OPTIMIZATION-BASED DATA CONTENT DETERMINATION

• US 20070239670A1
• Filed: 06/14/2007
• Published: 10/11/2007
• Est. Priority Date: 10/20/2004
• Status: Active Grant

• Current Assignee: LinkedIn Corporation (Microsoft Corporation)
• Original Assignee: International Business Machines Corporation
• Inventors: Aggarwal, Vikram, Zhou, Michelle
• Granted Patent: US 7,809,720 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: Y10S 707/946 Real estate

Abstract

Improved data content determination techniques are disclosed for use in accordance with information-seeking systems. For example, in one illustrative aspect of the invention, a technique for determining data content for a response to a query comprises obtaining a user query, and dynamically determining data content suitable for generating a response to the query, wherein data content determination is modeled as an optimization operation which attempts to balance context-based selection constraints. Further, the step of dynamically determining data content may further comprise modeling the context-based selection constraints as feature-based metrics. The feature-based metrics may be formulated using contextual information. Still further, the step of dynamically determining data content may further comprise performing the optimization operation such that one or more desirability metrics are maximized and one or more cost metrics are minimized, thus balancing the various constraints.

Claims

1. A method of determining data content for a response to a query, comprising the steps of:

obtaining a user query; and

dynamically determining data content suitable for generating a response to the query, wherein data content determination is modeled as an optimization operation which attempts to balance context-based selection constraints.

2. The method of claim 1, wherein the step of dynamically determining data content further comprises modeling the context-based selection constraints as feature-based metrics.

3. The method of claim 2, wherein the step of dynamically determining data content further comprises the step of the feature-based metrics measuring a presentation desirability value and a cost value.

4. The method of claim 3, wherein the step of the feature-based metrics measuring a presentation desirability value further comprises the step of using one or more data content properties.

5. The method of claim 4, wherein the step of using one or more data content properties further comprises using at least one of data volume, data availability and data informativeness.

6. The method of claim 3, wherein the step of the feature-based metrics measuring a presentation desirability value further comprises the step of using one or more data relationships.

7. The method of claim 6, wherein the step of using one or more data relationships further comprises using a data dependency.

8. The method of claim 3, wherein the step of the feature-based metrics measuring a presentation desirability value further comprises the step of using user information.

9. The method of claim 8, wherein the step of using user information further comprises using user interests.

10. The method of claim 3, wherein the step of the feature-based metrics measuring a presentation desirability value further comprises the step of using environment information.

11. The method of claim 10, wherein the step of using environment information further comprises using data associated with at least one of device features and availability.

12. The method of claim 3, wherein the step of the feature-based metrics measuring a presentation desirability value further comprises the step of using a conversation history.

13. The method of claim 12, wherein the step of using a conversation history further comprises using a sequence of previous user queries and system responses.

14. The method of claim 3, wherein the step of the feature-based metrics measuring a cost value further comprises the step of measuring a cost associated with a visual presentation.

15. The method of claim 3, wherein the step of the feature-based metrics measuring a cost value further comprises the step of measuring a cost associated with a spoken response.

16. The method of claim 2, wherein the step of dynamically determining data content further comprises the step of formulating the feature-based metrics using contextual information.

17. The method of claim 16, wherein the step of the feature-based metrics using contextual information further comprises the contextual information comprising at least one of query information, a conversation history, and a user model.

18. The method of claim 1, wherein the step of dynamically determining data content further comprises the step of performing the optimization operation such that one or more desirability metrics are maximized and one or more cost metrics are minimized.

19. Apparatus for determining data content for a response to a query, comprising:

a memory; and

at least one processor coupled to the memory and operative to obtain a user query, and dynamically determine data content suitable for generating a response to the query, wherein data content determination is modeled as an optimization operation which attempts to balance context-based selection constraints.

20. An article of manufacture for determining data content for a response to a query, comprising a machine readable medium containing one or more programs which when executed implement the steps of:

obtaining a user query; and

dynamically determining data content suitable for generating a response to the query, wherein data content determination is modeled as an optimization operation which attempts to balance context-based selection constraints.

21. A method of providing a service for determining data content for a response to a query, comprising the step of:

a service provider, in response to an obtained query, enabling the step of dynamically determining data content suitable for generating a response to the query, wherein data content determination is modeled as an optimization operation which attempts to balance context-based selection constraints.