System and method for providing profile matching with an unstructured document

• US 20070027859A1
• Filed: 07/27/2005
• Published: 02/01/2007
• Est. Priority Date: 07/27/2005
• Status: Active Grant

• Current Assignee: Janet Dwyer, John Harney
• Original Assignee: Janet Dwyer, John Harney
• Inventors: Harney, John, Dwyer, Janet
• Granted Patent: US 7,587,395 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/025 Coordination of plural reservations, e.g. plural trip segments, transportation combined with accommodation
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: Y10S 707/99936 Pattern matching access

Abstract

A method, system, and computer program product are disclosed for automatically matching the profile of unstructured electronic documents to objective sets of criteria. The is accomplished by evaluating text in the documents, comparing it to a set of weighted keyword criteria, generating a rating based on adherence to the criteria, rating and categorizing the results, sorting and viewing the results based on user defined criteria.

Claims

1. A method for providing profile matching within an unstructured electronic document, the method steps comprising:

loading one or more unstructured electronic documents from storage;

defining a set of requirements to be met by the unstructured electronic documents;

comparing the unstructured electronic documents to one or more criteria groups each containing one or more criteria; and

ranking the unstructured documents based on the documents'"'"' conformity to the criteria.

2. The method according to claim 1, wherein the defining includes creating a list of keywords from a set of requirements.

3. The method according to claim 2, wherein the defining includes expanding the keyword list using a thesaurus, wherein the thesaurus contains synonyms added via one or more methods from a group consisting of:

previously defined lists of synonyms,

previously defined subcategories of keyword,

automated generation of synonyms, and

user defined addition of synonyms.

4. The method according to claim 1, wherein the comparing is comprised of searching an unstructured document for criteria matches wherein searching includes one or more methods selected from a group consisting of:

evaluating the presence of keywords in a document,

evaluating the absence of keywords in a document,

evaluating the frequency of keywords in the document,

evaluating the context of keywords in the document,

evaluating the relationship of keywords to other keywords in the document and

evaluating the number of keywords in the document.

5. The method according to claim 1, wherein the comparing is comprised of generating a list of qualified documents based on meeting or surpassing one or more user defined requirements.

6. The method according to claim 5, wherein the generating includes tracking statistics associated with the documents'"'"' conformity to one or more user defined specifications.

7. The method according to claim 1, wherein the comparing includes generating a list of non-qualified documents based on failure to meet or surpass one or more user defined requirements.

8. The method according to claim 7, wherein the generating includes tracking the requirements each document failed to meet which in turn caused the document to be defined as non-qualifying.

9. The method according to claim 1, wherein the ranking includes rating documents by assigning one or more profile scores indicating a document'"'"'s degree of adherence to criteria.

10. The method according to claim 9, wherein assigning profile scores includes generating a profile score by assigning points to a document based on one or more statistics selected from a group consisting of:

the absence of keywords in the document,

the presence of keywords in the document,

the frequency of keywords in the document,

the context of keywords in the document,

the placement of keywords in the document,

the relationship of keywords to other keywords in the document, and

the number of keywords in the document.

11. The method according to claim 10, wherein assigning points includes assigning a point value for the first occurrence of each keyword in a document, and lesser point values for subsequent occurrences of keywords in the document.

12. The method according to claim 9, wherein the categorizing includes generating a category definition scheme which contains one or more methodologies for assigning scored documents to one or more categories based on one or more of a document'"'"'s profile scores.

13. An automated method of scoring one or more unstructured electronic documents, the method comprising the steps of:

searching for keywords in unstructured electronic documents;

assigning one or more point values for statistics related to the keyword occurrences in the documents;

aggregating the point values to generate one or more profile scores in one or more profile score categories for the documents; and

categorizing the documents based on one or more profile scores in one or more profile score categories.

14. The method according to claim 13, wherein assigning point values includes weighting a profile score by assigning one or more numeric point values to an unstructured electronic document.

15. The method according to claim 14, wherein weighting a profile score includes assigning point values to an unstructured electronic document based on the frequency of each keyword'"'"'s appearance in the document, the point values aggregated to generate one or more profile scores in one or more profile score categories.

16. The method according to claim 15, wherein assigning point values includes one or more methods from a group consisting of:

assigning fixed point values for each occurrence of each keyword in the unstructured electronic document,

assigning a first point value for the first occurrence of each keyword in the structured electronic document, and a lower, fixed point value for each subsequent occurrence of each keyword, and

assigning a first point value for the first occurrence of each keyword in the unstructured electronic document, and decreasing point values for each subsequent occurrence of each keyword.

17. The method according to claim 13, wherein assigned point values are aggregated into one or more categories to generate a profile score including one or more profile score categories.

18. The method according to claim 17, wherein the profile score in each category is normalized to generate a relative score reflecting the correlation between the desired and actual profile score in each category.

19. The method according to claim 13, wherein the categorizing includes separating one or more unstructured electronic documents into one or more document categories based on one or more profile score categories.

20. The method as recited in claim 13, wherein the assigning to a category includes listing in order two or more unstructured electronic documents from a category into a list, the ordering based on profile score.