System and process for automatically explaining probabilistic predictions

• US 20020180805A1
• Filed: 05/24/2001
• Published: 12/05/2002
• Est. Priority Date: 05/24/2001
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Rounthwaite, Robert, Chickering, David Maxwell, Heckerman, David E.
• Granted Patent: US 6,831,663 B2
• Time in Patent Office: Days
• US Class Current: 715/812
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

Optimizing storage and retrieval of monitoring data. In one aspect of this disclosure, there is a system, method and computer readable medium that stores instructions for instructing a computer system, to optimize storage and retrieval of data. In this embodiment, a transfer manager component acquires the data from an archive and assigns predetermined storage values to specified parameters that form the data structure of the acquired data. A database stores the data acquired by the transfer manager component in accordance with the predetermined storage values. A middle tier component extracts the data in the database and interpolates the data in accordance with the predetermined storage values.

Claims

1. A computer-readable medium having computer executable instructions for determining an influence of at least one possible user choice on at least one prediction of likely user choices, said computer executable instructions comprising:

predicting at least one likely user choice based on a probabilistic model and a set of preferences for the user; and

determining the influence of at least one of the possible user choices on the prediction of at least one likely user choice.

2. The computer-readable medium of claim 1 wherein the set of user preferences is determined implicitly.

3. The computer-readable medium of claim 1 wherein the set of user preferences is determined explicitly.

4. The computer-readable medium of claim 1 wherein determining the influence of at least one of the possible user choices comprises calculating explanation scores for each set of at least one predictor/variable value pairs for each prediction of likely user choices.

5. The computer-readable medium of claim 4 wherein each explanation score is sorted in order from highest to lowest.

6. The computer-readable medium of claim 5 wherein a predetermined number of highest value explanation scores from the sorted explanation scores are provided via a user interface.

7. The computer-readable medium of claim 5 wherein a predetermined number of highest value explanation scores from the sorted explanation scores are automatically provided as part a natural language explanation for explaining each prediction of likely user choices.

8. The computer-readable medium of claim 5 wherein a predetermined number of lowest value explanation scores from the sorted explanation scores are provided via a user interface.

9. The computer-readable medium of claim 5 wherein all of the explanation scores are provided via a user interface.

10. A system for automatically assigning scores to members of a set of at least one predictor/variable value pair representing likely user choices, comprising:

obtaining a set of user preferences for a particular user;

computing predictor/variable value pairs for each possible user choice using the set of user preferences in combination with a probabilistic model;

computing at least one prediction of likely user choices based upon the predictor/variable value pairs;

calculating explanation scores for at least one of the predictor/variable value pairs for each prediction; and

determining the influence of at least one member of the set of predictor/variable value pairs for each prediction based upon the scores calculated for the predictor/variable value pairs.

11. The system of claim 10 wherein the set of user preferences is obtained by examining a set of responses to a set of questions provided to the particular user.

12. The system of claim 10 wherein the set of user preferences is obtained by automatically examining at least one pattern of behavior of the particular user.

13. The system of claim 10 wherein the set of user preferences is obtained automatically by using a combination of implicit and explicit methods for determining the preferences of the particular user.

14. The system of claim 10 wherein the probabilistic model is automatically generated by examining a behavior of a group of at least one user.

15. The system of claim 10 wherein the probabilistic model is automatically generated using machine learning techniques.

16. The system of claim 10 wherein at least one explanation score is provided via a user interface as part of a natural language explanation for explaining why each prediction was made.

18. The method of claim 17 wherein the set of user preferences is provided by user input via a user interface.

19. The method of claim 17 wherein the set of user preferences is provided by examining Internet web browsing behavior of a user.

20. The method of claim 17 wherein the probabilistic model is automatically learned using a set of behavioral data from a set of at least one user.

21. The method of claim 20 wherein the probabilistic model is automatically learned using machine learning techniques.

22. The method of claim 17 wherein the influence of at least one possible user choice is provided as a natural language explanation via a user interface.

17. A method for identifying positive and negative influences on recommendations of likely user choices, comprising:

automatically computing predictor/variable value pairs for each possible user choice using a set of user preferences in combination with a probabilistic model;

automatically providing at least one recommendation of likely user choices based upon the predictor/variable value pairs;

automatically calculating an influence of each possible user choice for each recommendation based upon scores calculated for the predictor/variable value pairs.