COMBINING NETWORK ANALYSIS AND PREDICTIVE ANALYTICS

• US 20160127195A1
• Filed: 11/05/2014
• Published: 05/05/2016
• Est. Priority Date: 11/05/2014
• Status: Active Grant

• Current Assignee: Fair Isaac Corporation
• Original Assignee: Fair Isaac Corporation
• Inventors: Ripley, John R., Calvert, Brian E., Betron, Michael
• Granted Patent: US 9,660,869 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 7/36 Combined merging and sorting
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04W 4/21 for social networking applications

Abstract

Records are obtained from sources and typed datasets are assembled based on the obtained records. Relationships are identified among the typed datasets and at least one network comprising nodes and edges are assembled based on the identified relationships. A network analyzer assembles a vector and the vector is passed to analytics. The analytics then generate an output that is provided to a user or other device in the form of data.

Claims

1. A method for implementation by one or more data processors forming part of at least one computing system, the method comprising:

obtaining, by at least one data processor, a plurality of records from a plurality of sources;

assembling, by at least one data processor, a plurality of typed datasets based on the obtained records;

identifying, by at least one data processor, relationships among the typed datasets;

assembling, by at least one data processor, at least one network comprising nodes and edges, the assembling based on the identified relationships;

assembling, by at least one data processor, a vector by using a network analyzer;

passing, by at least one data processor, each vector to at least one analytic;

generating, by at least one data processor, by the at least one analytic, an output; and

providing, by at least one data processor, data comprising the output.

2. The method of claim 1, wherein providing the data comprises at least one of:

displaying the data, transmitting the data to a remote node, storing the data, or loading the data into a memory.

3. The method of claim 1, wherein the model is a predictive analytic.

4. The method of claim 1, wherein the model is a decision analytic.

5. The method of claim 1, wherein the model is a descriptive analytic.

6. The method of claim 1, wherein the at least one analytic includes a node analytic.

7. The method of claim 1, wherein the at least one analytic includes an edge analytic.

8. The method of claim 1, wherein the at least one analytic includes a network analytic.

9. The method of claim 1 further comprising:

reducing, by at least one data processor, the dimensionality of the vector to allow the at least one analytic to process the vector.

10. The method of claim 1 further comprising:

passing, by at least one data processor, the output back, as input, into the at least one analytic based on a determination by the at least one analytic that the output is to be passed back into the at least one analytic.

11. The method of claim 1 further comprising, by at least one data processor, sorting the output into a sorted output.

12. The method of claim 11 further comprising assigning, by at least one data processor, the output into a plurality of categories based on the sorting.

13. A non-transitory computer program product storing instructions which, when executed by at least one data processor forming part of at least one computing system, result in operations comprising:

obtaining, by at least one data processor, a plurality of records from a plurality of sources;

assembling, by at least one data processor, a plurality of typed datasets based on the obtained records;

identifying, by at least one data processor, relationships among the typed datasets;

assembling, by at least one data processor, at least one network comprising nodes and edges, the assembling based on the identified relationships;

assembling, by at least one data processor, a vector by using a network analyzer;

passing, by at least one data processor, each vector to at least one analytic;

generating, by at least one data processor, by the at least one analytic, an output; and

providing, by at least one data processor, data comprising the output.

14. The method of claim 13, wherein providing the data comprises at least one of:

displaying the data, transmitting the data to a remote node, storing the data, or loading the data into a memory.

15. The method of claim 13 further comprising:

reducing, by at least one data processor, the dimensionality of the vector to allow the at least one analytic to process the vector.

16. The method of claim 13 further comprising:

passing, by at least one data processor, the output back, as input, into the at least one analytic based on a determination by the at least one analytic that the output is to be passed back into the at least one analytic.

17. A method for implementation by one or more data processors forming part of at least one computing system, the method comprising:

obtaining, by at least one data processor, a plurality of records from a plurality of sources;

assembling, by at least one data processor, a plurality of typed datasets based on the obtained records;

identifying, by at least one data processor, relationships among the typed datasets;

assembling, by at least one data processor, at least one network comprising nodes and edges, the assembling based on the identified relationships;

assembling, by at least one data processor, a vector by using a network analyzer;

passing, by at least one data processor, each vector to at least one analytic;

generating, by at least one data processor, by the at least one analytic, an output; and

providing, by at least one data processor, data comprising the output.

18. The method of claim 17, wherein providing the data comprises at least one of:

displaying the data, transmitting the data to a remote node, storing the data, or loading the data into a memory.

19. The method of claim 17 further comprising:

reducing, by at least one data processor, the dimensionality of the vector to allow the at least one analytic to process the vector.

20. The method of claim 17 further comprising:

passing, by at least one data processor, the output back, as input, into the at least one analytic based on a determination by the at least one analytic that the output is to be passed back into the at least one analytic.