METHOD AND SYSTEM TO COMPARE DATA ENTITIES

• US 20080250064A1
• Filed: 01/15/2008
• Published: 10/09/2008
• Est. Priority Date: 01/17/2007
• Status: Active Grant

• Current Assignee: Aptima Inc.
• Original Assignee: Aptima Inc.
• Inventors: Duchon, Andrew, Foster, Pacey, Kelton, Kari, Orvis, Kara
• Granted Patent: US 7,822,750 B2
• Time in Patent Office: Days
• US Class Current: 707/103Y
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

The present invention relates in general to methods and systems for comparing and maximizing the appropriateness of a first set of one or more data objects to a set of second data objects. In one embodiment, the first set of data objects represent one or more tasks to be fulfilled by a set of capabilities represented by the second data objects. In one embodiment, this invention provides an effective and accurate method and system to compare and maximize the appropriateness between the requirements of a task and the second set'"'"'s capabilities, while these capabilities and requirements are contained, even if only latently, in data objects such as written documents, electronic databases or other sources of data and information. In one embodiment, topic modeling techniques are utilized to compare the data objects.

Claims

1. A computer-based method for comparing a plurality of data objects comprising the steps of:

providing a first data object;

providing a plurality of second data objects;

applying a topic model technique to the first data object and the plurality of second data objects creating a topic model;

grouping the plurality of second data objects creating a plurality of groupings;

determining a similarity of the first data object and the plurality of groupings;

determining an optimum similarity of the first data object and the plurality of groupings; and

representing the optimum similarity.

2. The computer-based method of claim 1 wherein the topic model technique comprises Probabilistic Latent Semantic Analysis (PLSA).

3. The computer-based method of claim 1 wherein the topic model technique comprises Latent Dirichlet Allocation (LDA).

4. The computer-based method of claim 1 wherein the step of applying a topic model technique further comprises pre-processing the first data object and the plurality of second data objects whereby the data objects are normalized.

5. The computer-based method of claim 1 further comprising:

providing at least one third data object; and

applying the topic model technique to the first data object, the plurality of second data objects and the at least one third data object to create the topic model.

6. The computer-based method of claim 5 wherein the step of applying the topic model technique further comprises pre-processing the first data object, the plurality of second data object and the at least one third data object whereby the data objects are normalized.

7. The computer-based method of claim 1 wherein the step of determining a similarity of the first data object and the plurality of groupings comprises applying a similarity metric.

8. The computer-based method of claim 1 wherein the first data object comprises at least one requirement and the plurality of second data objects comprises a plurality of capabilities of a plurality of individuals.

9. The computer-based method of claim 1 wherein the step of determining the optimum similarity of the first data object and the plurality of groupings comprises determining the smallest number of the second data objects to maximize the similarity of the first data object.

10. A computer-based method for comparing a plurality of data objects comprising the steps of:

providing a first data object;

providing a plurality of second data objects;

applying a topic model technique to the first data object and the plurality of second data objects creating a topic model;

grouping the plurality of second data objects creating a plurality of groupings;

determining a first similarity of the first data object and the plurality of groupings;

determining a second similarity of the plurality of second data objects in each of the plurality of groupings;

comparing the first similarity and the second similarity to determine an optimum similarity of the first data object and the plurality of groupings; and

representing the optimum similarity.

11. The computer-based method of claim 10 wherein the topic model technique comprises Probabilistic Latent Semantic Analysis (PLSA).

12. The computer-based method of claim 10 wherein the topic model technique comprises Latent Dirichlet Allocation (LDA).

13. The computer-based method of claim 10 wherein the step of applying a topic model technique further comprises pre-processing the first data object and the plurality of second data objects whereby the data objects are normalized.

14. The computer-based method of claim 10 further comprising:

providing at least one third data object; and

applying the topic model technique to the first data object, the plurality of second data objects and the at least one third data object to create the topic model.

15. The computer-based method of claim 14 wherein the step of applying the topic model technique further comprises pre-processing the first data object, the plurality of second data object and the at least one third data object whereby the data objects are normalized.

16. The computer-based method of claim 10 wherein the step of determining the first similarity and the second similarity comprises applying a similarity metric.

17. The computer-based method of claim 10 wherein the first data object comprises at least one requirement and the plurality of second data objects comprises a plurality of capabilities of a plurality of individuals.

18. The computer-based method of claim 10 wherein the step of determining the optimum similarity of the first data object and the plurality of groupings comprises minimizing the second similarity and maximizing the first similarity.

19. A computer-based method for comparing a plurality of data objects comprising the steps of:

providing a plurality of first data objects;

providing a plurality of second data objects;

applying a topic model technique to the plurality of first data objects and the plurality of second data objects creating a topic model;

grouping the plurality of second data objects creating a plurality of groupings;

determining a first similarity of each of the plurality of first data objects and each of the plurality of groupings;

determining a second similarity of the plurality of second data objects in each of the plurality of groupings;

comparing the first similarity and the second similarity to determine a plurality of optimum similarities of the plurality of first data objects and the plurality of groupings; and

representing the plurality of optimum similarities.

20. The computer-based method of claim 19 wherein the topic model technique comprises Probabilistic Latent Semantic Analysis (PLSA).

21. The computer-based method of claim 19 wherein the topic model technique comprises Latent Dirichlet Allocation (LDA).

22. The computer-based method of claim 19 wherein the step of applying a topic model technique further comprises pre-processing the plurality of first and second data objects whereby the data objects are normalized.

23. The computer-based method of claim 19 further comprising:

providing at least one third data object; and

applying the topic model technique to the first data object, the plurality of second data objects and the at least one third data object to create the topic model.

24. The computer-based method of claim 23 wherein the step of applying the topic model technique further comprises pre-processing the first data object, the plurality of second data objects and the at least one third data object whereby the data objects are normalized.

25. The computer-based method of claim 19 wherein the step of determining the first similarity and the second similarity comprises applying at least one similarity metric.

26. The computer-based method of claim 19 wherein the first plurality of data objects comprises a plurality of requirements and the plurality of second data objects comprises a plurality of capabilities of a plurality of individuals.

27. The computer-based method of claim 19 wherein the step of determining the plurality of optimum similarities of the plurality of first data objects and the plurality of groupings comprises minimizing the second similarity and maximizing the first similarity.

28. The computer-based method of claim 19 wherein the step of representing the plurality of optimum similarities further comprises representing a ranking of the plurality of optimal similarities.

29. The computer-based method of claim 28 wherein the ranking of the plurality of optimal similarities comprises ranking the plurality of optimal similarities to maximize a superset of optimal similarities.

30. A computer readable medium having stored thereon a computer program that, when executed, causes a computer to perform the steps of:

providing a first data object;

providing a plurality of second data objects;

applying a topic model technique to the first data object and the plurality of second data objects creating a topic model;

grouping the plurality of second data objects creating a plurality of groupings;

determining a similarity of the first data object and the plurality of groupings;

determining an optimum similarity of the first data object and the plurality of groupings; and

representing the optimum similarity.