Method and system for rating patents and other intangible assets

• US 7,962,511 B2
• Filed: 04/29/2003
• Issued: 06/14/2011
• Est. Priority Date: 09/14/1999
• Status: Active Grant

• Current Assignee: PatentRatings, LLC
• Original Assignee: PatentRatings, LLC
• Inventors: Barney, Jonathan A.
• Primary Examiner(s): Wassum; Luke S
• Application Number:
  US10/425,554
• Publication Number:
  US 20040220842A1
• Time in Patent Office: 2,968 Days
• Field of Search: None
• US Class Current: 707/776
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis
• CPC Class Codes: G06Q 50/184 Intellectual property management
• CPC Class Codes: G06Q 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: Y10S 707/93 intellectual property analysis
• CPC Class Codes: Y10S 707/99936 Pattern matching access
• CPC Class Codes: Y10S 707/99937 Sorting

Abstract

A statistical patent rating method and system is provided for independently assessing the relative breadth (“B”), defensibility (“D”) and commercial relevance (“R”) of individual patent assets and other intangible intellectual property assets. The invention provides new and valuable information that can be used by patent valuation experts, investment advisors, economists and others to help guide future patent investment decisions, licensing programs, patent appraisals, tax valuations, transfer pricing, economic forecasting and planning, and even mediation and/or settlement of patent litigation lawsuits. In one embodiment the invention provides a statistically-based patent rating method and system whereby relative ratings or rankings are generated using a database of patent information by identifying and comparing various characteristics of each individual patent to a statistically determined distribution of the same characteristics within a given patent population. For example, a first population of patents having a known relatively high intrinsic value or quality (e.g. successfully litigated patents) is compared to a second population of patents having a known relatively low intrinsic value or quality (e.g. unsuccessfully litigated patents). Based on a statistical comparison of the two populations, certain characteristics are identified as being more prevalent or more pronounced in one population group or the other to a statistically significant degree. Multiple such statistical comparisons are used to construct and optimize a computer model or computer algorithm that can then be used to predict and/or provide statistically-accurate probabilities of a desired value or quality being present or a future event occurring, given the identified characteristics of an individual patent or group of patents.

Claims

1. A computer-implemented automated method for enabling a user to access and operate a predetermined predictive computer model to score or rate a selected patent or group of patents in accordance with user-defined patent metrics, comprising:

selecting a patent or group of patents to be rated;

obtaining by a computer system from a database for each selected patent or group of patents a substantial full-text computer accessible file of the written description and/or claims thereof;

extracting from each said file certain patent metrics, either predetermined or as selected or directed by said user;

inputting said patent metrics into said predictive computer model, said predictive computer model being operable to input said patent metrics and based thereon to calculate a corresponding output rating or probability that is generally predictive of a particular quality being present and/or an event occurring relative to the selected patent or group of patents, wherein said predictive computer model comprises a multiple regression model that correlates multiple individual predictor variables comprising said extracted patent metrics to one or more desired criterion variables comprising the corresponding output rating or probability; and

generating by the computer system a patent rating report for said selected patent or group of patents, said patent rating report including basic information identifying said selected patent or group of patents and said corresponding output rating or probability determined therefore;

wherein said patent rating report contains at least one reported rating or ranking that is generally representative of the breadth (“

B”

) or likely infringement of the selected patent or group of patents, at least one reported rating or ranking that is generally representative of the defensibility (“

D”

) or likely validity of the selected patent or group of patents, and at least one reported rating or ranking that is generally representative of the commercial relevance (“

R”

) or technical merit of the selected patent or group of patents.

2. The computer-implemented method of claim 1 comprising the further step of ranking said output rating or probability against other output ratings or probabilities calculated for other patents within a predetermined or user-defined patent population.

3. The computer-implemented method of claim 2 comprising the further step of storing relevant user-provided information pertaining to said selected patent or group of patents in a computer-accessible database in association with other information identifying said selected patent or group of patents.

4. The computer-implemented method of claim 3 wherein said user-provided information comprises patent marking information and/or information pertaining to patented products.

5. The computer-implemented method of claim 3 wherein said user-provided information comprises one or more of the following:

relevant prior art that was not cited in the selected patent or group of patents, license terms, information about named inventors, information relating to sales of patented products, legal opinions, related litigation, other related or unrelated patents of interest, electronic file wrappers, hot-links to web sites.

6. The computer-implemented method of claim 3 wherein said user-provided information comprises user-provided ratings or rankings of said selected patent or group of patents.

7. The computer-implemented method of claim 3 comprising the further step of displaying said stored user-provided information pertaining to said selected patent or group of patents in association with said selected patent or group of patents such that each selected patent comprises its own bulletin board or note pad associated with it, upon which users may post and/or obtain relevant information.

8. The computer-implemented method of claim 3 wherein said extracted patent metrics comprise one or more characteristics of said selected patent or group of patents that are determined or assumed to have either a positive or negative correlation with the presence or absence of said particular quality and/or said event occurring relative to the selected patent or group of patents.

9. The computer-implemented method of claim 1 wherein said extracted patent metrics include one or more of the following:

number of claims per patent, number of words per claim, different words per claim, length of patent specification, number of drawing pages or figures, number of cited prior art references, age of cited references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent, payment of maintenance fees, name of prosecuting attorney or law firm, examination art group, or length of pendency in a patent office.

10. The computer-implemented method of claim 1 wherein said extracted patent metrics include one or more of the following:

patent marking data, claim relatedness, patent relatedness, or claim type.

11. The computer-implemented method of claim 1 wherein at least one of said extracted patent metrics comprises a modified claim word-count metric whereby each word and/or words, phrase in a patent claim of interest is assigned a certain point value generally proportional to its determined frequency of use in a relevant patent population and wherein the word-count metric is set equal to the sum of each of the individual word point values for essentially all of the words or word phrases contained within said claim.

12. The computer-implemented method of claim 1 wherein at least one of said extracted patent metrics comprises a relatedness metric generally indicative of the commonality of word or word phrase usage between one or more patent claims and/or patent written description.

13. The computer-implemented method of claim 1 wherein said multiple regression model has the form:

CV_m=ƒ

{PV₁,PV₂ . . . PV_n}

where;

CV_m=criterion variable or quality/event desired to be predicted

PV_n=predictor variables or selected patent metrics.

14. The computer-implemented method of claim 1 wherein said regression model includes between about 15 and 25 predictor variables.

15. The computer-implemented method of claim 1 comprising the further step of determining the statistical accuracy of the regression model in accordance with the general formula:

SA(m)=CO/(CO+IN)

where;

SA(m)=statistical accuracy of regression model (m)

CO=number of correct predictions for model (m)

IN=number of incorrect predictions for model (m).

16. The method of claim 1 wherein said patent rating report is generated in response to an electronic request transmitted over a computer network and wherein said report is generated and displayed automatically without further human intervention.

17. The method of claim 1 comprising the further step of, after generating said report, automatically without further human intervention transmitting said report electronically over a computer network to one or more intended recipients.

18. A computer-implemented method for rating a patent or group of patents, said method comprising:

gathering by a computer system data comprising one or more selected metrics generally identifying and/or quantifying certain selected characteristics of said patent or group of patents to be rated;

providing by the computer system said data as input to a mathematical rating model;

using by the computer system the mathematical rating model to calculate a numerical score or rating based on the data input, said mathematical rating model being statistically determined such that said numerical score or rating is generally predictive of the probability of said patent or group of patents being maintained or abandoned in the future, wherein said mathematical rating model comprises a multiple regression model that correlates multiple individual predictor variables comprising said selected metrics to one or more desired criterion variables comprising the corresponding output rating and/or probability of the patents being maintained or abandoned;

generating by the computer system a patent rating report for said selected patent or group of patents, said report including basic information identifying said selected patent or group of patents and said corresponding output rating or probability determined therefore;

wherein said patent rating report is generated in response to an electronic request transmitted over a computer network and wherein said report is generated and displayed automatically without further human intervention; and

wherein said patent rating report contains at least one reported rating or ranking that is generally representative of the breadth (“

B”

) or likely infringement of the selected patent or group of patents, at least one reported rating or ranking that is generally representative of the defensibility (“

D”

) or likely validity of the selected patent or group of patents, or at least one reported rating or ranking that is generally representative of the commercial relevance (“

R”

) or technical merit of the selected patent or group of patents.

19. The computer-implemented method of claim 18 wherein said mathematical rating model is derived by storing a first group of data comprising selected metrics identifying and/or quantifying said selected characteristics of a first population of patents for which maintenance fees have been paid, storing a second group of data comprising selected metrics identifying and/or quantifying said selected characteristics of a second population of patents for which maintenance fees have not been paid or for which it is undetermined whether maintenance fees have been paid, and using a statistical regression to create a predictive algorithm based on said stored first and second groups of data.

20. The computer-implemented method of claim 18 wherein said selected metrics include one or more of the following:

number of claims per patent, number of words per claim, different words per claim, length of patent written description, number of drawing pages or figures, number of cited prior art references, age of cited references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent, payment of maintenance fees, name of prosecuting attorney or law firm, examination art group, or length of pendency in a patent office.

21. The computer-implemented method of claim 18 wherein said multiple regression model has the form:

CV_m=ƒ

{PV₁,PV₂ . . . PV_n}

where;

CV_m=criterion variable or quality/event desired to be predicted

PV_n=predictor variables or selected patent metrics.

22. The computer-implemented method of claim 18 wherein said regression model includes between about 15 and 25 predictor variables.

23. The computer-implemented method of claim 18 comprising the further step of determining the statistical accuracy of the regression model in accordance with the general formula:

SA(m)=CO/(CO+IN)

where;

SA(m)=statistical accuracy of regression model (m)

CO=number of correct predictions for model (m)

IN=number of incorrect predictions for model (m).

24. A computer-implemented method for scoring or rating a selected patent or group of patents comprising:

accessing computer-executable instructions from at least one computer-readable storage medium; and

executing the computer-executable instructions, thereby causing computer hardware comprising at least one computer processor to perform operations comprising;

selecting a patent or group of patents to be rated;

obtaining for each selected patent or group of patents a substantial full-text computer accessible file of the written description and/or claims thereof;

extracting from each said file certain patent metrics, either predetermined or as selected or directed by said user;

inputting said patent metrics into said predictive computer model, said predictive computer model being operable to input said patent metrics and based thereon to calculate a corresponding output rating or probability that is generally predictive of a particular quality being present and/or an event occurring relative to the selected patent or group of patents, wherein said predictive computer model comprises a multiple regression model that correlates multiple individual predictor variables comprising said extracted patent metrics to one or more desired criterion variables comprising the corresponding output rating or probability; and

generating a patent rating report for said selected patent or group of patents, said patent rating report including basic information identifying said selected patent or group of patents and said corresponding output rating or probability determined therefore;

wherein said patent rating report contains at least one reported rating or ranking that is generally representative of the breadth (“

B”

) or likely infringement of the selected patent or group of patents, at least one reported rating or ranking that is generally representative of the defensibility (“

D”

) or likely validity of the selected patent or group of patents, or at least one reported rating or ranking that is generally representative of the commercial relevance (“

R”

) or technical merit of the selected patent or group of patents.

25. The computer-implemented method of claim 24, wherein the operations further comprise ranking said output rating or probability against other output ratings or probabilities calculated for other patents within a predetermined or user-defined patent population.

26. The computer-implemented method of claim 24, wherein the operations further comprise storing relevant user-provided information pertaining to said selected patent or group of patents in a computer-accessible database in association with other information identifying said selected patent or group of patents.

27. The computer-implemented method of claim 26 wherein said user-provided information comprises patent marking information and/or information pertaining to patented products.

28. The computer-implemented method of claim 26 wherein said user-provided information comprises one or more of the following:

relevant prior art that was not cited in the selected patent or group of patents, license terms, information about named inventors, information relating to sales of patented products, legal opinions, related litigation, other related or unrelated patents of interest, electronic file wrappers, hot-links to web sites.

29. The computer-implemented method of claim 26 wherein said user-provided information comprises user-provided ratings or rankings of said selected patent or group of patents.

30. The computer-implemented method of claim 26, wherein the operations further comprise displaying said stored user-provided information pertaining to said selected patent or group of patents in association with said selected patent or group of patents such that each selected patent comprises its own bulletin board or note pad associated with it, upon which users may post and/or obtain relevant information.

31. The computer-implemented method of claim 26 wherein said extracted patent metrics comprise one or more characteristics of said selected patent or group of patents that are determined or assumed to have either a positive or negative correlation with the presence or absence of said particular quality and/or said event occurring relative to the selected patent or group of patents.

32. The computer-implemented method of claim 24 wherein said extracted patent metrics include one or more of the following:

number of claims per patent, number of words per claim, different words per claim, length of patent specification, number of drawing pages or figures, number of cited prior art references, age of cited references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent, payment of maintenance fees, name of prosecuting attorney or law firm, examination art group, or length of pendency in a patent office.

33. The computer-implemented method of claim 24 wherein said extracted patent metrics include one or more of the following:

patent marking data, claim relatedness, patent relatedness, or claim type.

34. The computer-implemented method of claim 24 wherein at least one of said extracted patent metrics comprises a modified claim word-count metric whereby each word and/or words, phrase in a patent claim of interest is assigned a certain point value generally proportional to its determined frequency of use in a relevant patent population and wherein the word-count metric is set equal to the sum of each of the individual word point values for essentially all of the words or word phrases contained within said claim.

35. The computer-implemented method of claim 24 wherein at least one of said extracted patent metrics comprises a relatedness metric generally indicative of the commonality of word or word phrase usage between one or more patent claims and/or patent written description.

36. The computer-implemented method of claim 24 wherein said multiple regression model has the form:

CV_m=ƒ

{PV₁,PV₂ . . . PV_n}

where;

CV_m=criterion variable or quality/event desired to be predicted

PV_n=predictor variables or selected patent metrics.

37. The computer-implemented method of claim 24 wherein said regression model includes between about 15 and 25 predictor variables.

38. The computer-implemented method of claim 24, wherein the operations further comprise determining the statistical accuracy of the regression model in accordance with the general formula:

SA(m)=CO/(CO+IN)

where;

SA(m)=statistical accuracy of regression model (m)

CO=number of correct predictions for model (m)

IN=number of incorrect predictions for model (m).