Method and system for probabilistically quantifying and visualizing relevance between two or more citationally or contextually related data objects

• US 8,504,560 B2
• Filed: 03/02/2012
• Issued: 08/06/2013
• Est. Priority Date: 09/27/2005
• Status: Expired due to Fees

• Current Assignee: PatentRatings, LLC
• Original Assignee: PatentRatings, LLC
• Inventors: Barney, Jonathan A.
• Primary Examiner(s): Jami, Hares
• Application Number:
  US13/411,441
• Publication Number:
  US 20120221580A1
• Time in Patent Office: 522 Days
• Field of Search: 707/705, 707/722, 707/726, 707/728, 707/731, 707/999.1, 707/923, 707/930, 707/933, 707/937
• US Class Current: 707/722
• CPC Class Codes: G06F 16/14 Details of searching files based on file metadata
• CPC Class Codes: G06F 16/2228 Indexing structures
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/26 Visual data mining; Browsing structured data
• CPC Class Codes: G06F 16/334 Query execution G06F16/335 takes precedence
• CPC Class Codes: G06F 16/3346 using probabilistic model
• CPC Class Codes: G06F 16/34 Browsing; Visualisation therefor
• CPC Class Codes: G06F 16/382 using citations hypermedia G06F16/94
• CPC Class Codes: G06F 16/93 Document management systems
• CPC Class Codes: G06F 16/95 Retrieval from the web
• CPC Class Codes: G06F 16/951 Indexing; Web crawling techniques
• CPC Class Codes: G06F 2216/11 Patent retrieval
• CPC Class Codes: Y10S 707/912 Applications of a database
• CPC Class Codes: Y10S 707/923 Intellectual property
• CPC Class Codes: Y10S 707/93 intellectual property analysis
• CPC Class Codes: Y10S 707/933 Citation analysis
• CPC Class Codes: Y10S 707/937 intellectual property searching

Abstract

In one embodiment a method for probabilistically quantifying a degree of relevance between two or more citationally or contextually related data objects, such as patent documents, non-patent documents, web pages, personal and corporate contacts information, product information, consumer to behavior, technical or scientific information, address information, and the like is provided. In another embodiment a method for visualizing and displaying relevance between two or more citationally or contextually related data objects is provided. In another embodiment a search input/output interface that utilizes an iterative self-organizing mapping technique to automatically generate a visual map of relevant patents and/or other related documents desired to be explored, searched or analyzed is provided. In another embodiment, a search input/output interface that displays and/or communicates search input criteria and corresponding search results in a way that facilitates intuitive understanding and visualization of the logical relationships between two or more related concepts being searched is provided.

Claims

1. A computer-implemented method, comprising:

receiving, by a computer system, a first set of information identifying an input set of documents, said input set comprising a plurality of documents;

identifying, by the computer system, an additional document that is not a member of the input set, but which is citationally related to at least some of the documents in the input set;

programmatically calculating, by the computer system, a data value that represents a degree to which said document is citationally related to the at least some of the documents in the input set, said data value dependent upon at least (a) how many citational relationships exist at generations higher than a first generation between the input set of documents and said additional document, and (b) generation levels of said citational relationships, wherein calculating said data value comprises assigning different amounts of weight to citational relationships of different generation levels, said amounts of weight being based at least in part on a generational citation count determined for each of the different generation levels and an analysis in which multi-generation citational relationships between documents are used to predict existences of first generation citational relationships between documents, said analysis performed over a document population; and

storing the data value in computer storage in association with identifiers of the input set of documents and the additional document.

2. The computer-implemented method of claim 1, wherein said generation levels comprise first generation citational relationships and higher generation citational relationships, wherein the first generation citational relationships between the input set of documents and the additional document represent a direct citation between the input set of documents and the additional document, and the higher generation citational citiational relationships between the input set of documents and the additional document represent indirect citational relationships through at least one intermediate document.

3. The computer-implemented method of claim 1, wherein the analysis performed over the document population comprises a statistical analysis.

4. The computer-implemented method of claim 1, wherein the analysis performed over the document population comprises a predictive analysis.

5. The computer-implemented method of claim 1, wherein the analysis performed over the document population comprises a multi-variate regression analysis.

6. A computing system comprising:

one or more processors; and

computer storage storing machine-executable instructions including one or more modules configured for execution by the one or more processors in order to cause the computing system to at least;

receive, by the one or more processors, a first set of information identifying an input set of documents, said input set comprising a plurality of documents;

identify, by the one or more processors, an additional document that is not a member of the input set, but which is citationally related to at least some of the documents in the input set;

calculate, by the one or more processors, a data value that represents a degree to which said document is citationally related to at least some of the documents in the input set, said data value dependent upon at least (a) how many citational relationships exist at generations higher than a first generation between the input set of documents and said additional document, and (b) generation levels of said citational relationships, wherein calculating said data value comprises assigning different amounts of weight to citational relationships of different generation levels, said amounts of weight being based at least in part on a generational citation count determined for each of the different generation levels and an analysis in which multi-generation citational relationships between documents are used to predict existences of first generation citational relationships between documents, said analysis performed over a document population; and

store the data value in computer storage in association with identifiers of the input set of documents.

7. The computing system of claim 6, wherein said generation levels comprise first generation citational relationships and higher generation citational relationships, wherein the first generation citational relationships between the input set of documents and the additional document represent a direct citation between the input set of documents and the additional document, and the higher generation citational relationships between the input set of documents and the additional document represent an indirect citational relationship through at least one intermediate document.

8. The computing system of claim 6, wherein the analysis performed over the document population comprises a statistical analysis.

9. The computing system of claim 6, wherein the analysis performed over the document population comprises a predictive analysis.

10. The computing system of claim 6, wherein the analysis performed over the document population comprises a multi-variate regression analysis.

11. A non-transitory computer-readable medium encoded with instructions thereon, wherein the instructions direct a computing system to perform operations comprising:

receiving, by the computing system, a first set of information identifying an input set of documents, said input set comprising a plurality of documents;

identifying, by the computing system, an additional document that is not a member of the input set, but which is citationally related to at least some of the documents in the input set;

calculating, by the computing system, a data value that represents a degree to which said document is citationally related to at least some of the documents in the input set, said data value dependent upon at least (a) how many citational relationships exist at generations higher than a first generation between the input set of documents and said additional document, and (b) generation levels of said citational relationships, wherein calculating said data value comprises assigning different amounts of weight to citational relationships of different generation levels, said amounts of weight being based at least in part on a generational citation count determined for each of the different generation levels and an analysis in which multi-generation citational relationships between documents are used to predict existences of first generation citational relationships between documents, said analysis performed over a document population; and

storing the data value in computer storage in association with identifiers of the input set of documents and the additional document.

12. The non-transitory computer-readable medium of claim 11, wherein said generation levels comprise first generation citational relationships and higher generation citational relationships, wherein the first generation citational relationships between the input set of documents and the additional document represent a direct citation between the input set of documents and the additional document, and the higher generation citational relationships between the input set of documents and the additional document represent an indirect citational relationship through at least one intermediate document.

13. The non-transitory computer-readable medium of claim 11, wherein the analysis performed over the document population comprises a statistical analysis.

14. The non-transitory computer-readable medium of claim 11, wherein the analysis performed over the document population comprises a predictive analysis.

15. The non-transitory computer-readable medium of claim 11, wherein the analysis performed over the document population comprises a multi-variate regression analysis.