AUTOMATIC DATA MINING PROCESS CONTROL

• US 20110191277A1
• Filed: 08/29/2008
• Published: 08/04/2011
• Est. Priority Date: 06/16/2008
• Status: Abandoned Application

• Current Assignee: Optis Wireless Technology, LLC (Brevet Capital)
• Original Assignee: Optis Wireless Technology, LLC (Brevet Capital)
• Inventors: Renero Quintero, Jesus, Agúndez Dominguez, José Luis
• Application Number:
  US12/999,396
• Publication Number:
  US 20110191277A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 5/025 Extracting rules from data

Abstract

A data mining system includes a planning and learning module which receives as input a knowledge model and a set of goals and automatically produces as output a plurality of plans. The system includes a data mining processing unit which receives the plans as instructions and automatically creates results which are provided back to the planning and learning module as feedback. A method for data mining includes the steps of receiving as input at a planning and learning module a knowledge model and a set of goals. There is the step of automatically producing as output of the planning and learning module a plurality of plans from the input. There is the step of receiving by a data mining processing unit the plans as instructions. There is the step of automatically creating results by the data mining processing unit. There is the step of providing back to the planning and learning module the results as feedback.

Claims

1. A data mining system comprising:

a planning and learning module having a first input unit, which receives as input a knowledge model with a number of data and a set of goals, a number of planners for producing the number of plans, and a first output unit for automatically submitting the number of plans and the number of data towards a data mining processing unit; and

the data mining processing unit having an evaluator module, which chooses a plan of the number of plans to execute, a data mining module which mines the number of data based on the plan chosen by the evaluator module and automatically produces an outcome, and a reinforcement learning module which receives the outcome from the data mining module and produces and sends reinforcement learning signals to the planning and learning module as feedback, wherein the reinforcement learning signals are used to correct or reinforce either the model used by the planning and learning module, or the plans produced therein, or both.

2. A system as described in claim 1 wherein the planning and learning module includes an automated learning part which receives the feedback to correct or reinforce either the model used, or the plans, or both.

3. A system as described in claim 2 wherein the outcome from the data mining module is ranked and scored according to the plan by the reinforcement learning module and included in the reinforcement learning signals that are sent as feedback to the learning part.

4. A system as described in claim 1 wherein the planners produce the number of plans as alternative sets of instructions that, by operating on the model, achieve the goals.

5. A system as described in claim 4 wherein the data mining processing unit applies the alternative sets of instructions on the number of data.

6. A system as described in claim 5 wherein the evaluator module evaluates the alternatives to determine the most appropriate alternative to produce a result.

7. A system as described in claim 6 wherein the data mining processing unit includes a second output unit for offering the number of results.

8. A system as described in claim 7 wherein the reinforcement learning module is coupled with the second output unit to feedback the planning and learning module with the number of results, along with transitions and rewards scoring each result and usable for reinforcement learning purposes.

9. A system as described in claim 8 wherein the planning and learning module includes a second input unit for receiving from the data mining processing unit the results obtained, along with transitions and rewards scoring each result.

10. A system as described in claim 9 wherein the planners are arranged for re-computing the sets of instructions, or the existing model, or both.

11. A system as described in claim 10 wherein the first output unit is arranged for submitting the recomputed sets of instructions and the number of data towards the data mining processing unit.

12. A method for data mining comprising the steps of:

receiving as input at a planning and learning module a knowledge model with a number of data and a set of goals;

automatically producing as output of the planning and learning module a number of plans from the input;

receiving by a data mining processing unit the number of plans and the number of data;

choosing with an evaluator module of the data mining processing unit which plan of the number of plans to execute;

mining the data with a data mining module of the data mining processing unit based on the plan chosen by the evaluator module to produce an outcome by the data mining module;

producing with a reinforcement learning module reinforcement learning signals from the outcome;

providing back to the planning and learning module the reinforcement learning signals as feedback; and

using the reinforcement learning signals by the planning and learning module to correct or reinforce either the model used by the planning and learning module, or the plans produced therein, or both.

13. A method as described in claim 12 including the step of receiving by a reinforcement learning module of the data mining processing unit the outcome from the data mining module.

14. A method as described in claim 12 including the step of receiving at an automated learning part of the planning and learning module the feedback to correct or reinforce either the model used, or the plans, or both.

15. A method as described in claim 14 including the step of ranking and scoring by the reinforcement learning module the outcome from the data mining module according to the plan and including the ranked and scored outcome in the learning signals that is sent as feedback to the learning part.