System and method for assessing risk through a social network

• US 9,996,811 B2
• Filed: 12/10/2014
• Issued: 06/12/2018
• Est. Priority Date: 12/10/2013
• Status: Active Grant

• Current Assignee: Credit Karma LLC (Intuit, Inc.)
• Original Assignee: Zendrive, Inc.
• Inventors: Matus, Jonathan, Risbood, Pankaj
• Primary Examiner(s): Apple, Kirsten S
• Application Number:
  US14/566,408
• Publication Number:
  US 20150161538A1
• Time in Patent Office: 1,280 Days
• Field of Search: 705 35
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A method for assessing risk through a social network includes receiving user social network data, generating a risk map from the user social network data, and calculating a risk assessment based on the risk map and the user social network data.

Claims

1. A method for calculating driver risk based on a GPS sensor and social network data to improve location sensor-based determination of the driver risk associated with a motor vehicle, the method comprising:

connecting to an account, associated with a user, on a social networking platform through an API;

receiving user social network data at a web hosted service system from the social networking platform, comprising a first list of entities connected to the user through the social networking platform;

collecting GPS data received at the GPS sensor of a mobile device comprising the GPS sensor, a processor, a display, and a wireless communication transceiver, wherein the GPS data is indicative of driving habits of the user;

wirelessly receiving the GPS data at the web hosted service system from the mobile device, where the web hosted service system comprises a computer processor system;

calculating, by the computer processor system of the web hosted service system, a local driving pattern risk factor metric based on the GPS data, wherein calculating comprises;

extracting an average route length from the GPS data, and

computing a value of the local driving pattern risk factor metric based on the average route length, wherein the value is positively correlated with the average route length;

calculating, by the computer processor system of the web hosted service system, a network risk factor metric using a set of network risk factor weightings based on the user social network data, wherein calculating comprises;

computing a value of each of the set of network risk factor weightings, wherein the set of network risk factor weightings and the list of entities defines a one to one correspondence between the value and an entity of the list of entities, wherein the value is proportional to an interaction frequency between the user and the entity;

computing a total value of the network risk factor metric, wherein the total value is proportional to a weighted sum over the list of entities of a set of individual risk factor metrics associated with the list of entities, wherein the set of individual risk factor metrics and the list of entities defines a one to one correspondence, wherein each summand of the weighted sum is weighted by the value of each of the set of network risk factor weightings;

controlling the computer processor system of the web hosted service system to generate a driver risk map based on the local driving pattern risk factor metric and the network risk factor metric;

using the driver risk map to calculate a driver risk assessment at the computer processor system of the web hosted service system, thereby improving quality of the driver risk assessment through using a combination of the local driving pattern risk metric and the network risk factor metric; and

in response to calculating the driver risk assessment, controlling a query interface to remotely provide the driver risk assessment.

2. The method of claim 1, wherein the driver risk assessment is an assessment of risk for motor vehicle insurance.

3. The method of claim 2, further comprising receiving historical logging data from a telematic application;

wherein calculating the driver risk assessment comprises calculating the driver risk assessment based on the historical logging data.

4. The method of claim 3, wherein historical logging data includes user driving pattern data.

5. The method of claim 1, wherein the driver risk assessment is an assessment of risk for health insurance.

6. The method of claim 1, further comprising receiving self-reported user social network data.

7. The method of claim 6, wherein self-reported user social network data comprises a second list of entities;

wherein the second list of entities is selected by the user from the first list of entities.

8. The method of claim 7, wherein network risk factors are evaluated for individual entities.

9. The method of claim 8, wherein network risk factors are evaluated only for individual entities of the second list of entities.

10. The method of claim 7, wherein network risk factors are evaluated for entity groups.

11. The method of claim 7, further comprising calculating network risk factor weightings based on a social network structure of the user.

12. The method of claim 11, wherein social network structure comprises order of connections and number of mutual connections for entities of the first list of entities.

13. The method of claim 8, further comprising calculating network risk factor weightings for each entity of the second list of entities based on order of connections and number of mutual connections.

14. The method of claim 12, further comprising calculating network risk factor weightings for each entity of the first list of entities based on order of connections and number of mutual connections.

15. The method of claim 10, further comprising calculating network risk factor weightings for entity groups.

16. The method of claim 1, wherein receiving the user social network data comprises receiving user social network data according to a set of permissions set by a user through the social networking platform.

17. The method of claim 1, further comprising processing user social network data to determine relationship context.

18. The method of claim 9, further comprising processing user social network data to determine user interests.

19. The method of claim 1, wherein the mobile device comprises a set of motion sensors mounted at the mobile device during the driving of the motor vehicle by the driver, and wherein the method comprises:

wirelessly receiving, at the web hosted service system, motion sensor data sampled at the set of motion sensors; and

in response to wirelessly receiving the motion sensor data at the web hosted service system, controlling the computer processor system of the web hosted service system to determine an orientation of the mobile device relative the motor vehicle based on the motion sensor data, wherein calculating the local driving pattern risk factor metrics comprises calculating the local driving pattern risk factor metrics based on the GPS data and the motion sensor data, thereby facilitating the improvement of the quality of the driver risk assessment.

20. A method for calculating risk based on location sensors and social network data to improve location sensor-based determination of the risk, the method comprising:

receiving user social network data at a web hosted service system from a set of users;

collecting GPS data received at GPS sensors of mobile devices, each comprising a GPS sensor of the GPS sensors, a processor, a display, and a wireless communication transceiver, wherein the GPS data is indicative of driving habits of the set of users;

wirelessly receiving the GPS data at the web hosted service system from the mobile device, where the web hosted service system comprises a computer processor system;

calculating, by the computer processor system of the web hosted service system, local driving pattern risk factor metrics based on the GPS data, wherein calculating comprises;

extracting an average route length associated with each of the set of users from the GPS data, and

computing a value of the local driving pattern risk factor metric associated with each of the set of users based on the average route length, wherein the value is positively correlated with the average route length;

calculating, by the computer processor system of the web hosted service system, network risk factor metrics using network risk factor weightings based on the user social network data, wherein calculating comprises;

computing a value of a network risk factor weighting associated with each of the set of users, wherein the value is proportional to an interaction frequency between each of the set of users and each other user of the set of users;

computing a value of a network risk factor metric associated with each of the set of users, wherein the value is proportional to a weighted sum over the set of users of a set of individual risk factor metrics associated with each of the set of users, wherein the set of individual risk factor metrics and the set of users defines a one to one correspondence, wherein the weighted sum is weighted by the value of the network risk factor weighting associated with each of the set of users;

controlling the computer processor system of the web hosted service system to generate a risk map for all users of the set of users based on the local driving pattern risk factor metrics and the network risk factor metrics;

calculating a risk assessment for each user of the set of users based on the risk map and the user social network data; and

in response to a request for a driver risk assessment for a target user, controlling a query interface to remotely provide the driver risk assessment.

21. The method of claim 20, wherein receiving user social network data comprises receiving from each user of the set of users a list of users;

wherein calculating the risk assessment for each user comprises calculating the risk assessment for each user based on the list of users selected by each user.

22. The method of claim 21, wherein the list of users is a list of drivers;

wherein the risk assessment is an assessment of risk for motor vehicle insurance.