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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 14 April 2026 has been entered.
Response to Arguments
Regarding the 35 USC 101 rejection, Examiner has fully considered Applicant’s arguments and amendments.
Regarding Applicant’s assertion of “The claimed insight recommendation model, is a machine learning model that is trained by a KPI design engine to predict the significance of an available KPI not in use. Further, the insight recommendation model i.e. a machine learning model is trained by a KPI design engine based on a training dataset, by obtaining test data from data dictionary for the data repository, KPI data in the KPI repository, and historical data insight reports for a plurality of operation processes. Training insight recommendation model by KPI design engine by using the training dataset cannot be considered as a generic computer function and accordingly the processor cannot be considered as generic processor. Further training dataset for training the insight recommendation model is obtained from test data dictionary for the data repository, KPI data in the KPI repository, and historical data insights reports for a plurality of operation processes cannot be considered as managing of personal behavior or relationship or interaction between people rather the present invention includes concrete series of technical steps which are performed by a specific engines and models like KPI design engine. Therefore, such operations cannot be categorized as certain methods of organizing human activity. Accordingly, the Applicant submits that the claimed features do not merely recite an abstract idea, law of nature or natural phenomenon, under prong 1 of step 2A, but instead the claimed features are directed to patent eligible subject matter under prong 1 of step 2A.,” Examiner respectfully disagrees. The argued limitations above related to machine learning, as drafted, are part of the additional elements for consideration under Step 2A, Prong 2 and are not part of the abstract limitations for consideration under Step 2A, Prong 1. With respect to these additional elements, when these additional elements are considered both individually and in combination, they are not sufficient to prove integration into a practical application or anything significantly more. Additionally, the independent claims recite several abstract limitations for consideration under Step 2A, Prong 1, which can be seen below. Therefore, Examiner respectfully disagrees with Applicant’s assertions and maintains that the present claims recite an abstract idea for consideration under Step 2A, Prong 1.
Regarding Applicant’s assertion of “Applicant submits that the above emphasized claimed technical features solve the technical problem of how to extract meaningful data insights from the unutilized data in the data repositories for further employment into reports or dashboards.,” Examiner respectfully disagrees with Applicant’s assertion. The present claims do not reflect any technological improvements to any technology or technical fields. An improvement to extracting meaningful data insights for use in reports, as drafted, is not an improvement to any of the additional elements of the claims, or any other technology or technical field. Examiner respectfully asserts that an improvement to generating reports using meaningful insights, as drafted, would be an improvement to the abstract limitations for consideration under Step 2A, Prong 1. An improvement to the abstract idea would not be an improvement to the additional elements of the claims. MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements...” Additionally, as discussed in 2106.05(a)(II) improvements to technology or technical fields, “an improvement in the abstract idea itself … is not an improvement in technology”
Regarding Applicant’s assertion of “The present invention solves the above technical problem by employing a KPI design engine that is used to train an insight recommendation model, which is a machine learning model. More precisely, the insight recommendation model i.e. the machine learning model is trained by a KPI design engine based on a training dataset, by obtaining test data from data dictionary for the data repository, KPI data in the KPI repository, and historical data insight reports for a plurality of operation processes. Further, the insight recommendation model is executed by the distribution engine to generate a KPI recommendation for the operation process based on the data enthalpy metric. Further, the KPI recommendation is used as data insights for unused data. This saves the storage space in the system by increasing the data enthalpy realization by intelligently improving the utilization of the data. This, in turn, conserves the storage space in the computing system or utilize the unused space of the computing resources for some other meaningful purpose, also saving the energy cost for computing purpose.,” Examiner respectfully disagrees with Applicant’s assertions. The training of the machine learning model using particular data does not improve the functioning of the machine learning model itself. Rather, the selection process associated with the data to be input into the machine learning model would be improved through the use of a data dictionary and/or historical data insight reports. The type of abstract data being input into the machine learning model does not improve the functioning of the machine learning model itself, or any other additional elements. Furthermore, the type or amount of data stored in a database does not improve the functioning of the computer itself, or the memory itself. Examiner respectfully asserts that the mere selection of better data does not in turn improve the functioning of the computer or the memory of the computer itself. Therefore, Examiner respectfully disagrees with Applicant’s assertion that the present claims improve the functioning of the computer itself.
Accordingly, the 35 USC 101 rejection is maintained.
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 1-3, 7-9, 11-14, 17-18, and 20 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 1-3, 7-9, and 11 are directed to a method, claims 12-14 and 17-18 are directed to a system, and claim 20 is directed to a non-transitory computer readable media. Therefore, the claims are directed to patent eligible categories of invention.
Step 2A, Prong 1: Independent claims 1, 12, and 20 are related to generating a KPI recommendation, constituting an abstract idea based on “Certain Methods of Organizing Human Activity” related to commercial interactions including advertising or marketing sales activities or behaviors. Independent claim 1 recites limitations, similarly recited in claims 12 and 20, including “obtaining, available Key Performance Indicators (KPIs) associated with an operation process, the available KPIs representing metrics that can be calculated based on data for the operation process stored in a data repository; identifying, in-use KPIs comprising a subset of the available KPIs, the in-use KPIs being outputted as data insights; calculating, a data enthalpy metric for the operation process based on a number of the available KPIs and a number of the in-use KPIs, the data enthalpy metric indicating a relative amount of unutilized data for calculating a KPI; obtaining, an insight recommendation model to predict a significance of an available KPI not in use; executing, the insight recommendation model to generate a KPI recommendation for the operation process based on the data enthalpy metric; wherein executing the insight recommendation model comprises: predicting, with the insight recommendation model, a KPI significance metric for each of the available KPIs not in use of a plurality of operation process, the KPI significance metric indicating a significance weight of a first KPI in comparison with other KPIs; for each of the available KPIs not in use, calculating a KPI enthalpy metric based on the KPI significance metric of the first KPI and a data enthalpy metric of an operation process associated with the first KPI, wherein calculating the KPI enthalpy metric comprises: identifying available KPIs not in use associated with a same operation process as the KPI; and adding KPI significance metrics of the available KPIs not in use to obtain a subtotal KPI significance metric.” These limitations, as drafted, is a process that, under its broadest reasonable interpretation, but for the language of “with a processing circuitry,” covers an abstract idea but for the recitation of generic computer components. That is, other than reciting “with a processing circuitry,” nothing in the claim elements preclude the steps from being interpreted as an abstract idea. For example, with the exception of the “with a processing circuitry” language, the claim steps in the context of the claim encompass an abstract idea directed to “Certain Methods of Organizing Human Activity.”
Dependent claims 2-3, 7-8, 13-14, and 17-18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 9 and 11 will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claims 1, 12, and 20 do not integrate the judicial exception into a practical application. Claim 1 is directed to a method performed “with a processor circuitry.” Claim 12 is directed to a system comprising “a memory having stored thereon executable instructions; a processor circuitry in communication with the memory, the processor circuitry when executing the instructions configured to.” Claim 20 is directed to a product comprising “non-transitory machine-readable media; and instructions stored on the machine-readable media, the instructions configured to, when executed, cause a processor circuitry to.” Claim 1 recites limitations, similarly recited in claims 12 and 20, including “obtaining, with a processor circuitry, available Key Performance Indicators (KPIs) associated with an operation process from a KPI repository” “the in-use KPIs being outputted as data insights via a user interface,” and “outputting, with the processor circuitry, the KPI recommendation via the user interface to generate a knowledge graph for tracking usage of data assets and individual elements within the data assets.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Additionally, claim 1 recites limitations, similarly recited in claims 12 and 20, including “obtaining, with the processor circuitry, an insight recommendation model trained to predict a significance of an available KPI not in use” and “executing, with the processor circuitry, the insight recommendation model to generate a KPI recommendation for the operation process based on the data enthalpy metric.” These limitations reciting a trained model, as drafted, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Claims 1, 12, and 20 further recite the additional elements of “wherein the insight recommendation model is a pretrained machine learning model, wherein the pretrained machine learning model is trained, by a KPI design engine, based on a training dataset, wherein the training dataset includes test data from a data dictionary for the data repository, KPI data in the KPI repository and historical data insight reports for a plurality of operation processes.” These limitations reciting training a model, as drafted, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 2-3, 7-8, 13-14, and 17-18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 9 recites the additional element of “monitoring the data repository to detect a new data in the data repository.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Dependent claim 11 recites the additional element of “receiving a validation result for the KPI recommendation via the user interface; in response to the validation result indicating the KPI recommendation being rejected, triggering to train the insight recommendation model with new training dataset and execute the trained insight recommendation model to generate a new KPI recommendation.” These limitations reciting training a model, as drafted, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claims 1, 12, and 20 do not comprise anything significantly more than the judicial exception. Claim 1 is directed to a method performed “with a processor circuitry.” Claim 12 is directed to a system comprising “a memory having stored thereon executable instructions; a processor circuitry in communication with the memory, the processor circuitry when executing the instructions configured to.” Claim 20 is directed to a product comprising “non-transitory machine-readable media; and instructions stored on the machine-readable media, the instructions configured to, when executed, cause a processor circuitry to.” Claim 1 recites limitations, similarly recited in claims 12 and 20, including “obtaining, with a processor circuitry, available Key Performance Indicators (KPIs) associated with an operation process from a KPI repository” “the in-use KPIs being outputted as data insights via a user interface,” and “outputting, with the processor circuitry, the KPI recommendation via the user interface to generate a knowledge graph for tracking usage of data assets and individual elements within the data assets.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Additionally, claim 1 recites limitations, similarly recited in claims 12 and 20, including “obtaining, with the processor circuitry, an insight recommendation model trained to predict a significance of an available KPI not in use” and “executing, with the processor circuitry, the insight recommendation model to generate a KPI recommendation for the operation process based on the data enthalpy metric.” These limitations reciting a trained model, as drafted, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Claims 1, 12, and 20 further recite the additional elements of “wherein the insight recommendation model is a pretrained machine learning model, wherein the pretrained machine learning model is trained, by a KPI design engine, based on a training dataset, wherein the training dataset includes test data from a data dictionary for the data repository, KPI data in the KPI repository and historical data insight reports for a plurality of operation processes.” These limitations reciting training a model, as drafted, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not anything significantly more than the judicial exception.
Dependent claims 2-3, 7-8, 13-14, and 17-18 further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 9 recites the additional element of “monitoring the data repository to detect a new data in the data repository.” Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Dependent claim 11 recites the additional element of “receiving a validation result for the KPI recommendation via the user interface; in response to the validation result indicating the KPI recommendation being rejected, triggering to train the insight recommendation model with new training dataset and execute the trained insight recommendation model to generate a new KPI recommendation.” These limitations reciting training a model, as drafted, provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Use of a computer or other machinery in its ordinary capacity for tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
Accordingly, claims 1-3, 7-9, 11-14, 17-18, and 20 are rejected under 35 USC 101.
Allowable Subject Matter
Independent claims 1, 12, and 20 are rendered neither obvious nor anticipated by the available field of prior art. The claims overcome the prior art of record such that none of the cited prior art references can be applied to form the basis of a 35 USC 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims. Therefore, the claims may be allowable if amended to overcome the rejection(s) under 35 USC 101, as set forth above.
With respect to independent claims 1, 12, and 20, the present claims overcome the prior art rejection(s). The prior art, including the combination of Singh in view of Bader in view of Warkentin in view of Bhattacharya, when taken alone or in combination, does not anticipate, teach, or suggest the combination of limitations of “wherein calculating the KPI enthalpy metric comprises: identifying available KPIs not in use associated with a same operation process as the KPI; and adding KPI significance metrics of the available KPIs not in use to obtain a subtotal KPI significance metric; and calculating the KPI enthalpy metric for the KPI based on the subtotal KPI significance metric, the KPI significance metric of the KPI, and the data enthalpy metric of the same operation process as the KPI; and determining recommended KPIs based on the KPI enthalpy metrics, by selecting the KPI with KPI enthalpy metrics that exceed a predetermined metric threshold.” Examiner notes that Bhattacharya et al. (US 20230245022 A1) discloses generating scores with KPIs with other KPIs being unavailable and weighted at zero. Sheen et al. (US 20170272319 A1) discloses a prediction impact score associated with a KPI being below a threshold and being considered of less importance when predicting the KQI, and thus can be excluded from the set of KPIs used to adjust parameters. However, neither Bhattacharya nor Sheen can be incorporated into the prior art combination of the record above in order to sufficiently teach or disclose the combination of claim limitations including “wherein calculating the KPI enthalpy metric comprises: identifying available KPIs not in use associated with a same operation process as the KPI; and adding KPI significance metrics of the available KPIs not in use to obtain a subtotal KPI significance metric; and calculating the KPI enthalpy metric for the KPI based on the subtotal KPI significance metric, the KPI significance metric of the KPI, and the data enthalpy metric of the same operation process as the KPI; and determining recommended KPIs based on the KPI enthalpy metrics, by selecting the KPI with KPI enthalpy metrics that exceed a predetermined metric threshold.” Therefore, the independent claims overcome the prior art of the record such that none of the cited references can be applied to form the basis of a 35 USC 102 rejection nor can they be combined to fairly suggest in combination, the basis of a 35 USC 103 rejection when the limitations are read in the particular environment of the claims.
As allowable subject matter has been indicated, applicant's reply must either comply with all formal requirements or specifically traverse each requirement not complied with. See 37 CFR 1.111(b) and MPEP § 707.07(a).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Peral et al. ("Application of data mining techniques to identify relevant key performance indicators." 2017) discloses determining whether the KPIs included in a model are adequate or reflect relationships between objectives used to select the KPIs
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/SARA GRACE BROWN/Primary Examiner, Art Unit 3625
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