DETAILED ACTION
Status of the Application
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This communication is a final action in response to application filed on 6/8/2026. Claims 1-9 are currently pending and have been considered below.
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 1-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 1-9 are determined to be directed to an abstract idea.
The claims 1-9 are directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea), without providing a practical application, and without providing significantly more.
Regarding Step 1 of the subject matter eligibility analysis per the most recent subject matter eligibility guidance (2019 PEG), Claims 1-9 are directed to a method (i.e., process), a system (i.e., apparatus/machine), and a non-transitory medium (i.e., product); therefore, all claims are directed to one of the four statutory categories of invention.
Regarding Step 2A-Prong 1 of the subject matter eligibility analysis per the most recent subject matter eligibility guidance (2019 PEG), Claims 1, 4 and 7 are directed specifically to the abstract idea of managing generation of business metrics by: receiving a multidimensional data pertaining to a plurality of enterprise accounts, wherein the multidimensional data comprises at least one of a set of structured data and a set of unstructured data, wherein the multidimensional data is collected; identifying a plurality of potential Key Performance Indicators (KPIs) by mapping a plurality of predefined KPIs with the multidimensional data; selecting a plurality of relevant KPIs associated with each of the plurality of enterprise accounts from among a plurality of potential KPIs using a classification technique, wherein each of the plurality of relevant KPIs are associated with a dynamic coefficient, wherein the dynamic coefficient is updated based on context and focus of the plurality of enterprise accounts; computing a raw innovation factor for each of the plurality of enterprise accounts based on a weighted score associated with each of the corresponding plurality of relevant KPIs and the dynamic coefficient associated with each of the plurality of relevant KPIs, wherein the dynamic coefficient is fine-tuned or optimized over time on the basis of a system and process learning as part of the system's insight generation process; computing a scaled innovation score for each of the plurality of enterprise accounts by applying a set of pre-defined normalization rules on the corresponding raw innovation factor, wherein the set of pre- defined normalization rules for each of the plurality of enterprise accounts are generated based on size, headcount and revenue associated with a corresponding enterprise account; identifying a plurality of similar enterprise accounts corresponding to each of the plurality of enterprise accounts based on the corresponding scaled innovation score using an Euclidean distance based approach; generating a plurality of recommendations comprising a first set of insights, a second set of insights and a third set of insights to a user based on the plurality of similar enterprise accounts corresponding to each of the plurality of enterprise accounts, wherein the second set of insights triggers an evaluate action to an account team to check a reason and enables the account team to take a remedial action; updating the dynamic coefficient associated with each of the plurality of relevant KPIs associated with each of the plurality of enterprise accounts by: obtaining a self-learning control factor configured for each of the plurality of enterprise accounts, wherein the self-learning control factor is either one or zero; computing a moving average of the plurality of relevant KPIs associated with the plurality of similar accounts; computing a current delta dynamic coefficient for each of the plurality of similar accounts based on the moving average; and updating the dynamic coefficient associated with each of the plurality of relevant KPIs of the plurality of enterprise accounts using a self-learning auto correlation if the current delta dynamic coefficient is greater than zero, wherein the self-learning auto correlation is calculated from the current delta dynamic coefficient and the self- learning control factor, wherein a Standard Deviation (SD) is taken into consideration to perform the self-learning correction and self-learning feedback loop picks up the standard deviation in terms of a gap between a current target set and a moving average across the cohort, wherein the self- learning feedback loop continuously adjusts the relevant dynamic coefficients based on actual values to recalibrate a standardized calculation engine and using a self-learning control parameter update target; and computing an innovation score percentile for each of the plurality of enterprise accounts based on an updated dynamic coefficient associated with each of the plurality of relevant KPIs; providing insights to share a view of innovation velocity and enable informed decisions; which include abstract idea of mental processes (observing and evaluating data regarding enterprise accounts and KPIs and making judgement/opinion on specific business metrics); and certain methods of organizing human activities based on fundamental economic practice (managing generation of business metrics) and managing personal behavior and interactions between people (following instructions/rules to generate/compute business metrics); and mathematical concepts (various math techniques used to calculate business metrics). Claims 2-3, 5-6, 8-9 further define the abstract idea of claims 1, 4, and 7; therefore, these claims also recite mental processes, certain methods of organizing human activity, and mathematical concepts. After considering all claim elements, both individually and in combination and in ordered combination, it has been determined that the claims do not amount to significantly more than the abstract idea itself.
Regarding Step 2A-Prong 2 of the subject matter eligibility analysis per the most recent subject matter eligibility guidance (2019 PEG), while the claims 1-9 recite additional limitations which are hardware or software elements such as hardware processors, generative Artificial Intelligence, a system comprising: at least one memory storing programmed instructions; one or more Input /Output (I/O) interfaces; and one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions, non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors, automatically/auto (i.e., by use of a computer/processor), real time (i.e., by use of a computer/processor), these limitations are not enough to qualify as “practical application” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)). The claims do not amount to "practical application" for the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
Regarding Step 2B of the subject matter eligibility analysis per the most recent subject matter eligibility guidance (2019 PEG), while the claims 1-9 recite additional limitations which are hardware or software elements such as hardware processors, generative Artificial Intelligence, a system comprising: at least one memory storing programmed instructions; one or more Input /Output (I/O) interfaces; and one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions, non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors, automatically/auto (i.e., by use of a computer/processor), real time (i.e., by use of a computer/processor), these limitations are not enough to qualify as “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked as a tool to perform instruction of Abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide significantly more to an abstract idea (MPEP 2106.05(f) & (h)). The claims do not amount to "significantly more" than the abstract idea because they neither (1) recite any improvements to another technology or technical field; (2) recite any improvements to the functioning of the computer itself; (3) apply the judicial exception with, or by use of, a particular machine; (4) effect a transformation or reduction of a particular article to a different state or thing; (5) add a specific limitation other than what is well-understood, routine and conventional in the field; (6) add unconventional steps that confine the claim to a particular useful application; nor (7) provide other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment. Therefore, since there are no limitations in the claims 1-9 that transform the exception into a patent eligible application such that the claims amount to significantly more than the exception itself, and looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, the claims are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicants argued that the claims are not directed to an abstract idea. Examiner respectfully disagrees.
Applicant’s claimed invention recites the abstract idea of mental processes (observing and evaluating data regarding enterprise accounts and KPIs and making judgement/opinion on specific business metrics); and certain methods of organizing human activities based on fundamental economic practice (managing generation of business metrics) and managing personal behavior and interactions between people (following instructions/rules to generate/compute business metrics); and mathematical concepts (various math techniques used to calculate business metrics). Further, applicant’s claimed invention does not pertain to the fact pattern of Example 39.
Applicants argued that the claims integrate the abstract idea into a practical application by improving computer technology and provide significantly more. Examiner respectfully disagrees.
Additional elements of the claims not enough to qualify as “practical application” or “significantly more” being recited in the claims along with the abstract idea since these limitations are merely invoked to perform instruction of abstract idea in a particular technological environment and/or are generally linking the use of the abstract idea to a particular technological environment or field of use, and merely applying and abstract idea in a particular technological environment and merely limiting use of an abstract idea to a particular field or a technological environment do not provide practical application for an abstract idea (MPEP 2106.05 (f) & (h)).
Conclusion
Closest prior art to the invention includes:
Dharmavaram et al (US 20230368110 A1), regarding “A system and method for analyzing businesses data to make business decisions is disclosed. The method includes receiving a request from one or more users via one or more electronic devices to predict a set of insights associated with a business enterprise and generating a set of KPIs and metrics. The method further includes determining health of the business enterprise and predicting the set of insights associated with the business enterprise based on the received request, the generated set of KPIs and metrics, the determined health of the business enterprise and one or more diagnosis parameters by using a data management-based AI model. Further, the method includes outputting the determined health of the business enterprise, the one or more diagnosis parameters and the predicted set of insights on user interface screen of the one or more electronic devices associated with the one or more users.”;
Mahindru et al (US 20230140553 A1), regarding “Systems, computer-implemented methods, and/or computer program products facilitating a process to monitor and evaluate the effects of an artificial intelligence (AI) model on enterprise performance metrics are provided. According to an embodiment, a computer implemented method can comprise determining a technical issue of candidate technical issues associated with an artificial intelligence model that correlates to a change associated with a performance metric, wherein the determination is based on using a first data model that defines first relationships between the key performance metrics and candidate technical issues and second relationships between the candidate technical issues and candidate solutions. The method further comprises determining a solution for the technical issue using the data model and recommending or automatically implementing the solution. The method further provides for updating/refining the data model over time using continuous learning based on evaluating whether and how implemented solutions impact the relevant performance metrics.”;
Venkitapathi et al (US 20180123909 A1), regarding “The present disclosure discloses a method and a system for dynamically managing performance indicators for an enterprise to address goals for facility operations management. The method comprises integrating operations data associated with an enterprise, deriving Key Performance Indicators (KPIs), thresholds and metrics, determining factors affecting performance of the KPIs and factors affecting performance of the thresholds, normalizing the factors affecting the performance of the KPIs based on a comparability matrix, assessing performance of KPIs based on the one or more thresholds, the comparability matrix and associated patterns of the normalized data to derive KPI performance insights, fine-tuning the KPIs and enhancing the system, along with the one or more KPIs and the interactive visualization based on one or more patterns of usage of the interactive visualization and the fine-tuning, thereby dynamically managing performance indicators for an enterprise to address the goals of facility operations management.”;
Bakagiannis et al (I. Bakagiannis, V. C. Gerogiannis, G. Kakarontzas and A. Karageorgos, "Machine learning product key performance indicators and alignment to model evaluation," 2021 3rd International Conference on Advances in Computer Technology, Information Science and Communication (CTISC), Shanghai, China, 2021, pp. 172-177, doi: 10.1109/CTISC52352.2021.00039.), regarding “Machine Learning has seen amazing progress the past years with increasing commercial use from industries across the business spectrum. Businesses strive for alignment of vision and mission statement to the actual products they sell. For that reason tools like the Key Performance Indicators exist in order to monitor such progress. Nevertheless, products that embed a machine learning component are being optimized with other objective functions and are being evaluated in a vacuum with specific performance evaluation metrics that often have nothing to do with the business vision. In this position paper, we highlight this gap in different instances of the machine learning life cycle, explore and critically evaluate the current available solutions in the literature and introduce Key Performance Indicators in the machine learning development process. The paper also discusses representative machine learning KPIs in the development and deployment process.”.
None of the prior art alone or in combination teaches the claimed invention, wherein the novelty is in combination of all of the limitations.
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 MEHMET YESILDAG whose telephone number is (571)272-3257. The examiner can normally be reached M-F 8:30 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jerry O'Connor can be reached on (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MEHMET YESILDAG/Primary Examiner, Art Unit 3624
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