DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 09 April 2026 has been entered.
Status of Claims
This is a non-final office action in response to the request for continued examination filed 09 April 2026. Claims 1, 8, and 9 have been amended. Claims 16-20 are newly added. Claims 1-20 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 20 May 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Response to Amendment
Applicant’s amendment to claims 1, 8, and 9 and addition of new claims 16-20 has been entered.
Applicant’s amendment is sufficient to overcome the 35 U.S.C. 112(b) rejection. The rejection is respectfully withdrawn.
Applicant’s amendment is insufficient to overcome the pending 35 U.S.C. 101 rejection. The rejection remains pending and is updated below, as necessitated by amendment.
Applicant’s amendment is sufficient to overcome the pending 35 U.S.C. 103 rejection. The rejection is respectfully withdrawn.
Response to Arguments
Applicant’s arguments regarding the 35 U.S.C. 103 rejection have been fully considered, and are persuasive. Examiner analyzed amended Claim 1, and similarly claim 8, in view of the prior art of record and an updated prior art search and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success as discussed below. As a result, claims 1-20 are eligible over the prior art.
Applicant’s arguments regarding the 35 U.S.C. 101 rejection have been fully considered, but are not persuasive. Applicant asserts that the amended claims recite a specific technical solution to a technical problem identified in the specification for selecting an optimal employee group for project execution based on predicted performance values that goes beyond merely outputting data for human consideration, including that “the model performance can be improved in terms of vias and variance” that the claims cannot practically be performed in the human mind and are not directed to mere data collection and analysis, particularly the limitation for “executing feature extraction on the project data and the employee data using at least one of Principal Component Analysis (PCA), Zero-phase Component Analysis (ZCA), or autoencoder techniques” and because (per the Specification) “model selection and ensemble are all done in an automated pipeline.” Applicant further asserts that the specific technical problem is address by a specific technical solution using multi-level matrix structure and feature extraction techniques that “reduce the feature space of the application for incorporation into machine learning techniques” resulting in improved “running time and model accuracy” that represents a specific technical improvement to computer-implemented collaborative filtering and machine learning systems, not merely an abstract idea applied on a generic computer. Examiner respectfully disagrees.
The amendment to claims 1 and 8 includes claim language that describes the type of data analyzed (employee data, team data, and organization data, project related data including individual project data, and project group data), the purpose of the matrix analysis, and how the data is further analyzed in the identifying, performance score aggregating, ranking employee groups, and selecting an employee group having a highest ran for execution of the project. These limitations are data processing and analysis steps that generate an output of a selected employee group for execution of the project that could be performed by a project manager. The data is analyzed using mathematical concepts and data association (matching, aggregating, filtering) processed on a computer in a manner that does not provide a practical application of the recited abstract idea.
The additional elements used for feature extraction, Principal Component Analysis (PCA), Zero-phase Component Analysis (ZCA), or autoencoder techniques, are data analysis tools applied to generated features using in further data analysis steps, and are used to implement the abstract idea. Principal Component Analysis is a statistical technique used for dimensionality reduction, zero-phase component analysis is a mathematical matrix formulation for dimensional reduction, and encoders are used to reduce dimensionality. These additional elements are generically applied methods of simplifying datasets and do not transform the recited abstract idea into patent eligible subject matter. Further, the steps for “executing a self-profiling algorithm configured with unsupervised machine learning” and “executing a supervised machine learning model” are generically and broadly claimed as a tools for processing data that are insufficient to show a practical application of the recited abstract idea. Therefore the 35 U.S.C. 101 rejection is proper and maintained.
The newly added limitations in claims 16-20 detail how the dimensionally reduced and filtered data is processed using a first unsupervised leaning model algorithm, a second unsupervised learning model algorithm, comparing an evaluation result of the second unsupervised model to a global best evaluation result; further details on how the step for executing the self-profiling algorithm using a recursive learning scheme and attaching the unsupervised output from the unsupervised model algorithm to the generated features; performing root cause analysis and linking the root causes to prescriptive actions based on domain knowledge; generating a separate matrix for each milestone of the project; and encoding the project data and decoding the coding into reconstructed data. Each of these limitations further describe how the data is processed and analyzed to select an employee group having a highest rank for execution of the project -which falls within the certain methods of organizing human activities grouping of abstract concepts. The data analysis and processing steps improve the business process for project management and employee selection for milestone risk management, but the underlying techniques used to analyze and process the data are insufficient to transform the abstract idea into a practical application.
Regarding dependent claim 17, claim 17 includes steps detailing a recursive method applied to the self-profiling algorithm for driving clusters and anomalies of the project. While the claim includes steps for generating unsupervised output form the best model for each unsupervised model until an exit criteria is met, “[i]terative training using selected training material and dynamic adjustments based on real-time changes [is] incident to the very nature of machine learning” and “do[es] not represent a technological improvement. Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) (“The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted . . . do not represent a technological improvement” at least because they are “incident to the very nature of machine learning.”). While the method for processing the data to achieve a certain output is improved, the underlying technologies are used as tools to implement the abstract idea and merely provide a general like to the related data processing techniques and technologies. The data that is pre-processed for the machine learning steps is improved, but executed the machine learning models are not improved. Because the claims do not include an improvement to machine learning or computer processing technologies, while the data processing techniques are detailed and specific, they do not transform the abstract idea into a practical application and do not amount to significantly more than the recited abstract idea.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of collecting data, analyzing it, and outputting results of the collection and analysis for predicting project performance, without significantly more. Independent claim 1 recites a process and independent claim 8 recites a process for performance based project management.
Under Step 1, independent claim 1 recites at least one step or act, including executing feature extraction on the project data and employee data. Independent claim 8 recites at least one step or act, including generating a matrix from project performance data. Thus, the claims fall within one of the statutory categories of invention.
Amended independent claim 1 recites at least the following limitations:
generating a multi-level matrix from project performance data, wherein: the multi-level matrix is structured with rows representing employee related data across different dimensions including individual employee data, team data, and organization data, and columns representing project related data including individual project data and project group data;
the employee related data at different dimensions creates overlapping employee groups having correlated performance scores that enable collaborative filtering by providing similar engagements with projects; and
for missing values in the multi-level matrix, generating the missing values using a collaborative filtering training process that leverages the correlated performance scores from the overlapping employee groups;
for an input of a project comprising project data and employee data, executing feature extraction on the project data and the employee data using Principal Component Analysis (PCA), Zero-phase Component Analysis (ZCA), and autoencoder techniques to generate features that reduce feature space and noise while preserving data variation;
executing a self-profiling algorithm configured with unsupervised machine learning on the generated features to derive clusters and anomalies of the project;
executing a performance monitoring process on the generated features to determine a probability of a key performance indicator value at a milestone;
executing a supervised machine learning model on the generated features, the derived clusters, the derived anomalies, and the probability of the key performance indicator value at the milestone to generate a predicted performance value of the project;
identifying ones of the rows and ones of the columns in the multi-level matrix that match the employee related data and the project related data of the project;
aggregating performance scores corresponding to the identified ones of the rows and the ones of the columns;
ranking employee groups according to the aggregated performance scores: and selecting an employee group having a highest rank for execution of the project.
Amended independent claim 8 recites at least the following limitations:
generating a matrix from project performance data, the matrix structured in rows of employee related data across different dimensions including individual employee data, team data, and organization data, and columns representing project related data including individual project data and project group data;
the employee related data at different dimensions creates overlapping employee groups having correlated performance scores that enable collaborative filtering by providing similar engagements with projects; and
for missing values in the multi-level matrix, generating the missing values using a collaborative filtering training process that leverages the correlated performance scores from the overlapping employee groups;
or an input of a project comprising project data and employee data, executing feature extraction on the project data and the employee data using at least one of Principal Component Analysis (PCA), Zero-phase Component Analysis (ZCA), or autoencoder techniques to generate features that reduce feature space and noise while preserving data variation;
executing a self-profiling algorithm configured with unsupervised machine learning on the generated features to derive clusters and anomalies of the project;
executing a performance monitoring process on the generated features to determine a probability of a key performance indicator value at a milestone;
executing a supervised machine learning model on the generated features, the derived clusters, the derived anomalies, and the probability of the key performance indicator value at the milestone to generate a predicted performance value of the project;
identifying ones of the rows and ones of the columns in the multi-level matrix that match the employee related data and the project related data of the project;
aggregating performance scores corresponding to the identified ones of the rows and the ones of the columns;
ranking employee groups according to the aggregated performance scores; and selecting an employee group having a highest rank for execution of the project.
Under Step 2A Prong One, the limitations of independent claims 1 and 8 for generating a multi-level matrix from project performance data, executing feature extraction on project data and employee data, executing a self-profiling algorithm, executing a performance monitoring process on generated features, executing a supervised machine learning model on the generated features, derived clusters, derived anomalies, probability of the key performance indicator value at the milestone, identifying rows and columns that match the employee related data and the project related data, aggregating performance scores, ranking employee groups, and selecting an employee group as drafted, illustrates a process that, under its broadest reasonable interpretation covers performance of the limitation in the mind (data collection, analysis, and result determination). None of the additional elements preclude the steps from practically being performed in the human mind, or by a human using a pen and paper. Specifically, predicting a performance value for a project is a mental process because the claimed prediction is a process that is practically performed in the human mind by a human project data and using “evaluation, judgment, and opinion” to detect whether key performance indicators and project milestones have been met. Therefore, the limitations fall into the mental processes grouping and accordingly the claims recite an abstract idea.
Per the Specification at [0046-0049]: “Project specific data includes the performance of an employee based on the performance of completed projects or the performance at each milestone of completed or ongoing projects.” The limitations of claim 1 additionally fall within certain methods of organizing human activity because the project data includes employee data related to individual employee data, team data, and organization data that creates overlapping employee groups and project groups that are used for feature extraction to determine the probability of a key performance indicator value at a milestone based on monitoring employee performance; and further because the data is analyzed and processed to select the highest ranking employee group based on aggregated performance scores for execution of the project. Selecting or assigning employees to a project is managing personal behavior, and relationships or interactions between people. Accordingly, the limitations of independent claims 1 and 8 are directed to abstract concepts that fall within certain methods of organizing human activity.
Under Step 2A Prong Two, the judicial exception of claims 1 and 8 are not integrated into a practical application. In particular, the claims only recite a processor, storage device, self-profiling algorithm, principal component analysis, zero-phase component analysis, autoencoder techniques, and supervised and unsupervised machine learning models for performing the recited steps. These elements are recited at a high level of generality (i.e., as a generic processor performing a generic computer function) and amount to no more than mere instructions to apply the exception using generic computer components. See MPEP 2106.05(f). For example, Applicant’s specification at paragraph [0148] states: “… the operations described above can be performed by hardware, software, or some combination of software and hardware … When performed by software, the methods may be executed by a processor, such as a general purpose computer.” Adding generic computer components to perform generic functions, such as data gathering, performing calculations, and outputting a result would not transform the claim into eligible subject matter. See MPEP 2106.05(h). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The focus of Applicant’s invention is not an improvement to computer performance or any underlying technology, instead, the focus us to user generic computer components and data analysis tools to gather, manipulate, and analyze business data to administer, create, or modify project tasks/milestones or identify project risks such as monitoring key performance indicators at each milestone to help make strategic business decisions (see Spec. at [0077]) and predicting project performance for each milestone (see Spec. at [0094]), without significantly more. While the data analysis includes reducing dimensionality using specific data analysis tools such as principal component analysis, machine learning, autoencoder, and zero-phase filtering, these additional elements are simply used as data analysis tools, without improvement to the underlying technology. As a result, they do not integrate the recited abstract idea into a practical application.
Regarding claim 1, the recitation of “executing a self-profiling algorithm and executing a supervised machine learning model in limitations (b) and (d) merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “executing a self-profiling algorithm configured with unsupervised machine learning” and “executing a supervised machine learning model” limits the identified judicial exception “determining a probability of a key performance value at a milestone” and “generating a predicted performance value of the project” using a self-profiling algorithm comprising unsupervised machine learning and a supervised machine learning model, merely confines the use of the abstract idea to a particular technological environment (machine learning) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Further, the claim does not include any details regarding how the machine learning algorithm and model operates. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application.
The newly added limitations in claims 16-20 detail how the dimensionally reduced and filtered data is processed using a first unsupervised leaning model algorithm, a second unsupervised learning model algorithm, comparing an evaluation result of the second unsupervised model to a global best evaluation result; further details on how the step for executing the self-profiling algorithm using a recursive learning scheme and attaching the unsupervised output from the unsupervised model algorithm to the generated features; performing root cause analysis and linking the root causes to prescriptive actions based on domain knowledge; generating a separate matrix for each milestone of the project; and encoding the project data and decoding the coding into reconstructed data. Each of these limitations further describe how the data is processed and analyzed to select an employee group having a highest rank for execution of the project. The data analysis and processing steps improve the business process for project management and employee selection for milestone risk management, but the underlying techniques used to analyze and process the data are insufficient to transform the abstract idea into a practical application.
claim 17 includes steps detailing a recursive method applied to the self-profiling algorithm for driving clusters and anomalies of the project. While the claim includes steps for generating unsupervised output form the best model for each unsupervised model until an exit criteria is met, “[i]terative training using selected training material and dynamic adjustments based on real-time changes [is] incident to the very nature of machine learning” and “do[es] not represent a technological improvement. Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) (“The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted . . . do not represent a technological improvement” at least because they are “incident to the very nature of machine learning.”). While the method for processing the data to achieve a certain output is improved, the underlying technologies are used as tools to implement the abstract idea and merely provide a general like to the related data processing techniques and technologies. The data that is pre-processed for the machine learning steps is improved, but executed the machine learning models are not improved. Because the claims do not include an improvement to machine learning or computer processing technologies, while the data processing techniques are detailed and specific, they do not transform the abstract idea into a practical application and do not amount to significantly more than the recited abstract idea.
Under Step 2B, the limitations of claims 1 and 8 are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements of a processor and storage device amount to no more than mere instructions to apply the exception using a generic computer component which cannot provide an inventive concept.
Dependent claims 2-7 and 9-20 include the abstract ideas of the independent claims. The limitations of the dependent claims merely narrow the mental process/certain methods of organizing human activity by describing how the data is analyzed to generate a prediction. The limitations of the dependent claims are not integrated into a practical application because none of the additional elements set forth any limitations that meaningfully limit the abstract idea implementation. There are no additional elements that transform the claim into a patent eligible idea by amounting to significantly more. The analysis above applies to all statutory categories of invention. Accordingly, claims 1 through 20 are ineligible under 35 U.S.C. 101.
Allowable Subject Matter
Claims 1-20 are rejected under 35 U.S.C. 101, but the claims would be allowable if the aforementioned rejections are overcome.
Examiner analyzed amended Claims 1 and 8, and newly added claims 16-20, in view of the prior art of record and an updated prior art search and finds not all claim limitations are explicitly taught nor would one of ordinary skill in the art find it obvious to combine these references with a reasonable expectation of success. Although the prior art of record, Widanapathirana et al. (US 2019/0108471) and Jersin et al. (US 2019/0197180) combined disclose project metric analysis, feature extraction, and application of ensemble models for suggesting candidates by predicting outcomes using a matrix defined problem space with multiple dimensions, none of the cited prior art references teach, suggest, or otherwise disclose the specific ordered sequence of limitations presented in independent claims 1 and 8. Moreover since the specific ordered combined sequence of claim elements recited in claims 1 and 8 can only be found as recited in Applicant’s specification, any combination of the cited references and/or additional references to teach all the claim elements, including the features discussed above, would be the result of impermissible hindsight reconstruction. Accordingly the prior art rejections set forth in the previous action are withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Hajian (US 11,568,367) – automated parameterized modeling and scoring intelligence system includes a parameterized score estimation software tool and a parameterized score optimization software tool. The parameterized score estimation software tool processes design metrics associated with a current project according to historical project data selected based on a similarity with at least some of the design metrics to determine a score estimation for the current project. The parameterized score optimization software tool processes the score estimation based on external application data retrieved from an external application to determine an expected yield for the current project. A user of the system may iterate against the score estimation or the expected yield by changing one or more of the parameters used to determine same. The iteration may result in a score estimation or expected yield different from the initial versions thereof, such as to identify an optimal design for the current project.
Roberts et al. (US 12,067,463) – The machine learning platform accesses a dataset from a datastore. A task that identifies a target of a machine learning algorithm from the machine learning platform is defined. The machine learning algorithm forms a machine learning model based on the dataset and the task. The machine learning platform deploys the machine learning model and monitors a performance of the machine learning model after deployment. The machine learning platform updates the machine learning model based on the monitoring.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LETORIA G KNIGHT whose telephone number is (571)270-0485. The examiner can normally be reached M-F 9am-5pm.
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/L.G.K/Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623
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