Graph pattern recognition interface

• US 8,046,324 B2
• Filed: 12/21/2007
• Issued: 10/25/2011
• Est. Priority Date: 11/30/2007
• Status: Active Grant

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: eBay Inc.
• Inventors: Riccomini, Chris, Patil, Dhanurjay A. S., Miller, Allison E., Jastrebski, Grahame Andrew
• Primary Examiner(s): FERNANDEZ RIVAS, OMAR F
• Application Number:
  US11/963,452
• Publication Number:
  US 20090144213A1
• Time in Patent Office: 1,404 Days
• Field of Search: None
• US Class Current: 706/62
• CPC Class Codes: G06F 18/40 Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/043 based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
• CPC Class Codes: G06N 3/044 Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/049 Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
• CPC Class Codes: G06N 3/088 Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/047 Pattern matching networks; Rete networks
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

In some example embodiments, a system and method are illustrated as including receive pattern data that includes transaction data relating to transactions between persons. Next, the system and method may include building at least one secondary network based upon the pattern data. Additionally, the system and method may include displaying the at least one secondary network.

Claims

1. A computer implemented method comprising:

receiving pattern data of a primary network that includes transaction data relating to transactions between persons;

building, using one or more processors, at least one secondary network based on the pattern data of the primary network, the building includes building the at least one secondary network by mapping manual patterns to the pattern data, the manual patterns being stored patterns that closely correspond to a pattern of the pattern data; and

graphically displaying, using nodes and edges, the primary network and the at least one secondary network.

2. The computer implemented method of claim 1, wherein the building includes building the at least one secondary network using an Artificial Intelligence (A.I.) algorithm that processes at least one of a pattern characteristic set or historical data.

3. The computer implemented method of claim 1, wherein the displaying includes displaying a plurality of secondary networks in a matrix.

4. The computer implemented method of claim 1, further comprising storing the at least one secondary network into a data store as a manual pattern, the manual pattern being a stored pattern that closely corresponds to a pattern of the pattern data.

5. The computer implemented method of claim 4, wherein the manual pattern includes a pattern selected by a user.

6. The computer implemented method of claim 1, further comprising requesting a quick reference matrix based upon the pattern data.

7. The computer implemented method of claim 1, wherein the secondary network includes at least one of node or edge data generated through an analysis of the pattern data.

8. The computer implemented method of claim 1, wherein a transaction data includes at least one of a sales transaction data or a payment transaction data.

9. A computer system comprising:

a receiver to receive pattern data of a primary network that includes transaction data relating to transactions between persons;

a building engine to build at least one secondary network based upon the pattern data of the primary network, the building engine includes building the at least one secondary network by mapping manual patterns to the pattern data, the manual patterns being stored patterns that closely correspond to a pattern of the pattern data; and

a display to graphically display, using nodes and edges, the pattern data of the primary network and the at least one secondary network.

10. The computer system of claim 9, wherein the building engine builds the at least one secondary network with an Artificial Intelligence (A.I.) algorithm that processes at least one of a pattern characteristic set or historical data.

11. The computer system of claim 9, wherein the display includes a plurality of secondary networks configured within a matrix.

12. The computer system of claim 9, further comprising a storage engine to store the at least one secondary network into a data store as a manual pattern, the manual pattern being a stored pattern that closely corresponds to a pattern of the pattern data.

13. The computer system of claim 12, wherein the manual pattern includes a pattern selected by a user.

14. The computer system of claim 9, further comprising a request engine to process a request for a quick reference matrix based upon the pattern data.

15. The computer system of claim 9, wherein the secondary network includes at least one of node or edge data generated through an analysis of pattern data.

16. The computer system of claim 9, wherein the transaction data includes at least one of a sales transaction data or a payment transaction data.

17. An apparatus comprising:

means for receiving pattern data of a primary network that includes transaction data relating to transactions between persons;

means for building, using one or more processors, at least one secondary network based on the pattern data of the primary network, the building engine includes building the at least one secondary network by mapping manual patterns to the pattern data, the manual patterns being stored patterns that closely correspond to a pattern of the pattern data; and

means for graphically displaying, using nodes and edges, the pattern data of the primary network and the at least one secondary network.

18. A non-transitory machine-readable medium comprising instructions, which when implemented by one or more machines cause the one or more machines to perform operations comprising:

receiving pattern data of a primary network that includes transaction data relating to transactions between persons;

building at least one secondary network based on the pattern data of the primary network, the building engine includes building the at least one secondary network by mapping manual patterns to the pattern data, the manual patterns being stored patterns that closely correspond to a pattern of the pattern data; and

graphically displaying, using nodes and edges, display the pattern data of the primary network and the at least one secondary network.