MACHINE LEARNING GENERATED ACTION PLAN

• US 20140358828A1
• Filed: 01/23/2014
• Published: 12/04/2014
• Est. Priority Date: 05/29/2013
• Status: Abandoned Application

• Current Assignee: PurePredictive, Inc.
• Original Assignee: PurePredictive, Inc.
• Inventors: Phillipps, Kelly D., Wellman, Richard W.
• Application Number:
  US14/162,571
• Publication Number:
  US 20140358828A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06Q 30/0241 Advertisements

Abstract

Apparatuses, systems, methods, and computer program products are disclosed for a machine learning generated action plan. A machine learning module is configured to process different instances of data using machine learning to produce one or more results. The different instances of data may comprise different values for one or more actionable features. A recommended action module is configured to select one or more recommended actions for achieving a goal associated with the machine learning. The recommended action module may select the one or more recommended actions based on the one or more results. An action plan interface module is configured to provide an action plan associated with the one or more recommended actions.

Claims

1. A method for an action plan, the method comprising:

processing different instances of data using machine learning to produce one or more results, the different instances of data comprising different values for one or more actionable features;

selecting one or more recommended actions based on the one or more results; and

providing an action plan associated with the one or more recommended actions.

2. The method of claim 1, wherein the one or more recommended actions are selected for achieving a goal associated with the machine learning.

3. The method of claim 1, wherein the one or more recommended actions are selected based on one or more of a confidence metric associated with the one or more results and a predicted outcome associated with the one or more recommended actions.

4. The method of claim 1, further comprising varying values of one or more non-actionable features for the different instances of data.

5. The method of claim 4, wherein the one or more recommended actions include a recommendation for data collection associated with the one or more non-actionable features.

6. The method of claim 1, further comprising storing the one or more results in a results data structure indexed by one or more of the one or more actionable features and the different instances of data.

7. The method of claim 6, further comprising dynamically updating the provided action plan based on the stored one or more results in response to user input changing one or more machine learning parameters associated with the action plan.

8. The method of claim 7, wherein the one or more machine learning parameters comprise one or more of:

a value for an actionable feature;

a recommended action;

a target for a recommended action;

a count for a recommended action;

an action time for a recommended action;

a cost for a recommended action; and

a predicted outcome for a recommended action.

9. The method of claim 1, wherein the action plan comprises one or more lists of target subjects for the one or more recommended actions, the target subjects in the one or more lists ordered by confidence metrics generated by the machine learning for the target subjects.

10. The method of claim 1, wherein processing the different instances comprises predetermining, using the machine learning, permutations of the one or more results at predefined increments between one or more minimum values for the one or more actionable features and one or more maximum values for the one or more actionable features.

11. The method of claim 1, further comprising increasing one or more of a range of and a frequency of the different values for the one or more actionable features until the one or more results satisfy a predefined threshold.

12. The method of claim 11, wherein the one or more results satisfy the predefined threshold in response to determining a predictable relationship between at least one set of adjacent results.

13. The method of claim 1, further comprising generating the machine learning, the machine learning comprising program code for a plurality of learned functions from multiple machine learning classes, the program code generated to predict the one or more results based on the different instances of data.

14. The method of claim 13, wherein the machine learning comprises a different predictive program for each different recommended action, the different instances of data processed based on one or more dependent relationships between the different predictive programs.

15. The method of claim 1, further comprising fitting the one or more recommended actions to an action function, the action plan comprising the action function.

16. The method of claim 15, wherein the action function interpolates recommended actions between the one or more recommended actions.

17. An apparatus for an action plan, the apparatus comprising:

a machine learning module configured to process different instances of data using machine learning to produce one or more results, the different instances of data comprising different values for one or more features, the one or more results comprising at least one of a confidence metric and a predicted outcome;

a recommended action module configured to select one or more recommended actions for achieving a goal associated with the machine learning, the recommended action module selecting the one or more recommended actions based on the one or more results; and

an action plan interface module configured to provide an action plan associated with the one or more recommended actions.

18. The apparatus of claim 17, further comprising a pre-compute module configured to store the one or more results in a results data structure indexed by the one or more features and the different instances of data.

19. The apparatus of claim 18, further comprising an update module configured to dynamically update the provided action plan based on the stored one or more results in response to user input changing one or more machine learning parameters associated with the action plan.

20. The apparatus of claim 17, further comprising a predictive compiler module configured to generate the machine learning, the machine learning comprising program code for a plurality of learned functions from multiple machine learning classes, the program code generated to predict the one or more results based on the different instances of data.

21. An apparatus for an action plan, the apparatus comprising:

means for processing different instances of data using machine learning to produce one or more results, the different instances of data comprising different values for one or more features;

means for selecting one or more recommended actions based on the one or more results; and

means for providing an action plan comprising the one or more recommended actions and one or more target subjects for the one or more recommended actions.

22. The apparatus of claim 21, further comprising means for generating the machine learning, the machine learning comprising program code for a plurality of learned functions from multiple machine learning classes, the program code generated to predict the one or more results based on the different instances of data.