DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . The following FINAL office action is in response to Applicant communication filed on 01/27/2026 regarding application 18/332,174. Claims 1-20 remain pending. Claims 1-20 have been rejected.
Response to 35 U.S.C. § 101 Arguments
2. Applicant’s 35 U.S.C. § 101 arguments, filed with respect to Claims 1-20 have been fully considered, but they are found not persuasive (see Applicant Remarks, Pages 2-6, dated 01/27/2026). Examiner respectfully disagrees.
Argument #1:
(A). Applicant asserts that such contention ignores the specific limitations and functionality associated with the claimed steps and elements for evaluating prospective project results, identifying KPIs that contribute to deficient performance, and providing recommendations that assist in remedying and improving project results via a user interface of the device. In this regard, while a technological solution may use generic components, these generic components can operate in an unconventional manner to achieve an improvement in computer functionality. Amdocs Ltd v. Opennet Telecom, Inc., 841 F.3d 1288. 1300-1301 (Fed. Cir. 2016) (see Applicant Remarks, 1st ¶ of Page 3, dated 01/27/2026). Examiner respectfully disagrees.
While it is argued that the claims describe specific, functional steps (evaluating projects, identifying deficiencies, recommending actions), these arguments fail to overcome § 101 for the following reasons. Specific Limitations are Abstract Results: The "specific limitations" mentioned (e.g., selecting a first KPI value < second KPI value, calculating a target time) are merely the results of applying standard business logic and arithmetic to data, not technical, unconventional steps. Lack of Technical Improvement: The "improvement in computer functionality" argument is unsupported if the algorithm merely generates a report. Even using machine learning, if the process is essentially automating a manual business analysis process (calculating KPIs and providing advice), it is considered an abstract idea. "Unconventional" Application Not Present: The claims do not describe a new way for the device to operate (e.g., a new data structure, specialized processor, or novel network architecture). The device is simply used as a tool to perform mathematical calculations and display data, which does not constitute an "unconventional manner" that improves computer technology. In summary, the claims are directed to a computer-implemented business process, is considered an abstract idea that does not become eligible simply because it is performed on a computer.
Moreover, while Amdocs Ltd. v. Opennet Telecom, Inc. found that distributed architecture, which allowed for centralized network monitoring without sending all data to a single node, was patent-eligible because it provided a technological improvement, Independent Claims 1, 9 and 17 of the instant application is distinguishable from the Amdocs case due to the following reasons. No Technical Solution to Technical Problem: In Amdocs, the claim specifically solved a technical, network-level problem by reducing data transfer load. The present claim, conversely, focuses on business analytics (project KPIs), which are abstract, not a specific technical improvement in how the computer or network operates. Generalized Components vs. Specialized Architecture: Unlike the specialized, distributed computing architecture in Amdocs, the present claim refers to a general "device" and general "affinity propagation" to analyze data, which falls closer to the ineligible category of using a computer as a tool for economic or organizational analysis. Result-Oriented: The present claim describes what to calculate (targets, recommendations) rather than how to structurally improve the computer to do it faster or more efficiently.
Argument #2:
(B). Applicant argues that the “calculating” features and the “selecting” features, as recited in Independent Claim 1 cannot be performed using a physical aid, such as pen to paper and states that the “calculating” features and the “selecting” feature could not be performed in the human mind or via the aid of pencil and paper and asserts this in a conclusionary fashion (see Applicant Remarks, 2nd ¶ of Page 3, dated 01/27/2026). Examiner respectfully disagrees.
The argument that these calculations cannot be performed using a physical aid (pen and paper) is insufficient for patentability for the following reasons. Calculation Feasibility: Affinity propagation, while complex, is essentially an iterative clustering algorithm based on matrix calculations. While tedious, the underlying operations—arithmetic comparisons, summation, and data selection—are not inherently tied to a machine and could be performed by a person with paper and a calculator over an extended period. "Mental Process" Test: The Supreme Court in Alice Corp. v. CLS Bank noted that "machine-or-transformation" is not the sole test. The key is whether the claim is directed to an abstract idea. The steps described are, in essence, mathematical manipulation of numerical data (KPI values) or mental processes. Conclusionary Nature: Arguing that the calculation "could not be performed in the human mind" is often considered a conclusionary statement unless the claim specifically recites a new, technical, or non-conventional algorithmic improvement in the underlying computation itself. These claims as stated focuses on the result (the recommendation), not a technical, computer-centric improvement.
Regarding rebuttal to lack of case comparison: The examiner’s finding is supported by established precedent where claims focused on managing business data via mathematical calculation are deemed abstract. Similar to cases where software merely automating business processes is rejected, the current claim relies on the algorithm itself to define the "innovation," lacking a specific technical improvement. The reliance on "affinity propagation" does not make an abstract business strategy a technical solution.
Argument #3:
(C). Applicant then argues that the use of a computer is required to perform the claimed method of Independent Claim 1 and cited TQP Development, LLC v. Intuit, Inc., 2014 WL651935 (E.D. Tex. Feb 19, 2014) (the specific data encryption method “could not conceivably be performed in the human mind or with pencil and paper”) (see Applicant Remarks, 2nd ¶ of Page 3, dated 01/27/2026). Examiner respectfully disagrees.
While it is true that the calculation of affinity propagation, massive data handling, and real-time visualization of project KPIs cannot be performed by a human, this does not automatically render the claim patent-eligible. Under Alice/Mayo guidelines, simply requiring a computer to do a complex, high-speed calculation that a human could not do on paper does not make it eligible if the underlying method is just an abstract formula applied to business data. Unlike the encryption method in TQP Development LLC v. Intuit, Inc., which solved a specific technical computer security problem, these claims solve a business problem (tracking project KPIs) using a general-purpose computer tool. The steps of the instant claimed invention describe “a device” but do not restrict it to a specific, specialized apparatus. The calculations, clustering (affinity propagation), and comparisons are mathematical in nature. A human analyst, could, with significant time, take project data, calculate cluster, set goals, and formulate recommendations, thus making the process fundamentally a “method of organizing human activities”. Generic Components: These claims use standard technology (“ML device”, “user interface”), and the steps do not require a specifically programmed computer, but rather use a general-purpose computer to perform activities (similar to arguments used to distinguish from TQP). The core of these claims is a business strategy (project improvement) that is merely accelerated by a computer, rather than being a technical solution of the computer. The computer simply acts as an “accelerator” of abstract, manual, or mental steps.
Argument #4:
(D). Applicant then argues with reference to the TQP Development case, the Examiner’s response amounts to a non-sequitur. The issue of whether the “calculating” features and the “selecting” feature can or cannot be performed in the human mind or with the aid of pen and paper does not speak to the what the focus of the claims are under the "directed to" inquiry relative to what is stated in the claims. The examiners reliance on case law (Erricson Inc and ChargePoint) that address such issue is not germane to the Applicant's argument and position” (see Applicant Remarks, last ¶ of Page 3 and 1st of Page 4, dated 01/27/2026). Examiner respectfully disagrees.
Applicant’s argument—that the ability to perform calculations in the human mind is irrelevant to the "directed to" inquiry—is flawed. Under the Alice/Mayo framework, determining whether steps are purely mental or capable of being performed via pen and paper is a key indicator of whether the claim is directed to an abstract, mathematical concept rather than a technological improvement. Non-Sequitur Rebuttal: The focus of the claim is whether it covers a fundamental, abstract process. If a step can be performed mentally or manually, it strongly suggests the claim covers an abstract idea rather than a tangible technological improvement. Relevance of Case Law: Contrary to Applicant's assertion, Ericsson Inc. v. TCL and ChargePoint, Inc. v. SemaConnect are highly relevant. They establish that merely applying generic, conventional technology to perform an abstract mathematical or business process does not make it patent-eligible, particularly when the claimed invention does not improve the computer's operation itself. Conclusion: The claims do not solve a technical problem; they solve a business problem (managing projects) using a conceptual framework (data analysis), which makes it an abstract idea, regardless of the "machine learning" label applied to it.
Argument #5:
(E). Applicant then argues the evidence is required that the “calculating” features and the “selecting” features could be performed in the human mind or with the aid of paper and disputes Examiner citation of FairWarning, OIP Techs and Content Extraction court cases (see Applicant Remarks, 2nd ¶ of Page 3, dated 01/27/2026). Examiner respectfully disagrees.
Evidence is not required to prove that "calculating" and "selecting" can be done by a human. Under Alice/Mayo, if a claim is "directed to" an abstract concept, the burden shifts to the applicant to demonstrate that the claims are not simply doing so on a computer, and the court can rely on the face of the claim. FairWarning Context: FairWarning v. Iatric Systems established that a claim directed to analyzing and auditing user activity in a computer system was invalid as an abstract idea. FairWarning held that the "mere interaction" of data, even with user interface components, does not transform an abstract process into a patentable application. The case demonstrates that courts, particularly during 12(b)(6) motions, determine the legal question of eligibility by evaluating if the claim's "focus" is on an abstract concept rather than a technological improvement, based on the claim language itself, without requiring extrinsic evidence that a human could have performed the math.
The premise that evidence is not required to show that calculating and selecting features could be performed in the human mind or with paper is fundamentally flawed in the context of patent law precedents like OIP Techs, Inc. v. Amazon.com, Inc. and Content Extraction and Transmission LLC v. Wells Fargo Bank, N.A.. While these cases do not mandate a "smoking gun" testimonial regarding human capability, they clearly dictate that courts must perform a case-precedential comparison and analysis of the claims to determine if the computer is merely acting as a calculator or storing/retrieving data. OIP Techs stands for the principle that merely collecting, analyzing, and presenting data using computer technology, even if done faster than a human, does not make a mathematical algorithm patent-eligible; the court specifically compared the claim to prior cases of "abstract, mental processes". Content Extraction demonstrated that the analysis is not about a lack of evidence, but rather the failure of the claims to represent a technical improvement. The court's analysis focused on whether the claims were directed to a process that could be accomplished by a person and simply added "using a computer" as an afterthought. Therefore, the argument that evidence is unnecessary misses that legal precedent is the necessary evidence in a 101 analysis—comparing the claimed "calculating/selecting" steps against established, non-technical, mental, or clerical, methods. The claim in question, by focusing on "calculating... using... data" and "providing via a user interface," is analogous to the "abstract ideas" rejected in the cited case law.
Argument #6:
(F). Applicant argues that Independent Claims 1, 9 and 17 recites an improved user interface of a computing device in which new information is provided in the technical field of project management and cites court case of Core Wireless Licensing S.A.R.L v. LG Electronics, Inc., 880 F.3d 1356 (Fed. Cir. 2018) (an improved user interface for computing devices) and also the court case of Data Engine Tech. LLC v. Google, Inc., 906 F.3d 999, 1011 (Fed. Cir. 2018) (an improved user interface for devices) (see Applicant Remarks, last ¶ of Page 4 and 1st ¶ of Page 5, dated 01/27/2026). Examiner respectfully disagrees.
In response to Applicant remarks here, Examiner notes that the claim limitation steps of Independent Claims 1, 9 and 17 provide no technical user interface improvement. Unlike the Core Wireless, which provided a specific, improved screen structure for mobile devices, or Data Engine, which detailed a unique tabbed interface structure, this method only describes providing information such as a prospective target KPI value, the corrective recommendation and the target time allotted value to a user. The UI is simply a display medium, not a technological improvement to the interaction or structure of the interface itself. Secondly, New Information ≠ Improved Interface: Data Engine and Core Wireless required a technical solution to a computer interface problem. In this case, providing "new information" (a predictive KPI) through a standard interface does not make the interface "improved" in a technical sense; it simply displays the results of the abstract calculation. Thirdly regarding Functional Claiming, the "corrective recommendation" and "prospective target" are functional outputs. The claim limitation steps for Independent Claims 1, 9 and 17 lack specificity regarding how the UI is specifically structured or programmed in a novel way to improve functionality.
Examiner points out that in Core Wireless, the court found that the claims were directed to a specific improved UI that reduced the need for navigation, resulting in a more efficient, faster interface. The present claim limitations for Independent Claims 1, 9 and 17, merely provides outputs (target values/recommendations) on a user interface, which is a standard display function. With respect to the Data Engine court case, the claims in this case were directed to a specific method of navigating within a complex, multi-dimensional spreadsheet, which the court deemed a specific improvement. The present claim limitation steps for Independent Claims 1, 9 and 17 does not define a new, improved UI structure but rather outputs analytical results. Thirdly, regarding no new information type, the information provided (KPIs, project status) is inherently known to the field of project management. The “new information” is simply the output of an algorithm, not a novel form of data representation or interface architecture that improves computer performance. In summary, the steps for Independent Claims 1, 9 and 17 constitute a mental process or business method implemented on a computer, and the user interface, while perhaps presenting useful business data, does not constitute a technical improvement to the functionality of the computing device.
Argument #7:
(G). Applicant argues that current systems that may assist in evaluating various metrics and current project results may be unable to evaluate prospective project results and/or identify achievement improvements of KPIs that may current contribute to deficient performance. The Applicant’s claims specifically for Independent Claims 1, 9 and 17 provide a claimed user interface that provides an improvement, by way of new information, in which each of a prospective target KPI value, corrective recommendation, and target time allotted value are provided (see Applicant Remarks, 2nd ¶ of Page 5, dated 01/27/2026). Examiner respectfully disagrees.
With respect to this argument, Examiner responds by stating that Independent Claims 1, 9 and 17 do not, in fact, "predict" or create new information in a non-obvious way. It takes a "second current KPI value" (high performance) and assigns it as a "prospective target" for a "first group" (low performance) based on a mathematical comparison. This is fundamentally a benchmarking/gap analysis process (e.g., "if Team B is doing well, make Team A do what Team B is doing"). Therefore, the system is simply applying data processing to known business metrics, not creating a new, inventive predictive algorithm.
Secondly, the UI provides output of a calculation, not an improvement to the computer's functionality or a new technical capability. Presenting data (KPIs, time, recommendations) on a screen is a conventional, well-understood, and routine activity in the context of dashboards, and therefore, it does not add an inventive concept to the claim. The "corrective recommendation" is likely a simple, automated directive derived from the gap analysis (e.g., if X is low, allocate more resources).
Argument #8:
(H). Applicant argues that Claims 1-20 recite additional elements that integrate the judicial exception into a practical application under revised step 2a prong two of the 35 U.S.C. § 101 analysis (see Applicant Remarks, last ¶ of Page 5 and 1st ¶ of Page 6, dated 01/27/2026). Examiner respectfully disagrees.
In response to Applicant’s remarks here, Examiner points out that Independent Claims 1, 9 and 17, the claim elements considered both individually and as an ordered combination do not integrate the judicial exception into a practical application under step 2a prong 2 under the 35 U.S.C. § 101 analysis. For instance, the steps, including using machine learning for clustering (affinity propagation), calculating targets, and providing recommendations via a user interface, do not constitute a "practical application" that transforms the abstract idea into a patent-eligible invention. Instead, they recite computer functions (data collection, calculation, UI display) applied to a business process, failing to improve the computer's functioning itself or providing a specific, technical improvement to a computing technology, thus failing 35 U.S.C. 101 Step 2a, Prong 2. The first step of "Obtaining... data... and calculating... affinity propagation": This is merely a data processing step using a known algorithm (affinity propagation) to identify patterns, which is a mental process or mathematical concept performed on a computer, not a technical improvement. The second step of "Selecting... a first... KPI value... less than the second": This is a comparison/analysis step that could be performed mentally or with basic analytical tools. The third and fourth steps of "Calculating... a prospective target KPI" and "corrective recommendation": The claim calculates a target based on comparing two existing values and provides a recommendation. This is, again, an analytical, business-focused calculation, not a technical solution to a technical problem. The last steps of "Providing... via a user interface": Simply displaying data on a screen is a well-known, conventional, and generic use of a user interface, failing to add any "significantly more" to the abstract idea. Machine Learning Device: Merely reciting that a machine learning device performs the analysis does not make the process technical; it just means a computer is used to do the calculation, rather than a human, which is insufficient.
Next Applicant argues that their method improves "project management, such as a software maturity project," but this is a business/management improvement, not a technical/technological improvement. Independent Claims 1, 9 and 17 of the claimed invention do not, for example, improve the way data is stored in memory, how the processor executes the algorithm, or how the machine operates (e.g., increased processor speed or reduced bandwidth usage). Evaluation of the alleged "Improvement": The alleged improvement is to the business process of managing a project (identifying deficient KPIs and offering advice), which is an abstract, non-technical activity. No Technological Solution: The steps of calculating KPIs and providing recommendations do not solve a technical problem in computer science (e.g., they don't solve a network latency problem, improve database architecture, or optimize compiler efficiency). They merely automate a business analytical process. Furthermore, for Claims 1, 9 and 17, certain/particular claim limitations reflect mere data gathering (e.g., “obtaining, by a device that includes a machine learning device, performance data that indicates a state of a project based on key performance indicators”) and mere data outputting (e.g., “providing, by the device via a user interface to a user, the prospective target KPI value, the corrective recommendation, and the target time allotted value”) wherein each of steps are reminiscent of mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). With respect to the additional element of (e.g., “a machine learning device”) and when considered in view of the claim limitations both individually and as an ordered combination (as a whole) are insufficient to add a practical application under step 2a prong 2 due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment to analyze the state of a project based on key performance indicators (KPIs) as well as calculating a corrective recommendation that indicates resources to be provided to attain a prospective target KPI value in the field of project management (see MPEP § 2106.05 (h)).
In conclusion, since the claims are directed to the use of a computer to analyze business data (an abstract idea) without providing a specific technical improvement in the functioning of the computer itself, Claims 1-20 are patent ineligible under 35 U.S.C. § 101 under step 2a prong 2.
Argument #9:
(I). Applicant argues that Applicant’s claims recite details of how a solution to the technical problem of accurately evaluating, predicting, and recommending prospective target KPI values, prospective time periods during which the prospective target KPI values may be achieved, and resource recommendations are accomplished for a project (see Applicant Remarks, 1st ¶ of Page 6, dated 01/27/2026). Examiner respectfully disagrees.
The rebuttal response to this assertion by the Applicant consist of the following points. First, the "Problem" is Business, Not Technical: Evaluating, predicting, and recommending project resources based on KPIs is a fundamental business practice designed for organizational management. It is not a computer-centric technical problem (such as memory management or data transmission speed). Generic Mathematical Application: The claim uses machine learning techniques—specifically, "affinity propagation" to cluster data. Simply applying an algorithm to a new set of data (KPIs) does not make the process a "technical solution". Lack of Specificity in "How": These claims define the inputs (KPIs) and outputs (recommendations), but lacks the necessary technical implementation details—such as specific network architecture, unique training methods, or proprietary algorithm modifications—that would constitute a technical improvement. It describes a high-level, functional workflow ("calculating a target value," "providing a recommendation") rather than a concrete, specialized, and novel technical, technological, or scientific advancement. No Improvement in Computing Functionality: The claimed invention does not make the computer, network, or data processing unit work faster, more efficiently, or differently than a standard machine; it simply uses the machine to calculate business data faster.
Argument #10:
(J). Applicant argues that Claims 1-20 do not recite an abstract idea, law of nature of natural phenomenon under revised step 2a prong one of the 35 U.S.C. § 101 analysis (see Applicant Remarks, 2nd ¶ of Page 6, dated 01/27/2026). Examiner respectfully disagrees.
Specifically, Applicant continues to argue that Applicant’s claims are not directed to mental processes and certain methods of organizing human activities under step 2a prong 1 of the 35 U.S.C. § 101 analysis. Examiner respectfully disagrees.
Examiner notes that Independent Claims 1, 9 and 17 are directed to an abstract idea of automating, managing, and optimizing project performance through data analysis and predictive modeling. Specifically, this falls under the category of methodologies for organizing human activity (project management/resource allocation) and mathematical concepts/data processing (clustering via affinity propagation, calculating predictive metrics). The steps of obtaining, clustering, selecting, and calculating prospective target values and recommendations are business processes or mathematical algorithms performed on a computer. Mathematical Concept: Clustering data using "affinity propagation" to group similar project data. Method of Organizing Human Activity/Business Process: The use of "Key Performance Indicators (KPIs)" to evaluate, compare, and set targets for project performance. Predictive Modeling: Calculating future, prospective target KPI values based on existing data clusters. Data Analysis and Resource Optimization: Determining corrective actions based on performance disparities. These claim limitations for Independent Claims 1, 9 and 17 describes executing these tasks—typically performed by a human manager analyzing spreadsheets—using a machine learning device, which does not render the underlying concept non-abstract. First, other than factoring the additional elements of (e.g., a device that includes a ML device), the step of “obtaining performance data that indicates a state of a project based on key performance indicators (KPIs)” is interpreted as Certain Method of Organizing Human Activity (data collection) which is based on managing personal behavior or relationships or interactions between people (which includes teachings or following rules or instructions. Next, other than factoring the additional element of (e.g., a device), the step of “calculating affinity propagation….” is interpreted as Mental Processes such as concepts performed in the human mind (including observations or evaluations or judgments) or using pen and paper as a physical aid or alternatively as “Mathematical Concepts” such as mathematical calculations. Thirdly, other than factoring the additional element of (e.g., a device), the step of “Selecting KPI Values and Groups within a Cluster” is interpreted as Mental Processes such as concepts performed in the human mind (including observations or evaluations or judgments) or using pen and paper as a physical aid or alternatively as “Mathematical Concepts” such as mathematical calculations. Fourth, other than factoring the additional element of (e.g., a device), the step of “Calculating a Prospective Target KPI Value” is interpreted as Mental Processes such as concepts performed in the human mind (including observations or evaluations or judgments) or using pen and paper as a physical aid or alternatively as “Mathematical Concepts” such as mathematical calculations. Fifth, other than factoring the additional element of (e.g., a device), the step of “Calculating a Target Time allocated value” is interpreted as Mental Processes such as concepts performed in the human mind (including observations or evaluations or judgments) or using pen and paper as a physical aid or alternatively as “Mathematical Concepts” such as mathematical calculations. Sixth, other than factoring the additional element of (e.g., a device), the step of “Calculating a Corrective Recommendation (Resources)…” is interpreted as Mental Processes such as 1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid or alternatively as “Mathematical Concepts” such as mathematical calculations. Lastly, other than factoring the additional elements of (e.g., “a device” and a “user interface”), this step of “providing via a user interface…..” is interpreted as Certain Methods of Organizing Human Activities which pertains to managing personal behavior or relationships or interactions between people (which includes teachings or following rules or instructions.
Because Claims 1-20 recite certain methods of organizing human activities and mental process and mathematical concepts, it is directed to an abstract idea under step 2a prong 1 via the 35 U.S.C. § 101 analysis. Therefore, Claims 1-20 remain patent ineligible under 35 U.S.C. § 101 analysis.
Claim Rejections - 35 USC § 101
3. 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.
4. Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-20 are each focused to a statutory category namely a “method” or a “process” (Claims 1-8), a “device” or an “apparatus” (Claims 9-16) and a “non-transitory computer-readable storage medium” or an “article of manufacture” (Claims 17-20).
Step 2A Prong One: Independent Claims 1, 9 and 17 recite limitations that set forth the abstract idea(s), namely (see in bold except where strikethrough):
“” (see Independent Claim 9);
“” (see Independent Claim 17);
“obtaining, , performance data that indicates a state of a project based on key performance indicators (KPIs)” (see Independent Claim 1);
“calculating, , affinity propagation using the performance data” (see Independent Claim 1);
“selecting, , a first current KPI value pertaining to one of the KPIs and a first group of the project within a cluster of the affinity propagation calculation that includes a corresponding second current KPI value pertaining to the one of the KPIs and a second group of the project, wherein the first current KPI value is less than the second current KPI value” (see Independent Claim 1);
“calculating, , a prospective target KPI value pertaining to the one of the KPIs and the first group based on the second current KPI value and a category of the one of the KPIs” (see Independent Claim 1);
“calculating, , a target time allotted value for the prospective target KPI value to be attained” (see Independent Claim 1);
“calculating, based on the one of the KPIs, a corrective recommendation that indicates resources to be provided to attain the prospective target KPI value” (see Independent Claim 1);
“providing, to a user, the prospective target KPI value, the corrective recommendation, and the target time allotted value” (see Independent Claim 1);
“obtain performance data that indicates a state of a project based on key performance indicators (KPIs), ” (see Independent Claim 9);
“obtain performance data that indicates a state of a project based on key performance indicators (KPIs)” (see Independent Claim 17);
“calculate affinity propagation using the performance data” (see Independent Claims 9 & 17);
“select a first current KPI value pertaining to one of the KPIs and a first group of the project within a cluster of the affinity propagation calculation that includes a corresponding second current KPI value pertaining to the one of the KPIs and a second group of the project, wherein the first current KPI value is less than the second current KPI value” (see Independent Claims 9 & 17);
“calculate a prospective target KPI value pertaining to the one of the KPIs and the first group based on the second current KPI value and a category of the one of the KPIs” (see Independent Claims 9 & 17);
“calculate a target time allotted value for the prospective target KPI value to be attained” (see Independent Claims 9 & 17);
“calculate, based on the one of the KPIs, a corrective recommendation that indicates resources to be provided to attain the prospective target KPI value” (see Independent Claims 9 & 17);
“provide to a user , the prospective target KPI value, the corrective recommendation and the target time allotted value” (see Independent Claims 9 & 17).
Here, for Independent Claims 1, 9 and 17, the claims are directed to the abstract idea of automating, managing, and optimizing project performance through data analysis and predictive modeling. Specifically, this falls under the category of methodologies for organizing human activity (project management/resource allocation) and mathematical concepts/data processing (clustering via affinity propagation, calculating predictive metrics). The steps of obtaining, clustering, selecting, and calculating prospective target values and recommendations are business processes or mathematical algorithms performed on a computer. Mathematical Concept: Clustering data using "affinity propagation" to group similar project data. Method of Organizing Human Activity/Business Process: The use of "Key Performance Indicators (KPIs)" to evaluate, compare, and set targets for project performance. Predictive Modeling: Calculating future, prospective target KPI values based on existing data clusters. Data Analysis and Resource Optimization: Determining corrective actions based on performance disparities. These claim limitations for Independent Claims 1, 9 and 17 describes executing these tasks—typically performed by a human manager analyzing spreadsheets—using a machine learning device, which does not render the underlying concept non-abstract.
Therefore, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid, which in order to help perform these mental steps does not negate the mental nature of these limitations. The use of "physical aids" in implementing the abstract mental process, does not preclude the claim from reciting an abstract idea. See MPEP § 2106.04(a) III C.
Additionally, or alternatively, these abstract idea limitations (as identified above in bold), under their broadest reasonable interpretation of the claims as a whole, cover performance of their limitations as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (which includes teachings or following rules or instructions) and additionally or alternatively as (4) “Mathematical Concepts” which pertains to mathematical calculations.
That is, other than reciting (e.g., “a processor” & “a device” & “wherein instructions are configured to” & “user interface”), nothing in the claim elements precludes the steps from “Mental Processes” which pertains to (1) concepts performed in the human mind (including observations or evaluations or judgments) or (2) using pen and paper as a physical aid and additionally or alternatively as “Certain Methods of Organizing Human Activities” which pertains to (3) managing personal behavior or relationships or interactions between people (which includes teachings or following rules or instructions) and additionally or alternatively as (4) “Mathematical Concepts” which pertains to mathematical calculations.
Therefore, at step 2a prong 1, Yes, Claims 1-20 recite an abstract idea. We proceed onto analyzing the claims at step 2a prong 2.
Step 2A Prong Two: With respect to Step 2A Prong Two of the eligibility inquiry (as explained in MPEP § 2106.04(d)), the judicial exception is not integrated into a practical application. Independent Claims 1, 9 and 17 recites additional elements directed to: (e.g., “a processor” & “user interface” & “a device” & “wherein instructions are configured to”). These additional elements have been considered individually and in combination, but fail to integrate the abstract idea into a practical application because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP § 2106.05(f) and MPEP § 2106.05(h). Furthermore, for Claims 1, 9 and 17, certain/particular claim limitations reflect mere data gathering (e.g., “obtaining, by a device that includes a machine learning device, performance data that indicates a state of a project based on key performance indicators”) and mere data outputting (e.g., “providing, by the device via a user interface to a user, the prospective target KPI value, the corrective recommendation, and the target time allotted value”) wherein each of steps are reminiscent of mere insignificant extra-solution activities (see MPEP § 2106.05 (g)).
In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception. Therefore, at step 2a prong 2, Claims 1-20 are directed to the abstract idea and do not recite additional elements that integrate into a practical application.
Step 2B: (As explained in MPEP § 2106.05), it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Independent Claims 1, 9 and 17 recites additional elements directed to: (e.g., “a processor” & “user interface” & “a device” & “wherein instructions are configured to”). These elements have been considered individually and in combination, but fail to add significantly more to the claims because they amount to using computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (computing environment) and does not amount to significantly more than the abstract idea itself. See MPEP § 2106.05 (f) and MPEP § 2106.05 (h). Notably, Applicant’s Specification suggests that the claimed invention relies on nothing more than a general-purpose computer executing the instructions to implement the invention (e.g., see at Applicant’s Specification ¶ [0054]: “As previously described, a network device may be implemented according to various computing architectures (e.g., in a cloud, etc.) and according to various network architectures (e.g., a virtualized function, etc.). Device 300 may be implemented in the same manner. For example, device 300 may be instantiated, created, deleted, or some other operational state during its life-cycle (e.g., refreshed, paused, suspended, rebooting, or another type of state or status), using well-known virtualization technologies.” Also see at Applicant’s Specification ¶ [0070]: “Additionally, embodiments described herein may be implemented as a non-transitory computer-readable storage medium that stores data and/or information, such as instructions, program code, a data structure, a program module, an application, a script, or other known or conventional form suitable for use in a computing environment.”).
Independent Claims 1, 9 and 17: With respect to the additional element of (e.g., “a machine learning device”) and when considered in view of the claim limitations both individually and as an ordered combination (as a whole) are insufficient to add a practical application under step 2a prong 2 and also are not significantly more under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment to analyze the state of a project based on key performance indicators (KPIs) as well as calculating a corrective recommendation that indicates resources to be provided to attain a prospective target KPI value in the field of project management (see MPEP § 2106.05 (h)). Also with respect to Independent Claims 1, 9 and 17, certain/particular limitations shown recite (1) mere data gathering (e.g., “obtaining, by a device that includes a machine learning device, performance data that indicates a state of a project based on key performance indicators”) and (2) mere data outputting (e.g., “providing, by the device via a user interface to a user, the prospective target KPI value, the corrective recommendation and the target time allotted value”) wherein each of steps are reminiscent of mere insignificant extra-solution activities (see MPEP § 2106.05 (g)). Furthermore, these certain/particular claim limitations as demonstrated above for Independent Claims 1, 9 and 17 reflect Well-Understood, Routine and Conventional Activities (WURC) under MPEP § 2106.05 (d) ii: See Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec,838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359,1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrates the abstract idea into a practical application. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that, as an ordered combination, amount to significantly more than the abstract idea itself.
Dependent Claims 2-8, 10-16 and 18-20 recite additional elements directed to: (e.g., “supervised learning algorithm” (Dependent Claims 6 and 14)), when considered individually and as an ordered combination (as a whole) with these claim limitations recite the same abstract idea(s) as shown in Independent Claims 1, 9 and 17 along with further steps/details that (1) could be performed in the human mind (including observations or evaluations or judgments) or (2) using pen to paper as a “physical aid”, which therefore fall under the “Mental Processes” Grouping and also “Certain Methods of Organizing Human Activities” Grouping which pertains to (3) managing personal behavior or relationships or interactions between people (including teachings or following rules or instructions) and additionally or alternatively as (4) “Mathematical Concepts” which pertains to mathematical calculations.
Dependent Claims 2-5, 7-8, 10-13, 15-16 and 18-20 further narrow the abstract ideas, and are therefore still ineligible for the reasons previously provided in Steps 2A Prong 2 and Step 2B for Independent Claims 1, 9 and 17. Dependent Claims 6 and 14: Moreover, with respect to the additional elements of (e.g., “supervised learning algorithm” & “machine learning device”) as recited in Dependent Claims 6 and 14, these additional elements do not provide limitations that are indicative of integration into a practical application under step 2a prong 2 and also do not recite additional elements that are significantly more than the recited judicial exceptions under step 2B due to: (1) reciting mere instructions to implement an abstract idea on a computer or using a computer as a tool to “apply” the recited judicial exceptions (see MPEP § 2106.05(f)) or (2) the claims as a whole are limited to a particular field of use or technological environment to analyze the state of a project based on key performance indicators (KPIs) as well as calculating a corrective recommendation that indicates resources to be provided to attain a prospective target KPI value in the field of project management (see MPEP 2106.05 (h)).
The additional element of “machine learning” in Dependent Claims 6 and 14 does not amount to significantly more than the judicial exceptions under step 2B due being expressly recognized as Well-Understood, Routine and Conventional (WURC) in the art.
See for example: US PG Pub (US 2021/0081848 A1) – “Techniques for Adaptive Pipeline Composition for Machine Learning (ML)”, hereinafter Polleri, et. al. Polleri at ¶ [0041]: “A weighted list of common representations of each feature for a particular machine learning solution can be generated and stored.” Polleri at ¶ [0045]: The complexity conventionally required to create the machine learning applications 112 can be performed largely automatically with the model composition engine 132.” Polleri at ¶ [0068]: “KPIs can be problem/solution specific and can include a measurement of the accuracy of the results of the machine learning application as compared with some ground truth test data.”
The ordered combination of elements in the Dependent Claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself. Therefore, under Step 2B, Claims 1-20 do not include additional elements that are sufficient to amount to significantly more than the recited judicial exceptions. Thus, Claims 1-20 are ineligible with respect to the 35 U.S.C. § 101 analysis.
Conclusion
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 DERICK HOLZMACHER whose telephone number is (571) 270-7853. The examiner can normally be reached on Monday-Friday 9:00 AM – 6:30 PM EST.
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, Brian Epstein can be reached on 571-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-270-8853.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/DERICK J HOLZMACHER/ Patent Examiner, Art Unit 3625A
/BRIAN M EPSTEIN/Supervisory Patent Examiner, Art Unit 3625
Prosecution Insights