Calculating a trust score

• US 9,578,043 B2
• Filed: 03/20/2015
• Issued: 02/21/2017
• Est. Priority Date: 03/20/2015
• Status: Active Grant

• Current Assignee: www.TrustScience.com Inc.
• Original Assignee: www.TrustScience.com Inc.
• Inventors: Mawji, Ashif, Chan, Leo M., Chrapko, Shane, Marsh, Stephen, Chrapko, Evan V
• Primary Examiner(s): POTRATZ, DANIEL B
• Application Number:
  US14/664,285
• Publication Number:
  US 20160277424A1
• Time in Patent Office: 704 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 7/026 Magnitude comparison, i.e. determining the relative order of operands based on their numerical value, e.g. window comparator
• CPC Class Codes: G06Q 20/384 using social networks
• CPC Class Codes: G06Q 20/4016 involving fraud or risk level assessment in transaction processing
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 63/1408 by monitoring network traffic monitoring network traffic per se H04L43/00

Abstract

Systems, devices, and methods are described herein for calculating a trust score. The trust score may be calculated between entities including, but not limited to, human users, groups of users, organizations, businesses/corporations, and locations. A system trust score may be calculated for an entity by combining a variety of factors, including verification data, a network connectivity score, publicly available information, and/or ratings data. A peer trust score targeted from a first entity to a second entity may also be calculated based on the above factors. In some embodiments, the peer trust score may be derived from the system trust score for the target entity and may take into account additional factors, including social network connections, group/demographic info, and location data. Finally, a contextual trust score may be calculated between the first and second entities based on a type of transaction or activity to be performed between the two entities.

Claims

1. A method for updating a trust score, the method comprising:

accessing, using processing circuitry, a database storing data associated with a network community, the data identifying paths from a first entity to a second entity;

calculating, using the processing circuitry, a network connectivity score based on the identified paths;

receiving data about the second entity from a remote source;

calculating, using the processing circuitry, a ratings score based on the received data from the remote source;

calculating, using the processing circuitry, a trust score for the second entity by calculating a weighted sum of the network connectivity score and the ratings score using a first set of weights;

receiving an indication of an activity to be performed in the future between the first entity and the second entity;

selecting a second set of weights corresponding to the activity to be performed in the future, the second set of weights being different than the first set of weights; and

recalculating, using the processing circuitry, the trust score by recalculating the weighted sum of the network connectivity score and the ratings score using the second set of weights.

2. The method of claim 1, wherein the first entity and the second entity are connected through a social network.

3. The method of claim 2, further comprising identifying one or more intermediate entities in the social network that connect the first entity to the second entity.

4. The method of claim 2, wherein calculating the network connectivity score comprises determining a number of mutual friends between the first entity and the second entity.

5. The method of claim 4, wherein calculating the network connectivity score comprises assigning the network connectivity score according to a graduated scale based on the number of mutual friends between the first entity and the second entity.

6. The method of claim 1, wherein calculating a network connectivity score based on the identified paths comprises determining whether the identified paths exceeds a threshold number of paths.

7. The method of claim 1, wherein the received data is one of:

a credit score, criminal history data, financial transaction history data, and/or business reviews data.

8. The method of claim 1, wherein the first set of weights is provided by a system administrator.

9. The method of claim 1, wherein the first set of weights is user-assigned.

10. The method of claim 1, wherein at least one of the first entity and the second entity is a human user.

11. The method of claim 1, wherein at least one of the first entity and the second entity is a business.

12. The method of claim 1, further comprising resolving a decision related to the activity based, at least in part, on the updated trust score.

13. The method of claim 1, wherein the trust score for the second entity comprises a confidence range determined based on the network connectivity score and the ratings score.

14. The method of claim 1, wherein calculating the trust score for the second entity comprises:

retrieving an initial trust score associated with the second entity; and

updating the initial trust score associated with the second entity based on the network connectivity score and the ratings score.

15. The method of claim 1, wherein the indication is received from a user device associated with the first entity or the second entity.

16. A system for updating a trust score, the system comprising:

processing circuitry configured to;

access a database storing data associated with a network community, the data identifying paths from a first entity to a second entity;

calculate a network connectivity score based on the identified paths;

receive data about the second entity from a remote source;

calculate a ratings score based on the received data from the remote source;

calculate a trust score for the second entity by calculating a weighted sum of the network connectivity score and the ratings score using a first set of weights;

receive an indication of an activity to be performed in the future between the first entity and the second entity;

select a second set of weights corresponding to the activity to be performed in the future, the second set of weights being different than the first set of weights; and

recalculate the trust score by recalculating the weighted sum of the network connectivity score and the ratings score using the second set of weights.

17. The system of claim 16, wherein the first entity and the second entity are connected through a social network.

18. The system of claim 17, wherein the processing circuitry is further configured to identify one or more intermediate entities in the social network that connect the first entity to the second entity.

19. The system of claim 16, wherein the processing circuitry is configured to calculate the network connectivity score by determining a number of mutual friends between the first entity and the second entity.

20. The system of claim 19, wherein the processing circuitry is configured to calculate the network connectivity score by assigning the network connectivity score according to a graduated scale based on the number of mutual friends between the first entity and the second entity.

21. The system of claim 16, wherein the processing circuitry is configured to calculate a network connectivity score based on the identified paths by determining whether the identified paths exceeds a threshold number of paths.

22. The system of claim 16, wherein the received data is one of:

a credit score, criminal history data, financial transaction history data, and/or business reviews data.

23. The system of claim 16, wherein the first set of weights is provided by a system administrator.

24. The system of claim 16, wherein the first set of weights is user-assigned.

25. The system of claim 16, wherein at least one of the first entity and the second entity is a human user.

26. The system of claim 16, wherein the processing circuitry is further configured to resolve a decision related to the activity based, at least in part, on the updated trust score.

27. The system of claim 16, wherein the trust score for the second entity comprises a confidence range determined based on the network connectivity score and the ratings score.

28. The system of claim 16, wherein the processing circuitry is configured to calculate the trust score for the second entity by:

retrieving an initial trust score associated with the second entity; and

updating the initial trust score associated with the second entity based on the network connectivity score and the ratings score.

29. The system of claim 16, wherein the indication is received from a user device associated with the first entity or the second entity.

30. A non-transitory computer readable medium comprising instructions encoded thereon for providing media guidance, the instructions comprising:

instructions for accessing, using processing circuitry, a database storing data associated with a network community, the data identifying paths from a first entity to a second entity;

instructions for calculating, using the processing circuitry, a network connectivity score based on the identified paths;

instructions for receiving data about the second entity from a remote source;

instructions for calculating, using the processing circuitry, a ratings score based on the received data from the remote source;

instructions for calculating, using the processing circuitry, a trust score for the second entity by calculating a weighted sum of the network connectivity score and the ratings score using a first set of weights;

instructions for receiving an indication of an activity to be performed in the future between the first entity and the second entity;

instructions for selecting a second set of weights corresponding to the activity to be performed in the future, the second set of weights being different than the first set of weights; and

instructions for recalculating, using the processing circuitry, the trust score by recalculating the weighted sum of the network connectivity score and the ratings score using the second set of weights.