sensor data;

pre-processing, in an on-board computer of the vehicle, the sensor data to generate data about vehicle operator behavior, wherein pre-processing the sensor data includes identifying a plurality of types of movement of the vehicle operator;

retrieving, by a computing device disposed at a location different from a location of the vehicle, the data about vehicle operator behavior from the vehicle via a computer network;

clustering, by one or more processors of the computing device that are specifically configured by a first routine, the data about vehicle operator behavior into a plurality of groups of data, each of the plurality of groups of data representing a different one of the plurality of types of movement of the vehicle operator;

determining, by the one or more processors, a numerical level of risk corresponding to each of the plurality of groups of data by executing a learning routine specifically configured to determine the numerical level of risk based on correlations between types of movement of vehicle operators and levels of risk;

generating, by the one or more processors, an insurance rate for an insurance product, wherein the insurance rate is consistent with the numerical levels of risk corresponding to each of the plurality of groups of data; and

communicating, via the computer network, insurance costs for the insurance product according to the generated insurance rate to an owner of the insurance product.

2. The method of claim 1, further comprising:

analyzing, by the one or more processors, the data about vehicle operator behavior to identify recommendations to reduce risk; and

electronically communicating the recommendations to reduce risk to the vehicle operator.

3. The method of claim 1, wherein the learning routine is trained by executing the learning routine with a set of training data about past vehicle operator movements.

4. The method of claim 1, wherein generating the insurance rate for an insurance product consistent with the numerical levels of risk corresponding to each of the plurality of groups of data includes generating a risk index corresponding to the vehicle operator, the risk index being a collective measure of risk based on the numerical levels of risk corresponding to the plurality of groups of data.

5. The method of claim 1, wherein clustering the data about vehicle operator behavior into the plurality of groups of data includes comparisons with pre-programmed movement categories and comparisons with newly created movement categories, the newly created movement categories being created for groups of data dissimilar to the pre-programmed movement categories.

6. The method of claim 1, wherein determining, with one or more processors, the numerical level of risk corresponding to each of the plurality of groups of data includes:

determining, by executing the trained learning routine with one or more processors, whether at least some of the plurality of groups of data represent a type of behavior associated with relatively high risk in comparison to other types of behavior.

7. The method of claim 6, wherein determining whether the at least some of the plurality of groups of data represent the type of behavior associated with relatively high risk includes identifying the presence of a particular ones of the plurality of groups of data, wherein the presence of the particular ones of the plurality of groups of data within the whole of the data about vehicle operator behavior is associated with relatively high risk.

8. The method of claim 6, further comprising:

when it is determined that the at least some of the plurality of groups of data represent the type of behavior associated with relatively high risk in comparison to the other types of behavior, alerting the vehicle operator using at least one or more of an audible alert, a visual alert, or a tactile alert.

9. The method of claim 1, further comprising:

updating the training of the learning routine based on the retrieved data about vehicle operator behavior corresponding to the vehicle operator.

10. The method of claim 1, further comprising:

in response to communicating the insurance costs, receiving, via the computer network, payment for the insurance product.

11. The method of claim 1, wherein communicating the insurance costs to the owner of the insurance product includes:

generating, by the one or more processors, a risk report, the risk report including at least two of the numerical levels of risk, the generated insurance rate, the insurance costs according to the generated insurance rate, or payment instructions; and

communicating, via the computer network, the risk report to the owner of the insurance product.

12. The method of claim 1, wherein the types of movement of the vehicle operator represented by the plurality of groups of data include two or more of:

mobile device use;

vehicle feature adjustments;

eating or drinking;

smoking;

grooming and personal hygiene;

reading a map or navigation device;

reaching for objects inside the vehicle;

or

looking at objects or events outside the vehicle.

13. The method of claim 1, wherein the types of movement of the vehicle operator represented by the plurality of groups of data include three or more of:

mobile device use;

vehicle feature adjustments;

eating or drinking;

smoking;

grooming and personal hygiene;

reading a map or navigation device;

reaching for objects inside the vehicle;

or

looking at objects or events outside the vehicle.

14. A system comprising:

a motion sensing module disposed in a vehicle and configured to;

monitor movements of a vehicle operator inside of the vehicle, and

generate sensor data indicative of the monitored movements of the vehicle operator;

an on-board computer of the vehicle configured to;

pre-process the sensor data to generate data about vehicle operator behavior, at least by identifying a plurality of types of movement of the vehicle operator;

an interface disposed in the vehicle, the interface connecting the on-board computer to a computing device disposed at a location different from a location of the vehicle; and

the computing device, wherein the computing device is specially configured to;

retrieve the data about vehicle operator behavior from the on-board computer via the interface,

cluster the data about vehicle operator behavior into a plurality of groups of data, each of the plurality of groups of data representing a different one of the plurality of types of movement of the vehicle operator,

determine a numerical level of risk corresponding to each of the plurality of groups of data by executing a learning routine specifically configured to determine the numerical level of risk based on correlations between types of movement of vehicle operators and levels of risk,

generate an insurance rate for an insurance product, wherein the insurance rate is consistent with the numerical levels of risk corresponding to each of the plurality of groups of data, and

communicate, via a computer network, insurance costs for the insurance product according to the generated insurance rate to an owner of the insurance product.

15. The system of claim 14, wherein the computer device is further configured to:

analyze the data about vehicle operator behavior to identify recommendations to reduce risk, and

electronically communicate the recommendations to reduce risk to the vehicle operator.

16. The system of claim 14, wherein clustering the motion sensing data into a plurality of groups of data includes utilizing the motion sensing data to at least one of locate and track hands of the vehicle operator, separate features of the vehicle operator from background features, track joint movements of the vehicle operator, or identify particular gestures of the vehicle operator.