Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Examiner Note
2. Michael Young is no longer continuing prosecution on application number 17/281,002. It has been transferred to Examiner Jessica Lemieux.
DETAILED ACTION
3. This Final Office action is in response to the application filed on March 29th, 2021 and in response to Applicant’s Arguments/Remarks filed on February 2nd, 2026. Claims 21-37 are pending.
Priority
4. Application 17/281,002 was filed on March 29th, 2021 which is a 371 of PCT/US18/53796 filed on October 1st, 2018
Examiner Request
5. The Applicant is requested to indicate where in the specification there is support for amendments to claims should Applicant amend. The purpose of this is to reduce potential 35 U.S.C. §112(a) or §112 1st paragraph issues that can arise when claims are amended without support in the specification. The Examiner thanks the Applicant in advance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
6. Claims 21-37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under MPEP 2106, when considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A prong 1), and if so, it must additionally be determined whether the claim is integrated into a practical application (step 2A prong 2). If an abstract idea is present in the claim without integration into a practical application, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself (step 2B).
In the instant case, claims 21-37 are directed to a system and method. Thus, each of the claims falls within one of the four statutory categories (step 1). However, the claims also fall within the judicial exception of an abstract idea (step 2A). While claims 21, 36, and 37, are directed to different categories, the language and scope are substantially the same and have been addressed together below.
Under Step 2A Prong 1, the test is to identify whether the claims are “directed to” a judicial exception. Examiner notes that the claimed invention is directed to an abstract idea in that the instant application is directed to certain methods of organizing human activity specifically legal or commercial interactions (innovation analysis) (see MPEP 2106.04(a)(2)(II)) and mental processes (see MPEP 2106.04(a)(2)(III).
Examiner notes that:
Claim 21 recites: analyzing an intangible asset of an organization, […] comprising: generating multiple […] processing frameworks, wherein individual ones of the multiple […] processing frameworks are associated with respective factor weightings, and wherein the individual ones of the multiple […] processing frameworks are configured to extract data from certain interfaces while refraining from extracting data from other interfaces; causing, […] a first qualitative analysis comprising: obtaining data from a data source, the data corresponding to the intangible asset; selecting a computer-centric processing framework from the multiple computer-centric processing frameworks including a predefined factor associated with a type of the intangible asset; and generating, […] with the […] framework and a first portion of the data, a first qualitative factor associated with one of the respective factor weightings from the data as extracted from the certain interfaces; transmitting, […], the first qualitative factor; and generating a user interface comprising a first graphic associated with the first qualitative factor based on the first qualitative analysis, wherein the user interface is updated based at least in part on inputting new data into the user interface. Claims 36 and 37 further recite the qualitative factor corresponding to: coverage corresponding to a determined relationship between a product or a service of the organization and the intangible asset; opportunity corresponding to a potential increase in revenue of the organization attributable to the intangible asset; or risk corresponding to a potential that the intangible asset contributes to a decrease in the opportunity; and wherein the user interface is updated based at least in part on inputting new data into the user interface and at least two qualitative factors based on the qualitative analysis. These limitations are similar to the abstract idea identified in the MPEP 2106.04(a)(2)(II) in that the claims are directed to certain methods of organizing human activity such as commercial or legal interactions. These limitations describe evaluating information to produce a qualitative assessment. Examiner notes that the court has been clear that inventions reciting methods of organizing human activity in the form of commercial or business interactions between people are directed to the judicial exception found in grouping “II”. The limitations above closely follow the steps of the claims involving organizing human activity set forth in the group “II” of the MPEP 2106.04(a)(2). Therefore, the claims recite a method of organizing human activity.
These limitations as drafted are processes that, under the broadest reasonable interpretation, covers certain methods of organizing human activity but for recitation of generic computer components. That is, other than reciting a computing device in communication with a server computing device via a network, and the computer-centric, the claimed invention amounts to commercial or legal interactions, i.e. business decision making which falls within the “certain methods of organizing human activity” grouping of abstract ideas. Accordingly, claims 21, 36 and 37 recite an abstract idea.
Alternatively, Furthermore, Examiner notes claims 21, 36 and 37 recite a mental process, specifically evaluation, judgement and analysis. Examiner notes that the claimed invention is directed to obtaining data, performing an analysis on the data, and outputting the result which is directed to concepts that are performed mentally and a product of human mental work. Examiner notes that quantitative and qualitative analysis have been applied to patent and intellectual property information long before the invention of computers or computer models. Value and quality assessments have been determined by the mind of inventors, experts, and customers long before the standard computer functions and models were established. Examiner notes that the claimed invention is directed to receiving information related to patents and applying a quantitative and qualitative analysis on the data, and outputting the result which is similar to the abstract ideas identified in MPEP 2106.04(a)(2)(III) and the steps involved human judgments, observations, and evaluations that can be practically or reasonably performed in the human mind consistent with the “mental process” grouping set forth in MPEP 2106.04(a)(2)(III).
The conclusion that the claim recites an abstract idea within the groupings of the MPEP 2106.04(a)(2) remains grounded in the broadest reasonable interpretation consistent with the description of the invention in the specification. For example, (App. Spec. ¶ 14), the “generating frameworks for the evaluation of intangible assets and utilizing the frameworks to perform various analyses of intangible assets.” Accordingly, the Examiner submits claims 21, 36, and 37 recite an abstract idea.
The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. (Step 2A- Prong 1: YES. The claims are abstract).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to a method instructing the reader to implement the identified method of organizing human activity of commercial or legal interactions such as business decision making relating to organizational intangible assets. Examiner notes that the system provided merely receives information from a user related to an invention and analyzes the data. The system connects sellers and buyers to facilitate a transaction. Nothing is presented as to how the server and device systems improved, how the network is improved, how the computer system used is improved by the claimed invention. Applicant merely states that the improvement is found in the abstract idea itself meaning the alleged improvement is in the process of analyzing intellectual property. The specific limitations of the claimed invention which are directed to a server, device and network amount to generic computer components. The invention merely directs the users to implement the method via generic computer structure. For instance, the additional elements or combination of elements other than the abstract idea itself include the elements such as a “system” recited at a high level of generality. Accordingly, the claimed computer structure read in light of the specification can be any device and includes any wide range of possible devices comprising a number of components that are “well-known” and include an indiscriminate “device” (e.g., processor, memory, server, network, etc.). Thus, the claimed structure amounts to appending generic computer elements to abstract idea comprising the body of the claim. The computing element is only involved at a general, high level, and do not have the particular role within any of the functions but to be a generically claimed “the server computing device(s)1002 can include one or more processors1008, one or more computer-readable media1010, one or more communication interfaces1012, and one or more input/output devices1014. Each processor1008 can be a single processing unit or a number of processing units and can include single or multiple computing units or multiple processing cores. The processor(s)1008 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions”. (App. Spec. ¶ 12).
Similarly, reciting the abstract idea as software functions used to program a generic computer is not significant or meaningful: generic computers are programmed with software to perform various functions every day. A programmed generic computer is not a particular machine and by itself does not amount to an inventive concept because, as discussed in MPEP 2106.05(a), adding the words “apply it” (or an equivalent) with the judicial exception, or more instructions to implement an abstract idea on a computer, as discussed in Alice, 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)), is not enough to integrate the exception into a practical application. Further, it is not relevant that a human may perform a task differently from a computer. It is necessarily true that a human might apply an abstract idea in a different manner from a computer. What matters is the application, “stating an abstract idea while adding the words ‘apply it with a computer’” will not render an abstract idea non-abstract. Tranxition v. Lenovo, Nos. 2015-1907, -1941, -1958 (Fed. Cir. Nov. 16, 2016), slip op. at 7-8. Here, the instructions entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the role of the generic computing element recited in claims 21, 36, and 37 are the same as the role of the computer in the claims considered by the Supreme Court in Alice, and the claim as whole amounts merely to an instruction to apply the abstract idea on the generic device. Applicant’s amendment further recites utilizing a specifically-trained machine learning model. However, the claim does not recite any particular machine learning architecture, training technique, feature selection methodology, inferencing technique, or improvement to machine learning technology itself. Rather, the machine learning model is invoked at a high level of generality merely as a tool for generating the claimed qualitative factor from collected information. The claim therefore does not improve the functioning of a computer, improve machine learning technology, improve another technology or technical field, or effect any other technological improvement. Instead, the claimed machine learning model merely automates the otherwise abstract process of evaluating information to product a qualitative assessment. Accordingly, the claims have failed to integrate a practical application (2106.04(d)). (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claims 21-37, do not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible. (Step 2B: NO. The claims do not provide significantly more).
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea.
Claim 22-35 are further embellishments of the abstract idea and does not amount to significantly more.
Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter. See MPEP 2106.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
7. Claims 21-37 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 20090259506 to Barney in view of U.S. Patent Application Publication No. 20190028909 to Mermoud et al.
Referring to Claim 21, Barney discloses a computer user interface method for analyzing an intangible asset of an organization, the computer user interface produced on a user computing device, for operation by a user, in communication with a server computing device executing code establishing computer processes (see at least Barney: ¶ 164 “A computer program is caused to automatically access and read each computer text file and to extract therefrom certain selected patent metrics representative of or describing particular observed characteristics or metrics of each patent in the sequential series. The extracted patent metrics are input into a previously determined computer regression model or predictive algorithm that is selected and adjusted to calculate a corresponding rating output or mathematical score that is generally predictive of a particular patent quality of interest and/or the probability of a particular future event occurring. Preferably, for each patent in the sequential series a rating output or mathematical score is directly calculated from the extracted metrics using a series of predefined equations, formulas and/or rules comprising the algorithm. The results are then preferably stored in a computer accessible memory device in association with other selected information identifying each rated patent such that the corresponding rating may be readily referenced or retrieved for each patent in the sequential series”) comprising:
generating multiple computer-centric processing frameworks, wherein individual ones of the multiple computer-centric processing frameworks are associated with respective factor weightings, and wherein the individual ones of the multiple computer- centric processing frameworks are configured to extract data from certain interfaces while refraining from extracting data from other interfaces (see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”);
causing, by the server computing device, a first qualitative analysis comprising: obtaining data from a data source, the data corresponding to the intangible asset (see at least Barney: ¶ 164 “a substantial full-text copy of each patent in the sequential series is obtained in a computer text file format or similar computer-accessible format. A computer program is caused to automatically access and read each computer text file and to extract therefrom certain selected patent metrics representative of or describing particular observed characteristics or metrics of each patent in the sequential series. The extracted patent metrics are input into a previously determined computer regression model or predictive algorithm that is selected and adjusted to calculate a corresponding rating output or mathematical score that is generally predictive of a particular patent quality of interest and/or the probability of a particular future event occurring. Preferably, for each patent in the sequential series a rating output or mathematical score is directly calculated from the extracted metrics using a series of predefined equations, formulas and/or rules comprising the algorithm. The results are then preferably stored in a computer accessible memory device in association with other selected information identifying each rated patent such that the corresponding rating may be readily referenced or retrieved for each patent in the sequential series.”);
selecting a computer-centric processing framework from the multiple computer-centric processing frameworks including a predefined factor associated with a type of the intangible asset (see at least Barney: ¶ 45 “A computer algorithm evaluates the full-text file of the patent to be rated and extracts certain selected patent metric(s), which may be predefined, user-defined, or both. Based on the selected patent metric(s), the algorithm computes a rating number or probability (e.g., between 0 and 1) corresponding to the likely presence or absence of one or more user-defined qualities of interest in the patent to be rated and/or the probability of one or more possible future events occurring relative to the patent. If desired, the rating number or probability can be further ranked against other similar ratings for patents within a selected patent population, which may be predetermined, user-defined, or both. Thus, the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 102 “Preferably, the characteristics X1, X2, X3 have been previously selected and determined to have a statistically significant impact on the selected patent quality desired to be measured. At step 208 the observed patent quality Y of patent n is inputted into the system. In this case, the patent quality of interest is the validity or invalidity of the patent as determined by a final judgment of a court. Alternatively, the measured patent quality could be any one or more of a number of other qualities of interest such as discussed above.”; see also Barney: ¶ 159 “Patent ratings or rankings as taught herein may be compiled and reported in a variety of suitable formats, including numerical ratings/rankings, alphanumeric ratings/rankings, percentile rankings, relative probabilities, absolute probabilities, and the like. Multiple ratings or rankings may also be provided corresponding to different patent qualities of interest or specific patent claims. FIG. 11 illustrates one possible form of a patent rating and valuation report 700 that may be generated in accordance with a preferred embodiment of the invention.”; see also Barney: ¶ 38 “The approach is not limited, however, to analyzing litigated patents. For example, fruitful comparisons may also be made between litigated patents (presumably the most valuable patents) and non-litigated patents; or between high-royalty-bearing patents and low-royalty-bearing patents; or between high-cost-basis patents and low-cost-basis patents; or between published patent applications and issued patents. The number and variety of definable patent populations having different desired qualities or characteristics capable of fruitful comparison in accordance with the invention herein is virtually unlimited. While not specifically discussed herein, those skilled in the art will also recognize that a similar approach may also be used for valuing and/or rating other intellectual property or intangible assets such as trademarks, copyrights, domain names, web sites, and the like.”; see also Barney: ¶ 184 “While the statistical rating method and system of one embodiment of the present invention is disclosed and discussed specifically in the context of rating utility patents, those skilled in the art will readily appreciate that the techniques and concepts disclosed herein may have equal applicability to rating other types of intellectual property assets, such as trademarks, copyrights, trade secrets, domain names, web sites and the like.”; see at least Barney: ¶ 164 “A computer program is caused to automatically access and read each computer text file and to extract therefrom certain selected patent metrics representative of or describing particular observed characteristics or metrics of each patent in the sequential series. The extracted patent metrics are input into a previously determined computer regression model or predictive algorithm that is selected and adjusted to calculate a corresponding rating output or mathematical score that is generally predictive of a particular patent quality of interest and/or the probability of a particular future event occurring. Preferably, for each patent in the sequential series a rating output or mathematical score is directly calculated from the extracted metrics using a series of predefined equations, formulas and/or rules comprising the algorithm. The results are then preferably stored in a computer accessible memory device in association with other selected information identifying each rated patent such that the corresponding rating may be readily referenced or retrieved for each patent in the sequential series”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”; see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”); and
generating, utilizing […] the computer-centric processing framework and a first portion of the data, a first qualitative factor associated with one of the respective factor weightings from the data as extracted from the certain interfaces (see at least Barney: ¶ 69 “In its simplest form one embodiment of the present invention provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc.”; see also Barney: ¶ 85 “At block 148 a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 102 “At step 206 the system inputs selected characteristics (Cn=X1, X2, X3) of the next patent (n) in the study population (e.g., litigated patents). Preferably, the characteristics X1, X2, X3 have been previously selected and determined to have a statistically significant impact on the selected patent quality desired to be measured”; see also Barney: ¶ 159 “Patent ratings or rankings as taught herein may be compiled and reported in a variety of suitable formats, including numerical ratings/rankings, alphanumeric ratings/rankings, percentile rankings, relative probabilities, absolute probabilities, and the like. Multiple ratings or rankings may also be provided corresponding to different patent qualities of interest or specific patent claims. FIG. 11 illustrates one possible form of a patent rating and valuation report 700 that may be generated in accordance with a preferred embodiment of the invention.”; see also Barney: ¶ 166 “While it is preferred to provide independent B/D/R ratings and/or an overall score for each rated patent asset, those skilled in the art will recognize that numerous other ranking or rating systems may be used with efficacy in accordance with the teachings herein. For example, individual patent/claim scores may be ranked relative to a given population such that ratings may be provided on a percentile basis. Alternatively, numerical and/or alphanumerical scores may be assigned on a scale from 1-5, 1-9, 1-10, or A-E, for example. Optionally, and as illustrated in FIG. 11, each claim of the reported patent may be analyzed and rated separately if desired. In that case, each claim (1-9 in the example illustrated in FIG. 11) is preferably indicated as being either independent (“I”) or dependent (“D”), as the case may be. Alternatively, only the independent claims of a reported patent may be rated if desired.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”; see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”);
transmitting, via a network by a communication protocol from the server computing device to the user computing device, the first qualitative factor (see at least Barney: ¶ 34-35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see also Barney: ¶ 37 “The selection of which study population(s) to use depends upon the focus of the statistical inquiry and the desired quality (e.g., claim scope, validity, enforceability, etc.) of the patent asset desired to be elicited”; see also Barney: ¶ 41 “method and automated system for rating or ranking patents or other intangible assets. In accordance with the method a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from or believed to be different from the first quality or characteristic. A computer accessible database is provided and is programmed to contain selected patent metrics representative of or describing particular corresponding characteristics observed for each patent in the first and second patent populations. A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations. The regression model may then be used to rate or rank one or more patents in a third patent population by inputting into the regression model selected patent metrics representative of or describing corresponding characteristics of one or more patents in the third population”; see also Barney: ¶ 44 “Individual selected patents from the population are ranked in accordance with selected patent metrics to determine an overall patent quality rating and ranking for each individual selected patent. The patent value distribution curve is then used to determine a corresponding estimated value for an individual selected patent in accordance with its overall patent quality ranking. If desired, the method may be used to generate a patent valuation report including basic information identifying a particular reported patent or patents of interest and one or more valuations determined in accordance with the method described above.”; see also Barney: ¶ 69 “provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc”; see also Barney: ¶ 70 “Indirect patent metrics measure or report a quality or characteristic of a patent that, while perhaps not directly revealed by the patent itself or the PTO records relating to the patent, can be determined or derived from such information (and/or other information sources) using a variety of algorithms or statistical methods including, but not limited to, the methods disclosed herein. Examples of indirect patent metrics include reported patent litigation results, published case opinions, patent licenses, marking of patented products, and the like. Indirect patent metrics may also include derived measures or measurement components such as frequency or infrequency of certain word usage relative to the general patent population or relative to a defined sub-population of patents in the same general field”; see also Barney: ¶ 73 “These statistical comparisons are then used to construct and optimize a computer model or computer algorithm comprising a series of operative rules and/or mathematical equations. The algorithm is used to predict and/or provide statistically determined probabilities of a desired value or quality being present and/or of a future event occurring, given the identified characteristics of an individual identified patent or group of patents. The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined (or assumed) to have statistical significance. For example, positive scores could generally be applied to those patent characteristics determined or believed to have desirable influence and negative scores could be applied to those patent characteristics determined or assumed to have undesirable influence on the particular quality or event of interest”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents.”); and
generating a user interface comprising a first graphic associated with the first qualitative factor based on the first qualitative analysis, wherein the user interface is updated based at least in part on inputting new data into the user interface (see at least Barney: ¶ 180-181: “users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age). If desired, each of the B/D/R ratings can be statistically adjusted relative to the remaining ratings using known statistical techniques so as to minimize any undesired collinearity or overlap in the reported ratings.”; see also Barney: ¶ 161 “In the particular example illustrated, ratings 720 are provided on a scale from 1 to 10. However, a variety of other suitable rating scales may also be used with efficacy, such as numerical rankings, percentile rankings, alphanumeric ratings, absolute or relative probabilities and the like. If desired, individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 166-167 “While it is preferred to provide independent B/D/R ratings and/or an overall score for each rated patent asset, those skilled in the art will recognize that numerous other ranking or rating systems may be used with efficacy in accordance with the teachings herein. For example, individual patent/claim scores may be ranked relative to a given population such that ratings may be provided on a percentile basis. Alternatively, numerical and/or alphanumerical scores may be assigned on a scale from 1-5, 1-9, 1-10, or A-E, for example. Optionally, and as illustrated in FIG. 11, each claim of the reported patent may be analyzed and rated separately if desired. In that case, each claim (1-9 in the example illustrated in FIG. 11) is preferably indicated as being either independent (“I”) or dependent (“D”), as the case may be. Alternatively, only the independent claims of a reported patent may be rated if desired. [0167] Individual ratings 740, 750 and 755 in report 700 preferably provide numerical ratings (1-10) of the likely breadth (“B”), defensibility (“D”), and relevance (“R”) of each claim of the reported patent (and/or the patent as a whole). Such “BDR” ratings may alternatively be expressed in a variety of other suitable formats, such as letters, symbols, integer numerals, decimal numerals, percentage probabilities, percentile rankings, and the like. For example, a letter scoring system (e.g., A-E) could be assigned for each of the individual B/D/R components. In that case, a BDR rating of “B/A/A” would represent a “B” rating for breadth, and “A” ratings for both defensibility and relevance. An overall rating could then be derived from the individual BDR component ratings using a suitable conversion index rating system as generally illustrated below in Table 4”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”).
Barney does not specifically teach utilizing a specifically-trained machine learning model associated with the computer-centric processing framework.
Mermoud teaches utilizing a trained machine learning model. Mermoud teaches supervised machine learning models that are trained using a training data set, thereby teaching a trained machine learning model corresponding to the claimed specifically-trained machine learning model (paragraph [0038] In various embodiments, network assurance process 248 may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data) and therefore utilized to generate predictive scores from input metrics (paragraphs [0061]: The device predicts a health status score for the networking equipment in the physical location using the received network metrics as input to a machine learning-based predictive scoring model. The device provides an indication of the predicted health status score in conjunction with a visualization of the physical location for display by an electronic display. The device adjusts the predictive scoring model based on feedback regarding the predicted health status score and [0069]: n various embodiments, SPM 408 may maintain a machine learning- based predictive scoring model that uses M.sub.i as an input feature vector and outputs S.sub.i. For example, the scoring model may be a regression model such as a random forest model, deep neural network, or the like, that learns the relationships between these two sets of data. At first, the predictive scoring model of SPM 408 may be trained using labels generated by manually defined health status rules. However, over time, the model may be adjusted automatically and dynamically based on feedback signals from the applications involved, the monitored network, and/or the network administrator). Mermoud further teaches generating predictive using the trained machine learning model from received input metrics (paragraphs [0061 & 0069]). Accordingly, Mermoud teaches utilizing a trained machine learning model to generate predictive scores from input data. Examiner notes that Barney is relied upon for teaching the claimed computer-centric processing framework. Mermoud is relied upon solely for teaching that the predictive model utilized within such a framework may be a trained machine learning model.
Therefore, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to implement Barney’s predictive regression model utilizing the trained machine learning model taught by Mermoud because Mermoud teaches that trained machine learning models, including regression models, generate predictive scores from input metrics and recognize patterns within empirical data to improve predictive analysis (paragraphs [0037, 0038, 0061 and 0069]).
Referring to Claim 22, Barney discloses the computer user interface method of claim 21, the first qualitative factor being at least one of: coverage corresponding to a determined relationship between a product or a service of the organization and the intangible asset; opportunity corresponding to a potential increase in revenue of the organization attributable to the intangible asset; or risk corresponding to a potential that the intangible asset contributes to a decrease in the opportunity (see at least Barney: ¶ 40 “In accordance with another embodiment the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest”; and ¶ 45 “the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 85 “a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 131 “Each of the patent metrics identified above is anticipated to have a statistically significant impact on the probability of a patent being litigated in the future. By undertaking a statistical study of these and other patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can calculate an estimated statistical probability of a given patent being litigated during a predetermined period of time in the future based on the identified patent characteristics. If desired, a numerical rating or ranking may be assigned to each patent indicating the relative likelihood of litigation”; see also Barney: ¶ 147 “the identified patent metrics are anticipated to have a statistically significant impact on the probability of a patent being litigated successfully or unsuccessfully. By undertaking a statistical study of these and other identified patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can accurately calculate an estimated statistical probability of a given patent being successfully litigated (found valid and/or infringed), taking into consideration all of the identified patent characteristics and statistical relationships simultaneously. If desired, a numerical rating or ranking may be automatically calculated and assigned to each patent indicating the relative likelihood of a particular event or quality. Such rating may be provided for the patent as a whole or, alternatively (or in addition), individual ratings may be provided for one or more individual claims of the patent, as desired”; see also Barney: ¶ 179 “Ideally, it would also be possible for a user to request reports on all patents associated with a specific commercial product. Such product patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products. Relevant patent marking data could be gathered either through private voluntary reporting by manufacturers of such products and/or it may be gathered through other available means, such as automated web crawlers, third-party reporting or inputting and the like. Patent marking data (e.g., the presence or absence of a patent notice on a corresponding commercial product) and/or other relevant data (e.g., sales volume, sales growth, profits, etc.) could provide additional objective metric(s) by which to rate relevant patents in accordance with the invention. Presumably, patents that are being actively commercialized are more valuable than “paper patents” for which there is no corresponding commercial product. Optionally, the patent marking database can also include the necessary URL address information and/or the like which will allow users to hot-link directly to a third-party web page for each corresponding product and/or associated product manufacturer.”; see also Barney: ¶ 180 “In another embodiment of the invention, users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 171 “Similarly, a modified income valuation approach could be used whereby a hypothetical future projected income stream or average industry royalty rate is multiplied by a suitable discount or enhancement factor corresponding to the rating that the patent receives in accordance with the methods disclosed herein. In this manner, patents that receive higher ratings would be valued at higher than industry averages. Conversely, patents that receive lower ratings would be valued at lower than industry averages.”).
Referring to Claim 23, Barney discloses the computer user interface method of claim 21, including further comprising: causing, by the server computing device, a second qualitative analysis comprising generating, based at least partly on the computer-centric processing framework and the first portion of the data, a second qualitative factor (see at least Barney: ¶ 72 “In accordance with one preferred embodiment of the invention relative ratings or, rankings are generated using a database of selected patent information by identifying and comparing various relevant characteristics or metrics of individual patents contained in the database. In one example, a first population of patents having a known or assumed relatively high intrinsic value (e.g. successfully litigated patents) are compared to a second population of patents having a known or assumed relatively low intrinsic value (e.g. unsuccessfully litigated patents). Based on the comparison, certain characteristics are identified as statistically more prevalent or more pronounced in one population group or the other to a significant degree.”; see also Barney: ¶ 69 “provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc”; see also Barney: ¶ 70 “Indirect patent metrics measure or report a quality or characteristic of a patent that, while perhaps not directly revealed by the patent itself or the PTO records relating to the patent, can be determined or derived from such information (and/or other information sources) using a variety of algorithms or statistical methods including, but not limited to, the methods disclosed herein. Examples of indirect patent metrics include reported patent litigation results, published case opinions, patent licenses, marking of patented products, and the like. Indirect patent metrics may also include derived measures or measurement components such as frequency or infrequency of certain word usage relative to the general patent population or relative to a defined sub-population of patents in the same general field”; see also Barney: ¶ 73 “These statistical comparisons are then used to construct and optimize a computer model or computer algorithm comprising a series of operative rules and/or mathematical equations. The algorithm is used to predict and/or provide statistically determined probabilities of a desired value or quality being present and/or of a future event occurring, given the identified characteristics of an individual identified patent or group of patents. The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined (or assumed) to have statistical significance. For example, positive scores could generally be applied to those patent characteristics determined or believed to have desirable influence and negative scores could be applied to those patent characteristics determined or assumed to have undesirable influence on the particular quality or event of interest”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”).
Referring to Claim 24, Barney discloses the computer user interface method of claim 23, including further comprising: transmitting, via the network by the communication protocol from the server computing device to the user computing device, the second qualitative factor; and displaying to a user on a screen, a user interface comprising a second graphic associated with the second qualitative factor based on the second qualitative analysis, wherein the display is updated based at least in part on inputting new data into the user interface (see at least Barney: ¶ 180-181: “users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 72 “In accordance with one preferred embodiment of the invention relative ratings or, rankings are generated using a database of selected patent information by identifying and comparing various relevant characteristics or metrics of individual patents contained in the database. In one example, a first population of patents having a known or assumed relatively high intrinsic value (e.g. successfully litigated patents) are compared to a second population of patents having a known or assumed relatively low intrinsic value (e.g. unsuccessfully litigated patents). Based on the comparison, certain characteristics are identified as statistically more prevalent or more pronounced in one population group or the other to a significant degree.”; see also Barney: ¶ 69 “provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc”; see also Barney: ¶ 70 “Indirect patent metrics measure or report a quality or characteristic of a patent that, while perhaps not directly revealed by the patent itself or the PTO records relating to the patent, can be determined or derived from such information (and/or other information sources) using a variety of algorithms or statistical methods including, but not limited to, the methods disclosed herein. Examples of indirect patent metrics include reported patent litigation results, published case opinions, patent licenses, marking of patented products, and the like. Indirect patent metrics may also include derived measures or measurement components such as frequency or infrequency of certain word usage relative to the general patent population or relative to a defined sub-population of patents in the same general field”; see also Barney: ¶ 73 “These statistical comparisons are then used to construct and optimize a computer model or computer algorithm comprising a series of operative rules and/or mathematical equations. The algorithm is used to predict and/or provide statistically determined probabilities of a desired value or quality being present and/or of a future event occurring, given the identified characteristics of an individual identified patent or group of patents. The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined (or assumed) to have statistical significance. For example, positive scores could generally be applied to those patent characteristics determined or believed to have desirable influence and negative scores could be applied to those patent characteristics determined or assumed to have undesirable influence on the particular quality or event of interest”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents.”).
Referring to Claim 25, Barney discloses the computer user interface method of claim 24, including each of the first and second qualitative factors being one of: coverage corresponding to a determined relationship between a product or a service of the organization and the intangible asset; opportunity corresponding to a potential increase in revenue of the organization attributable to the intangible asset; or risk corresponding to a potential that the intangible asset contributes to a decrease in the opportunity, wherein the first and second qualitative factors are different (see at least Barney: ¶ 40 “In accordance with another embodiment the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest”; and ¶ 45 “the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 85 “a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 131 “Each of the patent metrics identified above is anticipated to have a statistically significant impact on the probability of a patent being litigated in the future. By undertaking a statistical study of these and other patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can calculate an estimated statistical probability of a given patent being litigated during a predetermined period of time in the future based on the identified patent characteristics. If desired, a numerical rating or ranking may be assigned to each patent indicating the relative likelihood of litigation”; see also Barney: ¶ 147 “the identified patent metrics are anticipated to have a statistically significant impact on the probability of a patent being litigated successfully or unsuccessfully. By undertaking a statistical study of these and other identified patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can accurately calculate an estimated statistical probability of a given patent being successfully litigated (found valid and/or infringed), taking into consideration all of the identified patent characteristics and statistical relationships simultaneously. If desired, a numerical rating or ranking may be automatically calculated and assigned to each patent indicating the relative likelihood of a particular event or quality. Such rating may be provided for the patent as a whole or, alternatively (or in addition), individual ratings may be provided for one or more individual claims of the patent, as desired”; see also Barney: ¶ 179 “Ideally, it would also be possible for a user to request reports on all patents associated with a specific commercial product. Such product patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products. Relevant patent marking data could be gathered either through private voluntary reporting by manufacturers of such products and/or it may be gathered through other available means, such as automated web crawlers, third-party reporting or inputting and the like. Patent marking data (e.g., the presence or absence of a patent notice on a corresponding commercial product) and/or other relevant data (e.g., sales volume, sales growth, profits, etc.) could provide additional objective metric(s) by which to rate relevant patents in accordance with the invention. Presumably, patents that are being actively commercialized are more valuable than “paper patents” for which there is no corresponding commercial product. Optionally, the patent marking database can also include the necessary URL address information and/or the like which will allow users to hot-link directly to a third-party web page for each corresponding product and/or associated product manufacturer.”; see also Barney: ¶ 180 “In another embodiment of the invention, users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 171 “Similarly, a modified income valuation approach could be used whereby a hypothetical future projected income stream or average industry royalty rate is multiplied by a suitable discount or enhancement factor corresponding to the rating that the patent receives in accordance with the methods disclosed herein. In this manner, patents that receive higher ratings would be valued at higher than industry averages. Conversely, patents that receive lower ratings would be valued at lower than industry averages.”).
Referring to Claim 26, Barney discloses the computer user interface method of claim 23, including further comprising causing, by the server computing device, a third qualitative analysis comprising generating, based at least partly on the computer-centric processing framework and the first portion of the data, a third qualitative factor (see at least Barney: ¶ 72 “In accordance with one preferred embodiment of the invention relative ratings or, rankings are generated using a database of selected patent information by identifying and comparing various relevant characteristics or metrics of individual patents contained in the database. In one example, a first population of patents having a known or assumed relatively high intrinsic value (e.g. successfully litigated patents) are compared to a second population of patents having a known or assumed relatively low intrinsic value (e.g. unsuccessfully litigated patents). Based on the comparison, certain characteristics are identified as statistically more prevalent or more pronounced in one population group or the other to a significant degree.”; see also Barney: ¶ 69 “provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc”; see also Barney: ¶ 70 “Indirect patent metrics measure or report a quality or characteristic of a patent that, while perhaps not directly revealed by the patent itself or the PTO records relating to the patent, can be determined or derived from such information (and/or other information sources) using a variety of algorithms or statistical methods including, but not limited to, the methods disclosed herein. Examples of indirect patent metrics include reported patent litigation results, published case opinions, patent licenses, marking of patented products, and the like. Indirect patent metrics may also include derived measures or measurement components such as frequency or infrequency of certain word usage relative to the general patent population or relative to a defined sub-population of patents in the same general field”; see also Barney: ¶ 73 “These statistical comparisons are then used to construct and optimize a computer model or computer algorithm comprising a series of operative rules and/or mathematical equations. The algorithm is used to predict and/or provide statistically determined probabilities of a desired value or quality being present and/or of a future event occurring, given the identified characteristics of an individual identified patent or group of patents. The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined (or assumed) to have statistical significance. For example, positive scores could generally be applied to those patent characteristics determined or believed to have desirable influence and negative scores could be applied to those patent characteristics determined or assumed to have undesirable influence on the particular quality or event of interest”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”).
Referring to Claim 27, Barney discloses the computer user interface method of claim 26, including further comprising: transmitting, via the network by the communication protocol from the server computing device to the user computing device, the second qualitative factor, third qualitative factor, or both; and causing, by the server computing device, on the display of the user computing device, presentation on the screen, a second graphic associated with the second, qualitative factor based on the second qualitative analysis, a third graphic associated with the third qualitative factor based on the third qualitative analysis, or both (see at least Barney: ¶ 180-181: “users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 72 “In accordance with one preferred embodiment of the invention relative ratings or, rankings are generated using a database of selected patent information by identifying and comparing various relevant characteristics or metrics of individual patents contained in the database. In one example, a first population of patents having a known or assumed relatively high intrinsic value (e.g. successfully litigated patents) are compared to a second population of patents having a known or assumed relatively low intrinsic value (e.g. unsuccessfully litigated patents). Based on the comparison, certain characteristics are identified as statistically more prevalent or more pronounced in one population group or the other to a significant degree.”; see also Barney: ¶ 69 “provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc”; see also Barney: ¶ 70 “Indirect patent metrics measure or report a quality or characteristic of a patent that, while perhaps not directly revealed by the patent itself or the PTO records relating to the patent, can be determined or derived from such information (and/or other information sources) using a variety of algorithms or statistical methods including, but not limited to, the methods disclosed herein. Examples of indirect patent metrics include reported patent litigation results, published case opinions, patent licenses, marking of patented products, and the like. Indirect patent metrics may also include derived measures or measurement components such as frequency or infrequency of certain word usage relative to the general patent population or relative to a defined sub-population of patents in the same general field”; see also Barney: ¶ 73 “These statistical comparisons are then used to construct and optimize a computer model or computer algorithm comprising a series of operative rules and/or mathematical equations. The algorithm is used to predict and/or provide statistically determined probabilities of a desired value or quality being present and/or of a future event occurring, given the identified characteristics of an individual identified patent or group of patents. The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined (or assumed) to have statistical significance. For example, positive scores could generally be applied to those patent characteristics determined or believed to have desirable influence and negative scores could be applied to those patent characteristics determined or assumed to have undesirable influence on the particular quality or event of interest”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents.”).
Referring to Claim 28, Barney discloses the computer user interface method of claim 27, including wherein the first qualitative factor, the second qualitative factor, and the third qualitative factor are one of: coverage corresponding to a determined relationship between a product or a service of the organization and the intangible asset; opportunity corresponding to a potential increase in revenue of the organization attributable to the intangible asset; or risk corresponding to a potential that the intangible asset contributes to a decrease in the opportunity, wherein the first qualitative factor, the second qualitative factor, and the third qualitative factor are different (see at least Barney: ¶ 40 “In accordance with another embodiment the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest”; and ¶ 45 “the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 85 “a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 131 “Each of the patent metrics identified above is anticipated to have a statistically significant impact on the probability of a patent being litigated in the future. By undertaking a statistical study of these and other patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can calculate an estimated statistical probability of a given patent being litigated during a predetermined period of time in the future based on the identified patent characteristics. If desired, a numerical rating or ranking may be assigned to each patent indicating the relative likelihood of litigation”; see also Barney: ¶ 147 “the identified patent metrics are anticipated to have a statistically significant impact on the probability of a patent being litigated successfully or unsuccessfully. By undertaking a statistical study of these and other identified patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can accurately calculate an estimated statistical probability of a given patent being successfully litigated (found valid and/or infringed), taking into consideration all of the identified patent characteristics and statistical relationships simultaneously. If desired, a numerical rating or ranking may be automatically calculated and assigned to each patent indicating the relative likelihood of a particular event or quality. Such rating may be provided for the patent as a whole or, alternatively (or in addition), individual ratings may be provided for one or more individual claims of the patent, as desired”; see also Barney: ¶ 179 “Ideally, it would also be possible for a user to request reports on all patents associated with a specific commercial product. Such product patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products. Relevant patent marking data could be gathered either through private voluntary reporting by manufacturers of such products and/or it may be gathered through other available means, such as automated web crawlers, third-party reporting or inputting and the like. Patent marking data (e.g., the presence or absence of a patent notice on a corresponding commercial product) and/or other relevant data (e.g., sales volume, sales growth, profits, etc.) could provide additional objective metric(s) by which to rate relevant patents in accordance with the invention. Presumably, patents that are being actively commercialized are more valuable than “paper patents” for which there is no corresponding commercial product. Optionally, the patent marking database can also include the necessary URL address information and/or the like which will allow users to hot-link directly to a third-party web page for each corresponding product and/or associated product manufacturer.”; see also Barney: ¶ 180 “In another embodiment of the invention, users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 171 “Similarly, a modified income valuation approach could be used whereby a hypothetical future projected income stream or average industry royalty rate is multiplied by a suitable discount or enhancement factor corresponding to the rating that the patent receives in accordance with the methods disclosed herein. In this manner, patents that receive higher ratings would be valued at higher than industry averages. Conversely, patents that receive lower ratings would be valued at lower than industry averages.”).
Referring to Claim 29, Barney discloses the computer user interface method of claim 21, including further comprising: causing, by the server computing device, a quantitative analysis comprising generating, based at least partly on a second portion of the data, a quantitative factor; transmitting, via the network by the communication protocol from the server computing device to the user computing device, the quantitative factor; and causing, by the server computing device, on the display of the user computing device, presentation on the screen, a second graphic associated with the quantitative factor based on the quantitative analysis (see at least Barney: ¶ 180-181: “users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 72 “In accordance with one preferred embodiment of the invention relative ratings or, rankings are generated using a database of selected patent information by identifying and comparing various relevant characteristics or metrics of individual patents contained in the database. In one example, a first population of patents having a known or assumed relatively high intrinsic value (e.g. successfully litigated patents) are compared to a second population of patents having a known or assumed relatively low intrinsic value (e.g. unsuccessfully litigated patents). Based on the comparison, certain characteristics are identified as statistically more prevalent or more pronounced in one population group or the other to a significant degree.”; see also Barney: ¶ 69 “provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc”; see also Barney: ¶ 70 “Indirect patent metrics measure or report a quality or characteristic of a patent that, while perhaps not directly revealed by the patent itself or the PTO records relating to the patent, can be determined or derived from such information (and/or other information sources) using a variety of algorithms or statistical methods including, but not limited to, the methods disclosed herein. Examples of indirect patent metrics include reported patent litigation results, published case opinions, patent licenses, marking of patented products, and the like. Indirect patent metrics may also include derived measures or measurement components such as frequency or infrequency of certain word usage relative to the general patent population or relative to a defined sub-population of patents in the same general field”; see also Barney: ¶ 73 “These statistical comparisons are then used to construct and optimize a computer model or computer algorithm comprising a series of operative rules and/or mathematical equations. The algorithm is used to predict and/or provide statistically determined probabilities of a desired value or quality being present and/or of a future event occurring, given the identified characteristics of an individual identified patent or group of patents. The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined (or assumed) to have statistical significance. For example, positive scores could generally be applied to those patent characteristics determined or believed to have desirable influence and negative scores could be applied to those patent characteristics determined or assumed to have undesirable influence on the particular quality or event of interest”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents.”).
Referring to Claim 30, Barney discloses the computer user interface method of claim 29, including the quantitative factor corresponding to a monetary valuation of the intangible asset based on a valuation methodology (see at least Barney: ¶ 171 “Similarly, a modified income valuation approach could be used whereby a hypothetical future projected income stream or average industry royalty rate is multiplied by a suitable discount or enhancement factor corresponding to the rating that the patent receives in accordance with the methods disclosed herein. In this manner, patents that receive higher ratings would be valued at higher than industry averages. Conversely, patents that receive lower ratings would be valued at lower than industry averages.”).
Referring to Claim 31, Barney discloses the computer user interface method of claim 30, including the valuation methodology being a cost methodology, an income methodology, an income methodology, or a combination thereof (see at least Barney: ¶ 171 “Similarly, a modified income valuation approach could be used whereby a hypothetical future projected income stream or average industry royalty rate is multiplied by a suitable discount or enhancement factor corresponding to the rating that the patent receives in accordance with the methods disclosed herein. In this manner, patents that receive higher ratings would be valued at higher than industry averages. Conversely, patents that receive lower ratings would be valued at lower than industry averages”).
Referring to Claim 32, Barney discloses the computer user interface method of claim 29, including wherein the quantitative analysis is based at least partly on the first qualitative analysis (see at least Barney: ¶ 69 “In its simplest form one embodiment of the present invention provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc.”).
Referring to Claim 33, Barney discloses the computer user interface method of claim 21, including wherein the computer-centric processing framework is associated with a plurality of factors comprising: coverage corresponding to a determined relationship between at least one of products or services of one or more organizations and the intangible asset; opportunity corresponding to a potential increase in revenue of the organization attributable to the intangible asset; and risk corresponding to a potential that the intangible asset contributes to a decrease in the opportunity (see at least Barney: ¶ 40 “In accordance with another embodiment the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest”; and ¶ 45 “the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 85 “a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 131 “Each of the patent metrics identified above is anticipated to have a statistically significant impact on the probability of a patent being litigated in the future. By undertaking a statistical study of these and other patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can calculate an estimated statistical probability of a given patent being litigated during a predetermined period of time in the future based on the identified patent characteristics. If desired, a numerical rating or ranking may be assigned to each patent indicating the relative likelihood of litigation”; see also Barney: ¶ 147 “the identified patent metrics are anticipated to have a statistically significant impact on the probability of a patent being litigated successfully or unsuccessfully. By undertaking a statistical study of these and other identified patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can accurately calculate an estimated statistical probability of a given patent being successfully litigated (found valid and/or infringed), taking into consideration all of the identified patent characteristics and statistical relationships simultaneously. If desired, a numerical rating or ranking may be automatically calculated and assigned to each patent indicating the relative likelihood of a particular event or quality. Such rating may be provided for the patent as a whole or, alternatively (or in addition), individual ratings may be provided for one or more individual claims of the patent, as desired”; see also Barney: ¶ 179 “Ideally, it would also be possible for a user to request reports on all patents associated with a specific commercial product. Such product patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products. Relevant patent marking data could be gathered either through private voluntary reporting by manufacturers of such products and/or it may be gathered through other available means, such as automated web crawlers, third-party reporting or inputting and the like. Patent marking data (e.g., the presence or absence of a patent notice on a corresponding commercial product) and/or other relevant data (e.g., sales volume, sales growth, profits, etc.) could provide additional objective metric(s) by which to rate relevant patents in accordance with the invention. Presumably, patents that are being actively commercialized are more valuable than “paper patents” for which there is no corresponding commercial product. Optionally, the patent marking database can also include the necessary URL address information and/or the like which will allow users to hot-link directly to a third-party web page for each corresponding product and/or associated product manufacturer.”; see also Barney: ¶ 180 “In another embodiment of the invention, users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 171 “Similarly, a modified income valuation approach could be used whereby a hypothetical future projected income stream or average industry royalty rate is multiplied by a suitable discount or enhancement factor corresponding to the rating that the patent receives in accordance with the methods disclosed herein. In this manner, patents that receive higher ratings would be valued at higher than industry averages. Conversely, patents that receive lower ratings would be valued at lower than industry averages.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”).
Referring to Claim 34, Barney discloses the computer user interface method of claim 33, including the plurality of factors further comprising invalidity corresponding to a probability of the intangible asset being invalidated in a litigation proceeding or an administrative proceeding (see at last Barney: ¶ 85 “At block 148 a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”.
Referring to Claim 35, Barney discloses the computer user interface method of claim 21, including wherein the intangible asset is a patent, trademark, copyright, or trade secret (see also Barney: ¶ 159 “Patent ratings or rankings as taught herein may be compiled and reported in a variety of suitable formats, including numerical ratings/rankings, alphanumeric ratings/rankings, percentile rankings, relative probabilities, absolute probabilities, and the like. Multiple ratings or rankings may also be provided corresponding to different patent qualities of interest or specific patent claims. FIG. 11 illustrates one possible form of a patent rating and valuation report 700 that may be generated in accordance with a preferred embodiment of the invention.”; see also Barney: ¶ 38 “The approach is not limited, however, to analyzing litigated patents. For example, fruitful comparisons may also be made between litigated patents (presumably the most valuable patents) and non-litigated patents; or between high-royalty-bearing patents and low-royalty-bearing patents; or between high-cost-basis patents and low-cost-basis patents; or between published patent applications and issued patents. The number and variety of definable patent populations having different desired qualities or characteristics capable of fruitful comparison in accordance with the invention herein is virtually unlimited. While not specifically discussed herein, those skilled in the art will also recognize that a similar approach may also be used for valuing and/or rating other intellectual property or intangible assets such as trademarks, copyrights, domain names, web sites, and the like.”; see also Barney: ¶ 184 “While the statistical rating method and system of one embodiment of the present invention is disclosed and discussed specifically in the context of rating utility patents, those skilled in the art will readily appreciate that the techniques and concepts disclosed herein may have equal applicability to rating other types of intellectual property assets, such as trademarks, copyrights, trade secrets, domain names, web sites and the like.”).
Referring to Claim 36, Barney discloses a computer user interface method for analyzing an intangible asset of an organization, the computer user interface produced on a user computing device, for operation by a user, in communication with a server computing device executing code establishing computer processes comprising:
generating multiple computer-centric processing frameworks, wherein individual ones of the multiple computer-centric processing frameworks are associated with respective factor weightings, and wherein the individual ones of the multiple computer- centric processing frameworks are configured to extract data from certain interfaces while refraining from extracting data from other interfaces (see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”);
causing, by the server computing device, a qualitative analysis (see at least Barney: ¶ 164 “A computer program is caused to automatically access and read each computer text file and to extract therefrom certain selected patent metrics representative of or describing particular observed characteristics or metrics of each patent in the sequential series. The extracted patent metrics are input into a previously determined computer regression model or predictive algorithm that is selected and adjusted to calculate a corresponding rating output or mathematical score that is generally predictive of a particular patent quality of interest and/or the probability of a particular future event occurring. Preferably, for each patent in the sequential series a rating output or mathematical score is directly calculated from the extracted metrics using a series of predefined equations, formulas and/or rules comprising the algorithm. The results are then preferably stored in a computer accessible memory device in association with other selected information identifying each rated patent such that the corresponding rating may be readily referenced or retrieved for each patent in the sequential series”) comprising:
obtaining data from a data source, the data corresponding to the intangible asset (see at least Barney: ¶ 164 “a substantial full-text copy of each patent in the sequential series is obtained in a computer text file format or similar computer-accessible format. A computer program is caused to automatically access and read each computer text file and to extract therefrom certain selected patent metrics representative of or describing particular observed characteristics or metrics of each patent in the sequential series. The extracted patent metrics are input into a previously determined computer regression model or predictive algorithm that is selected and adjusted to calculate a corresponding rating output or mathematical score that is generally predictive of a particular patent quality of interest and/or the probability of a particular future event occurring. Preferably, for each patent in the sequential series a rating output or mathematical score is directly calculated from the extracted metrics using a series of predefined equations, formulas and/or rules comprising the algorithm. The results are then preferably stored in a computer accessible memory device in association with other selected information identifying each rated patent such that the corresponding rating may be readily referenced or retrieved for each patent in the sequential series.”);
selecting a computer-centric processing framework from the multiple computer-centric processing frameworks including a predefined factor associated with a type of the intangible asset (see at least Barney: ¶ 45 “A computer algorithm evaluates the full-text file of the patent to be rated and extracts certain selected patent metric(s), which may be predefined, user-defined, or both. Based on the selected patent metric(s), the algorithm computes a rating number or probability (e.g., between 0 and 1) corresponding to the likely presence or absence of one or more user-defined qualities of interest in the patent to be rated and/or the probability of one or more possible future events occurring relative to the patent. If desired, the rating number or probability can be further ranked against other similar ratings for patents within a selected patent population, which may be predetermined, user-defined, or both. Thus, the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 102 “Preferably, the characteristics X1, X2, X3 have been previously selected and determined to have a statistically significant impact on the selected patent quality desired to be measured. At step 208 the observed patent quality Y of patent n is inputted into the system. In this case, the patent quality of interest is the validity or invalidity of the patent as determined by a final judgment of a court. Alternatively, the measured patent quality could be any one or more of a number of other qualities of interest such as discussed above.”; see also Barney: ¶ 159 “Patent ratings or rankings as taught herein may be compiled and reported in a variety of suitable formats, including numerical ratings/rankings, alphanumeric ratings/rankings, percentile rankings, relative probabilities, absolute probabilities, and the like. Multiple ratings or rankings may also be provided corresponding to different patent qualities of interest or specific patent claims. FIG. 11 illustrates one possible form of a patent rating and valuation report 700 that may be generated in accordance with a preferred embodiment of the invention.”; see also Barney: ¶ 38 “The approach is not limited, however, to analyzing litigated patents. For example, fruitful comparisons may also be made between litigated patents (presumably the most valuable patents) and non-litigated patents; or between high-royalty-bearing patents and low-royalty-bearing patents; or between high-cost-basis patents and low-cost-basis patents; or between published patent applications and issued patents. The number and variety of definable patent populations having different desired qualities or characteristics capable of fruitful comparison in accordance with the invention herein is virtually unlimited. While not specifically discussed herein, those skilled in the art will also recognize that a similar approach may also be used for valuing and/or rating other intellectual property or intangible assets such as trademarks, copyrights, domain names, web sites, and the like.”; see also Barney: ¶ 184 “While the statistical rating method and system of one embodiment of the present invention is disclosed and discussed specifically in the context of rating utility patents, those skilled in the art will readily appreciate that the techniques and concepts disclosed herein may have equal applicability to rating other types of intellectual property assets, such as trademarks, copyrights, trade secrets, domain names, web sites and the like.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”; see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”); and
generating, utilizing […] the computer-centric processing framework and a first portion of the data, a qualitative factor associated with one of the respective factor weightings from the data as extracted from the certain interfaces (see at least Barney: ¶ 69 “In its simplest form one embodiment of the present invention provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc.”; see also Barney: ¶ 85 “At block 148 a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 102 “At step 206 the system inputs selected characteristics (Cn=X1, X2, X3) of the next patent (n) in the study population (e.g., litigated patents). Preferably, the characteristics X1, X2, X3 have been previously selected and determined to have a statistically significant impact on the selected patent quality desired to be measured”; see also Barney: ¶ 159 “Patent ratings or rankings as taught herein may be compiled and reported in a variety of suitable formats, including numerical ratings/rankings, alphanumeric ratings/rankings, percentile rankings, relative probabilities, absolute probabilities, and the like. Multiple ratings or rankings may also be provided corresponding to different patent qualities of interest or specific patent claims. FIG. 11 illustrates one possible form of a patent rating and valuation report 700 that may be generated in accordance with a preferred embodiment of the invention.”; see also Barney: ¶ 166 “While it is preferred to provide independent B/D/R ratings and/or an overall score for each rated patent asset, those skilled in the art will recognize that numerous other ranking or rating systems may be used with efficacy in accordance with the teachings herein. For example, individual patent/claim scores may be ranked relative to a given population such that ratings may be provided on a percentile basis. Alternatively, numerical and/or alphanumerical scores may be assigned on a scale from 1-5, 1-9, 1-10, or A-E, for example. Optionally, and as illustrated in FIG. 11, each claim of the reported patent may be analyzed and rated separately if desired. In that case, each claim (1-9 in the example illustrated in FIG. 11) is preferably indicated as being either independent (“I”) or dependent (“D”), as the case may be. Alternatively, only the independent claims of a reported patent may be rated if desired.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”);
transmitting, via a network by a communication protocol from the server computing device to the user computing device, the qualitative factor (see at least Barney: ¶ 34-35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see also Barney: ¶ 37 “The selection of which study population(s) to use depends upon the focus of the statistical inquiry and the desired quality (e.g., claim scope, validity, enforceability, etc.) of the patent asset desired to be elicited”; see also Barney: ¶ 41 “method and automated system for rating or ranking patents or other intangible assets. In accordance with the method a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from or believed to be different from the first quality or characteristic. A computer accessible database is provided and is programmed to contain selected patent metrics representative of or describing particular corresponding characteristics observed for each patent in the first and second patent populations. A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations. The regression model may then be used to rate or rank one or more patents in a third patent population by inputting into the regression model selected patent metrics representative of or describing corresponding characteristics of one or more patents in the third population”; see also Barney: ¶ 44 “Individual selected patents from the population are ranked in accordance with selected patent metrics to determine an overall patent quality rating and ranking for each individual selected patent. The patent value distribution curve is then used to determine a corresponding estimated value for an individual selected patent in accordance with its overall patent quality ranking. If desired, the method may be used to generate a patent valuation report including basic information identifying a particular reported patent or patents of interest and one or more valuations determined in accordance with the method described above.”; see also Barney: ¶ 69 “provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc”; see also Barney: ¶ 70 “Indirect patent metrics measure or report a quality or characteristic of a patent that, while perhaps not directly revealed by the patent itself or the PTO records relating to the patent, can be determined or derived from such information (and/or other information sources) using a variety of algorithms or statistical methods including, but not limited to, the methods disclosed herein. Examples of indirect patent metrics include reported patent litigation results, published case opinions, patent licenses, marking of patented products, and the like. Indirect patent metrics may also include derived measures or measurement components such as frequency or infrequency of certain word usage relative to the general patent population or relative to a defined sub-population of patents in the same general field”; see also Barney: ¶ 73 “These statistical comparisons are then used to construct and optimize a computer model or computer algorithm comprising a series of operative rules and/or mathematical equations. The algorithm is used to predict and/or provide statistically determined probabilities of a desired value or quality being present and/or of a future event occurring, given the identified characteristics of an individual identified patent or group of patents. The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined (or assumed) to have statistical significance. For example, positive scores could generally be applied to those patent characteristics determined or believed to have desirable influence and negative scores could be applied to those patent characteristics determined or assumed to have undesirable influence on the particular quality or event of interest”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents.”); and
generating a user interface comprising a graphic associated with the qualitative factor based on the qualitative analysis, and wherein the user interface is updated based at least in part on inputting new data into the user interface. (see at least Barney: ¶ 180-181: “users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age). If desired, each of the B/D/R ratings can be statistically adjusted relative to the remaining ratings using known statistical techniques so as to minimize any undesired collinearity or overlap in the reported ratings.”; see also Barney: ¶ 161 “In the particular example illustrated, ratings 720 are provided on a scale from 1 to 10. However, a variety of other suitable rating scales may also be used with efficacy, such as numerical rankings, percentile rankings, alphanumeric ratings, absolute or relative probabilities and the like. If desired, individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 166-167 “While it is preferred to provide independent B/D/R ratings and/or an overall score for each rated patent asset, those skilled in the art will recognize that numerous other ranking or rating systems may be used with efficacy in accordance with the teachings herein. For example, individual patent/claim scores may be ranked relative to a given population such that ratings may be provided on a percentile basis. Alternatively, numerical and/or alphanumerical scores may be assigned on a scale from 1-5, 1-9, 1-10, or A-E, for example. Optionally, and as illustrated in FIG. 11, each claim of the reported patent may be analyzed and rated separately if desired. In that case, each claim (1-9 in the example illustrated in FIG. 11) is preferably indicated as being either independent (“I”) or dependent (“D”), as the case may be. Alternatively, only the independent claims of a reported patent may be rated if desired. [0167] Individual ratings 740, 750 and 755 in report 700 preferably provide numerical ratings (1-10) of the likely breadth (“B”), defensibility (“D”), and relevance (“R”) of each claim of the reported patent (and/or the patent as a whole). Such “BDR” ratings may alternatively be expressed in a variety of other suitable formats, such as letters, symbols, integer numerals, decimal numerals, percentage probabilities, percentile rankings, and the like. For example, a letter scoring system (e.g., A-E) could be assigned for each of the individual B/D/R components. In that case, a BDR rating of “B/A/A” would represent a “B” rating for breadth, and “A” ratings for both defensibility and relevance. An overall rating could then be derived from the individual BDR component ratings using a suitable conversion index rating system as generally illustrated below in Table 4”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”),
the qualitative factor corresponding to: coverage corresponding to a determined relationship between a product or a service of the organization and the intangible asset; opportunity corresponding to a potential increase in revenue of the organization attributable to the intangible asset; or risk corresponding to a potential that the intangible asset contributes to a decrease in the opportunity (see at least Barney: ¶ 40 “In accordance with another embodiment the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest”; and ¶ 45 “the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 85 “a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 131 “Each of the patent metrics identified above is anticipated to have a statistically significant impact on the probability of a patent being litigated in the future. By undertaking a statistical study of these and other patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can calculate an estimated statistical probability of a given patent being litigated during a predetermined period of time in the future based on the identified patent characteristics. If desired, a numerical rating or ranking may be assigned to each patent indicating the relative likelihood of litigation”; see also Barney: ¶ 147 “the identified patent metrics are anticipated to have a statistically significant impact on the probability of a patent being litigated successfully or unsuccessfully. By undertaking a statistical study of these and other identified patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can accurately calculate an estimated statistical probability of a given patent being successfully litigated (found valid and/or infringed), taking into consideration all of the identified patent characteristics and statistical relationships simultaneously. If desired, a numerical rating or ranking may be automatically calculated and assigned to each patent indicating the relative likelihood of a particular event or quality. Such rating may be provided for the patent as a whole or, alternatively (or in addition), individual ratings may be provided for one or more individual claims of the patent, as desired”; see also Barney: ¶ 179 “Ideally, it would also be possible for a user to request reports on all patents associated with a specific commercial product. Such product patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products. Relevant patent marking data could be gathered either through private voluntary reporting by manufacturers of such products and/or it may be gathered through other available means, such as automated web crawlers, third-party reporting or inputting and the like. Patent marking data (e.g., the presence or absence of a patent notice on a corresponding commercial product) and/or other relevant data (e.g., sales volume, sales growth, profits, etc.) could provide additional objective metric(s) by which to rate relevant patents in accordance with the invention. Presumably, patents that are being actively commercialized are more valuable than “paper patents” for which there is no corresponding commercial product. Optionally, the patent marking database can also include the necessary URL address information and/or the like which will allow users to hot-link directly to a third-party web page for each corresponding product and/or associated product manufacturer.”; see also Barney: ¶ 180 “In another embodiment of the invention, users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 171 “Similarly, a modified income valuation approach could be used whereby a hypothetical future projected income stream or average industry royalty rate is multiplied by a suitable discount or enhancement factor corresponding to the rating that the patent receives in accordance with the methods disclosed herein. In this manner, patents that receive higher ratings would be valued at higher than industry averages. Conversely, patents that receive lower ratings would be valued at lower than industry averages.”).
Barney does not specifically teach utilizing a specifically-trained machine learning model associated with the computer-centric processing framework.
Mermoud teaches utilizing a trained machine learning model. Mermoud teaches supervised machine learning models that are trained using a training data set, thereby teaching a trained machine learning model corresponding to the claimed specifically-trained machine learning model (paragraph [0038] In various embodiments, network assurance process 248 may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data) and therefore utilized to generate predictive scores from input metrics (paragraphs [0061]: The device predicts a health status score for the networking equipment in the physical location using the received network metrics as input to a machine learning-based predictive scoring model. The device provides an indication of the predicted health status score in conjunction with a visualization of the physical location for display by an electronic display. The device adjusts the predictive scoring model based on feedback regarding the predicted health status score and [0069]: n various embodiments, SPM 408 may maintain a machine learning- based predictive scoring model that uses M.sub.i as an input feature vector and outputs S.sub.i. For example, the scoring model may be a regression model such as a random forest model, deep neural network, or the like, that learns the relationships between these two sets of data. At first, the predictive scoring model of SPM 408 may be trained using labels generated by manually defined health status rules. However, over time, the model may be adjusted automatically and dynamically based on feedback signals from the applications involved, the monitored network, and/or the network administrator). Mermoud further teaches generating predictive using the trained machine learning model from received input metrics (paragraphs [0061 & 0069]). Accordingly, Mermoud teaches utilizing a trained machine learning model to generate predictive scores from input data. Examiner notes that Barney is relied upon for teaching the claimed computer-centric processing framework. Mermoud is relied upon solely for teaching that the predictive model utilized within such a framework may be a trained machine learning model.
Therefore, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to implement Barney’s predictive regression model utilizing the trained machine learning model taught by Mermoud because Mermoud teaches that trained machine learning models, including regression models, generate predictive scores from input metrics and recognize patterns within empirical data to improve predictive analysis (paragraphs [0037, 0038, 0061 and 0069]).
Referring to Claim 37, Barney discloses a computer user interface method for analyzing an intangible asset of an organization, the computer user interface produced on a user computing device, for operation by a user, in communication with a server computing device executing code establishing computer processes (see at least Barney: ¶ 164 “A computer program is caused to automatically access and read each computer text file and to extract therefrom certain selected patent metrics representative of or describing particular observed characteristics or metrics of each patent in the sequential series. The extracted patent metrics are input into a previously determined computer regression model or predictive algorithm that is selected and adjusted to calculate a corresponding rating output or mathematical score that is generally predictive of a particular patent quality of interest and/or the probability of a particular future event occurring. Preferably, for each patent in the sequential series a rating output or mathematical score is directly calculated from the extracted metrics using a series of predefined equations, formulas and/or rules comprising the algorithm. The results are then preferably stored in a computer accessible memory device in association with other selected information identifying each rated patent such that the corresponding rating may be readily referenced or retrieved for each patent in the sequential series”) comprising:
generating multiple computer-centric processing frameworks, wherein individual ones of the multiple computer-centric processing frameworks are associated with respective factor weightings, and wherein the individual ones of the multiple computer- centric processing frameworks are configured to extract data from certain interfaces while refraining from extracting data from other interfaces (see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”);
causing, by the server computing device, a qualitative analysis comprising: obtaining data from a data source, the data corresponding to the intangible asset (see at least Barney: ¶ 164 “a substantial full-text copy of each patent in the sequential series is obtained in a computer text file format or similar computer-accessible format. A computer program is caused to automatically access and read each computer text file and to extract therefrom certain selected patent metrics representative of or describing particular observed characteristics or metrics of each patent in the sequential series. The extracted patent metrics are input into a previously determined computer regression model or predictive algorithm that is selected and adjusted to calculate a corresponding rating output or mathematical score that is generally predictive of a particular patent quality of interest and/or the probability of a particular future event occurring. Preferably, for each patent in the sequential series a rating output or mathematical score is directly calculated from the extracted metrics using a series of predefined equations, formulas and/or rules comprising the algorithm. The results are then preferably stored in a computer accessible memory device in association with other selected information identifying each rated patent such that the corresponding rating may be readily referenced or retrieved for each patent in the sequential series.”);
selecting a computer-centric processing framework including a predefined factor associated with a type of the intangible asset from the multiple computer-centric processing frameworks (see at least Barney: ¶ 45 “A computer algorithm evaluates the full-text file of the patent to be rated and extracts certain selected patent metric(s), which may be predefined, user-defined, or both. Based on the selected patent metric(s), the algorithm computes a rating number or probability (e.g., between 0 and 1) corresponding to the likely presence or absence of one or more user-defined qualities of interest in the patent to be rated and/or the probability of one or more possible future events occurring relative to the patent. If desired, the rating number or probability can be further ranked against other similar ratings for patents within a selected patent population, which may be predetermined, user-defined, or both. Thus, the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 102 “Preferably, the characteristics X1, X2, X3 have been previously selected and determined to have a statistically significant impact on the selected patent quality desired to be measured. At step 208 the observed patent quality Y of patent n is inputted into the system. In this case, the patent quality of interest is the validity or invalidity of the patent as determined by a final judgment of a court. Alternatively, the measured patent quality could be any one or more of a number of other qualities of interest such as discussed above.”; see also Barney: ¶ 159 “Patent ratings or rankings as taught herein may be compiled and reported in a variety of suitable formats, including numerical ratings/rankings, alphanumeric ratings/rankings, percentile rankings, relative probabilities, absolute probabilities, and the like. Multiple ratings or rankings may also be provided corresponding to different patent qualities of interest or specific patent claims. FIG. 11 illustrates one possible form of a patent rating and valuation report 700 that may be generated in accordance with a preferred embodiment of the invention.”; see also Barney: ¶ 38 “The approach is not limited, however, to analyzing litigated patents. For example, fruitful comparisons may also be made between litigated patents (presumably the most valuable patents) and non-litigated patents; or between high-royalty-bearing patents and low-royalty-bearing patents; or between high-cost-basis patents and low-cost-basis patents; or between published patent applications and issued patents. The number and variety of definable patent populations having different desired qualities or characteristics capable of fruitful comparison in accordance with the invention herein is virtually unlimited. While not specifically discussed herein, those skilled in the art will also recognize that a similar approach may also be used for valuing and/or rating other intellectual property or intangible assets such as trademarks, copyrights, domain names, web sites, and the like.”; see also Barney: ¶ 184 “While the statistical rating method and system of one embodiment of the present invention is disclosed and discussed specifically in the context of rating utility patents, those skilled in the art will readily appreciate that the techniques and concepts disclosed herein may have equal applicability to rating other types of intellectual property assets, such as trademarks, copyrights, trade secrets, domain names, web sites and the like.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”; see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”); and
generating, utilizing […] the computer-centric processing framework and a first portion of the data, at least two qualitative factors associated with one of the respective factor weightings from the data as extracted from the certain interfaces (see at least Barney: ¶ 69 “In its simplest form one embodiment of the present invention provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc.”; see also Barney: ¶ 85 “At block 148 a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 102 “At step 206 the system inputs selected characteristics (Cn=X1, X2, X3) of the next patent (n) in the study population (e.g., litigated patents). Preferably, the characteristics X1, X2, X3 have been previously selected and determined to have a statistically significant impact on the selected patent quality desired to be measured”; see also Barney: ¶ 159 “Patent ratings or rankings as taught herein may be compiled and reported in a variety of suitable formats, including numerical ratings/rankings, alphanumeric ratings/rankings, percentile rankings, relative probabilities, absolute probabilities, and the like. Multiple ratings or rankings may also be provided corresponding to different patent qualities of interest or specific patent claims. FIG. 11 illustrates one possible form of a patent rating and valuation report 700 that may be generated in accordance with a preferred embodiment of the invention.”; see also Barney: ¶ 166 “While it is preferred to provide independent B/D/R ratings and/or an overall score for each rated patent asset, those skilled in the art will recognize that numerous other ranking or rating systems may be used with efficacy in accordance with the teachings herein. For example, individual patent/claim scores may be ranked relative to a given population such that ratings may be provided on a percentile basis. Alternatively, numerical and/or alphanumerical scores may be assigned on a scale from 1-5, 1-9, 1-10, or A-E, for example. Optionally, and as illustrated in FIG. 11, each claim of the reported patent may be analyzed and rated separately if desired. In that case, each claim (1-9 in the example illustrated in FIG. 11) is preferably indicated as being either independent (“I”) or dependent (“D”), as the case may be. Alternatively, only the independent claims of a reported patent may be rated if desired.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”; see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”);
transmitting, via a network by a communication protocol from the server computing device to the user computing device, the qualitative factor (see at least Barney: ¶ 180-181: “users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 34-35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see also Barney: ¶ 37 “The selection of which study population(s) to use depends upon the focus of the statistical inquiry and the desired quality (e.g., claim scope, validity, enforceability, etc.) of the patent asset desired to be elicited”; see also Barney: ¶ 41 “method and automated system for rating or ranking patents or other intangible assets. In accordance with the method a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from or believed to be different from the first quality or characteristic. A computer accessible database is provided and is programmed to contain selected patent metrics representative of or describing particular corresponding characteristics observed for each patent in the first and second patent populations. A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations. The regression model may then be used to rate or rank one or more patents in a third patent population by inputting into the regression model selected patent metrics representative of or describing corresponding characteristics of one or more patents in the third population”; see also Barney: ¶ 44 “Individual selected patents from the population are ranked in accordance with selected patent metrics to determine an overall patent quality rating and ranking for each individual selected patent. The patent value distribution curve is then used to determine a corresponding estimated value for an individual selected patent in accordance with its overall patent quality ranking. If desired, the method may be used to generate a patent valuation report including basic information identifying a particular reported patent or patents of interest and one or more valuations determined in accordance with the method described above.”; see also Barney: ¶ 69 “provides a statistical patent rating method and system for rating or ranking patents based on certain selected patent characteristics or “patent metrics.” Such patent metrics may include any number of quantifiable parameters that directly or indirectly measure or report a quality or characteristic of a patent. Direct patent metrics measure or report those characteristics of a patent that are revealed by the patent itself, including its basic disclosure, drawings and claims, as well as the PTO record or file history relating to the patent. Specific patent metrics may include, for example and without limitation, the number of claims, number of words per claim, number of different words per claim, word density (e.g., different words/total-words), length of patent specification, number of drawings or figures, number of cited prior art references, age of cited prior art references, number of subsequent citations received, subject matter classification and sub-classification, origin of the patent (foreign vs. domestic), payment of maintenance fees, prosecuting attorney or firm, patent examiner, examination art group, length of pendency in the PTO, claim type (i.e. method, apparatus, system), etc”; see also Barney: ¶ 70 “Indirect patent metrics measure or report a quality or characteristic of a patent that, while perhaps not directly revealed by the patent itself or the PTO records relating to the patent, can be determined or derived from such information (and/or other information sources) using a variety of algorithms or statistical methods including, but not limited to, the methods disclosed herein. Examples of indirect patent metrics include reported patent litigation results, published case opinions, patent licenses, marking of patented products, and the like. Indirect patent metrics may also include derived measures or measurement components such as frequency or infrequency of certain word usage relative to the general patent population or relative to a defined sub-population of patents in the same general field”; see also Barney: ¶ 73 “These statistical comparisons are then used to construct and optimize a computer model or computer algorithm comprising a series of operative rules and/or mathematical equations. The algorithm is used to predict and/or provide statistically determined probabilities of a desired value or quality being present and/or of a future event occurring, given the identified characteristics of an individual identified patent or group of patents. The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined (or assumed) to have statistical significance. For example, positive scores could generally be applied to those patent characteristics determined or believed to have desirable influence and negative scores could be applied to those patent characteristics determined or assumed to have undesirable influence on the particular quality or event of interest”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents.”); and
generating a user interface comprising one or more graphics associated with the at least to qualitative factor based on the quality analysis and wherein the user interface is updated based at least in part on inputting new data into the user interface (see at least Barney: ¶ 180-181: “users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age). If desired, each of the B/D/R ratings can be statistically adjusted relative to the remaining ratings using known statistical techniques so as to minimize any undesired collinearity or overlap in the reported ratings.”; see also Barney: ¶ 161 “In the particular example illustrated, ratings 720 are provided on a scale from 1 to 10. However, a variety of other suitable rating scales may also be used with efficacy, such as numerical rankings, percentile rankings, alphanumeric ratings, absolute or relative probabilities and the like. If desired, individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 166-167 “While it is preferred to provide independent B/D/R ratings and/or an overall score for each rated patent asset, those skilled in the art will recognize that numerous other ranking or rating systems may be used with efficacy in accordance with the teachings herein. For example, individual patent/claim scores may be ranked relative to a given population such that ratings may be provided on a percentile basis. Alternatively, numerical and/or alphanumerical scores may be assigned on a scale from 1-5, 1-9, 1-10, or A-E, for example. Optionally, and as illustrated in FIG. 11, each claim of the reported patent may be analyzed and rated separately if desired. In that case, each claim (1-9 in the example illustrated in FIG. 11) is preferably indicated as being either independent (“I”) or dependent (“D”), as the case may be. Alternatively, only the independent claims of a reported patent may be rated if desired. [0167] Individual ratings 740, 750 and 755 in report 700 preferably provide numerical ratings (1-10) of the likely breadth (“B”), defensibility (“D”), and relevance (“R”) of each claim of the reported patent (and/or the patent as a whole). Such “BDR” ratings may alternatively be expressed in a variety of other suitable formats, such as letters, symbols, integer numerals, decimal numerals, percentage probabilities, percentile rankings, and the like. For example, a letter scoring system (e.g., A-E) could be assigned for each of the individual B/D/R components. In that case, a BDR rating of “B/A/A” would represent a “B” rating for breadth, and “A” ratings for both defensibility and relevance. An overall rating could then be derived from the individual BDR component ratings using a suitable conversion index rating system as generally illustrated below in Table 4”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized. Adjustments can be made automatically in an orderly convergence progression, and/or they can by made randomly or semi-randomly. The latter method is particularly preferred where there are any non-linearities in the equations or rules governing the algorithm. Algorithm results are preferably reported as statistical probabilities of a desired quality being present, or a future event occurring (e.g., patent being litigated, abandoned, reissued, etc.) during a specified period in the future. Algorithm results could also be provided as arbitrary raw scores representing the sum of an individual patent's weighted scores, which raw scores can be further ranked and reported on a percentile basis or other similar basis as desired.”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer.”; see also Barney: ¶ 160 “the report 700 contains some basic data 710 identifying the patent being reported, including the patent number, title of the invention, inventor(s), filing date, issue date and assignee (if any). Several individual patent ratings 720 are also provided, including overall patent breadth (“B”), defensibility (“D”), and commercial relevance (“R”). Breadth and Defensibility ratings are preferably generated by a computer algorithm that is selected and adjusted to be predictive of known litigation outcomes (e.g., infringement/non-infringement and validity/invalidity) of a selected population of litigated patents based on various comparative patent metrics. Relevance ratings are preferably generated using a computer algorithm selected and adjusted to be predictive of patent maintenance rates and/or mortality rates based on various comparative patent metrics including, preferably, at least one comparative metric based on a normalized forward patent citation rate (normalized according to patent age).”; see also Barney: ¶ 179 “patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products”; see also Barney: ¶ 176-177 “One embodiment of the present invention is ideally suited for Internet-based applications. In one preferred embodiment, the invention would be made available to Internet users on the World Wide Web (“the web”), or a similar public network, and would be accessible through a web page. Various services, embodying different aspects of one embodiment of the present invention, could be made available to users on a subscription or a pay-per-use basis… In an Internet-based application, users would preferably have access to automated patent ratings, consolidated patent ratings (i.e. grouped by technology, business sector, industry, etc.), and a host of ancillary information regarding particular patents or groups of patents.”; see at least Barney: ¶ 35 “The algorithm may comprise a simple scoring and weighting system which assigns scores and relative weightings to individual identified characteristics of a patent or group of patents determined to have statistical significance. For example, positive scores would generally be applied to those patent characteristics having desirable influence and negative scores would apply to those patent characteristics having undesirable influence on the particular quality or event of interest. A high-speed computer is then used to repeatedly test the algorithm against one or more known patent populations (e.g., patents declared to be valid/invalid or infringed/non-infringed).”; see at least Barney: ¶ 35 “During and/or following each such test the algorithm is refined by adjusting the scorings and/or weightings until the predictive accuracy of the algorithm is optimized. Once the algorithm is suitably optimized, selected metrics for an individual identified patent or group of patents to be rated are input into the algorithm and the algorithm is operated to calculate an estimated rating or mathematical score for that patent or group of patents. Individual results could be reported as statistical probabilities of a desired quality being present, or a future event occurring (patent being litigated, abandoned, reissued, etc.)”; see at least Barney: ¶ 39 “provides a method for rating or ranking patents. In accordance with the method, a first population of patents is selected having a first quality or characteristic and a second population of patents is selected having a second quality or characteristic that is different from the first quality or characteristic. Statistical analysis is performed to determine or identify one or more patent metrics having either a positive or a negative correlation with either said first or second quality to a statistically significant degree. A regression model is constructed using the identified patent metric(s). The regression model is iteratively adjusted to be generally predictive of either the first or the second quality being present in a given patent. The regression model is used to automatically rate or rank patents by positively weighting or scoring patents having the positively correlated patent metrics and negatively weighting or scoring patents having the negatively correlated patent metrics (“positive” and “negative” being used here in the relative sense only).”; see at least Barney: ¶ 40-41 “the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest. A second database (or identified subset of the first database) of selected patent information is also provided identifying and/or quantifying certain selected characteristics of individual patents from a second population of patents generally lacking or having reduced incidence of the selected patent quality of interest… A computer regression model is constructed and adjusted based on the selected patent metrics. The regression model is operable to input the selected patent metrics for each patent in the first and second patent populations and to output a corresponding rating or ranking that is generally predictive of the first and/or second quality being present in each patent in the first and second patent populations.”; see also Barney: ¶ 74 “a high-speed computer is preferably used to repeatedly test the algorithm against one or more known patent populations (e.g. patents declared to be valid/invalid or infringed/non-infringed). During and/or following each such test the algorithm is refined (preferably automatically) by iteratively adjusting the scorings and/or weightings assigned until the predictive accuracy of the algorithm is optimized”; see also Barney: ¶ 161 “individual ratings or rankings 720 may also be combined using a suitable weighting algorithm or the like to arrive at an overall score or rating 730 for a given patent, patent portfolio or other intellectual property asset. The particular weighting algorithm used would preferably be developed empirically or otherwise so as to provide useful and accurate overall patent rating information for a given application such as investment, licensing, litigation analysis, etc.”; see also Barney: ¶ 180-181 “Internet-based application of this invention is a user-updated information database. According to this embodiment, certain users and/or all users would be allowed to post information they believe is pertinent to a particular patent or group of patents.”; see at least Barney: ¶ 120-121 “a different adjustment is needed to be made to the coefficients a, b, c, and/or d in order to cause the system to reconverge toward the optimal solution providing for maximum predictive accuracy. This is done by directing the system to blocks 232-268 to test the impact of various changes to each predictor variable (a, b, c, d) and to change one or more of the coefficient adjustment amounts (Aa, Ab, Ac and Ad) as necessary to reconverge on the optimal solution. Preferably, course adjustments are made first and then finer and finer adjustments are continually made as the regression model converges on an optimal solution having maximized statistical accuracy SA. Thus, decision blocks 232, 242, 252 and 262 first preferably determine which of the adjustment amounts (Aa, Ab, Ac and Ad) is greatest in magnitude. For example, if it is determined that Aa is greater than each of the adjustment amounts Ab, Ac and Ad, then decision block 232 directs the system to block 234.”),
each of the at least two qualitative factors corresponding to: coverage corresponding to a determined relationship between a product or a service of the organization and the intangible asset; opportunity corresponding to a potential increase in revenue of the organization attributable to the intangible asset; or risk corresponding to a potential that the intangible asset contributes to a decrease in the opportunity, wherein each of the at least two qualitative factors are different (see at least Barney: ¶ 40 “In accordance with another embodiment the invention provides a statistical method for scoring or rating selected qualities of individual patents and for generating a rating report specific to each individual patent rated. The method begins by providing a first database of selected patent information identifying and/or quantifying certain selected characteristics of individual patents from a first population of patents having a selected patent quality of interest”; and ¶ 45 “the method in accordance with the preferred embodiment of the invention is capable of producing multiple independent ratings and/or rankings for a desired patent to be rated, each tailored to a different user-defined inquiry, such as likelihood of the patent being litigated in the future, being held invalid, likelihood of successful infringement litigation, predicted life span of the patent, relative value of the patent, etc.”; see also Barney: ¶ 85 “a multiple regression model is constructed using the identified statistically relevant characteristics determined at block 144. Multiple regression modeling is a well-known statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify the selected relevant characteristics of a particular patent population, e.g., class/sub-class, number of independent claims, number of patent citations, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of a particular patent population, such as likelihood of successful litigation (either validity or infringement). Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 93 “Many different methods of statistical analysis may be suitably employed to practice one embodiment of the present invention. The preferred methodology is a multiple regression technique performed, for example, by a high-speed computer. As noted above, multiple regression modeling is a statistical technique for examining the relationship between two or more predictor variables (PVs) and a criterion variable (CV). In the case of one embodiment of the present invention the predictor variables (or independent variables) describe or quantify certain observable characteristics of a particular patent population, e.g., number of independent claims, length of specification, etc. Criterion variables (or dependent variables) measure a selected quality of interest of a particular patent population, such as likelihood of successful litigation, validity or infringement. Multiple regression modeling allows the criterion variable to be studied as a function of the predictor variables in order to determine a relationship between selected variables. This data, in turn, can be used to predict the presence or absence of the selected quality in other patents”; see also Barney: ¶ 131 “Each of the patent metrics identified above is anticipated to have a statistically significant impact on the probability of a patent being litigated in the future. By undertaking a statistical study of these and other patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can calculate an estimated statistical probability of a given patent being litigated during a predetermined period of time in the future based on the identified patent characteristics. If desired, a numerical rating or ranking may be assigned to each patent indicating the relative likelihood of litigation”; see also Barney: ¶ 147 “the identified patent metrics are anticipated to have a statistically significant impact on the probability of a patent being litigated successfully or unsuccessfully. By undertaking a statistical study of these and other identified patent metrics and by constructing a suitable regression model in accordance with the invention disclosed herein, one can accurately calculate an estimated statistical probability of a given patent being successfully litigated (found valid and/or infringed), taking into consideration all of the identified patent characteristics and statistical relationships simultaneously. If desired, a numerical rating or ranking may be automatically calculated and assigned to each patent indicating the relative likelihood of a particular event or quality. Such rating may be provided for the patent as a whole or, alternatively (or in addition), individual ratings may be provided for one or more individual claims of the patent, as desired”; see also Barney: ¶ 179 “Ideally, it would also be possible for a user to request reports on all patents associated with a specific commercial product. Such product patent information could advantageously be collected and stored on a centralized, searchable computer network database or the like in order to allow users to search and obtain patent information on particular commercial products. Relevant patent marking data could be gathered either through private voluntary reporting by manufacturers of such products and/or it may be gathered through other available means, such as automated web crawlers, third-party reporting or inputting and the like. Patent marking data (e.g., the presence or absence of a patent notice on a corresponding commercial product) and/or other relevant data (e.g., sales volume, sales growth, profits, etc.) could provide additional objective metric(s) by which to rate relevant patents in accordance with the invention. Presumably, patents that are being actively commercialized are more valuable than “paper patents” for which there is no corresponding commercial product. Optionally, the patent marking database can also include the necessary URL address information and/or the like which will allow users to hot-link directly to a third-party web page for each corresponding product and/or associated product manufacturer.”; see also Barney: ¶ 180 “In another embodiment of the invention, users would be allowed to request automatic updates and patent ratings according to certain user-defined parameters. Thus, a user who is particularly interested in the XYZ company could request an automatic updated report sent to him substantially contemporaneously (preferably within a few days, more preferably within about 2-3 hours, and most preferably within less than about 5-10 minutes) via e-mail and/or facsimile-whenever the XYZ company obtains a newly issued patent. A similar updated report could be generated and sent any time a new patent issued or a new application is published in a particular technology field or class of interest. The updates would preferably contain a synopsis of each new patent or published application, as well as a patent rating performed according to that user's preferred criteria. Updated reports for each rated patent could also be generated periodically whenever one or more identified patent metrics changed (e.g., forward citation rate, change of ownership, litigation, etc.). Such automated updating of rating information would be particularly important to investment and financial analysts, who depend on rapid and reliable information to make minute-by-minute decisions. Updated report(s) could also be generated and published each week for all newly issued patents granted by the PTO for that current week. Thus, in accordance with one preferred embodiment of the invention, informative patent rating and/or ranking information may be provided within days or hours of a new patent being issued and published by the PTO.”; see at least Barney: ¶ 171 “Similarly, a modified income valuation approach could be used whereby a hypothetical future projected income stream or average industry royalty rate is multiplied by a suitable discount or enhancement factor corresponding to the rating that the patent receives in accordance with the methods disclosed herein. In this manner, patents that receive higher ratings would be valued at higher than industry averages. Conversely, patents that receive lower ratings would be valued at lower than industry averages.”).
Barney does not specifically teach utilizing a specifically-trained machine learning model associated with the computer-centric processing framework.
Mermoud teaches utilizing a trained machine learning model. Mermoud teaches supervised machine learning models that are trained using a training data set, thereby teaching a trained machine learning model corresponding to the claimed specifically-trained machine learning model (paragraph [0038] In various embodiments, network assurance process 248 may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data) and therefore utilized to generate predictive scores from input metrics (paragraphs [0061]: The device predicts a health status score for the networking equipment in the physical location using the received network metrics as input to a machine learning-based predictive scoring model. The device provides an indication of the predicted health status score in conjunction with a visualization of the physical location for display by an electronic display. The device adjusts the predictive scoring model based on feedback regarding the predicted health status score and [0069]: n various embodiments, SPM 408 may maintain a machine learning- based predictive scoring model that uses M.sub.i as an input feature vector and outputs S.sub.i. For example, the scoring model may be a regression model such as a random forest model, deep neural network, or the like, that learns the relationships between these two sets of data. At first, the predictive scoring model of SPM 408 may be trained using labels generated by manually defined health status rules. However, over time, the model may be adjusted automatically and dynamically based on feedback signals from the applications involved, the monitored network, and/or the network administrator). Mermoud further teaches generating predictive using the trained machine learning model from received input metrics (paragraphs [0061 & 0069]). Accordingly, Mermoud teaches utilizing a trained machine learning model to generate predictive scores from input data. Examiner notes that Barney is relied upon for teaching the claimed computer-centric processing framework. Mermoud is relied upon solely for teaching that the predictive model utilized within such a framework may be a trained machine learning model.
Therefore, it would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to implement Barney’s predictive regression model utilizing the trained machine learning model taught by Mermoud because Mermoud teaches that trained machine learning models, including regression models, generate predictive scores from input metrics and recognize patterns within empirical data to improve predictive analysis (paragraphs [0037, 0038, 0061 and 0069]).
Response to Arguments
101 Rejection
8. Applicant's arguments filed with respect to the rejection of the claims under 35 USC 101 have been fully considered but they are not persuasive considering the arguments merely amount to the statement of patentability of the amended claims. The rejection has been adjusted to account for the amendments.
Therefore, the claims stand rejected.
103 Rejection
9. Applicant states that the prior art doesn’t disclose “generating, utilizing a specifically-trained machine learning model associated with the computer-centric processing framework and a first portion of the data, a first qualitative factor associated with one of the respective factor weightings from the data as extracted from the certain interfaces.” Examiner notes that these arguments are made with respect to the amended claims. Examiner disagrees with the applicant’s conclusion that the pending claims as amended are in condition for allowance, as the amended claims have been considered but applicant’s arguments are moot in view of the new ground(s) of rejection.
Conclusion
10. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA LEMIEUX whose telephone number is (571)270-3445. The examiner can normally be reached Monday-Friday 7AM-3PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, TARIQ HAFIZ can be reached at (571) 272-5350. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JESSICA LEMIEUX/ Supervisory Patent Examiner, Art Unit 3626
Prosecution Insights