DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
• This action is in reply to the amendments filed on February 2, 2026.
• Claims 1, 7, and 20 have been amended and are hereby entered.
• Claims 1-20 are currently pending and have been examined.
• This action is made FINAL.
Response to Arguments
Applicant’s arguments filed February 2, 2026 have been fully considered but they are not persuasive.
New 35 USC § 112 rejections have been entered due to applicant’s amendments.
Applicant’s arguments with respect to 35 USC § 101 have been fully considered and are not persuasive.
Regarding Applicant’s argument on page 8, that the claims integrate a practical application and recite significantly more than the abstract idea, the Examiner respectfully disagrees. Under the Patent Subject Matter Eligibility analysis, Step 2A, prong two, integration into a practical application requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. Limitations that are not indicative of integration into a practical application are those that generally link the use of the judicial exception into a particular technological environment or field of use-see MPEP 2106.05(h). Here the claims recite one or more processors; and one or more non-transitory computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform claim operations; a machine learning model; generating feedback data indicating performance of the machine learning model over a period of time; transforming the feedback data into a training dataset that differs from and is configured to be utilized to train the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated that represents an improved version of the machine learning model; a trained machine learning model; a network protocol or interface over a network; a user device; an application on the user device; and display data such that they amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (e.g., a computer network) (see MPEP 2106.05(h)).
Furthermore, in determining whether a claim integrates a judicial exception into a practical application, a determination is made of whether the claimed invention pertains to an improvement in the functioning of the computer itself or any other technology or technical field (i.e., a technological solution to a technological problem). Here, the claims recite generic computer components, i.e., a generic processor, a memory storing a computer program executable by the processor to perform the claimed method steps and system functions. The processor, memory and system are recited at a high level of generality and are recited as performing generic computer functions customarily used in computer applications.
Furthermore, the Specification describes a problem and improvement to a business or commercial process at least at [0001], disclosing “Disclosed herein are improvements in technology and solutions to technical problems that can be used to, among other things, analyze and generate visual representations of intellectual-property portfolios of various entities.”
The claims are not patent eligible.
Applicant’s arguments with respect to 35 USC § 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
For the reasons above, Applicant’s arguments are not persuasive.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites the limitations of “generating feedback data indicating performance of the machine learning model over a period of time; transforming the feedback data into a training dataset that differs from and is configured to be utilized to train the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated that represents an improved version of the machine learning model.” The Specification does not have support for a re-training of a model; nor does the Specification describe inputting any type of output data back into a model to train the model. In fact, the Specification does not describe training a model at all; rather the Specification merely refers to a model as already trained at paragraphs [0093]-[0094]. Also the Specification does not describe any type of transformation of feedback data into training data. The Specification is devoid of support for these claimed feature. Therefore, it is new matter.
Claims 7 and 20 have similar limitations found in claim 1 above, and therefore are rejected by the same rationale.
The rest of the dependent claims are rejected due to their dependency to a rejected claim.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation “a training dataset that differs from and is configured to be utilized to train the machine learning model.” The limitation is confusing because it is not clear what the “training dataset” differs from. For examination purposes, the Examiner is interpreting the limitation as “a training dataset that differs from the feedback data and is configured to be utilized to train the machine learning model.”
Claims 7 and 20 have similar limitations found in claim 1 above, and therefore are rejected by the same rationale.
The rest of the dependent claims are rejected due to their dependency to a rejected claim.
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea without significantly more. Independent claims 1, 7, and 20 are directed to a method (claims 1 and 7) and a system (claim 20). Therefore, on its face, each independent claim 1, 7, and 20 are directed to a statutory category of invention under Step 1 of the Patent Subject Matter Eligibility analysis (see MPEP 2106.03).
Under Step 2A, Prong One of the Patent Subject Matter Eligibility analysis (see MPEP 2106.04), claims 1, 7, and 20 recite, in part, a method and a system of organizing human activity. Using the limitations in claim 1 to illustrate, the claim recites generating a model based, at least in part, on a first financial metric associated with a first IP asset within a technology area, the first IP asset being associated with a first entity; identifying a second IP asset associated with the technology area; automatically determining, via the model, a second financial metric associated with the second IP asset based, at least in part, on the first financial metric and on the second IP asset being associated with the technology area; identifying a second entity associated with the second IP asset; and sending data to cause to enable and to provide the second financial metric, the second IP asset, the second entity, or a combination thereof.
The limitations, as drafted, is a process that, under its broadest reasonable interpretation, covers fundamental economic principles or practices and commercial and legal interactions (certain methods of organizing human activity), but for the recitation of generic computer components. The claimed inventions allows for analyzing metrics of intellectual property asset based on financial metrics, including for analyzing risk (see Specification of the instant application at paragraph [0041]-[0043], [0087]), which is a fundamental economic principles or practices of mitigating risk and commercial and legal interaction, specifically a commercial interaction of marketing or sales activities or behaviors and business relations. The mere nominal recitation of a one or more processors; and one or more non-transitory computer-readable media comprising instructions do not take the claim out of the methods of organizing human activity grouping. Thus, the claims recite an abstract idea.
Under Step 2A, Prong Two of the Patent Subject Matter Eligibility analysis (see MPEP 2106.04), the judicial exception is not integrated into a practical application. In particular, the additional elements of one or more processors; and one or more non-transitory computer-readable media comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform claim operations; a machine learning model; generating feedback data indicating performance of the machine learning model over a period of time; transforming the feedback data into a training dataset that differs from and is configured to be utilized to train the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated that represents an improved version of the machine learning model; a trained machine learning model; a network protocol or interface over a network; a user device; an application on the user device; and display data are recited at a high-level of generality (i.e., as a generic computer components performing generic computer functions of generating metrics, identifying assets, determining metrics, and sending data) such that it amounts to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (e.g., a computer network).-see MPEP 2106.05(h).
Accordingly, the combination of the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Under Step 2B of the Patent Subject Matter Eligibility analysis (see MPEP 2106.05), the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements in the claims amount to no more than generally linking the use of the judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Generally linking the use of the judicial exception to a particular technological environment or field of use using generic computer components cannot provide an inventive concept.
The claims are not patent eligible.
The dependent claims have been given the full two part analysis including analyzing the additional limitations both individually and in combination. The dependent claim(s) when analyzed both individually and in combination are also held to be patent ineligible under 35 U.S.C. 101 because for the same reasoning as above and the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea. Dependent claims 6, 13, and 19 simply further describes the technological environment. Dependent claims 2-5 and 8-18 simply help to define the abstract idea. The additional limitations of the dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Viewing the claim limitations as an ordered combination does not add anything further than looking at the claim limitations individually. When viewed either individually, or as an ordered combination, the additional limitations do not amount to a claim as a whole that is significantly more than the abstract idea. Accordingly, claims 1-20 are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-7, 9, 11-13, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20120303537 A1 (“Bader”) in view of US 20230031691 A1 (“Carroll”), and in view of US 20160350886 A1 (“Jessen”).
Regarding claim 1, Bader discloses a method comprising:
generating a model based, at least in part, on a first financial metric associated with a first IP asset within a technology area, the first IP asset being associated with a first entity (IP right valuation index P to analyze the patent portfolio of a company. See at least [0158]. See also [0158]-[0165]. IP right valuation index and its three factors (R; M; N) for each company in the selected region/cluster. See at least [0110]. Market strength M of a patent portfolio. See at least [0093]. Company associated with a patent portfolio. See at least [0004].);
identifying a second IP asset associated with the technology area (Distinct definition of the field of technology/industry sector to be analyzed. See at least [0159].);
automatically determining, via the model, a second financial metric associated with the second IP asset based, at least in part, on the first financial metric and on the second IP asset being associated with the technology area (The weighted technology field/industry average will be calculated. Calculating Technology-Field-Index (TFx) as an average value. The outcome is the weighted average of the IP right valuation index P values. It can be used as a benchmark for the company in focus to analyze its portfolio and compare it to companies in the selected field of technology. See at least [0161]-[0164].);
identifying a second entity associated with the second IP asset (Selecting for each company selected. See at least [0159]-[0160].); and
sending, via a network protocol or interface over a network to a user device, data to the second financial metric, the second IP asset, the second entity, or a combination thereof (providing second metric, see at least [0161]-[0164]. Providing data over a network, see at least [0136].).
While Bader discloses a model, Bader does not expressly disclose a machine learning model. Furthermore, Bader does not expressly disclose generating feedback data indicating performance of the machine learning model over a period of time; transforming the feedback data into a training dataset that differs from and is configured to be utilized to train the machine learning model; training the machine learning model utilizing the training dataset such that a trained machine learning model is generated that represents an improved version of the machine learning model. Furthermore, while Bader discloses a model, Bader does not expressly disclose a trained machine learning model. Furthermore, while Bader discloses sending data, Bader does not expressly disclose sending to cause an application on the user device to enable and to display data.
However, Carroll discloses disclose a machine learning model; generating feedback data indicating performance of the machine learning model over a period of time (A first model may be trained using data from a first three-month period. The computing system may determine one or more performance metrics for each machine learning model (e.g., accuracy). The computing system may input test data into each trained machine learning model to determine how well each machine learning model performs. The computing system may obtain one or more performance metrics (e.g., precision, recall, accuracy, etc.) from the trained machine learning models using the test data. For example, the computing system may input test data received after the first, second, and third three-month periods into each machine learning model and determine the accuracy of the models. The performance of each model may give an indication into how effective the training data used for each model was. This may enable the computing system to determine whether to use a particular training dataset and/or how much weight to give each training dataset when using it to train a machine learning model. See at least [0014].);
transforming the feedback data into a training dataset that differs from and is configured to be utilized to train the machine learning model (This may enable the computing system to determine whether to use a particular training dataset and/or how much weight to give each training dataset when using it to train a machine learning model. The computing system may compare one or more performance metrics (e.g. precision, recall, log loss, and/or accuracy, etc.) of each machine learning model and assign a weight to each corresponding dataset based on the comparison. For example, if the first machine learning model performs better than the second machine learning model, the computing system may give the first dataset a higher weight than the second dataset. See at least [0014]. The Examiner interprets assigning weights to a dataset as transforming the feedback data.);
training the machine learning model utilizing the training dataset such that a trained machine learning model is generated that represents an improved version of the machine learning model; a trained machine learning model (The computing system may train a machine learning model using the weighted datasets. Using the weighted dataset may increase the efficiency of the computing system because it may be able to train machine learning models using less data and/or less computing resources. Using the weighted dataset may enable the computing system to train machine learning models to obtain better results (e.g., improved precision, recall, accuracy, etc.). See at least [0014].).
From the teaching of Carroll, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the model of Bader to be a machine learning model, as taught by Carroll, and to modify Bader to generate feedback data indicating performance of the machine learning model over a period of time, as taught by Carroll, and to modify Bader to transform the feedback data into a training dataset that differs from and is configured to be utilized to train the machine learning model, as taught by Carroll, and to modify Bader to train the machine learning model utilizing the training dataset such that a trained machine learning model is generated that represents an improved version of the machine learning model, as taught by Carroll, and to modify the model of Bader to be a trained machine learning model, as taught by Bader, and to modify the sending of data of Bader to cause an application on the user device to enable and to display data, as taught by Carroll, in order to improve accuracy of a machine learning model (see Carroll at least at [0001]), and in order to improve results that a model obtains to obtain better results (e.g., improved precision, recall, accuracy, etc.) (see Carroll at least at [0002]).
Furthermore, while Bader discloses sending data, Bader does not expressly disclose sending to cause an application on the user device to enable and to display data.
However, Jessen discloses cause an application on the user device to enable and to display data ([0041] and [0083].).
From the teaching of Jessen, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the sending of data of Bader to cause an application on the user device to enable and to display data, as taught by Jessen, in order to improve analysis of intellectual property assets (see Jessen at least at [0005]-[0006] and [0038]).
Regarding claim 4, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 1, as discussed above, and Bader further discloses the first financial metric is available market data (IP right valuation index P to analyze the patent portfolio of a company. See at least [0158]. IP right valuation index and its three factors (R; M; N) for each company in the selected region/cluster. See at least [0110]. Market strength M of a patent portfolio. See at least [0093].).
While Bader discloses available market data, Bader does not expressly disclose publicly available market data.
However, Jessen discloses publicly available market data (The non-IP database may include, for example, information of non-patent literature or documents, such as journal articles, conference articles, manuals, brochures, and other publications, etc. The information may include the entire data of the non-patent document. See at least [0043]. See also [0117] and [0139].)
From the teaching of Jessen, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the data of Bader to be publicly available data, as taught by Jessen, in order to improve analysis of intellectual property assets (see Jessen at least at [0005]-[0006] and [0038]).
Regarding claim 5, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 1, as discussed above. Bader does not expressly disclose the publicly available market data is at least one of: option expiration; historic volatility; implied volatility; moneyness; open interest; option price; P/E ratio; P/B ratio; put/call ratio; share price; stock exchange; strike price; intrinsic value; premium; volume; or research and development spending.
However, Jessen discloses the publicly available market data is at least one of: option expiration; historic volatility; implied volatility; moneyness; open interest; option price; P/E ratio; P/B ratio; put/call ratio; share price; stock exchange; strike price; intrinsic value; premium; volume; or research and development spending (Financial market data including P-E ratio. See at least [0043].).
From the teaching of Jessen, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the metric of Bader to be P/E ratio, as taught by Jessen, in order to improve analysis of intellectual property assets (see Jessen at least at [0005]-[0006] and [0038]).
Regarding claim 6, the combination of Bader, Carroll, and Jessen discloses the limitations of claim 1, as discussed above, and Bader further discloses generating the second financial metric, the second IP asset, the second entity, or a combination thereof (providing second metric, see at least [0161]-[0164].).
While Bader discloses generating, Bader does not expressly disclose generating a graphical user interface (GUI) on the user device, the GUI configured to display a visual representation.
However, Jessen discloses generating a graphical user interface (GUI) on the user device, the GUI configured to display a visual representation (a view patent screen in which individual patents or applications may be viewed. See at least [0025], [0165], and FIG. 18. See also [0041] and [0083].).
From the teaching of Jessen, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the generating of Bader to generate a graphical user interface (GUI) on the user device, the GUI configured to display a visual representation, as taught by Jessen, in order to improve analysis of intellectual property assets (see Jessen at least at [0005]-[0006] and [0038]).
Claims 7 and 9 have similar limitations found in claim 1 above, and therefore are rejected by the same art and rationale.
Claim 11 has similar limitations found in claim 4 above, and therefore is rejected by the same art and rationale.
Claim 12 has similar limitations found in claim 5 above, and therefore is rejected by the same art and rationale.
Claim 13 has similar limitations found in claim 6 above, and therefore is rejected by the same art and rationale.
Regarding claim 15, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 7, as discussed above. And Bader further discloses the first metric, the second metric, or both are an opportunity metric (Technology strength metrics, see at least [0062]. See also [0131]-[0132], describing patent value for market-based measures.).
Bader does not expressly disclose the opportunity metric being directed to filing velocity, prosecution analytics, precedence, or a combination thereof for at least one IP asset associated with the first entity or at least one IP asset associated with the second entity.
However, Jessen discloses the opportunity metric being directed to filing velocity, prosecution analytics, precedence, or a combination thereof for at least one IP asset associated with the first entity or at least one IP asset associated with the second entity (The file wrapper delta is another output which measures the change in claim scope as a result of prosecuting the patent from the time it was filed to the time it was issued. The scope change is a function of the ClaimScape™ metric. That is, the claims of the application, if available, are processed by the claim scope engine with the broadest claim being identified. The independent claims of the granted patent are also processed by the claim scope engine to identify the broadest claim. The variation from the broadest published claim to the broadest granted claim is then calculated and visually depicted to show a change in scope. The filing dates and issue dates are also provided to give the user an idea of how long it took to prosecute that case. See at least [0179]. See also [0219].).
From the teaching of Jessen, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the metrics of Bader to be opportunity metrics directed to prosecution analytics, as taught by Jessen, in order to improve analysis of intellectual property assets (see Jessen at least at [0005]-[0006] and [0038]).
Regarding claim 17, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 7, as discussed above, and Bader further discloses the first metric, the second metric, or both are a comprehensive metric, the comprehensive metric being directed to at least two of a coverage metric, an opportunity metric, and an exposure metric (The rating of an individual patent is named Competitive Impact. It is calculated by the multiplication of two factors called Technology Relevance and Market Coverage. The Patent Asset Index is the sum of the Competitive Impacts of a portfolio. See at least [0052. See also [0054]. Technology strength metrics, see at least [0062]. See also [0131]-[0132], describing patent value for market-based measures. An objective weighting factor based on different measures/indicators. See at least [0049]-[0050] See also [0073], [0139], and [0144].).
Regarding claim 18, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 17, as discussed above, and Bader further discloses the comprehensive metric includes a weighting factor for at least one of the coverage metric, the opportunity metric, or the exposure metric (The rating of an individual patent is named Competitive Impact. It is calculated by the multiplication of two factors called Technology Relevance and Market Coverage. The Patent Asset Index is the sum of the Competitive Impacts of a portfolio. See at least [0052. See also [0054]. Technology strength metrics, see at least [0062]. See also [0131]-[0132], describing patent value for market-based measures.).
Regarding claim 19, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 18, as discussed above, and Bader further discloses generating at least one of the comprehensive metric, the coverage metric, the opportunity metric, or the exposure metric (The rating of an individual patent is named Competitive Impact. It is calculated by the multiplication of two factors called Technology Relevance and Market Coverage. The Patent Asset Index is the sum of the Competitive Impacts of a portfolio. See at least [0052. See also [0054]. Technology strength metrics, see at least [0062]. See also [0131]-[0132], describing patent value for market-based measures. An objective weighting factor based on different measures/indicators. See at least [0049]-[0050] See also [0073], [0139], and [0144].).
While Bader discloses generating, Bader does not expressly disclose generating a graphical user interface (GUI) on the user device, the GUI configured to display a visual representation.
However, Jessen discloses generating a graphical user interface (GUI) on the user device, the GUI configured to display a visual representation (a view patent screen in which individual patents or applications may be viewed. See at least [0025], [0165], and FIG. 18. See also [0041] and [0083].).
From the teaching of Jessen, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify generating of Bader to generate a graphical user interface (GUI) on the user device, the GUI configured to display a visual representation, as taught by Jessen, in order to improve analysis of intellectual property assets (see Jessen at least at [0005]-[0006] and [0038]).
Claim 20 has similar limitations found in claim 1 above, and therefore is rejected by the same art and rationale.
Claims 10, 14, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Bader in view of Carroll, in further view of Jessen, and in further view of WO 2020072033 A1 (“Crouse”).
Regarding claim 10, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 9, as discussed above, and Bader further discloses determining the second financial metric based on an average financial metric of multiple financial metrics of multiple other IP assets having a similarity score (The weighted technology field/industry average will be calculated. Calculating Technology-Field-Index (TFx) as an average value. The outcome is the weighted average of the IP right valuation index P values. It can be used as a benchmark for the company in focus to analyze its portfolio and compare it to companies in the selected field of technology. See at least [0161]-[0164].).
While Bader discloses a similarity score, Bader does not expressly disclose a similarity score that is equal to or greater than the threshold value.
However, Crouse discloses a similarity score that is equal to or greater than the threshold value (Determining whether a patent asset has at least a threshold amount of similarity with a claim of a patent portfolio. See at least [0058]-[0059]. See also [0050].).
From the teaching of Crouse, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the similarity score of Bader to be equal to or greater than a threshold value, as taught by Crouse, in order to improve analysis of intellectual property data and to improve ease of identifying information that can be derived from data which can be used to make decisions (see Crouse at least at [0001] and [0013]).
Regarding claim 14, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 7, as discussed above, and Bader further discloses the first metric, the second metric, or both are a coverage metric, for at least one IP asset associated with the first entity or at least one IP asset associated with the second entity (The rating of an individual patent is named Competitive Impact. It is calculated by the multiplication of two factors called Technology Relevance and Market Coverage. The Patent Asset Index is the sum of the Competitive Impacts of a portfolio. See at least [0052. See also [0054].).
While Bader discloses a coverage metric, Bader does not expressly disclose the coverage metric being directed to geographic distribution, expiration, breadth, diversity, revenue alignment, invalidity, or a combination thereof.
However, Crouse discloses the coverage metric being directed to geographic distribution, expiration, breadth, diversity, revenue alignment, invalidity, or a combination thereof (Coverage for intellectual property may also relate to a level of quality of one or more intellectual property assets such as a measure of the breadth and/or strength of intellectual property assets. Determining the breadth of intellectual property, such as patents, may include performing a semantic analysis of the claims and analyzing the words included in the claims of a patent with respect to words included in a particular dataset of similar patents. Strength of patents may be determined by evaluating factors, such as age of the patent, number of other patents that cite the patent, word count of shortest independent claim. These factors may be utilized to determine an indicator of strength of the patent with respect to the strength of other patents in a dataset. See at least [0017]-[0019]. See also [0040] and [0058]-[0061].).
From the teaching of Crouse, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the coverage metric of Bader to be directed to expiration and breadth, as taught by Crouse, in order to improve analysis of intellectual property data and to improve ease of identifying information that can be derived from data which can be used to make decisions (see Crouse at least at [0001] and [0013]).
Regarding claim 16, the combination of Bader, Carroll, and Jessen disclose the limitations of claim 7, as discussed above. Bader does not expressly disclose the first metric, the second metric, or both are an exposure metric, the exposure metric being directed to litigation, exposure alignment, or a combination thereof for at least one IP asset associated with the first entity or at least one IP asset associated with the second entity
However, Crouse discloses the first metric, the second metric, or both are an exposure metric, the exposure metric being directed to litigation, exposure alignment, or a combination thereof for at least one IP asset associated with the first entity or at least one IP asset associated with the second entity (One or more of the data sources may store information corresponding to litigation proceedings and/or other enforcement proceedings associated with intangible assets of a number of organizations. See at least [0027]. See also [0031], [0034], and see also [0045] describing analyzing litigation data to determine a metric.)
From the teaching of Crouse, it would have been obvious to one having ordinary skill in the art before the effective filing date to modify the metrics of Bader to be an exposure metric directed to litigation, as taught by Crouse, in order to improve analysis of intellectual property data and to improve ease of identifying information that can be derived from data which can be used to make decisions (see Crouse at least at [0001] and [0013]).
No Prior Art Rejections
Based on the prior art search results, the prior art of record fails to anticipate or render obvious the claimed subject matter of claims 2-3 and 8. While some individual features of claims 2-3 and 8 may be shown in the prior art of record: no known reference, alone or in combination, would provide the invention of claims 2-3 and 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20100057533 A1 (“Martinez”) discloses a predetermined number of manifest or measurable variables is extracted from each of a plurality of patent documents obtained from a data base or another source and from these manifest variables four or more first order latent variables are defined comprising: knowledge stock, technological scope, international scope and patent value for each patent document and a dependency or causality relationship between the manifest and latent variables and between latent variables is defined
US 20220188322 A1 (“Adel”) discloses retrieve a data list from the electronic database based at least in part on the query, count data with common player names from the data list, sort by count to generate a discrete distribution, apply power law analysis to the discrete distribution to determine the value of an exponent S of the discrete distribution and use the exponent S as a metric of consolidation of the data list. Said exponent may then be stored in a lossy compressed database.
US 20020178029 A1 (“Nutter”) discloses an intellectual property evaluation method includes the steps of receiving a plurality of input scores for each of a plurality of patents by a intellectual property management software program. The plurality of input scores are combined to form a scale value for each of the plurality of patents. The scale values are compared to determine a select group of patents. A plurality of additional information is received by the intellectual property management software program for each of the select group of patents.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/RAVEN E YONO/Primary Examiner, Art Unit 3694