MARKET ORCHESTRATION SYSTEM FOR FACILITATING ELECTRONIC MARKETPLACE TRANSACTIONS

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 . DETAILED ACTION 1. The following is a NON-FINAL Office Action in response to the communication received on 05/18/26. Claims 141, 145, and 300-317 are now pending in this application. 2. A request for continued examination (RCE) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application AFTER FINAL rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the FINALITY of the previous Office Action has been WITHDRAWN pursuant to 37 CFR 1.114. Applicant's submission filed on 05/18/26 has been entered. Response to Amendments 3. Applicants’ Amendment has been acknowledged in that: Claims 141, 300, 302-314, 316-317 have been amended; hence such, claims 141, 145, 300-317 are now pending in this application. RESPONSE TO ARGUMENTS Applicant argues#1 Claim 302 Is Not Directed to Software Per Se The Office Action rejected claims 302-314 as being directed to non-statutory subject matter including software programs. Office Action at 11. Applicant has amended claim 302 to now recite "one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to implement" the features of the claim. At least because these are hardware components, the claims are not directed to software per se. Applicant respectfully requests withdrawal of the non-statutory subject matter rejection of claims 302-314. Examiner Response The section 101 rejection for claims 302-314 being directed to non-statutory subject matter is hereby withdrawn. Applicant argues#2 The Claims Integrate Any Alleged Abstract Idea into a Practical Application The Office Action characterizes the claims as being directed to "performance of the limitation as a commercial interaction, the steps for orchestrating a market transaction associated with [a] market orchestration twin." Office Action at 15. Applicant respectfully requests reconsideration of this characterization, which does not include important details of the claims. Especially as amended, the claims are not directed to a commercial activity such as trading, but instead are directed to a more specific recited method for training, executing, and reinforcing an intelligent agent that orchestrates automated market actions on behalf of a particular user. The claims provide a technical solution that includes training and operating a machine-learned agent to learn preferences of a user and to select and orchestrate automated actions on behalf of the particular user using a specific implementation that further involves simulations and a digital twin. Examiner Response Examiner respectfully disagrees. The claims are reciting a commercial activity (the identified abstract idea of orchestrating a market transaction (trading) with the market orchestration digital twin. Also see the section 101 rejection below. The rejection is maintained. Applicant argues#3 First, amended claim 141 recites a specific personalization mechanism that turns the machine-learned model into a technical solution for a technical problem, namely that agent- driven automation actions do not reflect a particular user's preferences. Accordingly, the machine-learned model is "trained on historical trading data relating to the plurality of trades associated with the user and executed on the digital marketplace, such that the machine-learned model is trained to select a market action for orchestration in accordance with preferences of the user." This recitation specifies that the model is trained for a particular user based on the user's own trading history and therefore that the resulting model is configured to make improved selections in order to better match user preferences. See, e.g., paras. [2055], [2063]-[2066] of US Pub. No. 2022/0366494 (describing the intelligent agent trained on the user's own interactions). The trained model therefore provides a better technical solution for users by providing more personalized recommendations. Examiner Response Examiner respectfully disagrees. The specification paras that applicant refers to and other spec paras are reproduced below: [0510] Referring to FIG. 137, an adaptive intelligence system 13704 may include an artificial intelligence system 13748, a digital twin system 13720, and an adaptive device (or edge) intelligence system 13730. The artificial intelligence system 13748 may define a machine learning model 13702 for performing analytics, simulation, decision making, and prediction making related to data processing, data analysis, simulation creation, and simulation analysis of one or more of the transaction entities. The machine learning model 13702 is an algorithm and/or statistical model that performs specific tasks without using explicit instructions, relying instead on patterns and inference. The machine learning model 13702 builds one or more mathematical models based on training data to make predictions and/or decisions without being explicitly programmed to perform the specific tasks. The machine learning model 13702 may receive inputs of sensor data as training data, including event data 13724 and state data 13772 related to one or more of the transaction entities through data collection systems 13718 and monitoring systems 13706 and connectivity facilities 13716. The event data 13724 and state data 13772 may be stored in a data storage system 13710 The sensor data input to the machine learning model 13702 may be used to train the machine learning model 13702 to perform the analytics, simulation, decision making, and prediction making relating to the data processing, data analysis, simulation creation, and simulation analysis of the one or more of the transaction entities. The machine learning model 13702 may also use input data from a user or users of the information technology system. The machine learning model 13702 may include an artificial neural network, a decision tree, a support vector machine, a Bayesian network, a genetic algorithm, any other suitable form of machine learning model, or a combination thereof. The machine learning model 13702 may be configured to learn through supervised learning, unsupervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, association rules, a combination thereof, or any other suitable algorithm for learning. [2055] In embodiments, the market orchestration system platform 20500 trains intelligent agents 20234 based on training data that includes actions taken by users and features relating to the circumstances surrounding the action (e.g., the type of action taken, the scenario that prompted the action, and the like). In embodiments, the market orchestration system platform 20500 receives telemetry data from a client application 20312 associated with a particular user and learns the workflows performed by the particular user based on the telemetry data and the surrounding circumstances. For example, the user may be a buyer in a marketplace that is presented an asset digital twin. Among the actions of the buyer may be to run simulations on asset digital twins wherein the asset digital twins represent assets for sale in the marketplace. The states depicted in the asset digital twin may include the condition of the asset digital twin as a result of one or more simulations. In this example, the buyer may buy the asset via the asset digital twin when the asset digital twin is determined to be in a first condition (e.g., a good condition) as a result of one or more simulations and may decline to purchase the asset and search for other assets for sale when the asset digital twin is determined to be in a second condition (e.g., a critical condition) as the result of one or more simulations. The intelligent agent may be trained to identify the buyer's tendencies based on the buyer's previous interaction with the asset digital twin. Once trained, the intelligent agent may automatically buy assets for sale when a particular asset's digital twin is determined to be in the first condition as the result of one or more simulations and may automatically decline to buy the asset and search for other assets if the asset digital twin is in the second condition as the result of one or more simulations. In embodiments, simulations may be based upon and/or incorporate behavioral models that predict behavior of assets (such as physical models that predict physical conditions (such as based on physical, chemical and biological principles), economic models that predict economic behavior (such as models that predict behavior of purchasers, sellers, prices, trading patterns, or the like), human behavioral models (such as psychological models, demographic models, population models, sociological models, game-theoretic models, and many others), and many others, including any of the models described herein or in the documents incorporated herein by reference and including hybrids and combinations of the foregoing. As one example among many possible models, in a marketplace for wine (or other asset that can improve or deteriorate over time), a simulation may include a physical model that uses sensor data from a storage environment for a unit, a chemical model that predicts the effects of the passage of time under the sensed storage conditions, and an economic model that predicts the value of a unit of a given level of quality based on historical pricing patterns for similar goods. The results may yield an expected value of the asset, as well as a simulation of the price of the asset at different points of time, which an intelligent agent may use as reference information in comparison to a current price and/or a predicted future price, such as to determine automatically (after training upon a set of interactions of users with similar comparisons) whether to hold, buy, or sell. While reference is made to an intelligent agent being trained for a particular user, it is understood that an intelligent agent may be trained using the actions of one or more different users and may be used in connection with users that were not involved in training the intelligent agent. Further discussion of intelligent agents is provided throughout the disclosure. [2063] In some embodiments, the intelligent agent system 20210 receives feedback from users regarding respective intelligent agents 20234. For example, in some embodiments, a client application 20312 that leverages an intelligent agent may provide an interface by which a user can provide feedback regarding an action output by an intelligent agent. In embodiments, the user provides the feedback that identifies and characterizes any errors by the intelligent agent. In some of these embodiments, a report may be generated (e.g., by the client application 20312 or the market orchestration system platform 20500) that indicates the set of errors encountered by the user. The report may be used to reconfigure/retrain the intelligent agent. In embodiments, the reconfiguring/retraining an intelligent agent may include removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system (such as placing gates on a recurrent neural network to render it a gated RNN that balances learning with the need to diminish certain inputs in order to avoid exploding error problems), reengineering the type of the artificial intelligence system (such as by modifying the neural network type among a convolutional neural network, a recurrent neural network, a feed forward neural network, a long-term/short-term memory (LS™) neural network, a self-organizing neural network, or many other types and combinations), and/or augmenting the set of inputs to the artificial intelligence system. [2064] In embodiments, the intelligent agent may be configured to, at least partially, operate as a double of the user having a role within a marketplace. In these embodiments, the intelligent agent system 20210 trains an intelligent agent based on a training data set that includes a set of interactions by a specific user during the performance of their respective role within the marketplace. For example, the set of interactions that may be used to train the intelligent agent may include interactions of the user with the entities of a marketplace, interactions of the user with other users of the marketplace, interactions of the user with assets listed in the marketplace, interactions of the user with a digital twin, interactions of the user with sensor data obtained from a sensor system, interactions of the user with data streams generated by physical entities, interactions of the user with the computational entities of the marketplace, and the like. In some embodiments, the intelligent agent system 20210 parses the training data set of interactions to identify a chain of reasoning of the user upon a set of interactions. In some of these embodiments, the chain of reasoning may be parsed to identify a type of reasoning of the user, which may be used as a basis for configuring/training the intelligent agent. For example, the chain of reasoning may be a deductive chain of reasoning, an inductive chain of reasoning, a predictive chain of reasoning, a classification chain of reasoning, an iterative chain of reasoning, a trial-and-error chain of reasoning, a Bayesian chain of reasoning, a scientific method chain of reasoning, and the like. In some embodiments, the intelligent agent system 20210 parses the training data set of interactions to identify a type of processing undertaking by the user in analyzing the set of interactions. For example, types of processing may include audio processing in analyzing audible information, tactile or “touch” processing in analyzing physical sensor information, textual information processing in analyzing text, motion processing in analyzing motion information, visual processing in analyzing visual information, spatiotemporal processing in processing spatiotemporal information, mathematical processing in mathematically operating on numerical data, creative processing when deriving alternative options, analytic processing when selecting from a set of options, and the like. In embodiments, identification of a type of reasoning and/or a type of processing may be informed by undertaking brain imaging, such as functional MRI or other magnetic imaging, electroencephalogram (EEG), or other imaging, such as by identifying broad brain activity (e.g., wave bands of activity, such as delta, theta, alpha and gamma waves), by identifying a set of brain regions that are activated and/or inactive during the set interactions of the user that are being used for training of the intelligent agent (such as neocortex regions, such as Fp1 (involved in judgment and decision making), F7 (involved in imagination and mimicry), F3 (involved in analytic deduction), T3 (involved in speech), C3 (involved in storage of facts), T5 (involved in mediation and empathy), P3 (involved in tactical navigation), 01 (involved in visual engineering), Fp2 (involved in process management), F8 (involved in belief systems), F4 (involved in expert classification), T4 (involved in listening and intuition), C4 (involved in artistic creativity), T6 (involved in prediction), P4 (involved in strategic gaming), 02 (involved in abstraction), and/or combinations of the foregoing) or by other neuroscientific, psychological, or similar techniques that provide insight into how humans upon which the intelligent agent is trained are solving particular types of problems that are involved in workflows for which intelligent agents are deployed. In embodiments, an intelligent agent may be configured with a neural network type, or combination of types, that is selected to replicate or simulate a processing activity that is similar to the activity of the brain regions of a human expert that is performing a set of activities for which the intelligent agent is to be trained. As one example among many possible, a trader may be shown to use visual processing region O1 and strategic gaming region P4 of the neocortex when making successful trades, and a neural network may be configured with a convolutional neural network to provide effective replication of visual pattern recognition and a gated recurrent neural network to replicate strategic gaming. In embodiments, a library of neural network resources representing combinations of neural network types that mimic or simulate neocortex activities may be configured to allow selection and implementation of modules that replicate the combinations used by human experts to undertake various activities that are subjects of development of intelligent agents, such as involving robotic process automation. In embodiments, various neural network types from the library may be configured in series and/or in parallel configurations to represent processing flows, which may be arranged to mimic or replicate flows of processing in the brain, such as based on spatiotemporal imaging of the brain when involved in the activity that is the subject of automation. In embodiments, an intelligent software agent for agent development may be trained, such as using any of the training techniques described herein, to select a set of neural network resource types, to arrange the neural network resource types according to a processing flow, to configure input data sources for the set of neural network resources, and/or to automatically deploy the set of neural network types on available computational resources to initiate training of the configured set of neural network resources to perform a desired intelligent agent/automation workflows. In embodiments, the intelligent software agent used for agent development operates on an input data set of spatiotemporal imaging data of a human brain, such as an expert who is performing the workflows that is the subject of development of a further, and uses the spatiotemporal imaging data to automatically select and configure the selection and arrangement of the set of neural network types to initiate learning. Thus, a system for developing an intelligent agent may be configured for (optionally automatic) selection of neural network types and/or arrangements based on spatiotemporal neocortical activity patterns of human users involved in workflows for which the agent is trained. Once developed, the resulting intelligent agent/process automation system may be trained as described throughout this disclosure. [2065] In embodiments, a system for developing an intelligent agent 20234 (including the aforementioned agent for development of intelligent agents) may use information from brain imaging of human users to infer (optionally automatically) what data sources should be selected as inputs for an intelligent agent. For example, for processes where neocortex region O1 is highly active (involving visual processing), visual inputs (such as available information from cameras, or visual representations of information like price patterns, among many others) may be selected as favorable data sources. Similarly, for processes involving region C3 (involving storage and retrieval of facts), data sources providing reliable factual information (such as blockchain-based distributed ledgers) may be selected. Thus, a system for developing an intelligent agent may be configured for (optionally automatic) selection of input data types and sources based on spatiotemporal neocortical activity patterns of human users involved in workflows for which the agent is trained. [2066] In embodiments, the intelligent agents 20234 are trained to output actions on behalf of a trader, a buyer, a seller, a broker, a marketplace host and/or an affiliate of a buyer, a seller, a broker, or marketplace host. In these embodiments, an intelligent agent may be trained for marketplace roles, such that a user in a particular marketplace role can train the intelligent agent by performing their respective role. For example, an intelligent agent may be trained for performing actions on behalf of or recommending actions to a user in a particular role within a marketplace. In some of these embodiments, the client application 20312 may provide the functionality of the market orchestration system platform 20500. For example, in some embodiments, users may view market orchestration digital twins and/or may use the exchange suite tools via the client application 20312. During the use of the client application 20312, a buyer may use a screener tool to filter assets by setting criteria via the graphical user interface. Each time the user interacts with the client application 20312, the client application 20312 may monitor the user's actions and may report the actions back to the intelligent agent system 20210. Over time, the intelligent agent system 20210 may learn how the particular user responds to certain situations. For instance, if the user is the seller and each time the price of an asset is within a specific range, the seller places an order request to sell assets, the intelligent agent system 20210 may learn to automatically sell assets when pricing for those assets is within the specific range. Further implementations of the intelligent agent system 20210 are discussed further in the disclosure. The above recited spec paras do not disclose a technical solution to a technical problem. The additional element of the (machine learning, which is operating in it ordinary capacity (for predicting hypothetical situations using historical data as inputs) The trained intelligent is also operating in its ordinary capacity (a trained intelligent learns from previous actions and by executing actions). Applicant argued the claims present a technical improvement. Examiner does not find this argument persuasive. Applicant’s claims do not improve technology; the underlying technology remains unaffected by the claims. Applicant is addressing a business problem (provide orchestration of a market transaction with a market orchestration digital twin) with a business solution. Applicant is merely using existing technology (for its intended purpose) to implement the business solution. Any improvements lie in the abstract idea itself, not in underlying technology. The rejection is maintained. Applicant argues#4 Additionally, amended claim 141 recites a simulation that is used for candidate selection before the agent makes an irreversible trade that the user cannot undo. Many market actions, once orchestrated, commit the user to a counterparty or are otherwise not unilaterally reversible. The claim uses a simulation system to provide the agent with a better basis on which to select among the candidates before any one of them is orchestrated. Specifically, the claim recites "executing, by the one or more processors, a simulation system to simulate one or more candidate market actions on the market orchestration digital twin, wherein the simulation system generates at least one simulated state of the digital marketplace that comprises respective simulated outcomes of the one or more candidate market actions." The trained intelligent agent is then executed to "select an automated market action from the one or more candidate market actions in accordance with the preferences of the user based on the at least one simulated state using the one or more machine-learned models." This sequence where the agent selects from candidates whose outcomes have been computed against the digital twin thus provides an improved solution over a naive generic solution where outcomes can only be observed by committing to them in a marketplace. See, e.g., paras. [2086], [2111]-[2120] of US Pub. No. 2022/0366494. Examiner Response Examiner respectfully disagrees. Examiner reproduces paras 2086, 2111below: [2086] In embodiments, the digital twin simulation system 20804 implements a set of models (e.g., physical mathematical forecasts, logical representations, or process diagrams) that develop the framework where data and the response of the digital twin can be simulated in response to different situational stimuli or sets of stimuli. In embodiments, the digital twin simulation system 20804 may include or leverage a computerized model builder that constructs a predicted future state of either the data and/or the response of the digital twin to the input data. In some embodiments, the computerized model library may be obtained from a behavior datastore 20840 that stores one or more behavior models that defines economic and/or scientific formulas or processes. The computerized digital twin model calculates the results of the model to build an interactive environment where users can watch and manipulate the simulated environment seeing how the entire system responds to specific changes in the environment. [2111 In embodiments, the marketplace host digital twin 20850 may be configured to simulate one or more aspects of the marketplace. Such simulations may assist the user in making decisions, including, but not limited to marketplace configuration decisions, fee decisions, and/or operational decisions. For example, simulations of a proposed marketplace configuration may be tested using the modeling, machine learning, and/or AI techniques, as described herein, by simulating marketplace configuration parameters 20306 (e.g., supported asset types, listing requirements, fees, and the like), simulating economic factors, simulating market participation, and/or other suitable marketplace-related parameters. In embodiments, the digital twin simulation system 20804 may receive a request to perform a simulation requested by the marketplace host digital twin 20850, where the request indicates one or more parameters that are to be varied in one or more market orchestration digital twins. In response, the digital twin simulation system 20804 may return the simulation results to the marketplace host digital twin 20850, which in turn outputs the results to the user via the client device display. In this way, the user may be provided with various outcomes corresponding to different marketplace configuration parameters. For example, a user may request a set of simulations to be run to test different fee strategies to see how the different strategies affect the overall impact on profits. The simulation system may perform the simulations by varying the different fee strategies and may output the profit forecasts for each respective fee strategy. In some embodiments, the user may select a parameter set based on the various outcomes, and iterate simulations based at least on the varied prior outcomes. Drawing from the previous example, the user may decide to select the fee strategy that maximizes profit forecasts. In some embodiments, an intelligent agent may be trained to recommend and/or select a parameter set based on the respective outcomes associated with each respective parameter set. The limitations (executing, a simulation system to simulate one or more candidate market actions on the market orchestration digital twin, wherein the simulation system generates at least one simulated state of the digital marketplace that comprises respective simulated outcomes of the one or more candidate market actions, select an automated market action from the one or more candidate market actions in accordance with the preferences of the user based on the at least one simulated state) is part of the identified abstract idea. Examiner submits that simulating outcomes of the one or more market actions (trading) falls squarely in the category of Commercial interactions (executing trades by testing potential scenarios) before executing of a trade. The additional element of the one or more machine-learned models is recited at a high level of generality, operating in its ordinary capacity, and is being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f). The rejection is maintained. Applicant argues#5 Additionally, amended claim 141 recites a reinforcement mechanism that uses post- orchestration observations from the digital twin to improve the model over time. The intelligent agent is executed to "monitor the market orchestration digital twin after executing the automated market trade" and to "record feedback data comprising subsequent transaction outcome data corresponding to the automated market trade based on the monitoring of the market orchestration digital twin," and the claim then includes "reinforcing, by the one or more processors, the intelligent agent based on the feedback data wherein reinforcing the intelligent agent includes reinforcing the machine-learned model based on the subsequent transaction outcome data." This further enhances the effectiveness of the user-personalized model via reinforcement. See, e.g., paras. [2063]-[2066], [2108]-[2110] of US Pub. No. 2022/0366494. These features, as a combination and considered with the remainder of the claim, go well beyond a generic recitation of technology for trading. Instead, the claims recite a specific, detailed combination that provides a set of features that amount to a personalized intelligent agent that can orchestrate particular actions based on particular inputs for a particular user. In other words, the claims do not merely implement an abstract idea on a generic computer, at least because they recite specific technical details about how the model is trained to be personalized for a user, how actions are evaluated using simulations, and how the model is reinforced using a digital twin to improve performance. These features in combination recite far more than a business outcome and far more than the abstract use of ordinary technology. The claims are therefore directed to more than any abstract idea or judicial exception, and are eligible under Step 2A. Independent claim 302 recites a similar set of features and is eligible for at least similar reasons. Examiner Response Examiner respectfully disagrees. The limitations in bold (the intelligent agent is executed to "monitor the market orchestration digital twin after executing the automated market trade" and to "record feedback data comprising subsequent transaction outcome data corresponding to the automated market trade based on the monitoring of the market orchestration digital twin," is part of the identified abstract idea. The elements (“reinforcing, by the one or more processors, the intelligent agent based on the feedback data wherein reinforcing the intelligent agent includes reinforcing the machine-learned model based on the subsequent transaction outcome data) are additional elements outside of the identified abstract idea. Examiner reproduces para 2063 below: [2063] In some embodiments, the intelligent agent system 20210 receives feedback from users regarding respective intelligent agents 20234. For example, in some embodiments, a client application 20312 that leverages an intelligent agent may provide an interface by which a user can provide feedback regarding an action output by an intelligent agent. In embodiments, the user provides the feedback that identifies and characterizes any errors by the intelligent agent. In some of these embodiments, a report may be generated (e.g., by the client application 20312 or the market orchestration system platform 20500) that indicates the set of errors encountered by the user. The report may be used to reconfigure/retrain the intelligent agent. In embodiments, the reconfiguring/retraining an intelligent agent may include removing an input that is the source of the error, reconfiguring a set of nodes of the artificial intelligence system, reconfiguring a set of weights of the artificial intelligence system, reconfiguring a set of outputs of the artificial intelligence system, reconfiguring a processing flow within the artificial intelligence system (such as placing gates on a recurrent neural network to render it a gated RNN that balances learning with the need to diminish certain inputs in order to avoid exploding error problems), reengineering the type of the artificial intelligence system (such as by modifying the neural network type among a convolutional neural network, a recurrent neural network, a feed forward neural network, a long-term/short-term memory (LS™) neural network, a self-organizing neural network, or many other types and combinations), and/or augmenting the set of inputs to the artificial intelligence system. Examiner submits that reinforcing an intelligent agent using machine learning is a commonly used technique in the field of machine learning. Applicant is using the intelligent agent and the machine learning model (both recited at a high level of generality, and are operating in their ordinary capacity and are being used as a tool to implement the steps of the identified abstract idea. Therefore there are no additional elements that are indicative of integration into a practical application. The rejection is maintained. Applicant argues#6 The Amended Claims Recite Significantly More Than Any Alleged Abstract Idea Even if the claims were considered to be directed to an abstract idea, they would still be eligible because they recite an inventive concept that amounts to "significantly more." See MPEP § 2106.05(d). As in BASCOM, the claims include an inventive concept in the unconventional arrangement and specific combination of claimed components. See BASCOM Global Internet Servs. V. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The 'inventive concept' may arise in one or more of the individual claim limitations or in the ordered combination of the limitations."). Amended claim 141 recites a specific, ordered combination including the features recited above as well as additional features such as receiving trading data associated with a particular user from a market orchestration digital twin presented by a client application, training, simulating, and orchestrating as described above, followed by monitoring the digital twin after execution to record subsequent transaction outcome data as feedback, and finally reinforcing the machine-learned model to further improve the process as described above. These limitations work together to provide a specific inventive concept in the same manner that led to eligibility in BASCOM. Id. at 1350 ("The claims do not merely recite the abstract idea of filtering content along with the requirement to perform it on the Internet, or to perform it on a set of generic computer components Nor do the claims preempt all ways of filtering content on the Internet; rather, they recite a specific, discrete implementation of the abstract idea of filtering content."). The combination is also unconventional. No cited reference discloses the claimed combination or any similar combination of technologies. For example, a generic approach to using an intelligent agent on a marketplace would not include the claimed personalization, the claimed simulation system for candidate generation prior to selection and execution of the action, or the claimed reinforcement. These specific, discrete implementation details, when combined in an unconventional manner, provide the type of combination BASCOM found eligible, which is more than a recitation of "apply it" on a computer. This combination of additional elements also imposes meaningful limits on the claim, confining it to a specific application. This combination goes far beyond merely linking an abstract idea such as "trading" to a particular technological environment. See MPEP § 2106.05(e). For at least these reasons, the amended claims define an inventive concept that is significantly more than any alleged abstract idea and that is patent eligible. Independent claim 302 recites a similar ordered combination and is eligible for at least similar reasons. Examiner Response Examiner respectfully disagrees. As far as novelty is concerned, Examiner would like to point out that, as made clear by the courts, the “novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.” /Intellectual Ventures | v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188-89, 209 USPQ at 9). Furthermore, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101. (MPEP 2106.05 (I)) As far as the comparison to BASCOM, BASCOM was found patent eligible because the ordered combination was directed toward solving a problem arising in the realm of computer networks, providing a solution rooted in computer technology. BASCOM provided a filtering of content that was not independent of the internet. The ordered combination of elements as set forth by BASCOM, describes a customized filtering tool with customizable features specific to each end user. It was the non-conventional and non-generic arrangement of known conventional elements which made BASCOM patent eligible. Whereas the instant application used a generic technology (digital twins, intelligent agents, machine learning) to implement the steps of the identified abstract idea. Therefore the claims of the instant invention are unlike the claims in BASCOM There are no additional elements that amount to significantly more than the identified abstract idea. The rejection is maintained. Claim Interpretation Examiner reproduces the following spec paras to determine the BRI (Broadest reasonable interpretation) of the claim, consistent with the specification. [0031] Other aspects of the present disclosure relate to a method for configuring role-based market orchestration digital twins. The method may include generating, by a processing system, a digital twin of a marketplace. The digital twin may be a digital representation of the structure of the marketplace. The method may include determining, by the processing system, a set of relationships between different roles within the set of roles based on the marketplace. [0032] Other aspects of the present invention relate to a method for configuring an intelligent agent. The method may include receiving digital twin data from a set of data sources. The digital data may include sensor data that is received from a set of sensors that monitor a set of monitored physical entities associated with a marketplace, the sensor data transported by a set of network entities. The digital data may include marketplace data streams generated by a set of marketplace assets, wherein the marketplace assets include at least one of physical entities associated with the marketplace and digital entities associated with the marketplace. The method may include structuring the digital twin data into a set of digital twin data structures that are configured to serve a plurality of different role-based digital twins. [0511] The artificial intelligence system 13748 may also define the digital twin system 13720 to create a digital replica of one or more of the transaction entities. The digital replica of the one or more of the transaction entities may use substantially real-time sensor data to provide for substantially real-time virtual representation of the transaction entity and provides for simulation of one or more possible future states of the one or more transaction entities. The digital replica exists simultaneously with the one or more transaction entities being replicated. The digital replica provides one or more simulations of both physical elements and properties of the one or more transaction entities being replicated and the dynamics thereof, Claim Rejections- 35 U.S.C § 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. 1. Claims 141, 145, and 300-317 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 141,302 are directed to a method & system which are statutory categories of invention. (Step 1: YES). Representative, claim 141 recites: A method for updating intelligent agents, comprising: receiving from a client application, by one or more processors, trading data associated with a plurality of trades associated with a user from a market orchestration digital twin presented by the client application, trading data comprising: action data comprising the one or more assets involved in a respective trade of the plurality of trades, respective states of one or more fields of a graphical user interface of the client application prior to the respective trade, and an action type indicating a type of the respective trade executed by the user via the graphical user interface of the client application; and transaction outcome data resulting from the respective trade executed by the user; training, by the one or more processors, an intelligent agent to orchestrate future market actions for the user with respect to a digital marketplace based on the trading data, wherein the intelligent agent is configured to leverage one or more machine- learned models to orchestrate the future market actions for the user using data obtained from the market orchestration digital twin, wherein the one or more machine-learned models include a machine- learned model trained on historical trading data relating to the plurality of trades associated with user and executed on the digital marketplace, such that the machine-learned model is trained to select a market action for orchestration in accordance with preferences of the user; executing, by the one or more processors, a simulation system to simulate one or more candidate market actions on the market orchestration digital twin, wherein the simulation system generates at least one simulated state of the digital market place that comprises respective simulated outcomes of the one or more candidate market actions; executing, by the one or more processors, the trained intelligent agent to: select an automated market action from the one or more candidate market actions in accordance with preferences of the user based on the at least one simulated state using the one or more machine-learned models; orchestrate the automated market action using the market orchestration digital twin; monitor the market orchestration digital twin after executing the automated market action; and record feedback data comprising subsequent transaction outcome data corresponding to the automated market action based on the monitoring of the market orchestration digital twin; and reinforcing, by the one or more processors, the intelligent agent based on the feedback data by creating a feedback loop that indicates the automated market action and the subsequent transaction outcome data corresponding to the automated market action, wherein reinforcing the intelligent agent includes reinforcing the machine-learned model, based on the subsequent transaction outcome data. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. The claim recites elements that are in bold above, which covers performance of the limitation as a commercial interaction, the steps for orchestrating a market transaction associated with market orchestration digital twin If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a Commercial Interaction, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Claim 302 is abstract for similar reasons. (Step 2A-Prong 1: YES. The claims are abstract). This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application include: (1) 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 (MPEP 2106.05.f), (2) Adding insignificant extra solution activity to the judicial exception (MPEP 2106.05.g), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05.h). Claims 141&302 includes the additional element of: -An intelligent agent -A client application -A graphical user interface of the client application -One or more processors -A machine learned model -A client application tool -An intelligent agent system -A digital twin system -A market orchestration system -One or more processors -Memory The additional elements of (an intelligent agent, a client application, a graphical user interface of the client application, one or more processors, machine learned model, client application tool, an intelligent agent system, a digital twin system, a market orchestration system, one or more processors, memory, “training of the intelligent agent using a machine learning model, wherein the machine learning model includes a machine learning mode for making a prediction of an outcome”, ” the reinforcing of the machine learning model used by the intelligent agent and the client application tool and the intelligent agent system) are recited at a high level of generality and are operating in their ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f), where applying a computer or using a computer as a tool to perform the abstract idea is not indicative of a practical application. Therefore, the claim as a whole, looking at the additional elements individually and in combination, are no more than mere instructions to apply the exception using generic computing components and is not a practical application. MPEP 2106.05(f). The additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. 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. Therefore claims 141, 302 are directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application) Representative claim 141 fails STEP 2B because the claims as a whole, looking at the additional elements individually and in combination, are not sufficient to amount to significantly more than the identified abstract idea, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional elements recited in the claim beyond the judicial exception. Mere instructions to implement an abstract idea, on or with the use of generic computer components, or even without any computer components, cannot provide an inventive concept - rendering the claim patent ineligible. Thus claims 141, 302 are not patent eligible. (Step 2B: NO. The claims do not provide significantly more) Dependent claims 145, 300-301, 303-317 further define the abstract idea that is present in respective independent claim 141&302 and thus correspond to Certain Methods of Organizing Human Activity and hence are abstract for the reasons presented above. Claim 300, further defines the abstract idea as recited in claim 141. The additional element of “emulating a trader’s style based on style interaction data.” The emulation of a human’s interaction action is recited at a high level of generality, (for its intended purpose) and is being used as a tool to implement the steps of the identified abstract idea, see MPEP 2106.05(f). Claim 312 further defines the abstract idea recited in claim 302. The additional element of “the client application comprising a strategy tool”, is recited a high level of generality, operating in its ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea. Claims 314, 315 further defines the abstract idea recited in claims 141&302. The additional element of “reinforcing the intelligent agent by reconfiguring at least one of a set of weights of the intelligent agent, reconfiguring a set of nodes of the intelligent agent, reconfiguring a processing flow of the intelligent agent, or reconfiguring a type of one machine-learned models of the intelligent agent”, is recited a high level of generality, operating in its ordinary capacity, and are being used as a tool to implement the steps of the identified abstract idea. Therefore, the dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims (145, 300-301, 303-317) are directed to an abstract idea. Thus, the claims 141, 145, and 300-317 are not patent-eligible. CONCLUSION Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD Z SHAIKH whose telephone number is (571)270-3444. The examiner can normally be reached M-T, 9-600; Fri, 8-11, 3-5. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BENNETT SIGMOND can be reached at 303-297-4411. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 9/29/2026