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. This office action is in response to an amendment received on 10/23/25.
2. Claims 141, 301-302, 305, 307, 310, and 312-313 are amended.
3. Claims 314-317 are added.
4. Claims 141, 145, and 300-317 are now pending.
Note:
Examiner had previously rejected claims 302-313 as being directed to non-statutory subject matter, see pages 14-15 of the non-final rejection mailed on 5/8/25. Applicant has not addressed this issue in the response filed on 10/23/25. Therefore, this rejection is being maintained. Furthermore, based on the amendment to claim 302, an additional software per-se rejection is made, see the section 101 rejection below.
RESPONSE TO ARGUMENTS
Applicant argues#1
In the present case, the claims are eligible as they recite a practical application that is more than a drafting effort designed to monopolize the exception. Generally, the claims are generally directed to a system and method for training an intelligent agent. The claims, however, include additional elements that meaningfully limit the claims to recite a practical application. For example, claim 141 has been amended to recite in part receiving from a client application, by one or more processors, interaction data that indicates a set of interactions of a trader with a market orchestration digital twin presented by the client application, wherein the market orchestration digital twin visually depicts a plurality of fields used by the trader when executing trades on a digital marketplace, wherein the plurality of fields is populated using data collected from a plurality of data sources including the digital marketplace, the interaction data comprising: a set of workflow interactions performed by the trader with respect to one or more fields of the plurality of fields of the market orchestration digital twin corresponding to the digital marketplace via a graphical user interface of the client application prior to the trader performing a market action; and respective states of the one or more fields with which the trader interacted with prior to executing the market action via the graphical user interface of the client application, wherein the market action includes a trade involving one or more assets executed on the digital marketplace in response to at least one of the set of interactions of the trader; in response to the trade, receiving, by the one or more processors, trade data relating to the trade, the trade data comprising: action data comprising the one or more assets involved in the trade, the respective states of the one or more fields prior to the trade, and an action type indicating a type of trade executed by the trader via the graphical user interface of the client application; and transaction outcome data resulting from the market action executed by the trader, wherein the transaction outcome data includes an outcome of the market action, wherein the outcome may include at least one of: a profitability outcome, an operational outcome, or a behavioral outcome of the market action; training, by the one or more processors, an intelligent agent to orchestrate future market actions-with respect to the digital marketplace based on the interaction data and the trade data, wherein the intelligent agent is configured to leverage one or more machine-learned models to orchestrate the future market actions using data obtained from the market orchestration digital twin, wherein the one or more machine-learned models includes a machine-learned mode trained on historical trading data relating to a plurality of trades executed on the digital marketplace that, for each respective trade, comprises respective action data corresponding to the respective trade, respective transaction outcome data of the respective trade and one or more respective states of the market orchestration digital twin at a respective time corresponding to the respective trade, such that the machine-learned model is trained to predict an outcome of a potential market action; executing, by the one or more processors, the trained intelligent agent to: interface with the market orchestration digital twin; orchestrate an automated market trade using the one or more machine-learned models and the market orchestration digital twin; monitor the market orchestration digital twin after executing the automated market trade; and 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 reinforcing, by the one or more processors, intelligent agent based on the feedback data by creating a feedback loop that indicates the automated market trade and the subsequent transaction outcome data corresponding to the automated market action.
Examiner Response
Examiner respectfully disagrees.
The limitations (receiving interaction data that indicates that indicates a set of interactions of a trader with a market orchestration twin, wherein the market orchestration twin visually depicts a plurality of fields used by the trader when executing trades on a marketplace, wherein the plurality of fields is populated using data collected from a plurality of data sources including the marketplace, the interaction data comprising: a set of workflow interactions performed by the trader with respect to one or more fields of the plurality of fields of the market orchestration twin corresponding to the marketplace, prior to the trader performing a market action; and respective states of the one or more fields with which the trader interacted with prior to executing the market action, wherein the market action includes a trade involving one or more assets executed on the marketplace in response to at least one of the set of interactions of the trader; in response to the trade, receiving, trade data relating to the trade, the trade data comprising: action data comprising the one or more assets involved in the trade, the respective states of the one or more fields prior to the trade, and an action type indicating a type of trade executed by the trader; and transaction outcome data resulting from the market action executed by the trader, wherein the transaction outcome data includes an outcome of the market action, wherein the outcome may include at least one of: a profitability outcome, an operational outcome, or a behavioral outcome of the market action; to orchestrate future market actions with respect to the marketplace based on the interaction data and the trade data, to orchestrate the future market actions using data obtained from the market orchestration twin, to predict an outcome of a potential market action; orchestrate an automated market trade; monitor the market orchestration twin after executing the automated market trade; and record feedback data comprising subsequent transaction outcome data corresponding to the automated market trade based on the monitoring of the market orchestration twin; and that indicates the automated market trade and the subsequent transaction outcome data corresponding to the automated market action) is part of the identified abstract idea.
The additional elements (the one or more processors, client application, a graphical user interface of the client application, “a digital twin, “a digital twin system”, “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 market orchestration digital twin, ” 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 there are no additional elements that are indicative of integration into a practical application.
The rejection is maintained.
Applicant argues#2
The specification discloses “client application may monitor the use of the client application by a user when using the client application. In these embodiments, the client application may monitor the states of a market orchestration digital twin that the user drills down into, decisions that are made, and the like.” “The types of data that may populate a marketplace host digital twin may include... order data, marketplace/exchange performance data ..., asset data, demand planning data, trader data, broker data, ... discussion board data, social media data, fee data, regulatory data, ...may include high-level views of different states and/or marketplace-related data, including trader data (e.g., number of traders), trading activity data, asset data, regulatory data, fee data, commission data, broker data, execution speed data, percentage of orders price improved data, net improvement per order data, liquidity multiple data, and many other types of data.” The client application “may initially depict the various states at a lower granularity level...a user that is viewing the marketplace host digital twin may select a state to drill down into the selected state and view the selected state at a higher level of granularity.” A user (trader) “may view market orchestration digital twins and/or may use the exchange suite tools via the client application. During the use of the client application, 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, the client application may monitor the user's actions and may report the actions back to the intelligent agent system. Over time, the intelligent agent system 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 may learn to automatically sell assets when pricing for those assets is within the specific range.” “The intelligent agent may record each time the user executes a trade (which is the action) as well as the features surrounding the trade (e.g., the type of action, the type of asset, the price of the asset, the counterparty or counterparties, the quantity of assets, market sentiment in relation to the asset, shipping and/or delivery information, and the like). The intelligent agent may report the actions and features to the intelligent agent system that may train the intelligent agent on the manner by which the intelligent agent can undertake or recommend trading tasks in the future. Once trained, the intelligent agent may automatically perform actions ... in embodiments, the intelligent agent 20234 may record outcomes related to the performed/recommended actions, thereby creating a feedback loop with the intelligent agent system.” “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 lintelligence system (such as by modifying the neural network type among a convolutional neural network ... .” Publication of Current Application, Pub No.: US20220366494, 2063, 2066, 2101, 2104, and 2108-2110.
Here, Applicant has defined the claims to avoid potential abstract ideas. As a threshold matter, Applicant respectfully submits that the claim is not directed to “financial activity” (e.g., trading); rather the claim has been amended to limit the claim to cover the training of an intelligent agent to orchestrate trader activities based in part on interaction data that indicates the actions of a trader performed with respect to a market orchestration digital twin. Furthermore, the intelligent agent leverages a machine learned model to
orchestrate a market action, monitors the market orchestration digital twin after execution of an automated market action, and records feedback data corresponding to the automated market action. This feedback data is used to reinforce a machine-learned model that is leveraged by the intelligent agent to orchestrate the market action, thereby creating a feedback loop that improves the functionality of the leveraged model over time. As such, the additional elements of claim 141 reflect both an improvement to the technical field of artificial intelligence and an application of the judicial exception in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
Examiner Response
Examiner respectfully disagrees.
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 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.
Also see the Response to Applicant argues#1 above.
The rejection is maintained.
Applicant argues#3
In view of the foregoing, Applicant submits that claim 141 is allowable, as are claims 145, 300-301, and 314-315 ultimately dependent thereon.
Claim 302 is a systems claim version of method claim 141 and has been similarly amended. Thus, Applicant submits, claim 302 as amended is allowable as are claims 303-313 ultimately dependent thereon.
Examiner Response
This argument has been addressed above with respect to applicant argues#1-2 above, with respect to claim 141.
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.
Claim 302-314 are rejected under 35 U.S.C. 101 because the claims are directed to a non-statutory subject matter.
Claim 302 recites, “a client application tool structured to” and an “intelligent agent system configured to”.
Claim 302 has been further amended to recite, “wherein the intelligent agent is configured to leverage one or more machine-learned models to orchestrate….”
The “intelligent agent” and the “one or more machine learned models” under BRI (Broadest Reasonable Interpretation) is interpreted to be directed to software per se.
Para 2056 of the published application (US 2022/03664494) is reproduced below:
[2056] In embodiments, the intelligent agent system 20210 trains intelligent agents 20234 that perform/recommend actions on behalf of a user. An intelligent agent may be a software module that implements and/or leverages artificial intelligence services to perform/recommend actions on behalf of or in lieu of a user. In embodiments, an intelligent agent may use, link to, integrate with, and/or include one or more machine-learned systems or models (e.g., neural networks, prediction models, classification models, Bayesian models, Gaussian models, decision trees, random forests, and the like, including any described herein or incorporated herein by reference) that perform machine-learning tasks in connection with a defined role. Additionally or alternatively, an intelligent agent may be configured with artificial intelligence rules that determine actions in connection with a defined role. The artificial intelligence rules may be programmed by a user or may be generated by the intelligent agent system 20210.
Under the BRI of the specification these elements are software programs. Software program per se represent data structure without being connected to a processor, computer, or server does not fit into any of the four statutory classes (process, apparatus, article of manufacture and composition of matter) and therefore is not a statutory subject matter under 35 USC 101. (MPEP 2106 Patent Subject Matter Eligibility [R-6]).
For these reasons, claims 302-314 fail to satisfy one of the statutory categories set forth in 35 U.S.C. 101 and are therefore considered to be non-statutory.
Claim 141 is directed to a method which is a statutory category of invention. (Step 1: YES).
Claim 141 recites:
A method for updating intelligent agents, comprising:
receiving from a client application, by one or more processors, interaction data that indicates that indicates a set of interactions of a trader with a market orchestration digital twin presented by the client application, wherein the market orchestration digital twin visually depicts a plurality of fields used by the trader when executing trades on a digital marketplace, wherein the plurality of fields is populated using data collected from a plurality of data sources including the digital marketplace, the interaction data comprising:
a set of workflow interactions performed by the trader with respect to one or more fields of the plurality of fields of the market orchestration digital twin corresponding to the digital marketplace, via a graphical user interface of the client application prior to the trader performing a market action; and
respective states of the one or more fields with which the trader interacted with prior to executing the market action via the graphical user interface of the client application, wherein the market action includes a trade involving one or more assets executed on the digital marketplace in response to at least one of the set of interactions of the trader;
in response to the trade, receiving, by the one or more processors, trade data relating to the trade, the trade data comprising:
action data comprising the one or more assets involved in the trade, the respective states of the one or more fields prior to the trade, and an action type indicating a type of trade executed by the trader via the graphical user interface of the client application; and
transaction outcome data resulting from the market action executed by the trader, wherein the transaction outcome data includes an outcome of the market action, wherein the outcome may include at least one of: a profitability outcome, an operational outcome, or a behavioral outcome of the market action;
training, by the one or more processors, an intelligent agent to orchestrate future market actions with respect to the digital marketplace based on the interaction data and the trade data, wherein the intelligent agent is configured to leverage one or more machine- learned models to orchestrate the future market actions using data obtained from the market orchestration digital twin, wherein the one or more machine-learned models include a machine- learned mode trained on historical trading data relating to a plurality of trades executed on the digital marketplace that, for each respective trade, comprises respective action data corresponding to the respective trade, respective transaction outcome data of the respective trade and one or more respective states of the market orchestration digital twin at a respective time corresponding to the respective trade, such that the machine-learned model is trained to predict an outcome of a potential market action;
executing, by the one or more processors, the trained intelligent agent to:
interface with the market orchestration digital twin;
orchestrate an automated market trade using the one or more machine- learned models and the market orchestration digital twin;
monitor the market orchestration digital twin after executing the automated market trade; and
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
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 trade 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 twin (e.g. receiving interaction data that indicates that indicates a set of interactions of a trader with a market orchestration twin, wherein the market orchestration twin visually depicts a plurality of fields used by the trader when executing trades on a marketplace, wherein the plurality of fields is populated using data collected from a plurality of data sources including the marketplace, the interaction data comprising: a set of workflow interactions performed by the trader with respect to one or more fields of the plurality of fields of the market orchestration twin corresponding to the marketplace, prior to the trader performing a market action; and respective states of the one or more fields with which the trader interacted with prior to executing the market action, wherein the market action includes a trade involving one or more assets executed on the marketplace in response to at least one of the set of interactions of the trader; in response to the trade, receiving, trade data relating to the trade, the trade data comprising: action data comprising the one or more assets involved in the trade, the respective states of the one or more fields prior to the trade, and an action type indicating a type of trade executed by the trader; and transaction outcome data resulting from the market action executed by the trader, wherein the transaction outcome data includes an outcome of the market action, wherein the outcome may include at least one of: a profitability outcome, an operational outcome, or a behavioral outcome of the market action; to orchestrate future market actions with respect to the marketplace based on the interaction data and the trade data, to orchestrate the future market actions using data obtained from the market orchestration twin, to predict an outcome of a potential market action; orchestrate an automated market trade; monitor the market orchestration twin after executing the automated market trade; and record feedback data comprising subsequent transaction outcome data corresponding to the automated market trade based on the monitoring of the market orchestration twin; and that indicates the automated market trade and the subsequent transaction outcome data corresponding to the automated market action)
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:
-A digital twin
-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
The additional elements of (the one or more processors, client application, a graphical user interface of the client application, “a digital twin, “a digital twin system”, “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 market orchestration digital twin, ” 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.
Accordingly, these additional elements, when considered separately and as an ordered combination, 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)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, 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 142, 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 (142, 300-301, 303-317) are directed to an abstract idea. Thus, the claims 141, 145, and 300-317 are not patent-eligible.
CONCLUSION
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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.
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/MOHAMMAD Z SHAIKH/Primary Examiner, Art Unit 3694 12/12/2025