DETAILED ACTION
Notice of Pre-AIA or AIA Status
[1] The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice to Applicant
[2] This communication is in response to the amendment filed 31 December 2025. This Application has been granted Track 1 status under the United States Patent and Trademark Office Prioritized Examination (Track 1) Program. Status under the Track 1 Program was granted by the United States Patent and Trademark Office 26 August 2025. It is noted that this application benefits from Provisional Patent Application Serial Nos. 63/672,571, 63/672,611, 63/672,606, 63/678,750, 63/704800 filed 17 July 2024, 2 August 2024, and 8 October 2024. The Information Disclosure Statements (IDS) filed 1 October 2025, 15 December 2025, and 2 February 2026 have been entered and considered. Claims 1, 11, and 21 have been amended. Claims 1-30 are pending.
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.
[3] Previous rejection(s) of claims 1-30 under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter are addressed as follows:
[i] Previous rejection(s) of claims 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claims are not directed to one of the four recognized classes of invention has/have been overcome by the amendments to the subject claims and is/are withdrawn.
[ii] Previous rejection of claims 1-30 under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter, specifically an abstract idea without significantly more has/have not been overcome by the amendments to the subject claims and is/are maintained. The revised statement of rejection presented below is necessitated by amendment and addresses the present amendments to the pending claims.
The following analysis is based on the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024 and further clarified in the Reminders on Evaluating Subject Matter Eligibility of claims under 35 U.S.C. 101 guidance memorandum published 4 August 2025. Claim(s) 1-30 as a whole is/are determined to be directed to an abstract idea. The rationale for this determination is explained below:
Abstract ideas are excluded from patent eligibility based on a concern that monopolization of the basic tools of scientific and technological work might serve to impede, rather than promote, innovation. Still, inventions that integrate the building blocks of human ingenuity into something more by applying the abstract idea in a meaningful way are patent eligible (See MPEP 2106.04).
Consistent with the findings of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. ineligible abstract ideas are defined in groups, namely: (1) Mathematical Concepts (e.g., mathematical relationships, mathematical formulas or equations, and mathematical calculations; (2) Mental Processes (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions); and (3) Certain Methods of Organizing Human Activity. Groupings of Certain Methods of Organizing Human Activity include three sub-categories within the group, namely: (1) fundamental economic principles or practices; (2) commercial or legal interactions (e.g., agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations); (3) managing personal behavior or relationships or interactions between people (e.g., social activities, teaching, and following rules or instructions) (See MPEP 2106.04(a).
Eligibility Step 1: Four Categories of Statutory Subject Matter (See MPEP 2106.03): Independent claims 1, 11, and 21 are directed to a method, computer program product comprising a computer-readable storage medium, and a system, respectively, and are reasonably understood to be properly directed to one of the four recognized statutory classes of invention designated by 35 U.S.C. 101; namely, a process or method, a machine or apparatus, an article of manufacture, or a composition of matter. While the claims, generally, are directed to recognized statutory classes of invention, each of method/process, system/apparatus claims, and computer-readable media/articles of manufacture are subject to additional analysis as defined by the Courts to determine whether the particularly claimed subject matter is patent-eligible with respect to these further requirements. In the case of the instant application, each of claims 1, 11, and 21 are determined to be directed to ineligible subject matter based on the following analysis/guidance:
Eligibility Step 2A prong 1: (See MPEP 2106.04): In reference to claim 1, the claimed invention is directed to non-statutory subject matter because the claim(s) as a whole, considering all claim elements both individually and in combination, do/does not amount to significantly more than an abstract idea. The claim(s) is/are directed to the abstract idea of selecting and arranging nodes in a workflow, i.e., a sequence of actionable tasks or action, which is reasonably considered to be method of limited to claimed ineligible steps/processes performable by Human Mental Processing (e.g., concepts performed or performable in the human mind including observations, evaluations, judgements, or opinions). In particular, the general subject matter to which the claims are directed illustrates a process in which a threat mitigation workflow is constructed based on selection and arrangement of performable functions/steps, which is an ineligible inventive process limited to human mental observations, evaluations, and determinations.
The courts have previously identified subject matter limited to the implementation of steps/processes performable by Human Mental Processing and/or by a human using pen and paper to be ineligible abstract ideas (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011). Further, mental processes or concepts performed in the human mind including observation and evaluation are considered to be ineligible abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for a recitation of generic computer components, then the claim is still to be grouped as a mental process unless the limitation cannot practically be performed in the human mind (See MPEP 2106.04(a)(2)).
With respect to functions/steps limited to processes performable by Human Mental Processing and/or by a human using pen and paper, as presented by amendment, representative claim 1 recites:
“…selecting two or more…nodes from a plurality of…nodes; and arranging the two or more…nodes to form a…workflow…defining utilization statistics for a plurality of discrete… resources providing the respective… resources; and routing requests for… resources to one of the plurality of discrete… resources based upon, at least in part, the utilization statistics…”
Respectfully, absent further clarification of the processing steps executed by the recited computer and/or “generative AI nodes”, one of ordinary skill in the art would readily understand that given a complement of functions and/or tools for performing specified functions, one of ordinary skill would be capable of selecting and arranging a sequence of functions and further allocating requests for operations to suitable resources based on observed statistics associated with the resource for the purpose of directing performance of a desired operation, i.e., a workflow employing by the human mental processing (See CyberSource Corp v. Retail Decisions, Inc., 654 F.3d 1366, 1373 (Fed. Cir. 2011) (“a method that can be performed by human thought alone is merely an abstract idea and is not patent eligible under 35 U.S.C 101).
Eligibility Step 2A prong 2: (See MPEP 2106.04(d)): Under step 2A prong two, Examiners are to consider additional elements recited in the claim beyond the judicial exception and evaluate whether those additional elements integrate the exception into a practical application. Further, to be considered a recitation of an element which integrates the judicial exception into a practical application, the additional elements must apply, rely on, or use the judicial exception in a manner that imposes meaningful limits on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
As presented by amendment, the additional/technical elements identified in claim 1 are limited to: “generative AI nodes”. Claim 1 further indicates, generally, that the claimed method is “computer-implemented” as designated in the preamble. Claims 5 and 6 further introduce a “database”. Claim 10 introduces a “generative AI models”. Claims 11 and 21, directed to a computer program product and computing system introduce a “processor” and processor-executable “instructions”. With respect to these potential additional elements:
(1) The “computer”, “processor”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “database” is identified as storing generative AI nodes.
(3) The “generative AI models” are identified as being defined for the generative AI nodes.
(4) The “generative AI nodes” is identified as being selected and arranged to form a workflow. With respect to the “generative AI nodes”, as presented by amendment, claim 1 specifies that the plurality of “…generative AI nodes… provide respective generative Al resources…”. Claim 1 as amended further provides “…defining utilization statistics for a plurality of discrete generative Al resources providing the respective generative Al resources; and routing requests for generative Al resources to one of the plurality of discrete generative Al resources based upon, at least in part, the utilization statistics for the plurality of discrete generative Al resources…”.
With respect to the elements added by amendment, the technical elements of providing AI resources by the nodes and routing tasks based on gathered statistics associated with the resources are reasonably limited to mere data collection/extra solution activity and mental processes of observing utilization for the purpose of allocating tasks/requests to an appropriate resource.
With respect to the above noted functions attributable to the identified additional elements, MPEP 2106.05 stipulates that: Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05(f); and/or Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) serve as indications that the use of the technology recited does not indicate integration into a practical application of the judicial exception.
With respect to the identification of the nodes as generative AI nodes and further utilizing the Ai nodes to generate a workflow output, Examiner notes the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update) published in the Federal Register, 17 July 2024 and further clarified in the Reminders on Evaluating Subject Matter Eligibility of claims under 35 U.S.C. 101 guidance memorandum published 4 August 2025. In consideration of the noted guidance and clarification, the recitation of mathematical processes or concepts utilized in the context of training and/or implementing a machine learning model constitute claimed ineligible mathematical concepts or processes. Additionally, Examiner respectfully directs Applicant’s attention to Example 47, claim 2. Specifically, the instant recitations of “arranging the two or more generative AI nodes to form a generative AI workflow” and “using the generative AI workflow” are analogous to the training of an artificial neural network based on input data and receiving continuous training data of Examiner 47. Reasonably, the training data and feedback data are limited to mere data gathering and generating an output at a high level of generality and, by extension, are reasonably understood to constitute insignificant extra solution activity (See MPEP 2106.05(g)). The recited training process is limited to a recitation of the inputs and outputs to be applied to an undefined training process absent any technical specificity regarding actual training. Accordingly, the recited AI processes and associated training are understood to be generic, commercially available, AI models. As presented, the Ai models and workflow are limited to recitation of an assembly of generic technical elements (e.g., models).
Each of the above noted limitations states a result (e.g., AI nodes are selected, arranged, stored, and visualized, and the resultant workflow is used to generate a workflow result, etc.) as associated with a respective “computer”, “processor” “instructions”, or “database”. Beyond the general statement that method is computer-implemented and generally employs a processor and/or computer, the limitations provide no further clarification with respect to the functions performed by the “AI nodes”, “computer”, and “processor” in producing the claimed result. A recitation of “by a computer”, “by a processor”, or “by a model” absent clarification of particular processing steps executed by the underlying technology to produce the result are reasonably understood to be an equivalent of “apply it”. The identified functions performed by the recited technology are limited to: (1) receiving and sending data via a computer network (e.g., input commands); (2) storing and retrieving information and data from a generic computer memory (e.g., AI nodes); (3) displaying data on a generic computer display (e.g., visualize the workflow); and (4) performing mental observations using the obtaining information/data (e.g., selecting and arranging nodes, analyzing statistics and allocating tasks/requests based on the analysis) (See MPEP 2106.05(f)).
Accordingly, claim 1 is reasonably understood to be conducting standard, and formally manually performed process of selecting and arranging nodes in a workflow using the generic devices as tools to perform the abstract idea. The identified functions of the recited additional elements reasonably constitute a general linking of the abstract idea to a generic technological environment. The claimed selecting and arranging nodes in a workflow benefit from the inherent efficiencies gained by data transmission, data storage, and information display capacities of generic computing devices, but fails to present an additional element(s) which practical integrates the judicial exception into a practical application of the judicial exception.
Eligibility Step 2B: (See MPEP 2106.05): Analysis under step 2B is further subject to the Revised Examination Procedure responsive to the Subject Matter Eligibility Decision in Berkheimer v. HP, Inc. issued by the United States Patent and Trademark Office (19 April 2018). Examiner respectfully submits that the recited uses of the underlying computer technology constitute well-known, routine, and conventional uses of generic computers operating in a network environment. In support of Examiner’s conclusion that the recited functions/role of the computer as presented in the present form of the claims constitutes known and conventional uses of generic computing technology, Examiner provides the following:
In reference to the Specification as originally filed, Examiner notes paragraphs [0060]-[0067]. In the noted disclosure, the Specification provides listings of generic computing systems, e.g., a general computing platform including exemplary servers, network configurations and various processor configuration which are identified as capable and interchangeable for performing the disclosed processes. The disclosure does not identify any particular modifications to the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that this disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
While the above noted disclosure serves to provide sufficient explanation of technical elements required to perform the inventive method using available computing technology, the disclosure does not appear to identify any particular modifications or inventive configurations of the underlying hardware elements required to perform the inventive methods and functions. Accordingly, it is reasonably understood that the disclosure indicates that the hardware elements and network configurations suitable for performing the inventive methods are limited to commercially available systems at the time of the invention. Further, absent further clarification, it is reasonably understood that any modifications/improvements to the underlying technology attributable to the inventive method/system are limited to improvements realized by the disclosed computer-executable routines and the associated processes performed.
As presented by amendment, the claims specify that the above identified generic computing structures and associated functions/routines include:
(1) The “computer”, “processor”, and “instructions” are identified as engaged in an unspecified, general manner in the performance of each of the recited steps/functions.
(2) The “database” is identified as storing generative AI nodes.
(3) The “generative AI models” are identified as being defined for the generative AI nodes.
(4) The “generative AI nodes” is identified as being selected and arranged to form a workflow. With respect to the “generative AI nodes”, as presented by amendment, claim 1 specifies that the plurality of “…generative AI nodes… provide respective generative Al resources…”. Claim 1 as amended further provides “…defining utilization statistics for a plurality of discrete generative Al resources providing the respective generative Al resources; and routing requests for generative Al resources to one of the plurality of discrete generative Al resources based upon, at least in part, the utilization statistics for the plurality of discrete generative Al resources…”.
While Examiner acknowledges that the noted limitations are computer-implemented, Examiner respectfully submits that, in aggregate (e.g., “as a whole”) they do not amount to significantly more than the abstract idea/ineligible subject matter to which the claimed invention is primarily directed.
While utilizing a computer, the claimed invention is not rooted in computer technology nor does it improve the performance of the underlying computer technology. The computer-implemented features of the claimed invention noted above are reasonably limited to: (1) receiving and sending data via a computer network (e.g., input commands); (2) storing and retrieving information and data from a generic computer memory (e.g., AI nodes); (3) displaying data on a generic computer display (e.g., visualize the workflow); and (4) performing mental observations using the obtaining information/data (e.g., selecting and arranging nodes, analyzing statistics and allocating tasks/requests based on the analysis).
The above listed computer-implemented functions are distinguished from the generic data storage, retrieval, transmission, and data manipulation/processing capacities of the generic systems identified in the Specification solely by the recited identification of particular data elements that are of utility to a user performing the specific method of selecting and arranging nodes in a workflow. In summary, the computer of the instant invention is facilitating non-technical aims, i.e., selecting and arranging nodes in a workflow, because it has been programmed to store, retrieve, and transmit specific data elements and/or instructions that is/are of utility to the user. The non-technical functions of selecting and arranging nodes in a workflow benefit from the use of computer technology, but fail to improve the underlying technology.
In support, the courts have previously found that utilization of a computer to receive or transmit data and communications over a network and/or employing generic computer memory and processor capacities store and retrieve information from a computer memory are insufficient computer-implemented functions to establish that an otherwise unpatentable judicial exception (e.g. abstract idea) is patent eligible. With respect to the determinations of the Courts regarding using a computer for sending and receiving data or information over a computer network and storing and retrieving information from computer memory, see at least: 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; sending messages over a network OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); receiving and sending information over a network buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93 and see performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199; and Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) with respect to the performance of repetitive calculations does not impose meaningful limits on the scope of the claims.
Independent claims 11 and 21, directed to computer program product storing computer-executable instructions and an apparatus/system for performing the method steps are rejected for substantially the same reasons, in that the generically recited computer components in the apparatus/system and computer readable media claims add nothing of substance to the underlying abstract idea.
Dependent claims 2-10, 12-20, and 22-30, when analyzed as a whole are held to be ineligible subject matter and are rejected under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claimed invention is not directed to an abstract idea.
In accordance with all relevant considerations and aligned with previous findings of the courts, the technical elements imparted on the method that would potentially provide a basis for meeting a “significantly more” threshold for establishing patent eligibility for an otherwise abstract concept by the use of computer technology fail to amount to significantly more than the abstract idea itself. For further guidance and authority, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al. 573 U.S.____ (2014)) (See MPEP 2106).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
[4] Claim(s) 1-6, 9-16, 19-26, and 29-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Munguia Tapia et al. (United States Patent Application Publication No. 2024/0362465, hereinafter ‘Munguia Tapia’) in view of Mystetskyi et al. (United States Patent Application Publication No. 2025/0244964 hereinafter ‘Mystetskyi’).
With respect to (currently amended) claim 1, Munguia Tapia discloses a computer-implemented method, executed on a computing device, comprising: selecting two or more generative AI nodes from a plurality of generative AI nodes (Munguia Tapia et al.; paragraphs [0076] [0088] [0089]; See at least system identifies generative AI nodes); and arranging the two or more generative AI nodes to form a generative AI workflow (Munguia Tapia et al.; paragraphs [0090] [0109]; See at least system assembles identified nodes into a sequence/workflow).
Claim 1 has been amended with respect to the previously recited “…plurality of generative Al nodes…” to further specify “…wherein each of the plurality of generative Al nodes provide respective generative Al resources…”.
With respect to this element, Munguia Tapia specifies that the workflow nodes provide generative AI resources in a workflow context (Munguia Tapia et al.; paragraphs [0076] [0088] [0089]; See at least generative AI nodes provided specified processing functions, i.e., “resources”).
Claim 1 has been further amended to include: “…defining utilization statistics for a plurality of discrete generative Al resources providing the respective generative Al resources; and
routing requests for generative Al resources to one of the plurality of discrete generative Al resources based upon, at least in part, the utilization statistics for the plurality of discrete generative Al resources…”.
With respect to these elements, Munguia Tapia discloses a routing an agent routing module which allocates requests to designated AI-nodes in the workflow (Munguia Tapia et al.; paragraphs [0118] [0128]; See at least generative AI nodes and routing of requests based on confidence score and context). While the routing occurs based on a confidence score and context, Munguia Tapia fail to specify that routing is performed based on utilization statistics for discrete sets of AI resources.
However, as evidenced by Mystetskyi, it is well-known in the art to analyze utilization information of generative AI resources including usage patterns, request context, load balancing considerations, and utilization rates of specific AI nodes/functions to determine routing of requests to specific AI resources (Mystetskyi et al.; paragraphs [0068] [0239] [0273] [0376] [0523]; See at least utilization analysis including usage patterns, load balancing including queuing, and utilization rates of specific resources (e.g., AI calendar functions) for the purpose of directing requests. Each of these routing functions are forms of routing based on “utilization statistics” associated with AI resources insofar as presently claimed).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the agent routing module and associated functions including allocation of requests to AI resources of Munguia Tapia by further including well-known functions of routing requests to AI resources based on capacity/load balancing requirements, resource utilize rates, and/or usage patterns as taught by Mystetskyi. The instant invention is directed to a system and method of assembling and defining generative AI workflows. As Munguia Tapia disclose the use of agent routing module and associated functions including allocation of requests to AI resources in the context of a system and method for assembling and defining generative AI workflows and Mystetskyi similarly discloses the utility of routing requests to AI resources based on capacity/load balancing requirements, resource utilize rates, and/or usage patterns in the context of a system and method for assembling and defining generative AI workflows, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by combining prior art elements accordingly to known methods to yield the predictable results of enabling deployment of multiple instances of each generative AI agent as limited resources, and managing their deployment to ensure they do not exceed assigned resource limits (Mystetskyi et al.; paragraph [0011]).
With respect to claim 2, Munguia Tapia discloses a computer-implemented method further comprising: visually rendering the generative AI workflow, thus defining a visualized workflow (Munguia Tapia et al.; paragraphs [0109] [0139] Figs. 5A-5D; See at least interface and composer. See further visual rendering of nodes).
With respect to claim 3, Munguia Tapia discloses a computer-implemented method further comprising: enabling a user to visually revise the visualized workflow (Munguia Tapia et al.; paragraphs [0115] [0151]; See at least drag and drop nodes using composer/interface).
With respect to claim 4, Munguia Tapia discloses a computer-implemented method further comprising: defining the plurality of generative AI nodes (Munguia Tapia et al.; paragraphs [0091]-[0093] [0160]; See at least generating workflow descriptor and definitions for service and processing nodes).
With respect to claim 5, Munguia Tapia discloses a computer-implemented method further comprising: storing the plurality of generative AI nodes within a generative AI node database (Munguia Tapia et al.; paragraphs [0107]-[0109]; See at least identify nodes for workflow. See further nodes and instances stored in database).
With respect to claim 6, Munguia Tapia discloses a computer-implemented method wherein selecting two or more generative AI nodes from a plurality of generative AI nodes includes:
selecting the two or more generative AI nodes from the generative AI node database (Munguia Tapia et al.; paragraphs [0107]-[0109]; See at least identify nodes for workflow. See further nodes and instances stored in database).
With respect to claim 9, Munguia Tapia discloses a computer-implemented method further comprising: providing an input command to the generative AI workflow (Munguia Tapia et al.; paragraphs [0076] [0089]; See at least request and inputs and execution of workflow nodes in sequence); and processing the input command using the generative AI workflow to produce a workflow result (Munguia Tapia et al.; paragraphs [0089]-[0092] [0099]-[0100]; See at least execution of workflow and outputs).
With respect to claim 10, Munguia Tapia discloses a computer-implemented method further comprising: defining one or more target generative AI models for at least one of the two or more generative AI nodes (Munguia Tapia et al.; paragraphs [0076] [0088] [0173]; See at least nodes and models).
Claims 11-16, 19, and 20 and 21-26, 29, and 30 substantially repeat the subject matter addressed above with respect to claims 1-6, 9, and 10 as directed to the enabling system and computer-readable medium storing computer-executable instructions. With respect to these elements, Munguia Tapia disclose enabling the disclosed method employing analogous systems and executable instructions. Accordingly, claims 11-16, 19, and 20 and 21-26, 29, and 30 are rejected under the applied teachings as discussed above with respect to claims 1-6, 9, and 10.
[5] Claim(s) 7-8, 17-18, and 27-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Munguia Tapia et al. (United States Patent Application Publication No. 2024/0362465, hereinafter ‘Munguia Tapia’) in view of Mystetskyi et al. (United States Patent Application Publication No. 2025/0244964 hereinafter ‘Mystetskyi’), as applied to claim 1 above, and further in view of Zhang et al. (United States Patent Application Publication No. 2025/0190449, hereinafter ‘Zhang’).
With respect to claim 7, while Munguia Tapia discloses a composer that enables a user to define an AI node sequence, Munguia Tapia fails to specify that defining the workflow includes generation of modification of prompts associated with nodes.
However, as evidenced by Zhang, it is well-known in the art to provide for user generation and modification of prompts associated with generative AI agents/nodes in an assembled AI workflow (Zhang et al.; paragraphs [0032] [0034]; See at least generating and modifying prompts associated with AI agent nodes).
With respect to claim 8, Munguia Tapia disclose assembling a path sequence/execution order using a composer tool. The specific paths defined by Munguia Tapia include a loop, splits and routing paths (Munguia Tapia et al.; paragraphs [0115] [0132] [0134] [0151]; See at least drag and drop nodes using composer/interface. See further split flows and conditional sequencing of AI nodes).
Munguia Tapia fails to disclose iterative paths.
However, Zhang discloses defining iterative path implementations for an AI agent-based workflow (Zhang et al.; paragraphs [0032] [0034]; See at least generating and modifying AI agent nodes sequencing including iterative selection of generative AI agents).
Regarding claims 7 and 8, it would have been obvious to one of ordinary skill in the art at the time the invention was made to have modified the workflow definition and functionality including path determinations provided by the workflow composer of Munguia Tapia by further including well-known workflow design functions including prompt modification and iterative node selection as taught by Zhang. The instant invention is directed to a system and method of assembling and defining generative AI workflows. As Munguia Tapia disclose the use of workflow definition and functionality including path determinations provided by the workflow composer in the context of a system and method for assembling and defining generative AI workflows and Zhang similarly discloses the utility of workflow design functions including prompt modification and iterative node selection in the context of a system and method for assembling and defining generative AI workflows, the teachings are reasonably considered to have been derived from analogous references and applied in the manner disclosed by the respective references. Accordingly, one of ordinary skill in the art would have been motivated to make the noted combination/modification as rationalized by combining prior art elements accordingly to known methods to yield the predictable results of improving user experience by increasing the accuracy of AI agent selection for dynamic workflows (Zhang et al.; paragraphs [0005]).
Claims 17-18 and 27-28 substantially repeat the subject matter addressed above with respect to claims 7-8 as directed to the enabling system and computer-readable medium storing computer-executable instructions. With respect to these elements, Munguia Tapia disclose enabling the disclosed method employing analogous systems and executable instructions. Accordingly, claims 17-18 and 27-28 are rejected under the applied teachings, conclusions obviousness, and rationale to modify as discussed above with respect to claims 7-8.
Response to Remarks/Amendment
[6] Applicant's remarks filed 31 December 2025 have been fully considered and are addressed as follows:
[i] Applicant’s remarks in response to previous rejection(s) of claim(s) 1-30 under 35 U.S.C. 101 as being directed to non-statutory subject matter as set forth in the previous Office Action mailed 1 October 2025 are reasonably considered to have been fully addressed in the context of the revised rejection of the claims presented above responsive to the amendments to the subject claims and in consideration of the framework for determining patent subject matter eligibility under 35 U.S.C. 101 established in the decisions of the Supreme Court in Mayo Collaborative Services v. Prometheus Labs., Incorporated and Alice Corporation Pty. Ltd. v. CLS Bank International, et al. (See MPEP 2106 subsection III and 2106.03-2106.05) and the 2024 Guidance Update on Patent Subject Matter Eligibility, Including Artificial Intelligence (2024 AI SME Update), published in the Federal Register, 17 July 2024. Additionally, Applicant substantially rehashes arguments previously presented in the prior response. These arguments are addressed in accordance with Examiner’s response in the prior Office Action(s) mailed 1 October 2025 incorporated in its entirety in response.
[ii] Applicant’s remarks directed to previous rejection(s) of claim(s) 1-30 under 35 U.S.C. 103(a) as being unpatentable as set forth in the previous Office Action mailed 1 October 2025 have been fully considered and are moot in light of newly added grounds of rejection responsive to the amendments to the subject claims. See revised rejection under 35 U.S.C. 103 presented herein.
Conclusion
[7] The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Singh et al., WORKFLOW OPTIMIZATION LEVERAGING GENERATIVE AI AND QUANTUM SIMULATION, United States Patent Application Publication No. 2025/0252376, paragraphs [0059]-[0068]: Relevant Teachings: Singh discloses a system/method that includes steps/functions of using a resource allocation algorithms for optimizing resource utilization in an AI-workflow environment.
Ahmed et al., DYNAMIC THREAT MITIGATING OF GENERATIVE ARTIFICIAL INTELLIGENCE MODELS, United States Patent Application Publication No. 2025/0055867, paragraphs [0028]-[0029]: Relevant Teachings: Ahmed discloses a system/method that includes functions of analyzing usage and utilization patterns of AI-models for the purpose of identifying threats to the model.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/ROBERT D RINES/Primary Examiner, Art Unit 3625