Method and system for rating patents and other intangible assets

• US 20040220842A1
• Filed: 04/29/2003
• Published: 11/04/2004
• Est. Priority Date: 09/14/1999
• Status: Active Grant

• Current Assignee: PatentRatings, LLC
• Original Assignee: PatentRatings, LLC
• Inventors: Barney, Jonathan A.
• Granted Patent: US 7,962,511 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• 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 method for estimating the probability of a future event occurring relative to a particular identified intellectual property asset or group of intellectual property assets of interest, comprising:

storing a first series of data comprising selected metrics identifying and/or quantifying certain selected characteristics of a first population of intellectual property assets for which the event has occurred;

storing a second series of data comprising selected metrics identifying and/or quantifying said selected characteristics of a second population of intellectual property assets for which the event has not occurred or for which it is undetermined whether the event has occurred;

constructing a predictive computer model or algorithm based on said stored first and second series of data, said algorithm being operable to retrieve said first and second series of stored data and to perform certain mathematical or statistical calculations thereon to generate an output score or estimated probability that is generally predictive of the event having either occurred or not occurred relative to each intellectual property asset in said first or second populations of intellectual property assets; and

providing as input to said algorithm a third series of data comprising selected metrics identifying and/or quantifying certain selected characteristics of said particular identified intellectual property asset or group of intellectual property assets of interest and operating said computer model to calculate a relative ranking or estimated probability of the event occurring in the future relative to said identified intellectual property asset or group of intellectual property assets of interest.

2. The method of claim 1 wherein said first and/or second populations of intellectual property assets comprise patents that have been the subject of prior litigation through final judgment and wherein said event comprises one or more of the following:

a final judgment of infringement, a final judgment of non-infringement, a final judgment of invalidity, a final judgment of non-invalidity, a final judgment in favor of the patentee, a final judgment in favor of the accused.

3. The method of claim 1 wherein said first and/or second populations of intellectual property assets comprise selected samples of issued U.S. patents and wherein said event comprises one or more of the following:

payment of the first scheduled maintenance fee, payment of the second scheduled maintenance fee, payment of the third scheduled maintenance fee.

4. The method of claim 1 wherein said first and second populations of intellectual property assets have roughly the same population size.

5. The method of claim 4 wherein said first and second populations of intellectual property assets have a population size of greater than about 1000.

6. The method of claim 5 wherein said first and second populations of intellectual property assets have a population size of between about 20,000 and 80,000.

7. The method of claim 1 wherein said selected metrics comprise one or more characteristics of each said intellectual property asset in said first and second populations of intellectual property assets that are determined or assumed to have either a positive or negative correlation with the occurrence or non-occurrence of said event.

8. The method of claim 1 wherein said algorithm comprises a multiple regression model that correlates multiple individual predictor variables comprising said selected metrics to a single desired criterion variable comprising the approximated probability of said event either occurring or not occurring.

9. The method of claim 8 wherein said multiple regression model has the form:

CV_m=ƒ

{PV₁, PV₂ . . . PV_n}

where;

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

 

PV_n=predictor variables or selected patent metrics.

10. The method of claim 9 wherein said regression includes between about 15 and 25 predictor variables.

11. The method of claim 10 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).

12. The method of claim 11 comprising the further steps of:

incrementally modifying the regression model (m) to produce a modified regression model (m+1);

determining the statistical accuracy of the modified regression model (m+1);

comparing the statistical accuracy of the modified regression model (m+1) to the previously determined statistical accuracy of regression model (m); and

either repeating said incremental modification of the regression model (m+1) to produce a further modified regression model (m+2) if the determined statistical accuracy of the modified regression model (m+1) is greater than the determined statistical accuracy of the regression model (m), or reversing said incremental modification of regression model (m+1) to produce the original regression model (m) if the determined statistical accuracy of the modified regression model (m+1) is less than the determined statistical accuracy of the regression model (m).

13. The method of claim 1 comprising the further step of generating an event probability report for said identified selected intellectual property asset or group of intellectual property assets of interest, said report including basic information identifying said selected intellectual property asset or group of intellectual property assets of interest and said estimated probability of said event occurring or not occurring within a predetermined period of time in the future.

14. An event probability report generated according to the method of claim 13.

15. The method of claim 1 comprising the further steps of:

providing data representative of an intellectual property asset value distribution curve, the shape of the curve generally representing an approximated distribution of patent value according to an estimated probability of said event either occurring or not occurring; and

using said representative data to estimate a value or value range for said selected intellectual property asset of interest according to the estimated probability of said event either occurring or not occurring relative to said selected intellectual property asset of interest.

16. An automated method for enabling a user to access and operate a predetermined predictive computer model or algorithm 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 for each selected patent or group of patents a substantial full-text computer accessible file of the specification 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 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; and

displaying the resulting output rating or probability such that it may be viewed by said user.

17. The method of claim 16 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.

18. The method of claim 17 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.

19. The method of claim 18 wherein said user-provided information comprises patent marking information and/or information pertaining to patented products.

20. The method of claim 18 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, possible 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 other web sites.

21. The method of claim 18 wherein said user-provided information comprises user-provided ratings or rankings of said selected patent or group of patents.

22. The method of claim 18 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 from the user'"'"'s perspective each selected patent would, in effect, have its own bulletin board or note pad associated with it, upon which users may post and/or obtain relevant information.

23. The method of claim 18 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.

24. The method of claim 16 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 the PTO.

25. The method of claim 16 wherein said extracted patent metrics include one or more of the following:

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

26. The method of claim 16 wherein at least one of said extracted patent metrics comprises a modified claim word-count metric whereby each word and/or word 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.

27. The method of claim 16 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 specifications.

28. The method of claim 16 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.

29. The method of claim 28 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.

30. The method of claim 28 wherein said regression model includes between about 15 and 25 predictor variables.

31. The method of claim 28 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).

32. The method of claim 31 comprising the further steps of:

incrementally modifying the regression model (m) to produce a modified regression model (m+1);

determining the statistical accuracy of the modified regression model (m+1);

comparing the statistical accuracy of the modified regression model (m+1) to the previously determined statistical accuracy of regression model (m); and

either repeating said incremental modification of the regression model (m+1) to produce a further modified regression model (m+2) if the determined statistical accuracy of the modified regression model (m+1) is greater than the determined statistical accuracy of the regression model (m), or reversing said incremental modification of regression model (m+1) to produce the original regression model (m) if the determined statistical accuracy of the modified regression model (m+1) is less than the determined statistical accuracy of the regression model (m).

33. The method of claim 16 comprising the further step of generating 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 therefor.

34. The method of claim 33 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.

35. The method of claim 33 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.

36. The method of claim 33 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.

37. A method for estimating or rating the probability of a future event occurring relative to a patent or group of patents, said method comprising:

gathering data comprising two or more selected metrics generally identifying and/or quantifying certain selected characteristics of said patent or group of patents;

providing said data as input to a mathematical estimating model;

using the mathematical estimating model to calculate a numerical output based on the data input, said numerical output being generally predictive of said future event occurring relative to said patent or group of patents; and

wherein said mathematical estimating model is derived from statistical data correlating said two or more selected patent characteristics to the occurrence or non-occurrence of said event

38. The method of claim 37 wherein said mathematical estimating 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 the event has occurred, storing a second group of data comprising selected metrics identifying and/or quantifying said selected characteristics of a second population of patents for which the event has not occurred or for which it is undetermined whether the event has occurred, and using a statistical regression to create a predictive algorithm based on said stored first and second groups of data, said algorithm being operable to perform certain mathematical or statistical calculations using said first and second groups of stored data and to generate in each case an output score or estimated probability that is generally predictive of the event having either occurred or not occurred relative to each patent or group of patents.

39. The method of claim 37 wherein said future event comprises one or more of the following:

payment of the first scheduled maintenance fee, payment of the second scheduled maintenance fee, payment of the third scheduled maintenance fee.

40. The method of claim 37 wherein said future event comprises the filing of a litigation lawsuit involving said patent or group of patents.

41. The method of claim 37 wherein said future event comprises the successful prosecution of a litigation lawsuit involving said patent or group of patents.

42. The method of claim 37 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 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 the PTO.

43. A method for rating a patent or group of patents, said method comprising:

gathering 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 said data as input to a mathematical rating model;

using the mathematical rating model to calculate a numerical score or rating based on the data input, said mathematical rating model being statistically determined such 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.

44. The method of claim 43 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.

45. The method of claim 43 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 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 the PTO.

46. The method of claim 43 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 patent or group of patents being maintained or abandoned.

47. The method of claim 46 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.

48. The method of claim 46 wherein said regression model includes between about 15 and 25 predictor variables.

49. The method of claim 46 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).

50. The method of claim 49 comprising the further steps of:

incrementally modifying the regression model (m) to produce a modified regression model (m+1);

determining the statistical accuracy of the modified regression model (m+1);

comparing the statistical accuracy of the modified regression model (m+1) to the previously determined statistical accuracy of regression model (m); and

either repeating said incremental modification of the regression model (m+1) to produce a further modified regression model (m+2) if the determined statistical accuracy of the modified regression model (m+1) is greater than the determined statistical accuracy of the regression model (m), or reversing said incremental modification of regression model (m+1) to produce the original regression model (m) if the determined statistical accuracy of the modified regression model (m+1) is less than the determined statistical accuracy of the regression model (m).

51. The method of claim 43 comprising the further step of generating 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 therefor.

52. The method of claim 51 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.

53. The method of claim 52 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.