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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 3/12/2026 has been entered.
Status of Claims
Office Action is in response to the Applicant's amendments and remarks filed3/12/2026. Claims 1, 8 and 15 were amended. Claims 1-20 are presently pending and presented for examination.
Response to Remarks/Arguments
In regards to rejection under 35 U.S.C. § 101: Applicant’s arguments, filed 3/12/2026, with respect to claims 1-20 have been fully considered and are not persuasive.
In regards to Applicant’s arguments that “Applicant respectfully submits that one or more features of amended independent claim 1 cannot be performed or executed by the human mind. The amended claim now recites specific technological operations implemented through a machine-learning framework. In particular, the claim requires generating a first set of data points by a generative model and generating data labels based on business rules associated with compliance requirements. These operations involve automated computational processing to generate structured datasets representing compliance scenarios, which cannot reasonably be performed mentally or manually. Further, the amended claim introduces a confidence- based validation mechanism, wherein a second set of labels is generated and associated with confidence estimates, and additional data points having confidence estimates below a predetermined threshold are automatically selected and presented to the user for validation via a user interface. This process implements a machine-learning training workflow that algorithmically identifies uncertain data instances and routes them for targeted validation to improve model accuracy. The determination of confidence values, threshold comparison, automated selection of data points, and presentation through a user interface require specialized computing operations and cannot be carried out by human mental processes. Additionally, the claim recites generating training-ready data and determining model accuracy, followed by updating the training data based on validation of the selected additional data points when the model accuracy falls below a predetermined value. This establishes an iterative feedback mechanism for improving a machine-learning compliance model. Accordingly, the subject matter of amended independent claim 1 is not merely directed to organizing human activity or mental processes. Therefore, the subject matter of independent claim 1 is not similar to the alleged abstract idea, but instead is directed to a concrete technological solution that cannot be executed without specialized computing resources… the subject matter of the amended independent claim 1 facilitates a practical application of a compliance monitoring system that generates validated and confidence-scored data labels for training machine learning models used in regulatory oversight. The claimed method serves as an intelligent interface for acquiring compliance- related inputs, creating data points and corresponding labels, and validating those labels with user interaction. It further leverages automated confidence estimation, accuracy queries, and iterative retraining of a compliance model to ensure that the system continually improves its predictive reliability. These compliance models are generally applied in regulated industries such as pharmaceuticals, finance, or healthcare, where inaccurate monitoring of compliance requirements can result in severe operational, legal, and financial risks… the claimed method can be used in monitoring and managing compliance workflows across an enterprise. In this, the method receives user-defined compliance requirements, generates structured data points from user profiles, and automatically generates structured data points by a generative model based on those inputs. The generative model further produces initial data labels derived from business rules corresponding to the compliance requirements, which are then validated by the user through the system interface. Based on the validated labels, the system then generates a second set of labels, each associated with a confidence estimate that reflects the system's level of certainty in its predictions. These confidence estimates allow the system to prioritize uncertain labels for human validation via a user interface, thereby reducing manual burden while ensuring data integrity and prioritizing uncertain instances for targeted review. The system subsequently generates training-ready data from the validated labels for training a data model and performs automated queries to determine the accuracy of the model. This ensures that the compliance model adapts dynamically to evolving requirements and improves over time. See at least paragraphs [0028]- [0036], [0039]-[0045], [0047]-[00S2], [0055], [0057], [0059], [0067], [0073], and [0076]- [0083] of the as filed Specification. Accordingly, the subject matter of amended independent claim 1 provides a machine-implemented compliance monitoring framework that goes well beyond the alleged abstract idea of "organizing human activity." The system requires computing resources to generate confidence-scored labels, perform automated accuracy checks, and iteratively retrain compliance models, all of which integrate the abstract idea into a practical application. The claimed subject matter therefore provides a technological improvement to compliance data processing and machine learning, and cannot be characterized as a mere collection or analysis of information under prong 2 of step 2A… Applicant asserts that effective compliance monitoring and model training in modern regulatory and enterprise environments require dynamic data labeling, probabilistic validation, and iterative retraining of compliance models using user feedback. Traditional approaches to compliance management, such as manual rule-checking, static reporting, and isolated label generation, are highly inefficient, error-prone, and unable to adapt to evolving compliance requirements. These conventional systems fail to provide confidence scoring for generated labels and cannot automatically refine training datasets in response to model accuracy checks. As a result, existing solutions are unable to ensure reliability or scalability when compliance models are deployed across large organizations handling diverse and rapidly changing regulatory data. The challenges become particularly acute in scenarios where compliance data must be processed across multiple sources, validated against user inputs, and retrained iteratively to maintain accuracy thresholds for predictive compliance analytics. The claimed subject matter overcomes these limitations by introducing a machine- implemented system that not only generates first and second sets of compliance labels but also associates each of the second set of labels with a confidence estimate indicating its correctness. The claimed subject matter further generates automated queries to determine the accuracy of the compliance model and, when the accuracy falls below a predetermined value, updates the training-ready data dynamically with user-provided inputs. This creates a continuous feedback loop where uncertain data is flagged through confidence estimates, validated efficiently by users, and integrated into the compliance model for iterative retraining. Further, the method of claim 1 recites that the additional data points having confidence estimates below a predetermined threshold are automatically selected and presented to the user for validation via a user interface. By embedding confidence-driven labeling and automated retraining mechanisms, the system enables compliance monitoring frameworks to self-correct and adapt to real-world conditions, providing a level of accuracy and responsiveness unattainable through manual methods. As a result, the subject matter of amended independent claim 1 represents a significant technological improvement over traditional compliance monitoring approaches. See at least paragraphs [0002]-[0005], [0026]-[0028], [0046]-[0052] and [0076]-[0083] of the as filed Specification”, (see remarks , pg. 8-12).
Examiner respectfully disagrees, the current claims are not statutory because they are directed towards an abstract idea without significantly more. The claims recite method for generating and validating labels for compliance to be monitored, which is a method of managing interactions between people, which falls into the methods of organizing human activity grouping as well as Mathematical concepts in form of mathematical relationships, mathematical formals or equations and mathematical calculations and Mental processes such as concepts performed in the human mind which include observation, evaluation, judgement and opinion, as the utilization of the training in the claims can be formed through the method of mathematical calculations to conclude with an estimate/prediction of data model used for accuracy within the data labels generated. Additionally, the improvement is applied within the business method, but upon analysis of the computing elements, there are no improvements applied to the computing elements, aside from the computing elements merely applying the abstract idea. The computing elements such as “data points, memory, data model, generative model, user interface of claim 1; data points, memory, data model, processor, program instructions, generative model, user interface of claim 8; data points, memory, data model, computer-readable storage medium, program instructions, generative model, user interface of claim 15” are recited at a high level of generality and are generically recited computer elements. The generically recited computer elements amount to simply implementing the abstract idea on a computer. The combination of these additional elements are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these 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. Therefore, elements being analyzed for significantly more are mere generic computer components being implemented to implement the abstract idea on a computer.
Response to Prior Art Arguments
Applicant's prior art arguments filed 3/12/2026 are persuasive, the prior art rejection has been withdrawn.
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 without significantly more. The claim recites method for generating and validating labels for compliance to be monitored.
Step 2A – Prong 1
Independent Claims 1, 8 and 15 as a whole recite a method of organizing human activity. The limitations from exemplary Claim 1 reciting “method for monitoring compliance, comprising: receiving a first set of inputs from a user, the first set of inputs comprising information relating to one or more compliance requirements to be monitored; acquiring a profile associated with said user; generating a first set of based on the first set of inputs and one or more attributes acquired from the profile associated with the user; generating a first set of labels for at least one of the from the first set, wherein the first set of labels comprises of data labels generated based on business rules associated with compliance requirements; validating the first set of labels by said user; generating a second set of labels for additional based on validation of first set of labels wherein each of the second set of labels comprises a corresponding confidence estimate indicating a level of correctness for a corresponding first set of labels; automatically selecting the additional data points having confidence estimates below a predetermined threshold and presenting the selected additional data points to the user for validation; storing in the first and second set of labels; and generating training ready data from the first and second set of labels for training; generating a query to determine an accuracy; and updating the training ready data based on one or more inputs received from the user if the accuracy is below a predetermined value” is a method of managing interactions between people, which falls into the certain methods of organizing human activity grouping, additionally mathematical concepts such a mathematical relationships, mathematical formulas or equations and mathematical calculations as the models can be laid out by pen and paper as a human can perform calculation to present a data model. The mere recitation of a generic computer (data points, memory, data model, generative model, user interface of claim 1; data points, memory, data model, processor, program instructions, generative model, user interface of claim 8; data points, memory, data model, computer-readable storage medium, program instructions, generative model, user interface of claim 15) does not take the claim out of the methods of organizing human activity grouping. Thus, the claim recites an abstract idea.
Step 2A - Prong 2: Claims 1-20 and their underlining limitations, steps, features and terms, are further inspected by the Examiner under the current examining guidelines, and found, both individually and as a whole, not to include additional elements that are sufficient to integrate the abstract idea into a practical application. The limitations are directed to limitations referenced in MPEP 2106.05 that are not enough to integrate the abstract idea into a practical application. Limitations that are not enough include, as a non-limiting or non-exclusive examples, such as: (i) adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions, (ii) insignificant extra solution activity, and/or (iii) generally linking the use of the judicial exception to a particular technological environment or field of use.
This judicial exception is not integrated into a practical application because the claim recites the additional elements of (data points, memory, data model, generative model, user interface of claim 1; data points, memory, data model, processor, program instructions, generative model, user interface of claim 8; data points, memory, data model, computer-readable storage medium, program instructions, generative model, user interface of claim 15). The data points, memory, data model, generative model, user interface of claim 1; data points, memory, data model, processor, program instructions, generative model, user interface of claim 8; data points, memory, data model, computer-readable storage medium, program instructions, generative model, user interface of claim 15, are recited at a high level of generality and are generically recited computer elements. The generically recited computer elements amount to simply implementing the abstract idea on a computer. The combination of these additional elements are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these 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 claim do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Thus, even when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are ineligible.
Dependent claims 2-7, 9-14 and 16-20 are also directed to same grouping of methods of organizing human activity. The additional elements of the data points and compliance model in claim 6, 13 and 20; memory in claims 2-3, 6, 9-10, 13, 16-17 and 20; data model of claim 3-5, 7, 10-12, 14, 17-19; processor in claims 9-11, 13-14; program instructions of claim 13, 16-17, 19; computer-readable storage medium in claims 16-20; program instructions of claim 15, are additional elements do no more than generally link the use of the judicial exception to a particular technological environment or field of use. Accordingly, in combination, these 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.
Novel/Non-Obvious Subject Matter
Examiner has determined that all of Applicant’s claims have overcome having prior art rejections. The reason for this is that Examiner does not believe that, at the time of Applicant’s priority date, it would have been obvious for a person of ordinary skill in the art to combine prior art disclosures to result in the particular combination of elements/limitations in that claim, including the particular configuration of the elements/limitations with respect to each other in the particular combination, without the use of impermissible hindsight.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to IBRAHIM EL-BATHY whose telephone number is (571)272-7545. The examiner can normally be reached Monday - Friday 9am - 7pm.
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/IBRAHIM N EL-BATHY/Primary Examiner, Art Unit 3628
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