DETAILED ACTION
This action is in response to the claims filed 11/24/2025 for Application number 17/256,686. Claims 70 and 73-76 have been amended. Thus, claims 59-77 are currently pending.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/27/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered 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 60-61, 63-72 and 74-77 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 59, the claim does not recite any abstract idea therefore it is eligible under 35 U.S.C. 101
Regarding claim 60,
Step 1 Analysis: Claim 60 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 60 recites, in part, The limitation of:
…evaluating real-time data inputs [through the FBN and EBN] and explicitly analyzing these inputs in a closed-loop manner to select optimal decision-making processes can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “central HUB”, “ensemble hyperstructures”, and “through the FBN and EBN”. Thus, these elements in the claim 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 computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing a central HUB and ensemble hyperstructures to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 61,
Step 1 Analysis: Claim 61 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 61 recites, in part, The limitation of:
…implements a structured real-time orchestration mechanism governing probabilistic logic pathways within the ensemble hyperstructures, to ensure that the system remains adaptive and responsive to changing environmental conditions can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “central HUB” and “ensemble hyperstructures”. Thus, these elements in the claim 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 computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing a central HUB and ensemble hyperstructures to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 62, the claim does not recite any abstract idea therefore it is eligible under 35 U.S.C. 101.
Regarding claim 63,
Step 1 Analysis: Claim 63 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 63 recites, in part, The limitation of:
…continuously monitors and updates the [AOPM and Bayesian networks] in real time ensuring that decision-making processes are dynamically adjusted and optimized via real-time feedback can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “central HUB”, “an adaptive Difference Engine that synchronizes…”, “AOPM” and “Bayesian networks”. Thus, these elements in the claim 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 computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing a central HUB, an adaptive Difference Engine that synchronizes…, AOPM and Bayesian networks to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 64,
Step 1 Analysis: Claim 64 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 64 recites, in part, The limitation of:
…dynamically updates the AND/OR Process Model (AOPM) based on real-time data inputs and probabilistic logic encoded as Guarded Horn Clauses (GHCs) providing a structured, agent-based reasoning loop for continuous adaptation.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers mathematical calculations which falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “central HUB”, “Difference Engine”, and “AOPM”. Thus, these elements in the claim 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 computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing a central HUB, Difference Engine, and AOPM to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 65,
Step 1 Analysis: Claim 65 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 65 recites, in part, The limitation of:
…monitors discrepancies between expected and actual outcomes in real time and refines decision pathways to align system actions with current environmental conditions, thereby optimizing performance and adaptability can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “central HUB”. Thus, this element in the claim is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing a central HUB to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 66,
Step 1 Analysis: Claim 66 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 66 recites, in part, The limitation of:
…ensemble reasoning framework…explicitly coordinating the FBN and FBN in an adaptive, agent-based decision-making architecture can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “AOPM” and “FBN and EBN”. Thus, these elements in the claim is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing a central HUB to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 67, the rejection of claim 66 is further incorporated, and further, the claim recites: explicitly orchestrates decision-making processes by real-time evaluation and selection of probabilistic actions through the hyperstructures, ensuring optimized decision pathways beyond standard Bayesian belief propagation
This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 66, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 68, the rejection of claim 66 is further incorporated, and further, the claim recites: [the FBN and EBN are employed] for predictive real-time analytics, processing both stable and dynamic data inputs to enable forward-looking adaptation rather than reactive-only learning.
This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 66, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 69, the rejection of claim 66 is further incorporated, and further, the claim recites: continuously refining decision pathways based on evolving contextual data and ensuring system adaptivity is guided by current environmental context and learned knowledge.
This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 66, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 70,
Step 1 Analysis: Claim 70 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 70 recites, in part, The limitation of:
…generates ensembles of Communicating Sequential Processes (CSP) for structured, parallel task coordination in real time (note: The portion of the limitation which recites “for structured, parallel task coordination in real time” is merely intended use. This portion is given no patentable weight because the limitation, or portion thereof, does not claim the function(s) as being positively recited actions or functions, and or it does not add any meaning or purpose to the associated manipulative step(s).) can be considered to be an evaluation in the human mind.
the GHCs defining conditional logic guard-action rules that govern the system’s decision-making processes
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “a complex adaptive process control system”, “AND/OR Process Model (AOPM)”, and “wherein the system continuously evaluates and updates the GHC-defined logic to adapt the AOPM’s decision making pathways based on real-time data inputs”. Thus, these elements in the claim 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 computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing a complex adaptive process control system, and AND/OR Process Model (AOPM) to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 71, the rejection of claim 70 is further incorporated, and further, the claim recites: wherein the GHC-specified AOPM explicitly defines the generation and coordination of CSP ensembles, ensuring that tasks are executed in a structured and synchronized manner across distributed processes.
This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 70, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 72, the rejection of claim 70 is further incorporated, and further, the claim recites: wherein the system dynamically adapts to real-time data inputs through continuous evaluation and updating of the GHCs, allowing the AOPM to adjust its logic pathways in real time to optimize decision-making under current environmental conditions.
This claim recites additional mental steps in addition to the judicial exception identified in the rejection of claim 70, thus recites a judicial exception.
The claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 73, the claim does not recite any abstract idea therefore it is eligible under 35 U.S.C. 101.
Regarding claim 74,
Step 1 Analysis: Claim 74 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 74 recites, in part, The limitation of:
further comprising probabilistic logic pathways integrated within the AOPM to optimize decision-making based on real-time data, coordinating task execution across distributed CSP processes can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “AOPM”. Thus, these elements in the claim 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 computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing an AOPM to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 75, the claim does not recite any abstract idea therefore it is eligible under 35 U.S.C. 101.
Regarding claim 76,
Step 1 Analysis: Claim 76 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 76 recites, in part, The limitation of:
wherein the [probabilistic hyperstructures, the FBN and EBN that interact dynamically with the [AOPM and CSP ensembles] to process both stable and evolving data patterns in a unified framework can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements - “AOPM” and “wherein the probabilistic hyperstructures, the FBN and EBN”. Thus, these elements in the claim 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 computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing an AOPM and FBN and EBN hyperstructures to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
Regarding claim 77,
Step 1 Analysis: Claim 77 is directed to a process, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 77 recites, in part, The limitation of:
maintaining adaptive decision-making aligned with dynamic environmental conditions can be considered to be an evaluation in the human mind.
These limitations as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements – “a Difference Engine which continuously updates the AOPM and the probabilistic hyperstructures in real time”. Thus, these elements in the claim 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 computer component. Please see MPEP 2106.05(f). Accordingly, 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.
Step 2B Analysis: 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 utilizing a Difference Engine, AOPM and FBN and EBN hyperstructures to perform the steps of the claimed process amount 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. Therefore, the claim does not include any additional elements that amount to an integration of the judicial exceptions into a practical application, nor to significantly more than the judicial exceptions. The claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 59-77 are rejected under 35 U.S.C. 103 as being unpatentable over Potgieter ("The Engineering of Emergence in Complex Adaptive Systems", hereinafter "Potgieter") in view of Kochut ("Execution Kernel for Parallel Logic Programming", hereinafter "Kochut").
Regarding claim 59, Potgieter teaches A real-time complex adaptive process control system (“We propose the BaBe methodology that will enable a complex system to be adaptive by learning from its environment and modifying its behaviour during run-time.” [Synopsis, ¶2]),
comprising a central HUB configured to dynamically coordinate data flow and processing tasks across multiple integrated subsystems in a continuous, closed-loop feedback environment (See Figure 32, pg. 119, See also “The engineering of emergence in a complex adaptive system involves a continuous process employing an (external or internal) observation mechanism to identify regularities in the information about the system’s environment” pg. 16),
the HUB comprises ensemble hyperstructures (“All complex adaptive systems maintain internal models, consisting of hyperstructures representing “regularities” in the information about the system’s environment and its own interaction with that environment. Hyperstructures are higher-order structures that emerge from the collective behaviour of the agents” [pg. 1, §1.1, ¶3]) that include [an AND/OR Process Model (AOPM)],
a Fixed-Structure Bayesian Network (FBN) (“The BaBe agent architecture is based on Minsky’s model and “watches itself” in the way described above. A belief propagation agency (corresponds to “Fixed Structure Bayesian Networks”) can be viewed as an A-Brain that is connected to the real world. As soon as evidence is received from the environment, belief is propagated through the underlying Bayesian behaviour network” [pg. 112-113, bottom para]), and
a Emergent-Structure Bayesian Networks (EBN). (“The competence agencies (corresponds to “Emergent Structure Bayesian Networks”) can be viewed as constituting the “B-Brain”. These agencies can “see” inside the “A-Brain” by inspecting the beliefs of nodes and acting upon these beliefs and possibly changing the state of the environment, influencing the beliefs propagated by the belief propagation agency – the “A-Brain”.” [pg. 113, top para]])
Note: The specification describes “FBN” and “EBN” to be both distributed Bayesian Networks, thus Potgieter explicitly teaches that these agencies are distributed Bayesian Networks (See Synopsis, ¶2)
However Potgieter does not explicitly disclose the HUB comprising an AND/OR Process MODEL (AOPM)
Kochut teaches an AND/OR Process MODEL (AOPM) (“The AND/OR process model, though attractive because it allows for maximum use of parallelism available in a given logic program, is very difficult to realize efficiently on commercially available multi-processors.” [pg. 493, § Processing Model, ¶1])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the complex adaptive process control system of Potgieter to implement the AND/OR process model of Kochut. Potgieter describes the use of a decision tree that is used for an agency to achieve its goals (pg. 45, ¶4). Thus, one would have been motivated to make this modification as an AND/OR Process Model allows for maximum use of parallelism in a logic program. [pg. 493, § Processing Model, ¶1, Kochut]
Regarding claim 60, Potgieter/Kochut teaches The system of claim 59, where Potgieter further teaches wherein the central HUB continuously orchestrates the ensemble hyperstructures to interact, evaluating real-time data inputs through the FBN and EBN and explicitly analyzing these inputs in a closed-loop manner to select optimal decision-making processes (“Bayesian networks are ideally suited to be used as adaptive hyperstructures in internal models. Bayesian learning can be used to adapt to environmental changes (based on real-time data inputs), and belief propagation to reason about what action to take next given environmental conditions (select a workflow that determines which action is to be taken)” [pg. 4, 1.5., first bullet])
Regarding claim 61, Potgieter/Kochut teaches The system of claim 59, where Potgieter further teaches wherein the central HUB implements a structured real-time orchestration mechanism governing probabilistic logic pathways within the ensemble hyperstructures, ensuring that the system remains adaptive and responsive to changing environmental conditions. (“Our BaBe agent architecture is adaptive through the use of specialized Bayesian networks, which we call Bayesian behaviour networks, as hyperstructures. Bayesian networks are ideally suited for probabilistic reasoning (probabilistic logic pathways) in uncertain environments and can be used by agent architectures to evolve and adapt in response to environmental changes.” [bottom of pg. 3 – top of pg. 4])
Regarding claim 62, Potgieter/Kochut teaches The system of claim 59, where Potgieter teaches wherein the central HUB provides an explicit adaptive synchronization framework, coordinating and synchronizing the operation of FBN, EBN, and AOPM hyperstructures to maintain coherent and efficient system behavior (“An agent architecture can be a single-agent system or a multi-agent system, composed of agents, coordinated through their relationships with one another. (“synchronizing the operation”) ” [pg. 1, 1.2. Adaptive Agent Architectures, ¶1; note: Kochut discloses the AOPM as recited in claim 59 and further discloses “parallel logic programming” (corresponds to “synchronizing operations”) thus the combination of Potgieter/Kochut teaches the limitation as recited.])
Note: The portion of the limitation which recites “to maintain coherent and efficient system behavior” is merely intended use. This portion is given no patentable weight because the limitation, or portion thereof, does not claim the function(s) as being positively recited actions or functions, and or it does not add any meaning or purpose to the associated manipulative step(s).
Same motivation to combine the teachings of Potgieter/Kochut as claim 1.
Regarding claim 63, Potgieter/Kochut teaches The system of claim 59, where Kochut further teaches wherein the central HUB controls an adaptive Difference Engine (“Our execution kernel (“Difference engine”) is based on the model of Communicating Sequential Processes [7]. The programmer is able to split the usual backtrackingbased sequential control into parallel threads of execution, realized by separate concurrent processes. Each process is represented by a separate Prolog-like task with the usual data structures for its own environment (rule base, variable bindings, backtrack points, etc.).” [pg. 493, Processing Model, ¶2]) that continuously monitors, updates, and synchronizes the (AOPM) and Bayesian networks in real time (“Concurrently active processes are able to communicate with each other by exchanging messages. (corresponds to “continuously monitors”) Messages are transmitted via channels. (“updates”) A message can be used to either synchronize the processing or transmit a partial result (variable binding) of some larger computation.” [pg. 493, § Processing Model, ¶2]), ensuring that decision-making processes are dynamically adjusted and optimized via real-time feedback.
Note: The portion of the limitation which recites “ensuring that decision-making processes are dynamically adjusted and optimized via real-time feedback” is merely an intended result. This portion is given no patentable weight because the limitation, or portion thereof, does not claim the function(s) as being positively recited actions or functions, and or it does not add any meaning or purpose to the associated manipulative step(s).
Potgieter teaches continuously monitors, updates, and synchronizes Bayesian networks in real time (“The Bayesian behaviour networks implemented by the Bayesian agencies sense changes in real-time by the environment components from the different data sources in the environment and the patterns are integrated into the Bayesian behaviour networks as they emerge” [pg. 107, top para])
Same motivation to combine the teachings of Potgieter/Kochut as claim 1.
Regarding claim 64, Potgieter/Kochut teaches The system of claim 59, where Kochut teaches wherein the Difference Engine dynamically updates the AND/OR Process Model (AOPM) based on real-time data inputs and probabilistic logic encoded Guarded Horn Clauses (GHCs). (“The most prominent among such languages are Concurrent Prolog, PARLOG, and Guarded Horn Clauses (GHC, in short). A program written in any of these languages is represented as a collection of guarded clauses. Literals in a guarded clause are divided into the guard part and the body part (the guard part may be empty). The computation is organized around the AND/OR process tree, where AND processes are responsible for solving conjunctive goals, and OR processes solve single literals. OR processes used here are different than those used in concurrent search strategies for the evaluation "normal" logic program” [pg. 492, bottom left col – top right col]) providing a structured, agent-based reasoning loop for continuous adaptation
Note: The portion of the limitation which recites “providing a structured, agent-based reasoning loop for continuous adaptation” is merely an intended result. This portion is given no patentable weight because the limitation, or portion thereof, does not claim the function(s) as being positively recited actions or functions, and or it does not add any meaning or purpose to the associated manipulative step(s).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the complex adaptive process control system of Potgieter to implement the Guarded Horn Clauses alongside the AND/OR Process Model of Kochut. One would have been motivated to make this modification as such languages allow the programmer to decide which portions of the program should be done in parallel and how to synchronize the cooperation of processes. [pg. 492, bottom left col, Kochut]
Regarding claim 65, Potgieter/Kochut teaches The system of claim 64, where Potgieter teaches wherein the HUB monitors discrepancies between expected and actual outcomes, in real time and refines decision pathways to align system actions with current environmental conditions thereby optimizing performance and adaptability. (“The engineering of emergence in a complex adaptive system involves a continuous process employing an (external or internal) observation mechanism to identify regularities in the information about the system’s environment and about its own interaction with that environment (the input stream) and updating the hyperstructures in the internal model whenever new regularities are identified in the input stream. Emergence refers to the unexpected deviation of the regularities (“discrepancies between expected and actual outcomes”) in the input stream from what is expected from the hyperstructures in the internal model.” [pg. 16, top para])
Regarding claim 66, Potgieter teaches The system of claim 58, further comprising an ensemble reasoning framework that integrates multiple probabilistic logic models within the (AOPM), explicitly coordinating the FBN and EBN in an adaptive, agent based decision-making architecture. (“A complex adaptive system can use a Bayesian network as a probabilistic model of what the emergent effects are of certain interactions and behaviours in response to certain environmental states (the causes). Such a causal model can then be queried by an arbitration process to decide which action(s) are most relevant given a certain state of the environment. Bayesian networks are therefore ideally suited to be used as hyperstructures in the internal models of complex adaptive systems” [pg. 18, top para])
However, Potgieter fails to explicitly teach within an AND/OR Process Model (AOPM).
Kochut teaches within the AOPM. (“The AND/OR process model, though attractive because it allows for maximum use of parallelism available in a given logic program, is very difficult to realize efficiently on commercially available multi-processors.” [pg. 493, § Processing Model, ¶1])
Note: The specification discloses AND/OR process trees/models can be controlled by distributed software agents thus is analogous to the agents recited in Potgieter. Therefore, a substitution between the agent of Potgieter and the AND/OR Process Model of Kochut would be obvious and teach the limitation as recited.
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the complex adaptive process control system of Potgieter to implement the AND/OR process model of Kochut. Potgieter describes the use of a decision tree that is used for an agency to achieve its goals (pg. 45, ¶4). Thus, one would have been motivated to make this modification as an AND/OR Process Model allows for maximum use of parallelism in a logic program. [pg. 493, § Processing Model, ¶1, Kochut]
Regarding claim 67, Potgieter/Kochut teaches The system of claim 59, where Potgieter further teaches wherein the ensemble reasoning framework explicitly orchestrates decision-making processes by real-time evaluation and selection of probabilistic actions through the hyperstructures, ensuring optimized decision pathways beyond standard Bayesian belief propagation (“Bayesian networks are ideally suited to be used as adaptive hyperstructures in internal models. Bayesian learning can be used to adapt to environmental changes (based on real-time data inputs), and belief propagation to reason about what action to take next given environmental conditions (select a workflow that determines which action is to be taken)” [pg. 4, 1.5., first bullet])
Regarding claim 68, Potgieter/Kochut teaches The system of claim 66, where Potgieter teaches wherein the ensemble hyperstructures, including the FBN (“The BaBe agent architecture is based on Minsky’s model and “watches itself” in the way described above. A belief propagation agency (corresponds to “Fixed Structure Bayesian Networks”) can be viewed as an A-Brain that is connected to the real world. As soon as evidence is received from the environment, belief is propagated through the underlying Bayesian behaviour network” [pg. 112-113, bottom para]) and (EBN) (“The competence agencies (corresponds to “Emergent Structure Bayesian Networks”) can be viewed as constituting the “B-Brain”. These agencies can “see” inside the “A-Brain” by inspecting the beliefs of nodes and acting upon these beliefs and possibly changing the state of the environment, influencing the beliefs propagated by the belief propagation agency – the “A-Brain”.” [pg. 113, top para]]),
are employed for predictive real-time analytics, processing both stable and dynamic data inputs. (“The engineering of emergence in a complex adaptive system involves a continuous process employing an (external or internal) observation mechanism to identify regularities in the information about the system’s environment and about its own interaction with that environment (the input stream) and updating the hyperstructures in the internal model whenever new regularities are identified in the input stream” [pg. 16, top para])
Note: The specification describes “FBN” and “EBN” to be both distributed Bayesian Networks, thus Potgieter explicitly teaches that these agencies are distributed Bayesian Networks (See Synopsis, ¶2) to enable forward-looking adaptation rather than reactive-only learning
Note: The portion of the limitation which recites “to enable forward-looking adaptation rather than reactive-only learning” is merely an intended result. This portion is given no patentable weight because the limitation, or portion thereof, does not claim the function(s) as being positively recited actions or functions, and or it does not add any meaning or purpose to the associated manipulative step(s).
Regarding claim 69, Potgieter/Kochut teaches The system of claim 66, where Potgieter teaches wherein the ensemble reasoning framework incorporates context-aware processing, continuously refining decision pathways based on evolving contextual data and ensuring system adaptivity is guided by current environmental context and learned knowledge. (“The engineering of emergence in a complex adaptive system involves a continuous process employing an (external or internal) observation mechanism to identify regularities in the information about the system’s environment and about its own interaction with that environment (the input stream) and updating the hyperstructures in the internal model whenever new regularities are identified in the input stream” [pg. 16, top para])
Regarding claim 70, Potgieter teaches A complex adaptive process control system, wherein the system continuously evaluates and updates the GHC-defined logic to adapt the AOPM's decision-making pathways based on real-time data inputs (“The engineering of emergence in a complex adaptive system involves a continuous process employing an (external or internal) observation mechanism to identify regularities in the information about the system’s environment and about its own interaction with that environment (the input stream) and updating the hyperstructures in the internal model whenever new regularities are identified in the input stream.” [pg. 16, top para]).
However fails to explicitly teach A complex adaptive process control system, comprising a Guarded Horn Clause (GHC) specification of an AND/OR Process Model (AOPM)
Kochut teaches A complex adaptive process control system, comprising a Guarded Horn Clause (GHC) specification of an AND/OR Process Model (AOPM) (“Such languages allow the programmer to decide which portions of the program should be done in parallel and how to synchronize the cooperation of processes. The most prominent among such languages are Concurrent Prolog [ 181, PARLOG [ 11, and Guarded Horn Clauses (GHC, in short)” [pg. 492, bottom left col – top right col])
that generates ensembles of Communicating Sequential Processes (CSP) (“Our execution kernel is based on the model of Communicating Sequential Processes [7]. The programmer is able to split the usual backtrackingbased sequential control into parallel threads of execution, realized by separate concurrent processes. Each process is represented by a separate Prolog-like task with the usual data structures for its own environment (rule base, variable bindings, backtrack points, etc.).” [pg. 493, Processing Model, ¶2]) for structured, parallel task coordination in real time (note: This limitation is merely intended use. This portion is given no patentable weight because the limitation, or portion thereof, does not claim the function(s) as being positively recited actions or functions, and or it does not add any meaning or purpose to the associated manipulative step(s).)
the GHCs define the conditional logic guard-action rules that govern the system's decision-making processes(“The procedural meaning of the polling goal can be stated as follows: “try each guarded goal in sequence, until one of them successfully terminates its guard part”. In that case, the computation “commits” to the corresponding goal.” [pg. 493, bottom right col]).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the complex adaptive process control system of Potgieter to implement the AND/OR process model of Kochut. Potgieter describes the use of a decision tree that is used for an agency to achieve its goals (pg. 45, ¶4). Thus, one would have been motivated to make this modification as an AND/OR Process Model allows for maximum use of parallelism in a logic program. [pg. 493, § Processing Model, ¶1, Kochut]
Regarding claim 71, Potgieter/Kochut teaches The system of claim 70, where Kochut teaches wherein the GHC-specified AOPM explicitly defines the generation and coordination of CSP ensembles (See rejection of claim 70), ensuring tasks are executed in a structured and synchronized manner across distributed processes. (“The second direction involves development of various parallel logic programming languages, with different than Prolog syntax and semantics. Such languages allow the programmer to decide which portions of the program should be done in parallel and how to synchronize the cooperation of processes.” [pg. 492, bottom left col])
Same motivation to combine the teachings of Kochut/Potgieter as claim 70.
Regarding claim 72, Potgieter/Kochut teaches The system of claim 70, where Kochut teaches wherein the system dynamically adapts to real-time data inputs through continuous evaluation and updating of the GHCs, allowing the AOPM to adjust its logic pathways in real time to optimize decision-making under current environmental conditions. (“Our execution kernel is based on the model of Communicating Sequential Processes [7]. The programmer is able to split the usual backtrackingbased sequential control into parallel threads of execution, realized by separate concurrent processes. Each process is represented by a separate Prolog-like task with the usual data structures for its own environment (rule base, variable bindings, backtrack points, etc.).” [pg. 493, § Processing Model, ¶2; Potgieter teaches the complex system being able to dynamically adapt to real-time inputs via continuous evaluation and updating of its subsystems, thus when combined with Kochut would teach the limitation as claimed.])
Same motivation to combine the teachings of Kochut/Potgieter as claim 70.
Regarding claim 73, Potgieter teaches A complex adaptive process control system (“We propose the BaBe methodology that will enable a complex system to be adaptive by learning from its environment and modifying its behaviour during run-time.” [Synopsis, ¶2])… incorporating probabilistic hyperstructures (Fixed and Emergent Bayesian Networks, FBN and EBN) that interact dynamically with the AOPM and CSP to process stable and emergent data patterns (“All complex adaptive systems maintain internal models, consisting of hyperstructures representing “regularities” in the information about the system’s environment and its own interaction with that environment. Hyperstructures are higher-order structures that emerge from the collective behaviour of the agents” [pg. 1, §1.1, ¶3]).
However Potgieter fails to explicitly teach implementing an AND/OR Process Model (AOPM) realized through ensembles of Communicating Sequential Processes (CSP) running in parallel
Kochut also teaches A complex adaptive process control system, implementing an AND/OR Process Model (AOPM) realized through ensembles of Communicating Sequential Processes (CSP) running in parallel. (“Our execution kernel is based on the model of Communicating Sequential Processes [7]. The programmer is able to split the usual backtrackingbased sequential control into parallel threads of execution, realized by separate concurrent processes. Each process is represented by a separate Prolog-like task with the usual data structures for its own environment (rule base, variable bindings, backtrack points, etc.).” [pg. 493, Processing Model, ¶2])
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the complex adaptive process control system of Potgieter to implement the AND/OR process model of Kochut. Potgieter describes the use of a decision tree that is used for an agency to achieve its goals (pg. 45, ¶4). Thus, one would have been motivated to make this modification as an AND/OR Process Model allows for maximum use of parallelism in a logic program. [pg. 493, § Processing Model, ¶1, Kochut]
Regarding claim 74, Potgieter/Kochut teaches The system of claim 73, Kochut teaches further comprising probabilistic logic pathways integrated within the AOPM to optimize decision-making based on real-time data, coordinating task execution across distributed CSP processes. (“Our execution kernel is based on the model of Communicating Sequential Processes [7]. The programmer is able to split the usual backtrackingbased sequential control into parallel threads of execution, realized by separate concurrent processes. Each process is represented by a separate Prolog-like task with the usual data structures for its own environment (rule base, variable bindings, backtrack points, etc.)... A message can be used to either synchronize the processing or transmit a partial result (variable binding) of some larger computation” [pg. 493, § Processing Model, ¶2; Potgieter teaches the complex system being able to dynamically adapt to real-time inputs via continuous evaluation and updating of its subsystems, thus when combined with Kochut would teach the limitation as claimed.])
Note: The portion of the limitation which recites “to optimize decision-making based on real-time data, coordinating task execution across distributed CSP processes” is merely an intended use. This portion is given no patentable weight because the limitation, or portion thereof, does not claim the function(s) as being positively recited actions or functions, and or it does not add any meaning or purpose to the associated manipulative step(s).
Same motivation to combine the teachings of Kochut/Potgieter as claim 73.
Regarding claim 75, Potgieter/Kochut teaches The system of claim 73, where Kochut teaches wherein the CSP framework manages structured communication between system components, to ensure synchronized and efficient operations, providing the Communicating Sequential Processes (CSP) framework that underlies the real-time process orchestration (“Communication among concurrently active processes is handled by sending and receiving messages. Messages travel via communication channels, similarly as in the latest version of the Communicating Sequential Processes [7] (in the original CSP report a message had to be sent directly to a named process). The system predicates send and receive can be used to do just that.” [pg. 493, § Communication and Synchronization, ¶1]),
Same motivation to combine the teachings of Kochut/Potgieter as claim 73.
Regarding claim 76, Potgieter/Kochut teaches The system of claim 73,
Potgieter teaches wherein the probabilistic hyperstructures (FBN) (“The BaBe agent architecture is based on Minsky’s model and “watches itself” in the way described above. A belief propagation agency (corresponds to “Fixed Structure Bayesian Networks”) can be viewed as an A-Brain that is connected to the real world. As soon as evidence is received from the environment, belief is propagated through the underlying Bayesian behaviour network” [pg. 112-113, bottom para]) and (EBN) (“The competence agencies (corresponds to “Emergent Structure Bayesian Networks”) can be viewed as constituting the “B-Brain”. These agencies can “see” inside the “A-Brain” by inspecting the beliefs of nodes and acting upon these beliefs and possibly changing the state of the environment, influencing the beliefs propagated by the belief propagation agency – the “A-Brain”.” [pg. 113, top para]]),
That interact dynamically with the [AOPM] and [CSP] ensembles to process both stable and evolving data patterns in a unified framework. (“The engineering of emergence in a complex adaptive system involves a continuous process employing an (external or internal) observation mechanism to identify regularities in the information about the system’s environment and about its own interaction with that environment (the input stream) and updating the hyperstructures in the internal model whenever new regularities are identified in the input stream” [pg. 16, top para; note: AOPM and CSP are taught by Kochut which are used in a logic program thus would be obvious to combine into the system of Potgieter.])
Note: The specification describes “FBN” and “EBN” to be both distributed Bayesian Networks, thus Potgieter explicitly teaches that these agencies are distributed Bayesian Networks (See Synopsis, ¶2)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the Execution kernel method of Kochut by implementing it into the complex adaptive system of Potgieter. One would have been motivated to make this modification as logical or pseudo-logical based reasoning for decision making is the most popular and widely used agent architectures. [pg. 46, 4.6.2.1. Overview, Potgieter]
Regarding claim 77, Potgieter/Kochut teaches The system of claim 73, Kochut further comprising a Difference Engine that continuously updates the AOPM (“(“Our execution kernel (“Difference engine”) is based on the model of Communicating Sequential Processes…(“Concurrently active processes are able to communicate with each other by exchanging messages. Messages are transmitted via channels. (“updates”) A message can be used to either synchronize the processing or transmit a partial result (variable binding) of some larger computation.” [pg. 493, § Processing Model, ¶2]) and
However Kochut doesn’t explicitly teach continuously updates the probabilistic hyperstructures in real time, maintaining adaptive decision-making aligned with dynamic environmental conditions.
Potgieter teaches continuously updates hyperstructures in real time, maintaining adaptive decision-making aligned with dynamic environmental conditions (“The engineering of emergence in a complex adaptive system involves a continuous process employing an (external or internal) observation mechanism to identify regularities in the information about the system’s environment and about its own interaction with that environment (the input stream) and updating the hyperstructures in the internal model whenever new regularities are identified in the input stream. Emergence refers to the unexpected deviation of the regularities in the input stream from what is expected from the hyperstructures in the internal model.” [pg. 16, top para])
Same motivation to combine the teachings of Kochut/Potgieter as claim 76.
Response to Arguments
Applicant's arguments filed 11/24/2025 have been fully considered but they are not persuasive.
Regarding the 35 U.S.C. §112(a) and §112(b) Rejections:
Applicant’s amendments appear to have overcome the previous 112(a) and 112(b) rejections. Therefore, the rejections have been withdrawn.
Regarding the 35 U.S.C. §101 Rejection:
Applicant appears to assert on pg. 8 of the remarks that claim 60 recites “continuously [orchestrating]…” and “a central HUB” coordinating ensemble hyperstructures... which cannot be practically performed in the human mind. Examiner respectfully disagrees. Although the claim recites the central HUB continuously orchestrates the ensemble hyperstructures to interact…the examiner asserts merely using the central HUB to perform this step amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f). The claim does explicitly recite “evaluating real-time data inputs…to select optimal decision-making processes” which under BRI amounts to an abstract idea.
Applicant further asserts the claimed architecture is a novel configuration of interacting components with defined technical roles. Examiner respectfully disagrees. The claim is recited in a broad and generic manner such that the “central HUB” is merely orchestrating/coordinating the ensemble hyperstructures without any specific details on the actual architecture of the system other than that it comprises all of these networks/subsystems. The claims also fail to recite any specific details of the interactions between any of these networks/subsystems within the complex adaptive process control system. Therefore, the claim does not recite any additional elements that amount to an integration of the judicial exception into a practical application.
Applicant further asserts by combining distributed Bayesian network hyperstructures with a real-time process orchestration mechanism, the claimed system achieves technical capabilities significantly beyond human mental processes or basic computing and further leads to technical improvements. Examiner respectfully disagrees. As noted above, the claims as currently recited fail to explicitly reflect any improvement in computer technology. The claims never recite any specific details on how any of the ensemble hyperstructures operate other than merely providing real-time data into these networks/subsystems. As noted throughout the office action, the language of the claims also appears to be intended use/intended results thus carries little to no patentable weight. Examiner suggests implementing more positively recited language, in particular, positively functional language of the functions of each network/subsystem and how they are operating/interacting with each other to fully capture the technological improvements argued by the applicant.
Regarding applicant’s arguments regarding claims 70-72 and 74-77, applicant merely asserts the limitations of claim 70 cannot be practically performed in the human mind. As noted above, merely stating that the system is “continuously” evaluating… does not mean that the step is not practically performed in the human mind. As stated in MPEP §2106.04.(a)(2).III.C. “A Claim That Requires a Computer May Still Recite a Mental Process”. Merely using a generic computer to perform this step amounts to no more than mere instructions to apply the exception using a generic computer component. Please see MPEP 2106.05(f).
Regarding the Prior Art Rejections:
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). Applicant asserts the prior art of Kochut is directed to a different system architecture from the claims and thus fails to recite a central controller integrating Bayesian networks with a process model. Examiner respectfully disagrees. The claims are rejected under the combination of Potgieter in view of Kochut. Potgieter explicitly teaches the central HUB which coordinates several subsystems however fails to explicitly teach the AOPM. Kochut is relied upon to teach the deficiencies of Potgieter (i.e. AOPM and CSP framework) thus the examiner asserts that the combination of the two prior art references would teach the claims.
Applicant further asserts the absence of Distribution Bayesian Network Hyperstructures within Kochut’s disclosure. While examiner agrees that Kochut fails to explicitly teach these features recited in the claims, the examiner asserts Kochut was not relied upon to teach these features and the prior art of Potgieter was relied upon to teach these specific features argued by the applicant.
Applicant further asserts Kochut’s disclosure fails to describe continuous monitoring an updating of its logic based on streaming external data. While examiner partially agrees with this assertion, the newly amended features of claim 70 recite the continuously and updating of the system’s logic which are now taught by the prior art of Potgieter. Therefore, examiner asserts Potgieter teaches the complex system being able to dynamically adapt to real-time inputs via continuous evaluation and updating of its subsystems, thus when combined with Kochut would teach the limitation as claimed.
Applicant further asserts the “ensemble hyperstructures” and “cognitive agents” provide a system functionality that is absent in Kochut. While the examiner agrees these features are not taught by Kochut, these newly amended limitations specifically including the FBN and EBN hyperstructures into claim 73 are now taught by Potgieter. Therefore, applicant’s arguments regarding the prior art of Kochut was considered to be moot.
Applicant argues the two prior arts focuses on different problems and the mere presence of decision trees does not suggest any compatibility with AOPM. In this case, the two prior arts fall within the same endeavor of logical programming. Potgieter explicitly suggests "an agent architecture uses "logical or pseudo-logical reasoning". Thus, examiner believes both references would be analogous art and it would have been obvious to one of ordinary skill in the art before the effective filing to combine the two references. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, examiner asserts that there would be some motivation to combine the teachings of Kochut with the system disclosed by Potgieter. Potgieter describes the use of a decision tree that is used for an agency to achieve its goals (pg. 45, ¶4). The AOPM model disclosed by Kochut can be used as a process tree for solving conjunctive goals or single literals therefore it appears there would be motivation to combine the teachings of the two prior art references because an AND/OR Process Model allows for maximum use of parallelism in a logic program. [pg. 493, § Processing Model, ¶1, Kochut].
Applicant further asserts the claimed system uniquely unites probabilistic hyperstructures with a logic AOPM under a unified orchestration thus there would be a non-obvious integration of Bayesian Hyperstructures with AOPM. In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, as noted above, the examiner believes both references fall within the same field of endeavor of logical programming thus would be considered analogous prior arts. Potgieter suggests the use of multiple models such as a decision tree where the agent can decide which possible actions to take. This follows the same logical processing concept as Kochut as the prior art provides teaching of sequential logic processing. Examiner believes both references would be analogous art and it would have been obvious to one of ordinary skill in the art before the effective filing to combine the two references.
Applicant further asserts of the present inventors’ HUB is more than just a network router or a generic processor and suggests that neither Potgieter or Kochut describes such a feedback-controlled orchestration. Examiner respectfully disagrees. Potgieter explicitly teaches the central HUB as claimed while also disclosing continuous evaluation and adaptation to process real-time data input. The claims offer little to no details on “how” the claimed orchestration of actual process flows or tasks are using these observations. Thus, the examiner asserts under BRI, the prior art of Potgieter would read on the recited claim limitations.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986). The difference engine as claimed under BRI is merely an element that can monitors changes, updates and synchronizes two components within a system. As noted in the rejection Kochut’s “execution kernel” can be interpreted as a “difference engine” because it can be used to synchronize and allow concurrently active processes to communicate with each other via exchanging and transmitting messages. Additionally, the prior art of Pogieter describes continuous observation and updating of network subsystems thus when combined with Kochut would read on the claim limitation under BRI.
Applicant further asserts the claimed system incorporates EBN for emergent patterns but extends it by embedding those agents into a larger orchestrated that can be directed and queried through a formal process model and thus a design would not naturally flow from simply knowing Potgieter and Kochut. Examiner respectfully disagrees. As noted in the prior art rejection above, the instant specification discloses AND/OR process trees/models can be controlled by distributed software agents thus is analogous to the agents recited in Potgieter. Thus, a mere substitution between the agent disclosed by Potgieter and the AND/OR Process Model of Kochut would be obvious to one of ordinary skill in the art and teach the limitation as recited.
Regarding applicant’s arguments specifically to claim 74 and Kochut’s AOPM failing to suggest those messages or process triggers are guided by probabilistic inferences or real-time learned probabilities. Examiner respectfully disagrees. The claim merely states probabilistic pathways are integrated within the AOPM but never specifics how they are being integrated. Therefore, the examiner asserts the claims is not specific enough to require that any of the messages of process triggers are being guided by probabilistic inferences or real-time learned probabilities as being argued by applicant.
Applicant further asserts to arrive at the claim 74 scenario, one would to envision modifying Kochut’s execution model to continuously accept inputs from a Bayesian network. In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). There is no evidence to suggest that an integrating Kochut's AOPM into Potgieter's HUB would require any substantial transformation. Thus, merely implementing a AND/OR process model into Potgieter's HUB would merely be equivalent to implementing another model in software. Thus, applicant's arguments are not persuasive.
Applicant’s arguments regarding claim 76 appear to repeat the same argument provided previously regarding the failure of the prior art of Potgieter failing to teach the FBN and EBN being associated with a CSP model. Again, applicant appears to be attacking the references individually rather than the combination of the references. As noted in the rejection, the AOPM and CSP are taught by Kochut which are used within a logic program thus would be obvious to implement into the system of Potgieter. There would be motivation to do this as noted by Potgieter on pg. 46, 4.6.2.1 “logical or pseudo logical based reasoning for decision making is the most popular and widely used agent architectures. Therefore, applicant’s arguments are not persuasive.
Applicant’s arguments regarding the presently claimed Difference Engine being unprecedented and representing an emergent concept from the union of two fields has been considered but are not persuasive. Applicant argues that the difference engine links the logic layer and the learning layer such that both are iteratively refined as conditions changed however this specific concept is not clearly recited in the claims. Claim 63 merely states the difference engine “synchronizes” the AOPM and Bayesian networks but has no details of how that particular step is done and claim 77 merely states the difference engine continuously updates but has not details of anything regarding any linking between AOPM and probabilistic hyperstructures. Therefore, the examiner asserts the claims lack the detail required to be interpreted as argued by the applicant. The prior art rejection has been maintained.
Conclusion
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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/MICHAEL H HOANG/PRIMARY EXAMINER, Art Unit 2122