Contextual data mapping, searching and retrieval

• US 20080228761A1
• Filed: 03/10/2008
• Published: 09/18/2008
• Est. Priority Date: 03/16/2007
• Status: Active Grant

• Current Assignee: 1759304 Ontario, Inc.
• Original Assignee: 1759304 Ontario, Inc.
• Inventors: Ravandi, Mehdi, Kei Leung, David Yum
• Granted Patent: US 8,266,145 B2
• Time in Patent Office: Days
• US Class Current: 707/5
• CPC Class Codes: G06F 16/90 Details of database functions independent of the retrieved data types
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06F 16/9538 Presentation of query results

Abstract

An example method is illustrated as including receiving a first content set, the first content set organized according to a rules set, using the rules set to parse the first content set to generate a first pattern set having a plurality of members, assigning a weighted value to each member of the first and a second pattern set based on a frequency of occurrence of each member in the first and second pattern sets, wherein each member of the first and second pattern sets includes digital content, and determining a relevancy score linking each of the members of the first and second pattern set in a one to one mapping of the members of the first pattern set to each of the members of the second pattern set, wherein the relevancy score is based upon the weighted value assigned to each member of the first and second pattern sets.

Claims

1. A computer implemented method comprising:

receiving a first content set, wherein the first content set is organized according to a rules set;

using the rules set to parse the first content set to generate a first pattern set having a plurality of members;

assigning a weighted value to each member of the first and a second pattern set based on a frequency of occurrence of each member in the first and second pattern sets, wherein each member of the first and second pattern sets includes digital content; and

determining a relevancy score linking each of the members of the first and second pattern sets in a one to one mapping of the members of the first pattern set to each of the members of the second pattern set, wherein the relevancy score is based upon the weighted value assigned to each member of the first and second pattern sets.

2. The computer implemented method of claim 1, further comprising storing each member of the first and second pattern sets in a data structure.

3. The computer implemented method of claim 2, further comprising executing an algorithm to find a highest relevancy score linking a member of the first pattern set and a member of the second pattern set.

4. The computer implemented method of claim 3, wherein the algorithm is a linear programming algorithm.

5. The computer implemented method of claim 1, further comprising assigning the weighted value based upon one or more weighting types including at least one of relevance weighting, relationship weighting, and review weighting.

6. The computer implemented method of claim 1, wherein the weighted value includes a Fuzzy Logic score.

7. The computer implemented method of claim 1, further comprising applying one or more constraint values to the relevancy score, wherein the constraint value is selected from a group of constraint values consisting of a fastest to locate value, an ease of access value, and a co-expected relevancy value.

8. The computer implemented method of claim 1, wherein the digital content includes at least one of web pages, on-line blog entries, on-line chat entries, on-line bulletin board postings, and social network postings.

9. The computer implemented method of claim 3, further comprising presenting the highest relevancy score for one or more members of the first and second pattern sets as an optimized model.

10. A computer system comprising:

a receiver residing on the computer system to receive a first content set, wherein the first content set is organized according to some rules set;

a parser to parse the first content set to generate a first pattern set;

an assignor residing on the computer system to assign a weighted value to each member of the first and a second pattern sets based on a frequency of occurrence of each member in the first and second pattern sets, wherein each member of the first and second pattern sets includes digital content; and

a first calculator residing on the computer system to determine a relevancy score linking each of the members of the first and second pattern sets in a one to one mapping of the members of the first pattern set to each of the members of the second pattern set, wherein the relevancy score is based upon the weighted value assigned to each member of the first and second pattern sets.

11. The computer system of claim 10, further comprising a database operatively coupled to the computer system to store each member of the first and second pattern sets in a data structure

12. The computer system of claim 11, further comprising a second calculator, residing on the computer system, to traverse the data structure using an algorithm to find a link representing a highest relevancy score between a member of the first pattern set and a member of the second pattern set.

13. The computer system of claim 12, wherein the algorithm is a linear programming algorithm.

14. The computer system of claim 10, further comprising a third calculator residing on the computer system to assign the weighted value based upon one or more weighting types including at least one of relevance weighting, relationship weighting, and review weighting.

15. The computer system of claim 10, wherein the weighted value includes a fuzzy logic score.

16. The computer system of claim 10, further comprising a fourth calculator residing on the computer system to apply one or more constraint values to the relevancy score, wherein the one or more constraint value is selected form a group of constraint values consisting of a fastest to locate value, an ease of access value, and a co-expected relevancy value.

17. The computer system of claim 10, wherein the digital content includes at least one of web pages, on-line blog entries, on-line chat entries, on-line bulletin board postings, and social network postings.

18. The computer system of claim 12, further comprising a model generator residing on the computer system to present the highest relevancy score for one or more members of the first and second pattern sets as an optimized model.

19. An apparatus comprising:

means for receiving a first content set, wherein the first content set is organized according to a rules set;

means for using the rules set to parse the first content set to generate a first pattern set having a plurality of members;

means for assigning a weighted value to each member of the first and a second pattern set based on a frequency of occurrence of each member in the first and second pattern sets, wherein each member of the first and second pattern sets includes digital content; and

means for determining a relevancy score linking each of the members of the first and second pattern sets in a one to one mapping of the members of the first pattern set to each of the members of the second pattern set, wherein the relevancy score is based upon the weighted value assigned to each member of the first and second pattern sets.

20. A machine-readable medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform the following operations:

receive a first content set, wherein the first content set is organized according to a rules set;

use the rules set to parse the first content set to generate a first pattern set having a plurality of members;

assign a weighted value to each member of the first and a second pattern set based on a frequency of occurrence of each member in the first and second pattern sets, wherein each member of the first and second pattern sets includes digital content; and

determine a relevancy score linking each of the members of the first and second pattern set in a one to one mapping of the members of the first pattern set to each of the members of the second pattern set, wherein the relevancy score is based upon the weighted value assigned to each member of the first and second pattern sets.