Method and system for allowing multiple applications to utilize customized feedback with a shared machine learning engine

• US 20070239630A1
• Filed: 02/28/2006
• Published: 10/11/2007
• Est. Priority Date: 02/28/2006
• Status: Abandoned Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Sow, Daby Mousse, Davis, John Sidney
• Application Number:
  US11/363,238
• Publication Number:
  US 20070239630A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A method (and system) of using a machine learning infrastructure to support the learning requirements of a plurality of goal-oriented applications includes instantiating a machine learning infrastructure that includes a plurality of learning algorithms with an interface to the algorithms, and registering a plurality of goal-oriented applications with the machine learning infrastructure

Claims

1. A method of using a machine learning infrastructure to support the learning requirements of a plurality of goal-oriented applications, the method comprising:

instantiating a machine learning infrastructure that includes a plurality of learning algorithms with an interface to said algorithms; and

registering a plurality of goal-oriented applications with said machine learning infrastructure.

2. The method of claim 1, further comprising:

registering a plurality of users with said machine learning infrastructure, wherein each of said registered goal-oriented applications is associated with at least one registered user.

3. The method of claim 2, further comprising:

generating at least one pattern for each user with said machine learning infrastructure for each of said goal-oriented applications.

4. The method of claim 1, wherein said machine learning infrastructure comprises a single reinforcement learning feedback facility for said machine learning infrastructure.

5. The method of claim 1, further comprising:

associating each of said goal-oriented applications with a learning classification.

6. The method of claim 5, further comprising:

grouping said goal-oriented applications according to said associated learning classifications; and

transforming said grouped goal-oriented applications into a common list.

7. The method of claim 6, further comprising:

using values of said goal-oriented applications according to said common list to route information into a reinforcement learning facility of said machine learning infrastructure.

8. The method of claim 2, further comprising:

automatically determining a best learning algorithm to apply for each relevant pair of said registered users and applications.

9. The method of claim 1, wherein said machine learning infrastructure comprises a single machine learning middleware infrastructure.

10. A signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus to perform the method of claim 1.

11. A method of using a machine learning infrastructure to support a plurality of goal-oriented applications, the method comprising:

instantiating a machine learning infrastructure comprising a machine learning algorithm, wherein said plurality of goal-oriented applications simultaneously use said machine learning algorithm.

12. The method of claim 11, further comprising:

registering said plurality of goal-oriented applications with said machine learning infrastructure.

13. The method of claim 11, further comprising:

registering a plurality of users with said machine learning infrastructure, wherein each of said goal-oriented applications is associated with at least one registered user.

14. The method of claim 13, further comprising:

generating a pattern for each user with said machine learning infrastructure for each of said goal-oriented applications.

15. The method of claim 11, further comprising:

grouping said goal-oriented applications according to learning classifications associated with each of said goal oriented applications; and

transforming said grouped goal-oriented applications into a common list.

16. The method of claim 15, further comprising:

using values of said goal-oriented applications according to said common list to route information into said machine learning algorithm of said machine learning infrastructure.

17. A system for using a machine learning infrastructure to support a plurality of goal-oriented applications, comprising:

a machine learning engine, said plurality of goal-oriented applications being registered with said machine learning engine,

wherein said machine learning engine comprises a pattern recognition unit that identifies patterns in an event log based on said plurality of goal-oriented applications.

18. The system of claim 17, further comprising:

a data aggregation unit that aggregates event data received from a plurality of data sources and generates said event log based on said event data received from said data sources.

19. The system of claim 18, further comprising:

a pattern matching unit that receives event data from said data aggregation unit, compares said received event data with said patterns identified by the machine learning engine, and initiates execution of one of said goal-oriented applications upon correlation of said received event data with a pattern identified by said machine learning engine to be associated with said one of said goal-oriented applications.

20. The system of claim 19, further comprising:

a feedback unit that receives feedback data regarding said one of said goal-oriented applications initiated by said pattern matching unit and updates said pattern associated with said one of said goal-oriented applications based on said feedback data.