GENERATING DESCRIPTIONS OF MATCHING RESOURCES BASED ON THE KIND, QUALITY, AND RELEVANCE OF AVAILABLE SOURCES OF INFORMATION ABOUT THE MATCHING RESOURCES

• US 20080270394A1
• Filed: 07/11/2008
• Published: 10/30/2008
• Est. Priority Date: 09/17/2002
• Status: Active Grant

• Current Assignee: R2 Solutions LLC (Acacia Research Corporation)
• Original Assignee: Chad Carson, Dmitri Pavlovski, Douglas Cook, Mohan V. Nibhanupudi, Robert Meyers
• Inventors: Pavlovski, Dmitri, Carson, Chad, Nibhanupudi, Mohan V., Meyers, Robert, Cook, Douglas
• Granted Patent: US 8,745,044 B2
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access

Abstract

Techniques are provided for generating descriptions of matching resources in a manner that takes into account the kind, quality, and relevance of the available sources of information about the matching resources. For example, after the search engine identifies matching resources based on the query terms, the search engine determines the kinds of available sources of information about each matching resource. For each matching resource, based on the kinds of available sources of information about the matching resource, one of a plurality of processes is selected to generate a description for the matching resource. Using the content-sensitive description generation techniques described herein, a single result set may include abstracts that were generated using several different processes, where the difference in process corresponds to a difference in the kind, quality, and relevance of the available sources of information about each matching resource.

Claims

1. A method of generating a composite abstract, the method comprising:

based on a proportion of (a) words that are contained in both a first text string and a first data set relative to (b) words that are contained in the first text string but not in the first data set, determining whether to remove the first text string from a first set of one or more text strings;

based on a proportion of (a) words that are contained in both a second text string and a second data set relative to (b) words that are contained in the second text string but not in the second data set, determining whether to remove the second text string from a second set of one or more text strings; and

combining the set of sentences with the set of text strings.

2. The method of claim 1, wherein:

the first data set contains the second set of text strings and a title; and

the second data set contains the first set of text strings and the title.

3. The method of claim 1, wherein the second text string is a last text string that occurs in an abstract.

4. The method of claim 1, wherein each text string in the first set of text strings satisfies specified criteria.

5. The method of claim 1, further comprising removing a part of the first set of text strings, wherein said part is contained in the second set of text strings.

6. The method of claim 1, further comprising removing the second text string from the second set of text strings if words from the second text string that occur in the second data set comprise less than a specified percentage of words that are contained in the second text string.

7. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 1.

8. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 2.

9. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 3.

10. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 4.

11. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 5.

12. A computer-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, causes the one or more processors to perform the method recited in claim 6.