Methods for document indexing and analysis

• US 20020062302A1
• Filed: 08/07/2001
• Published: 05/23/2002
• Est. Priority Date: 08/09/2000
• Status: Active Grant

• Current Assignee: Gary Martin Oosta
• Original Assignee: Gary Martin Oosta
• Inventors: Oosta, Gary Martin
• Granted Patent: US 7,130,848 B2
• Time in Patent Office: Days
• US Class Current: 707/1
• CPC Class Codes: G06F 16/358 Browsing; Visualisation therefor
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: Y10S 707/931 Patent comparison
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99935 Query augmenting and refining, e.g. inexact access
• CPC Class Codes: Y10S 707/99936 Pattern matching access
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

The present invention describes a method that is based on an analysis of document information, and which can be used for conducting and potentially accelerating business opportunity assessments and technology investment decisions. Documents include patent documents, scientific and trade literature, magazines, e-zines, Internet search results and the like. The system can be implemented as an automatic or semi-automatic analysis system, and it ultimately provides a document index or visual index from which technology, investment or business decisions can be drawn. The method comprises the steps of selecting a document data set for analysis, selecting one or more analysis means, such as means to determine relationships among the documents in the selected set, and then, forming a representation of the result for further analysis, display or planning.

Claims

1. A method for analysis of a collection of documents, which method comprises the steps of:

a. selecting a data set for analysis from said collection of documents;

b. selecting at least one analysis means for said data set including an analysis algorithm based on the structure of said data set; and

c. using the selected analysis means to produce a result from the analysis comprising relationships among the documents in the selected set.

2. The method of claim 1, wherein the result comprises an index.

3. The method of claim 1, wherein the result comprises a graphical representation.

4. The method of claim 1, wherein the result is in a visual index form suitable for display or further analysis.

5. The method of claim 1, wherein said analysis means includes means to identify and display patterns in patenting activity, patterns in scientific research, patterns in commercial activity or patterns in popular interest in an area that can affect business or technology decisions.

7. The method of claim 1, wherein said analysis means comprises an algorithm selected from the group consisting of co-occurrence analysis, co-word analysis and co-citation analysis.

9. The method of claim 7 or 8, wherein the data set comprises information from issued patents or patent applications.

10. The method of claim 7 or 8, wherein said analysis means utilizes one or more of the group consisting of genetic algorithms, Bayesian learning, neural networks, Markov models, hidden Markov models, co-word analysis, co-citation analysis, partial least square, or principle component analysis.

11. The method of claim 1, wherein the method further includes the step of formatting at least a portion of the data set which is not in a standard format into a standard format prior to utilizing the analysis means.

12. The method of claim 1, wherein said analysis means utilizes algorithms selected from the group consisting of co-citation analysis, co-word analysis, genetic algorithms, Bayesian learning, neural networks, Markov models, hidden Markov models, partial least squares, or principal component analysis, to create a visually displayable index to a selected set of documents.

14. The expert systems as claimed in claim 13, wherein the result comprises an index.

15. The expert systems as claimed in claim 13, wherein the result comprises a graphical representation.

16. The expert systems as claimed in claim 13, wherein the result is in a visual index form suitable for display or further analysis.

17. The expert systems as claimed in claim 13, wherein said analysis means includes means to identify and display patterns in patenting activity, patterns in scientific research, patterns in commercial activity or patterns in popular interest in an area that can affect business or technology decisions.

18. The expert systems as claimed in claim 13, wherein said analysis means comprises an algorithm capable of bibliographic analysis.

19. The expert systems as claimed in claim 13, wherein said analysis means comprises an algorithm selected from the group consisting of co-occurrence analysis, co-word analysis and co-citation analysis.

20. The expert systems as claimed in claim 13, wherein said analysis means comprises an algorithm selected from the group consisting of Markov models, hidden Markov models, partial least squares and principal component analysis.

21. The expert systems as claimed in claim 13, wherein the data set comprises information from issued patents or patent applications.

22. The expert systems as claimed in claim 13, wherein said analysis means utilizes one or more of the group consisting of genetic algorithms, Bayesian learning, neural networks, Markov models, hidden Markov models, co-word analysis, co-citation analysis, partial least square, or principle component analysis.

23. The expert systems as claimed in claim 13, wherein the method further includes the step of formatting at least a portion of the data set which is not in a standard format into a standard format prior to utilizing the analysis means.

24. The expert systems as claimed in claim 13, wherein said analysis means utilizes algorithms selected from the group consisting of co-word, cocitation analysis, genetic algorithms, Bayesian learning, neural networks, Markov models, hidden Markov models, partial least squares, or principal component analysis, to create a visually displayable index to a selected set of documents.

6. The method of claim l wherein said analysis means comprises an algorithm capable of bibliographic analysis.

8. The method of claims, wherein said analysis means comprises an algorithm selected from the group consisting of Markov models, hidden Markov models, partial least squares and principal component analysis.

13. An expert system for analysis of a collection of documents, comprising:

a) means for selecting a data set for analysis from the collection of documents;

b) means for selecting at least one analysis algorithm for said data set based on the structure of said data set; and

c) means for utilizing said at least one analysis algorithm to perform an analysis of said data set to produce a result comprising relationships among the documents in said data set.