Contextual data mapping, searching and retrieval

• US 8,266,145 B2
• Filed: 03/10/2008
• Issued: 09/11/2012
• Est. Priority Date: 03/16/2007
• Status: Expired due to Fees

• Current Assignee: 1759304 Ontario, Inc.
• Original Assignee: 1759304 Ontario, Inc.
• Inventors: Ravandi, Mehdi, Leung, David Yum Kei
• Primary Examiner(s): Chbouki, Tarek
• Application Number:
  US12/075,207
• Publication Number:
  US 20080228761A1
• Time in Patent Office: 1,646 Days
• Field of Search: 707/736
• US Class Current: 707/736
• 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 and represents a relationship between the members of the first and second pattern sets,

wherein determining includes using at least one of a contextual pattern operation;

a structural relationship operation;

a perspective operation;

or combinations thereof; and

wherein assigning a weighted value determining the relevancy score includes applying a formula of P_n=Σ

f_ip_i/n, where;

n is the total number of known patterns in the system;

i is 0 to n;

p_i is 1 if the bit pattern or co-related patterns exists; and

f is the fuzziness of a pattern towards another pattern, and wherein determining a relevancy score includes performing a union function on the weighted values.

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

6. 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.

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

8. 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.

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

10. The method of claim 1, wherein determining the relevancy score includes performing a logical intersection of the weighted value between the members of the first and second pattern set, applying a minimization function to the logical intersection to produce a minimized output, and applying a maximization function to the minimized output to produce the relevancy score.

11. A computer system comprising:

a receiver residing on the computer system to receive a first content set and store the first content set in a memory, 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 and including a processor 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;

a first calculator, including a processor, 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 and represents a relationship between the members of the first and second pattern sets, wherein the first calculator uses at least one of a contextual pattern operation;

a structural relationship operation;

a perspective operation;

or combinations thereof; and

wherein the assignor is to assign the weighted value determining the relevancy score by applying a formula of Pn=Σ

fipi/n, where;

n is the total number of known patterns in the system;

i is 0 to n;

pi is 1 if the bit pattern or co-related patterns exists; and

f is the fuzziness of a pattern towards another pattern, and wherein determining a relevancy score includes performing a union function on the weighted values.

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

13. The computer system of claim 12, further comprising a second calculator, residing on the computer system and including a processor, 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.

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

15. The computer system of claim 13, further comprising a model generator, residing on the computer system and including a processor, to present the highest relevancy score for one or more members of the first and second pattern sets as an optimized model.

16. The computer system of claim 11, further comprising a third calculator, residing on the computer system and including a processor, to assign the weighted value based upon one or more weighting types including at least one of relevance weighting, relationship weighting, and review weighting.

17. The computer system of claim 11, wherein the weighted value includes a fuzzy logic score.

18. The computer system of claim 11, further comprising a fourth calculator, residing on the computer system and including a processor, to apply one or more constraint values to the relevancy score, wherein the one or more 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.

19. The computer system of claim 11, 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.

20. The computer system of claim 11, wherein the first calculator is to calculate using all of a total number of known patterns in the system, whether a bit pattern or co-related pattern exists, fuzziness value of one pattern to another pattern, and a union function.

21. An apparatus comprising:

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

means for using the rules set and a processor 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, wherein the means for assigning includes a processor; 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 and represents a relationship between the members of the first and second pattern sets, wherein the means for determining includes a processor;

wherein the means for assigning a weighted value determining the relevancy score includes means for applying a formula of P_n=Σ

f_ip_i/n, where;

n is the total number of known patterns in the system;

i is 0 to n;

p_i is 1 if the bit pattern or co-related patterns exists; and

f is the fuzziness of a pattern towards another pattern, and wherein determining a relevancy score includes performing a union function on the weighted values.

22. A non-transitory machine-readable storage 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 and represents a relationship between the members of the first and second pattern sets;

wherein the assign operation includes applying a formula of P_n=Σ

f_ip_i/n, where;

n is the total number of known patterns in the system;

i is 0 to n;

p_i is 1 if the bit pattern or co-related patterns exists; and

f is the fuzziness of a pattern towards another pattern, and wherein determining a relevancy score includes performing a union function on the weighted values.