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 .
Status of Claims
This Final Office Action is in response to the amendments filed 06/25/2026. Claim(s) 1-26 are pending. Claim(s) 20-26 are new.
Priority
Application 19/000,608 was filed on 12/23/2024, and is a continuation of Application 18/673,570 which was filed 05/24/2024 and has a provisional Application 63/593,163 filed 10/25/2023, and provisional Application 63/589,256 filed 10/10/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/21/2026 was filed after the mailing date. The submission is partially in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered, in part, by the examiner.
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 1-26 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-26 are directed to a system, method, or product which are/is one of the statutory categories of invention. (Step 1: YES).
Claims 1, and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method and computing device for managing data during sessions and displaying information. For Claims 1 and 13 the limitations of (Claim 1 being representative):
[…]:
execute an interactive session configured to serve a plurality of respondents via a plurality of […] displays that accept information from and tailor subsequent […] displays dynamically during the interactive session on […] outputs returned during the interactive session;
instantiate a first […] model configured to accept execution requirement inputs and in response further configured to generate output questions and output requests for validation data associated with at least some of the questions, the output questions and requests for display in the plurality of user interface displays;
analyze a free form input received in displayed visual […] objects from the interactive session display, the visual interface objects including at least respective questions or respective requests for validation data;
instantiate a second […] model configured to accept the free form input and in response further configured to generate a prediction to identify complete free form responses and incomplete free form responses, at least as part of analysis of the free form input;
automatically, generate, at least in part, using the prediction from the second […] model, on retrieved free form responses displays of supplemental visual […] objects in response to determining a user […] input for a respective free form response is incomplete or partially complete, wherein some of the supplemental visual […] objects are automatically generated to include at least questions and requests for validation data;
identify, using the prediction from the second […] model, missing information in the incomplete or partially complete free form response and include, via the supplemental visual […] objects, request for specific validation data sufficient to resolve the incomplete or partially complete free form response.
The above limitations have a scope that includes a process that is used to determine if responses are complete or partially incomplete in regards to requesting validation data. This is construed as reciting a legal interaction that determines compliance on whether or not a response meets a compliance threshold of being complete or partially complete. The claim is simply receiving data and processing the data to determine if a free form response is complete or partially complete, and displaying the result. This qualifies as a certain method of organizing human activities type of abstract idea.
Additionally, the claimed use of the data and the claimed determinations can be practically performed by a human being mentally where a user is reading incoming data including free form responses to questions, determining if the responses are incomplete or partially complete, and visually marking the result. A human being can perform those actions mentally. The claimed receipt of the data and the use of the data to make the claimed determinations can be easily performed by a person who is reading data and mentally making the claimed determinations. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes. Accordingly, Claims 1, and 13 recite an abstract idea. (Step 2A- Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. Claims 1 and 13 recites the additional elements of a processor (Claims 1 and 13), memory (Claim 1), user interface (Claims 1 and 13), first and second artificial intelligence model (Claims 1 and 13), that implements the identified abstract idea. These additional elements are not described by the applicant and are recited at a high-level of generality (i.e., one or more generic computers performing a generic computer functions) such that it amounts no more than mere instructions to apply the exception using a generic computer components. Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Claims 1, and 13 are directed to an abstract idea. (Step 2A-Prong 2: NO: the additional claimed elements are not integrated into a practical application).
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 integration of the abstract idea into a practical application, the additional elements of a processor (Claims 1 and 13), memory (Claim 1), user interface (Claims 1 and 13), first and second artificial intelligence model (Claims 1 and 13) to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”). Accordingly, even in combination, these additional elements do not provide significantly more. As such claims 1 and 13 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more).
Dependent Claims 2-12, 14-18-26 are similarly rejected because they either further define/narrow the abstract idea of independent claims 1, and 13 as discussed above. Claim(s) 2 and 14 merely describe(s) a third model outputting a set of requirements associated with regulatory information and custom client policy information. Claim(s) 3 and 15 merely describe(s) evaluating a plurality of constraints defined by regulatory information and custom policy information and identifying a set of execution requirements for one or more targets. Claim(s) 4 and 16 merely describe(s) tailoring the interactive session and user display based on a client location and respective requirements associated with the interactive session. Claim(s) 5 and 17 merely describe(s) generating an assessment responsive to completion of analysis of results for each of one or more execution targets and updating a status associated with an execution evaluation, wherein the assessment includes analysis of the results generated from the displayed object, supplemental visual interface objects, and any additional data source. Claim(s) 6 merely describe(s) selecting and executing a respective instance of the first model trained on a plurality of constraints and linked information requirements responsive to definition of a set of execution requirements. Claim(s) 7 merely describe(s) the first model accepting the set of execution requirements as input and generating text output during prediction to solicit information to verify execution requirements and any of the plurality of constraints and linked information requirements. Claim(s) 8 and 18 merely describe(s) tailoring the output to a plurality of execution targets and representing the text outputs as part of a visual interface object. Claim(s) 9 merely describe(s) the first model accepting specification of an execution target and generating the text outputs tailored to the execution target. Claim(s) 10 merely describe(s) executing a second model trained on answers to information requests and labeled responses. Claim(s) 11 merely describe(s) the labeled responses including complete and incomplete responses. Claim(s) 12 and 19 merely describe(s) the second model configured to accept response answers to information requests and predict output evaluation of complete or incomplete. Claim(s) 20 merely describe(s) a closed-loop feedback systems to identify missing information in the incomplete or partially complete free form response based on the prediction, automatically generating the supplemental visual objects including the requests for specific validation data sufficient to resolve the incomplete or partially complete free form response, assigning a completed status or the incomplete invalid status to a respective request and question based on the prediction, and adjusting a risk evaluation associated with an execution target based on the assigned status. Claim(s) 21 merely describe(s) dynamically generating and modifying the plurality of user displays and the supplemental visual objects in real time during the interactive session based on the prediction from the second model, wherein content of the supplemental visual objects is determined by the prediction and includes at least the requests for specific validation data tailored to missing information identified in the incomplete or partially complete free form response. Claim(s) 22 merely describe(s) the second model is further configured to evaluate the free form input using context provided by first model processing, including a set of rules, regulations, or requirements linked to the interactive session, and use the context provided to generate a respective prediction. Claim(s) 23 merely describe(s) the context provided by the first model processing is further tailored to a respective respondent and a location associated with the respective respondent, and the second model is configured to use the context provided to generate a respective prediction. Claim(s) 24 merely describe(s) setting a timing constraint for completion of a free form response including evaluation for completeness of any response to the requests for specific validation data, assign a completed status to a respective request and question in response to determining the free form response satisfies the respective request based on another prediction generated by the second AI model; and assign an incomplete or invalid status to the respective request and question in response to determining the free form response fails to satisfy the respective request. Claim(s) 25 merely describe(s) reducing a risk metric associated with an execution target in response to the completed status and increasing a risk metric associated with the execution target in response to the incomplete or invalid status. Claim(s) 26 merely describe(s) the first and second models being implemented as a combined model.
Dependent Claim(s) 2, 5, 7, 8, 9, 14, 17, and 18 recite limitations that further define the abstract idea noted in independent claims 1, and 13. In addition, it recites the additional elements of a third Al model, fourth AI model, and natural language processing. The third Al model, fourth AI model, and natural language processing are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computing component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself.
Claims 2-4, 6-7, 14-18, 20-24, and 26 include the additional elements of a processor, first Al model, user interface, and second AI model. The processor, first Al model, user interface, and second AI model are analyzed in the same manner as the processor, first Al model, user interface, and second AI model in the independent claim and do not provide a practical application or significantly more for the same reasons above. Therefore claims 2-12, 14-26 are considered patent ineligible for the reasons given above.
Response to Arguments
Applicant's arguments filed 06/25/2026 with respect to Double Patenting Rejection, have been fully considered and are persuasive. The Double Patenting Rejection has been withdrawn in light of the amendments.
Applicant's arguments filed 06/25/2026 with respect to the Claim Objections, have been fully considered and are persuasive. The Claim Objections have been withdrawn in light of the amendments.
Applicant's arguments filed 06/25/2026 with respect to 35 U.S.C. § 112, have been fully considered and are persuasive. The 35 U.S.C. § 112 Rejection has been withdrawn in light of the amendments.
Applicant's arguments filed 06/25/2026 with respect to 35 U.S.C. § 101, have been fully considered but they are not persuasive. The Applicant argues that the claims do not recite mental processes since the human mind cannot instantiate a machine learning model. The Examiner respectfully disagrees. The claimed use of the data and the claimed determinations where a user is reading incoming data including free form responses to questions, determining if the responses are incomplete or partially complete, and visually marking the result involve human judgments, observations, and evaluations that can be practically or reasonably performed in the human mind. The first, and second AI models, visual interface objects, and AI model predictions, is merely using the AI as a tool, and the machine learning itself is not improved. The Applicants invention merely results in the claimed invention reciting an improvement to the abstract idea, rather than a specific improvement in the capabilities of computing devices (i.e. machine learning). The Applicant cites SRI Int’l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019), where the court declined to identify claimed collection and analysis of network data as abstract because “the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims”. However, in SRI, the claims sought to solve the problem of detecting security threats when the number of login attempts for each computer may below the threshold to trigger an alert, and the claims were found to be more complex than merely reciting the performance of a known business practice on the internet since the claims were “using a specific technique—using a plurality of network monitors that each analyze specific types of data on the network and integrating reports from the monitors—to solve a technological problem arising in computer networks; identifying hackers or potential intruders into the network.” Looking at the limitations of Applicant’s claimed invention there is no indication that the claims solve a technological problem, improve the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. In other words, the claims simply require the performance of the abstract idea of managing data and displaying information on generic computer components using conventional computer activities and unlike SRI, they are not drawn to solve a technological problem arising in computer networks.
The Applicant further argues that the claims are not directed to certain methods of organizing human activity since the claim recites a specific AI system architectures that instantiates specialized AI models, dynamically generates user interface displays based on AI predictions, and identifies missing information using machine learning predictions, and that the technical operations do not fall within any of the subgroupings. The Examiner respectfully disagrees. The claims are considered to fall into the commercial or legal interaction sub-grouping because the claims recite a legal interaction of determining compliance of whether or not a response meets a compliance threshold of being complete or partially complete. The specific AI system architecture with AI models, generating user interface displays based on AI predictions, and using machine learning predictions are not limited to a specific technical solution of the abstract idea.
The Applicant further argues under Step 2A, Prong 2 that the claim integrates any alleged abstract idea into a practical application because it improves the functioning of an AI-driven compliance data collection system. The Applicant first argues that the claim recites a multi-modal AI architecture with specialized, coordinated workflow, and that the models are not generic models, but specialized models with defined inputs and outputs that coordinate in a workflow that improves the operation of the claimed system, and is analogous to Example 47, Claim 3 where the claim was eligible because the trained ANN’s output drove specific remedial actions that improved network security. The Examiner respectfully disagrees. MPEP 2106.04(d)(1) states "the word 'improvements' in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B." Here, there is no improvement to the technological environment to which the claims are confined (a general purpose computer using machine learning); put another way, the computer is implementing what it was programmed to implement. The computer did not cause the problem of incomplete answers. Further, the use of AI models to accept inputs and generate outputs is not a technical solution to a technical problem. Looking at the limitations of Applicant's claimed invention there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Merely performing an abstract idea process more efficiently using generic computer components does not render the claims patent-eligible. Further, Example 47 Claim 3 which recites “training… the ANN based on input data … to generated a trained ANN” recites performing the training using a backpropagation algorithm and a gradient descent algorithm. The claims of the instant application are not the same as the specific narrow limitation of real time security enhancement.
The Applicant then argues that claim 1 recites AI-driven dynamic generation of user interface displays, by “automatically generating, at least in part, using the prediction from the second AI model, on retrieved free form responses displays of supplemental visual interface objects in response to determining a user interface input for a respective free form response is incomplete or partially complete, wherein some of the supplemental visual interface objects are automatically generated to include at least questions and requests for validation data”, and that the AI prediction directly controls what is displays. The Examiner respectfully disagrees. “Automatically” is interpreted to be the equivalent to “using a computer” which is treated as an additional element. Generating questions and requests for validation data from free form response displays in response to determining user input is incomplete or partially complete is results-oriented statements of intended benefit, not evidence of a technological improvement. Merely performing an abstract idea process more efficiently using conventional computer components does not render the claims patent-eligible. In other words, “automatically” tailoring content is not a technical solution to a technical problem and there is no indication that the elements improve the functioning of the computer or any other technology.
The Applicant further argues that the claim recites AI-driven identification of incomplete responses and automatic generation of supplemental displays, and that the AI prediction directly controls what supplemental content is generated and displayed, and that the second AI model’s operation drives specific automated actions that improved the functioning of the compliance data collection system and is similar to Example 47, Claim 3, where the ANN’s output drove specific actions (dropping packets, blocking traffic) that improved network security. The Examiner respectfully disagrees. As mentioned above, “automatically” is interpreted to be the equivalent to “using a computer” which is treated as an additional element. Automatically displaying content does not amount to a practical application that integrates the abstract idea into a specific technical improvement in computer functionality or another technology, but rather use the additional elements as a tool to perform the abstract analysis. The argument of the claim being similar to Example 47, Claim 3 is not persuasive because Example 47, Claim 3 recites “training… the ANN based on input data … to generated a trained ANN” recites performing the training using a backpropagation algorithm and a gradient descent algorithm. The claims of the instant application are not the same as the specific narrow limitation of real time security enhancement.
The Applicant further argues that the ordered combination of elements (1), (2), and (3) listed on page 14, and page 15 of the response on 06/25/2026 taken alone and in combination provides a specific technical solution that improves AI-driven interactive data collection systems, and that the elements provides significantly more than the abstract idea by describing an unconventional technical arrangement that improves the functioning of compliance data collection systems by using specialized AI models in a coordinated pipeline where the output of one model drives the input and actions of the next, dynamically generating and tailoring user interface content based on real-time AI predictions rather than static templates, and implement a closed-loop feedback system that automatically identifies missing information and generates targeted requests. The Examiner respectfully disagrees. Looking at the limitations of the Applicant’s claimed invention there is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implantation. In other words, the claims simply require the performance of the abstract idea of managing the completeness of data during interactive sessions and displaying subsequential information using conventional computer activities, and are not drawn to an improvement in computer related technology. MPEP 2106.05(a) states, "If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art." An improvement to "the functioning of compliance data collection system", should be accompanied with a description of how the compliance data system is improved. The use of “AI models”, and “automatic display”, does not reflect an improvement, especially when it merely recites using an AI model at a high level of generality. Further, it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology.
The Applicant further argues that the Office Action provides no evidentiary basis for treating the additional claim elements as well-understood, routine, and conventional. The Examiner respectfully disagrees. The rejection does not rely on an assertion that the additional elements are well-understood, routine, or conventional. MPEP 2106.05(d) states, "If the additional element (or combination of elements) is a specific limitation other than what is well- understood, routine and conventional in the field, for instance because it is an unconventional step that confines the claim to a particular useful application of the judicial exception, then this consideration favors eligibility. If, however, the additional element (or combination of elements) is no more than well-understood, routine, conventional activities previously known to the industry, which is recited at a high level of generality, then this consideration does not favor eligibility." Since the rejection does not rely on this consideration to show that the claims are ineligible, the applicant's arguments are not persuasive because the consideration is based on the additional elements and it overlaps with the improvement consideration (MPEP 2106.05(a), and mere instructions to apply an exception (MPEP 2106.05(f)).
The Applicant further argues that Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205 (Fed. Cir. 2025), does not apply to the present claims. The rejection does not assert the claims being analogous to Recentive Analytics, Inc. v. Fox Corp., therefore, this argument is moot. Further, the Federal Circuit held that the patents were directed to abstract ideas and did not contain an inventive concept that would transform them into patent-eligible applications. The court noted that the use of generic machine learning technology in a new environment, such as event scheduling or network map creation, does not make the patents eligible. However, here, the claims of the instant application are similar because the claims “do no more than claim the application of generic machine learning to new to new data environments, without disclosing improvements to the machine learning models to be applied.”
The Applicant further argues that the claims are analogous to Ex Parte Desjardin since the specification describes improvements to AI-driven compliance data collection – including the use of specialized AI models in a coordinated pipeline, and dynamic generation of user interface displays based on AI predictions. The Examiner respectfully disagrees. In Ex Parte Desjardin, Decision on Request for Rehearing of Appeal No. 2024-000567, the patent eligibility was due to a concrete improvement to the functioning of a machine-learning model, specifically a reduction in system complexity and an improvement in how the computer itself operated. In contrast, the present claims do not recite any improvement to the functioning of a computer, processor, or other technology, rather they utilize generic computing components to perform data collection analysis, and presentation functions that merely implement an abstract idea on a computer.
The Applicant further argues that the dependent claims are independently subject matter eligibility. The closed-loop feedback system, AI driven dynamic UI generation, context-aware AI Evaluation, respondent and location-specific context, and timing constraints, status assignments, and risk evaluation have been fully considered but are not persuasive because they merely recite the use of device in their ordinary capacity. There is no improvement to a computing or machine learning system, even when considering the additional elements individually or as a combination, because the use of machine learning/AI still fall within “apply it”. Therefore, the dependent claims are a further extension of the abstract idea without integration into a practical application or significantly more. Claims 1-26 remain rejected under 35 U.S.C. 101.
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.
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/E.M.K./Examiner, Art Unit 3626
/JESSICA LEMIEUX/Supervisory Patent Examiner, Art Unit 3626
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