Database system and method for retrieving records from a record library

• US 20060259475A1
• Filed: 04/12/2006
• Published: 11/16/2006
• Est. Priority Date: 05/10/2005
• Status: Abandoned Application

• Current Assignee: Word Data Corporation
• Original Assignee: Word Data Corporation
• Inventors: Dehlinger, Peter J.
• Application Number:
  US11/403,280
• Publication Number:
  US 20060259475A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/907 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/93 Document management systems

Abstract

Disclosed are a computer-readable code, system and method for retrieving one or more records stored in electronic form in a library of records. The program that executes the method accesses a database table to identify, from user-generated information, one or more phrases likely to be contained in or associated with a record of interest, and from these phrase(s), identifies one or more phrase-related tags. The program uses the one or more tags so identified to find, independent of user input, test tags associated with those already identified, and to present to the user the number of records associated with the test tags, allowing the user to find records based on the inclusion of known tags and associated phrases.

Claims

1. A computer database method for finding a record of interest in a library of records characterized by distinction subsets of tag descriptors, comprising

(a) accessing a database table to identify, from user-generated information, one or more tag-descriptive phrases likely to be contained in or associated with a record of interest,

(b) from the phrase(s) identified in step (a), identifying one or more tags associated with the identified phrase(s),

(c) accessing a tag-affinity database table to identify test tags associated in the library records with those identified in step (b),

(d) accessing a database table of searchable tags, to generate for each of the test tags identified in step (c), data related to the number of library records containing in or associated with that test tag and the tags identified in step (b), and

(e) presenting the number-of-records data generated in (d) to a user.

2. The method of claim 1, wherein step (a) includes the steps of (ai) accessing a word-records database table composed of searchable words, and for each word in said table, a list of identifiers of phrases containing that word, to identify from a user-generated, word-based query, those phrases having the highest element overlap with the query words, and (ai) presenting those highest-overlap phrases to the user, for user selection of one or more phrases.

3. The method of claim 2, wherein step (b) includes accessing a phrase database table composed of phrase identifiers, and for each phrase identifier, a list of one or more tags associated with that phrase, to identify one or more tags associated with the phrase(s) identified in step (a).

4. The method of claim 3, wherein the phrase database table further includes, for each phrase identifier, the actual phrase associated with each phrase identifier, and step (a) includes accessing the searchable-phrase table to retrieve and present to the user, the actual phrase(s) associated with the identified phrase identifier(s).

5. The method of claim 1, wherein steps (a) and (b) are carried out iteratively, prior to step (c), where each successive iteration yields one or more newly identified phrases and associated tags to add to the previously identified phrases and associated tags from all previous iterations.

6. The method of claim 5, wherein at each iteration, there is displayed along with those phrases identified in step (a), the number of library records containing both previously identified and newly identified tags, where the iterations of steps (a) and (b) are continued until the number of records containing the selected and identified citations is desirably small.

7. The method of claim 1, wherein the affinity database table accessed in step (c) is a t×

t matrix of all tags t associated with said records, and the matrix values for each word pair in the matrix is related to the number occurrence of both tags in the pair in said records.

8. The method of claim 1, wherein step (d) includes (d1) determining for each of the tags identified in (c), the total number of library records containing that test tag and one or more of the previously identified tags previously identified by steps (a) and (b), (d2) displaying those test tags identified from step (c) having the highest total number of library records determined from (d1), along with the number of records so determined, and (d3) allowing the user to select one or more tags displayed in (d2).

11. The method of claim 1, wherein step (e) further includes selecting one or more tags presented in step (e), adding the selected tags to those identified in step (b), and repeating steps (c)-(e), until a desirably small number of records are presented in step (e).

12. The method of claim 1, for finding a record document of interest in a library of citation-rich documents, wherein said tags are citations appearing in said documents and said phrases are statements or propositions in said documents in close proximity to said citations.

13. The method of claim 1, for finding a record patent of interest in a library of patents, wherein said tags are class and subclass numbers assigned to said patents and said phrases are definitions of the classes and subclasses associated with said numbers.

14. The method of claim 1, for finding a disease record in a library of disease records, wherein said tags are symptoms identifiers, and said phrases are descriptions of symptoms associated with said tags.

15. The method of claim 1, for finding a subject record in a library of subject records, wherein said tags are personality or preference identifiers and said phrases are descriptions of personality or preference traits associated with said tags.

9. The method of 8, wherein each tag in the database table of searchable tags accessed in step (d) is represented as an N-dimensional vector, where N is the total number of library records in the system, and the coefficient of each vector term is a binary coefficient that indicates whether that tag is in the associated library record represented by that term, and step (d1) includes adding the vectors corresponding to one or more previously identified tags with that of a test tag by AND addition of the vector coefficients, and counting the coefficients from the added vectors.

10. The method of claim 9, wherein the one of more tags identified in step (b) include two of more groups of tags identified from two or more iterations of steps (a) and (b), respectively, where each group includes one or more tags, and step (d1) includes adding the coefficients of vectors in each group by OR addition, to generate a group vector, then adding the group vector(s) with that of a test tag by AND addition, and counting the coefficients in the summed vector.

16. A database system for finding a record of interest in a library of records characterized by distinction subsets of tag descriptors, comprising

(a) a computer,

(b) database tables accessible by said computer, including:

(i) a word-records table composed of searchable words, and for each word in said table, a list of identifiers of phrases containing that word,

(ii) a phrase table composed of phrase identifiers, and for each phrase identifier, a list of one or more tags associated with that phrase,

(iii) an affinity matrix whose matrix values represent, for each pair of tags in the system, a number related to the affinity of the two tags of the pair in said records, and

(iv) a tag table in which each tag is represented as an N-dimensional vector, where N is the total number of library records in the system, and the coefficient of each vector term is a binary coefficient that indicates whether that tag is in the associated library record represented by that term, and

(c) computer-readable code executable by said computer to;

(i) access the word-records table to identify, from user-generated information, one or more phrases likely to be contained in or associated with a record of interest,

(ii) access the phrase table to identify one or more tags associated with the phrase(s) identified in (i),

(iii) access the affinity matrix to identify additional test tags associated in the library records with those identified in step (ii),

(iv) access the tag table to generate for each of the test tags identified in step (iii), data related to the number of library records containing in or associated with that test tag and the tags identified in step (ii), and

(v) present the number-of-records data generated in (iv) to a user.

17. The system of claim 16, wherein said affinity matrix is a t×

t matrix of all tags t associated with said records, and the matrix values for each word pair in the matrix is related to the number occurrence of both tags in the pair in said records.

18. The system of claim 17, wherein the sum of the matrix values of each row of the matrix are normalized to a common value.

19. A database for use by an electronic computer for finding a record of interest in a library of records, comprising

(i) a word-records table composed of searchable words, and for each word in said table, a list of identifiers of phrases containing that word,

(ii) a phrase table composed of phrase identifiers, and for each phrase identifier, a list of one or more tags associated with that phrase,

(iii) an affinity matrix whose matrix values represent, for each pair of tags in the system, a number related to the affinity of the two tags of the pair in said records, and

(iv) a tag table in which each tag is represented as an N-dimensional vector, where N is the total number of library records in the system, and the coefficient of each vector term is a binary coefficient that indicates whether that tag is in the associated library record represented by that term.

20. The system of claim 19, wherein said affinity matrix is a t×

t matrix of all tags t associated with said records, and the matrix values for each word pair in the matrix is related to the number occurrence of both tags in the pair in said records.

21. The system of claim 20, wherein the sum of the matrix values of each row of the matrix is normalized to a common value.