Analysis of third party networks

• US 8,161,062 B2
• Filed: 05/11/2010
• Issued: 04/17/2012
• Est. Priority Date: 02/12/2002
• Status: Active Grant

• Current Assignee: Mantas, Inc. (Oracle Corporation)
• Original Assignee: Mantas, Inc. (Oracle Corporation)
• Inventors: Zhang, Tao, Donoho, Steven Kirk
• Primary Examiner(s): Colbert, Ella
• Application Number:
  US12/777,988
• Publication Number:
  US 20100223199A1
• Time in Patent Office: 707 Days
• Field of Search: 705/1, 705/1.1, 705/14, 705/14.26, 705/14.51, 705/26, 705/26.1, 705/35, 705/39, 705/40, 705/36, 705/36.R, 705/348, 709/201, 709/203, 709/217, 709/218, 707/607, 707/776, 707/780, 707/800, 707/950, 707/760
• US Class Current: 707/760
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/06 Buying, selling or leasing transactions
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: Y10S 707/95 Financial

Abstract

A method of analyzing customer behavior, where customers are engaged in customer-to-customer transactions in the third-party network, includes the transformation of data representing the customer-to-customer transactions from a data representation to a network representation, and then analyzing the network representation. The network representation includes a set of nodes and a set of links where each node represents a customer and each link represents a transaction between two of the customers.

Claims

1. A method of analyzing characteristics or behaviors of customers engaged in customer-to-customer transactions in a third party network, the method comprising:

transforming data representative of a plurality of individual customer-to-customer transactions from a data representation to a network representation;

analyzing the network representation, wherein the analyzing comprises;

identifying at least one transaction pattern within the network representation; and

identifying transactions that correspond to a pattern and transactions that deviate from the pattern; and

performing third-party network analysis on the network representation, wherein the performing comprises;

analyzing third-party networks, wherein each third-party network represents a group of customers connected through customer-to-customer transactions, wherein the analyzing comprises identifying internal characteristics and behaviors of each network.

2. The method of claim 1, further including the step of building one or more third-party networks corresponding to customer-to-customer transactions.

3. The method of claim 2, wherein each third-party network comprises a plurality of nodes and a plurality of links, wherein each node is associated with at least one link and each link is associated with at least two nodes.

4. The method of claim 3, wherein each node in a network represents a member, customer or individual involved in third-party business activity.

5. The method of claim 3, wherein each link represents a connection or transaction between two member customers.

6. The method of claim 2, wherein each third-party network represents a group of customers connected through customer-to-customer transactions.

7. The method of claim 2, wherein each network has internal link pattern characteristics corresponding to interactions and connections between member customers in the network.

8. The method of claim 2, wherein each network has external network pattern characteristics, representing interactions and relationships between a network and external entities outside the network.

9. The method of claim 1, wherein the step of analyzing third-party networks identifies external characteristics and behaviors of each network by treating each network as a network object.

10. The method of claim 1, wherein the performing step further comprises the step of network mining using one or more mining algorithms.

11. The method of claim 10, wherein the network mining step comprises using decision tree mining to store third-party network patterns in nodes.

12. The method of claim 11, wherein at least one of the nodes stores a subset of networks having similar group pattern behaviors.

13. The method of claim 10, wherein the network mining step comprises using association rule mining to find networks having important association relationships with external patterns outside networks.

14. The method of claim 10, wherein the network mining step comprises using clustering group networks having similar network properties.

15. The method of claim 10 wherein the network mining step comprises detecting transactions that correspond to a pattern.

16. The method of claim 10 wherein the network mining step comprises detecting transactions that deviate from a pattern.

17. A non-transitory computer-readable medium of analyzing characteristics or behaviors of customers engaged in customer-to-customer transactions in a third party network, having sets of instructions stored thereon which, when executed by a computer, cause the computer to:

transform data representative of a plurality of individual customer-to-customer transactions from a data representation to a network representation;

analyze the network representation, wherein the analyzing comprises;

identifying at least one transaction pattern within the network representation; and

identifying transactions that correspond to a pattern and transactions that deviate from the pattern; and

perform third-party network analysis on the network representation, wherein the performing comprises;

analyzing third-party networks, wherein each third-party network represents a group of customers connected through customer-to-customer transactions, wherein the analyzing comprises identifying internal characteristics and behaviors of each network.

18. A system for analyzing characteristics or behaviors of customers engaged in customer-to-customer transactions in a third party network, the system comprising:

a memory device; and

a processor coupled with the memory device, wherein the memory device has sets of instructions stored thereon which, when execute by the processor, cause the processor to;

transform data representative of a plurality of individual customer-to-customer transactions from a data representation to a network representation;

analyze the network representation, wherein the analyzing comprises;

identifying at least one transaction pattern within the network representation; and

identifying transactions that correspond to a pattern and transactions that deviate from the pattern; and

perform third-party network analysis on the network representation, wherein the performing comprises;

analyzing third-party networks, wherein each third-party network represents a group of customers connected through customer-to-customer transactions, wherein the analyzing comprises identifying internal characteristics and behaviors of each network.