Methods to determine a vehicle insurance premium based on vehicle operation data collected via a mobile device

• US 10,402,907 B2
• Filed: 02/08/2013
• Issued: 09/03/2019
• Est. Priority Date: 06/29/2011
• Status: Active Grant

• Current Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Original Assignee: State Farm Mutual Automobile Insurance Company (State Farm)
• Inventors: Bowne, Benjamin F., Peng, Jufeng "James", Baker, Nicholas R., Marzinzik, Duane Lee, Riley, Sr., Matthew Eric, Christopulos, Nick U., Fields, Brian Mark, Wilson, J Lynn, Wilkerson, Bryan T., Thurber, David W.
• Primary Examiner(s): Hamilton, Sara C
• Application Number:
  US13/763,231
• Publication Number:
  US 20130179198A1
• Time in Patent Office: 2,398 Days
• Field of Search: 705 4, 705 35
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G01C 21/3676 Overview of the route on the road map
• CPC Class Codes: G01C 21/3697 Output of additional, non-guidance related information, e.g. low fuel level G01C21/3679 takes precedence
• CPC Class Codes: G01S 19/13 Receivers
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 10/0833 Tracking
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G07C 5/008 communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/08 Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
• CPC Class Codes: G07C 5/0858 wherein the data carrier is removable
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/026 using orientation information, e.g. compass
• CPC Class Codes: H04W 4/40 for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

A method for determining a vehicle insurance premium for a period of time based at least in part on collected vehicle operation data, wherein a remote computer receives the collected vehicle operation data from a mobile device and calculates an insurance premium based at least in part on the collected vehicle operation data. A method for determining a vehicle insurance premium for a period of time based at least in part on collected vehicle operation data, wherein a server receives collected vehicle operation data, a database stores the collected vehicle operation data, and a rating engine determines a vehicle insurance premium based at least in part on collected vehicle operation data.

Claims

1. A method for identifying and displaying at least one notable driving event, the method comprising:

collecting, by a processor of a mobile device and via one or more accelerometers integrated in the mobile device, raw driving data corresponding to movements of a vehicle during a driving data collection session, the collected raw driving data comprising G-force data;

time-stamping by the processor of the mobile device, the collected raw driving data comprising the G-force data during the driving data collection session;

calculating, by the processor of the mobile device, a moving average of the G-force data at each time stamp based on the G-force data at time stamps within a period of time during the driving data collection session;

determining, by the processor of the mobile device, a G-force percentile at each time stamp by comparing the calculated moving average of G-force data at each time stamp to a G-force percentile in a look-up table accessible by the mobile device;

calculating, by the processor of the mobile device, jerk data at each time stamp during the driving data collection session as an absolute value of, a difference between the calculated moving average of G-force data at each time stamp and the calculated moving average of G-force data at a prior time stamp and the difference divided by an amount of time between the time stamps;

calculating, by the processor of the mobile device, a moving average of jerk data at each time stamp based on the jerk data at time stamps within a period of time during the driving data collection session;

determining, by the processor of the mobile device, a jerk percentile at each time stamp by comparing the calculated moving average of jerk data at each time stamp to a jerk percentile in a look-up table accessible by the mobile device;

calculating, by the processor of the mobile device, a driving score based on an average of the G-force percentiles at time stamps during the driving data collection session and an average of the jerk percentiles at time stamps during the driving data collection session;

identifying, by the processor of the mobile device, at least one notable driving event by comparing the G-force data and the jerk data at each time stamp to threshold values, wherein for each respective time stamp a notable driving event is identified when an absolute value of the G-force data is greater than a G-force threshold value, or the absolute value of the jerk data is greater than a jerk data threshold value;

displaying, by the processor of the mobile device, the driving score on a display of the mobile device; and

displaying, by the processor of the mobile device, the at least one notable driving event on the display of the mobile device.

2. The method of claim 1, wherein displaying the driving score comprises displaying the driving score immediately after the driving data collection session is concluded.

3. The method of claim 1, wherein the displaying the at least one-a notable driving event comprises displaying the at least one notable driving event immediately after the driving data collection session is concluded.

4. The method of claim 1, further comprising transmitting, by the processor of the mobile device, at least one of:

the time-stamped raw driving data;

the moving average G-force data;

the moving average jerk data;

the driving score; and

the at least one notable driving event.

5. The method of claim 1, wherein the G-force data comprises a lateral G-force and a longitudinal G-force, further comprising classifying, by the processor of the mobile device, the at least one notable driving event:

as left cornering when the lateral G-force is greater than 0;

as right cornering when the lateral G-force is less than 0;

as accelerating when the longitudinal G-force is less than 0; and

as decelerating when the longitudinal G-force is greater than 0.

6. The method of claim 1, wherein calculating the driving score further comprises comparing the collected raw driving data to driving behavior metrics collected during a different driving data collection session including a trip by a different driver, the driving behavior metrics stored in a memory accessible by the mobile device.

7. The method of claim 5, further comprising displaying, by the processor of the mobile device, positive and negative impact on fuel efficiency corresponding to the classified at least one notable driving event.

8. The method of claim 5, further comprising generating, by the processor of the mobile device, a notification to a monitor device that the classified at least one notable driving event and the calculated driving score are available for display without accessing the mobile device, and transmitting, by the processor of the mobile device, the notification to the monitor device.