Graph pattern recognition interface

• US 8,341,111 B2
• Filed: 08/29/2011
• Issued: 12/25/2012
• Est. Priority Date: 11/30/2007
• Status: Active Grant

• Current Assignee: PayPal, Inc. (PayPal Holdings, Inc.)
• Original Assignee: eBay Inc.
• Inventors: Patil, Dhanurjay A. S., Jastrebski, Grahame Andrew, Miller, Allison E., Riccomini, Chris
• Primary Examiner(s): FERNANDEZ RIVAS, OMAR F
• Application Number:
  US13/220,209
• Publication Number:
  US 20110313960A1
• Time in Patent Office: 484 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 method comprising:

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

building, using at least one processor, at least one secondary network based on the pattern data of the primary network, the building including using an Artificial Intelligence (AI) algorithm that processes a pattern characteristic set by combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and

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

2. The method of claim 1, wherein the building comprises parsing the pattern data using a retrieved parsing grammar to generate the pattern characteristic set.

3. The method of claim 1, wherein the building comprises building an AI data structure using the AI algorithm, historical data;

and the pattern characteristic set.

4. The method of claim 3, further comprising classifying the AI data structure based on a historical taxonomy.

5. The method of claim 1, wherein the building comprises applying a learning paradigm to historical data, the learning paradigm being a selection from the group consisting of supervised learning, unsupervised learning, and reinforcement learning.

6. The 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.

7. The method of claim 1, wherein a transaction data comprises at least one of sales transaction data or a payment transaction data.

16. The method of claim 1, further comprising:

determining whether diversity is established based on execution of the crossover or mutation function; and

re-executing the combining of the selected historical data and the pattern characteristic set in response to a determination that diversity is not established.

8. A system comprising:

a processor of a machine;

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

a building engine to build, using the processor of the machine, at least one secondary network based upon the pattern data of the primary network, the building, engine to build by using an Artificial Intelligence (AI) algorithm that processes a pattern characteristic set by combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and

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

17. The system of claim 8, wherein the building engine is further to:

determine whether diversity is established based on execution of the crossover or mutation function, and

re-execute the combining of the selected historical data and the pattern characteristic set in response to a determination that diversity is not established.

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

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

building at least one secondary network based on the pattern data of the primary network, the building including using an Artificial Intelligence (AI) algorithm that processes a pattern characteristic set by combining selected historical data and the pattern characteristic set based on a crossover or mutation function; and

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

10. The non-transitory machine-readable medium of claim 9, wherein the building comprises parsing the pattern data using a retrieved parsing grammar to generate the pattern characteristic set.

11. The non-transitory machine-readable medium of claim 9, wherein the building comprises building an AI data structure using the AI algorithm, historical data, and the pattern characteristic set.

12. The non-transitory machine-readable medium of claim 11, wherein the operation further comprise classifying the AI data structure based on a historical taxonomy.

13. The non-transitory machine-readable medium of claim 9, wherein the building comprises applying a learning paradigm to historical data, the learning paradigm being a selection from the group consisting of supervised learning, unsupervised learning, and reinforcement learning.

14. The non-transitory machine-readable medium of claim 9, wherein the operations further comprise 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.

15. The non-transitory machine-readable medium of claim 9, wherein a transaction data comprises at least one of sales transaction data or a payment transaction data.

18. The non-transitory machine-readable medium if claim 9, wherein the operations further comprise:

determining whether diversity is established based on execution of the crossover or mutation function; and

re-executing the combining of the selected historical data and the pattern characteristic set in response to a determination that diversity is not established.