DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This office action is in response to arguments and amendments entered on June 18, 2026 for the patent application 18/598,504 originally filed on March 7, 2024. Claims 1, 9,and 16 are amended. Claims 1-20 are pending. The first office action of March 18, 2026 is fully incorporated by reference into this Final Office Action.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1 – “Statutory Category Identification”
Claim 1 is directed to “a coaching simulator system” (i.e. a machine), claim 9 is directed to “a method” (i.e. a process), and claim 16 is directed to “a non-transitory computer-readable medium” (i.e. a machine), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 1 “Abstract Idea Identification”
However, the claims are drawn to an abstract idea of “simulating a coaching session,” in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion). Regardless, the claims are reasonably understood as either “certain methods of organizing human activity” or “mental processes,” which require the following limitations:
Per claim 1:
“receiving an interaction between a customer and an agent;
scoring the interaction using an evaluation form;
automatically identifying a recurring improvement area for the agent based on the scored interaction and past scored interactions, wherein identifying the recurring improvement area comprises programmatically analyzing historical scored interactions and extracting frequent low-scoring evaluation scenarios;
creating a prompt for a large language model (LLM) by populating a prompt template with a definition of the evaluation form, evaluation questions, a simulation objective, and a simulation example, wherein the definition of the evaluation form, the evaluation questions, the simulation objective and the simulation example are based on the recurring improvement area;
providing a framework to invoke the LLM using the created prompt, a model, and a plurality of hyperparameters;
starting a first coaching simulation scenario by invoking the LLM, via the framework, to present a first question to the agent;
receiving a first answer to the first question from the agent;
querying the LLM, via the framework, to analyze the first answer to the first question;
querying the LLM, via the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the first question; and
automatically control progression of the simulation scenario based on the score, including selectively repeating the question or advancing to a subsequent question.”
Per claim 9:
“receiving an interaction between a customer and an agent;
scoring the interaction using an evaluation form;
automatically identifying a recurring improvement area for the agent based on the scored interaction and past scored interactions, wherein identifying the recurring improvement area comprises programmatically analyzing historical scored interactions and extracting frequent low-scoring evaluation scenarios;
creating a prompt for a large language model (LLM) by populating a prompt template with a definition of the evaluation form, evaluation questions, a simulation objective, and a simulation example, wherein the definition of the evaluation form, the evaluation questions, the simulation objective and the simulation example are based on the recurring improvement area;
providing a framework to invoke the LLM using the created prompt, a model, and a plurality of hyperparameters;
starting a first coaching simulation scenario by invoking the LLM, via the framework, to present a first question to the agent;
receiving a first answer to the first question from the agent;
querying the LLM, via the framework, to analyze the first answer to the first question;
querying the LLM, via the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the first question; and
automatically control progression of the simulation scenario based on the score, including selectively repeating the question or advancing to a subsequent question.”
Per claim 16:
“receiving an interaction between a customer and an agent;
scoring the interaction using an evaluation form;
automatically identifying a recurring improvement area for the agent based on the scored interaction and past scored interactions, wherein identifying the recurring improvement area comprises programmatically analyzing historical scored interactions and extracting frequent low-scoring evaluation scenarios;
creating a prompt for a large language model (LLM) by populating a prompt template with a definition of the evaluation form, evaluation questions, a simulation objective, and a simulation example, wherein the definition of the evaluation form, the evaluation questions, the simulation objective and the simulation example are based on the recurring improvement area;
providing a framework to invoke the LLM using the created prompt, a model, and a plurality of hyperparameters;
starting a first coaching simulation scenario by invoking the LLM, via the framework, to present a first question to the agent;
receiving a first answer to the first question from the agent;
querying the LLM, via the framework, to analyze the first answer to the first question;
querying the LLM, via the framework, to provide real-time feedback and a score for the agent based on the analyzed first answer to the first question; and
automatically control progression of the simulation scenario based on the score, including selectively repeating the question or advancing to a subsequent question.”
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Step 2A, Prong 2 – “Practical Application”
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “a processor” and “a computer readable medium,” are claimed, as these are merely claimed to generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “simulating a coaching session,” is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Step 2B – “Significantly More”
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “a processor” and “a computer readable medium,” are claimed, these are generic, well-known, and conventional elements. As evidence that these are generic, well-known, and a conventional elements (or an equivalent term), as a commercially available product, or in a manner that indicates that the additional elements are sufficiently well-known, the Applicant’s specification discloses these in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a). As such, this satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Moreover, the element of “a processor” is described in para. [0090] as follows:
“[0090] Referring now to FIG. 11, illustrated is a block diagram of a system 1100 suitable for implementing embodiments of the present disclosure. System 1100, such as part of a computer and/or a network server, includes a bus 1102 or other communication mechanism for communicating information, which interconnects subsystems and components, including one or more of a processing component 1104 (e.g., processor, micro-controller, digital signal processor (DSP), etc.), a system memory component 1106 (e.g., RAM), a static storage component 1108 (e.g., ROM), a network interface component 1112, a display component 1114 (or alternatively, an interface to an external display), an input component 1116 (e.g., keypad or keyboard), and a cursor control component 1118 (e.g., a mouse pad).”
Likewise, the element of “a computer readable medium,” is described in para. [0092] as follows:
“[0092] Logic may be encoded in a computer readable medium, which may refer to any medium that participates in providing instructions to processor 1104 for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. In various implementations, volatile media includes dynamic memory, such as system memory component 1106, and transmission media includes coaxial cables, copper wire, and fiber optics, including wires that comprise bus 1102. Memory may be used to store visual representations of the different options for searching or auto-synchronizing. In one example, transmission media may take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications. Some common forms of computer readable media include, for example, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, carrier wave, or any other medium from which a computer is adapted to read.”
These elements are reasonably interpreted as part of a generic computer having generic computer components which provides no details of anything beyond ubiquitous standard off-the-shelf equipment.
Therefore, the Applicant’s own specification discloses ubiquitous standard equipment that is (1) generic, routine, conventional, and/or commercially available; and (2) does not provide anything significantly more. Thus, Step 2B, of the subject-matter eligibility analysis is “No.”
In addition, dependent claims 2-8, 10-15 and 17-20 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-8, 10-15 and 17-20 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to claim 1, 9 or 16. Therefore, claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
The Applicant’s arguments filed on June 18, 2026 related to claims 1-20 are fully considered, but are not persuasive.
Claim Rejections - 35 U.S.C. § 101
The Applicant respectfully argues “Applicant respectfully traverses both rejections. As discussed below, claim 1 has been amended to (i) recite a "non-transitory" computer readable medium, which Applicant submits resolves the second rejection.”
The Examiner respectfully agrees. As such, the argument is persuasive. Therefore, “the second rejection,” to claims 1-8 are withdrawn.
Step 2A, Prong One - The Claims Recite Limitations That Cannot Practically Be Performed in the Human Mind or as Routine Human Activity
The Applicant respectfully argues “Applicant submits that the claims are not directed to an abstract idea, but rather are rooted in computer technology and recite a specific technical architecture for orchestrating a Large Language Model (LLM). The claimed system performs programmatic orchestration and state management of a Generative AI model that cannot be performed manually.
A human coach cannot manually tune a "temperature hyperparameter" or maintain programmatic memory buffers-these are uniquely technological operations.
Furthermore, the LLM simultaneously performs dual computational roles-acting as an unpredictable, adversarial customer while simultaneously serving as an objective, rule-bound evaluator-in real-time. Managing these dual computational states is a uniquely technological challenge that cannot be replicated by human mental processes.
Applicant further submits that the claims involve big data processing and personalization that constitutes a data-mining operation, not a mental process. As amended, claim 1 now expressly recites that the system "automatically identif[ies] a recurring improvement area for the agent based on the scored interaction and past scored interactions, wherein identifying the recurring improvement area comprises programmatically analyzing historical scored interactions and extracting frequent low-scoring evaluation scenarios."
This programmatic parsing of historical Quality Management data to identify recurring failure patterns is a computational operation that cannot practically be performed in the human mind.
The invention addresses a specific technical problem, namely that generic large language models cannot reliably operate as continuous, dual-role, stateful simulation engines.
The recited solution employs structured prompt templates with multiple constrained variables, bounded generative output through hyperparameter settings, and persistent conversational memory buffers. This constitutes a specific technical architecture for large language model orchestration rather than an abstract idea.
Thus, the pending claims are not directed to a mere abstract idea such as "coaching" or "training," but rather to a specific, technical implementation of a generative AI-based orchestration system that performs automated, iterative coaching using a structured large language model (LLM) framework.
As reflected in the specification:
Figure 1 discloses a particularized LLM orchestration architecture, including:
an LLM framework,
a model selector,
configurable hyperparameters,
conversational memory, and
structured prompts.
This is not generic computing hardware, but a specialized arrangement of interacting AI components configured for continuous performance improvement of an agent. The claimed system is therefore directed to a technological solution rooted in artificial intelligence system design, not an abstract mental process.
Further, Figure 3 provides concrete prompt template code, which:
uses defined input variables, and
ties those inputs directly to a recurring improvement area of the agent.
This demonstrates that the present is not conceptual or result-oriented, but instead recites a specific implementation for controlling LLM behavior using structured inputs and parameterization.”
The Examiner respectfully disagrees. First, the abstract idea is related to following rules or instructions, which are categorized as “certain methods of organizing human activity.” Also, MPEP §2106 under “II. Certain Methods Of organizing Human Activity,” certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping. As applied in this case, a person interacting with a computer for “simulating a coaching session,” related to “interaction between a customer and an agent,” reasonably constitutes identifying the Applicant’s claims as an abstract idea in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions).
With respect to mental processes, actual mental performance of the abstract idea is not required. Further, the MPEP § 2106.04(a)(2)(III)(C) states that “claims can recite a mental process even if they are claimed as being performed on a computer” and that “examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and Applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite “a mental process.” In the present case, the claim limitations perform steps that are performed on a generic computer and/or computer environment, and merely uses a computer as a tool to perform the concept of “coaching” which has been done in the analog for decades if not centuries. As such, the argument is not persuasive.
Step 2A, Prong Two --- The Claims Integrate Any Alleged Abstract Idea Into a Practical Application
The Applicant respectfully argues “Applicant also submits that the Examiner's reliance on Electric Power Group, LLC V. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016), is misplaced. Unlike Electric Power Group, where the claims merely collected, analyzed, and displayed data for human review, the present claims operate as a closed-loop control system where the algorithm's outputs trigger automated system actions. As shown in Figure 2B and described in paragraphs [0071]-[0073] of the specification, the LLM's calculated score is a programmatic trigger that directly controls the state machine of the coaching simulator. If the score is below a predefined minimum, the system "programmatically halts progression" and "forces the application state to loop back to the previous question" (Step 232). As-Filed Specification, paragraph [0071]. If the score meets the threshold, the system triggers a different action, "advanc[ing] the Conversation Buffer Memory state and prompting the next scenario" (Step 234). As-Filed Specification, paragraph [0073]. The system does not merely display a score for human review and uses that output to immediately alter the operational state of the simulator.
Indeed, claim 1 has been amended to expressly recite this closed-loop, state-machine functionality, and it is not merely described in the specification or relegated to dependent claims. As amended, claim 1 now recites "automatically controlling progression of the first coaching simulation scenario based on the score, including selectively repeating the first question to the agent or advancing to a subsequent question of the first coaching simulation scenario."
Dependent claims 5-7 (and similarly claims 13-15 and 18-20) further particularize the specific branches of this state machine: claim 5 recites "determining that the score for the agent is below a predefined minimum score; querying the LLM, by the framework, to ask the agent to answer the first question again; receiving a second answer to the first question from the agent; querying the LLM, by the framework, to analyze the second answer to the first question; and querying the LLM, by the framework, to provide real-time feedback and a score for the agent based on the analyzed second answer to the first question"--i.e., the "repeating the first question" branch now recited in claim 1. Likewise, claim 6 (and similarly claims 14 and 19) recites the branch in which the score exceeds the predefined minimum and a second question is presented, and claim 7 (and similarly claims 15 and 20) recites the branch in which the system provides a summary report and confirms whether the agent wishes to continue to a second coaching simulation scenario.
These claim limitations are the very "programmatic trigger" and state-machine transitions described above, confirming that the claims, considered as a whole, integrate any alleged abstract idea into a specific, technological process for orchestrating an LLM-based coaching simulation, rather than merely collecting, analyzing, and displaying data for human consideration as in Electric Power Group.”.”
The Examiner respectfully disagrees. As evidenced by the Applicants written description as originally filed in para. [0002], said para. provides the following:
“The present disclosure relates generally to methods and systems for simulated coaching of contact center agents, and more particularly to methods and systems that provide simulated coaching where a simulator seamlessly acts as both a customer and a coach.” That being said, Applicant’s claims are also output for “human review,” and are on-point with Electric Power Group. The fact that the claims appear to have adaptive analysis/scoring (i.e. “to ask the agent to answer the first question again; receiving a second answer to the first question from the agent; querying the LLM, by the framework, to analyze the second answer to the first question; and querying the LLM, by the framework, to provide real-time feedback and a score for the agent based on the analyzed second answer to the first question"), this adaptation of analysis to provide feedback is also reasonably analogous to Electric Power Group by further collecting data, analyzing it, and outputting the results of the analysis to further coach an agent. As such, the argument is not persuasive.
The Applicant respectfully argues “Additionally, the claims recite an active generative execution environment, not merely a passive data analyzer. The identification of an improvement area (Step 906 in Figure 9) is merely a trigger for complex technical execution. The system immediately uses that data to create a structured prompt (Step 908), spin up an LLM framework with specific hyperparameters (Step 910), and initiate a continuous, real-time conversational loop (Steps 912-918). See As-Filed Specification, paragraphs [0078]-[0086]. The analysis is not the end product; it is the raw material used to build the simulation engine.
Even assuming arguendo that an abstract idea is recited, the claims integrate it into a practical application.
1. Closed-Loop Control System (Critical Distinction)
Unlike cases such as Electric Power Group, the invention does not merely analyze and
display data.
Instead:
the LLM generates a score, and
the score automatically controls execution flow (retry vs advance).
Specifically:
if the score is below threshold
system forces retry loop
if above threshold
advances simulation state
This is a feedback-controlled state machine, not passive analytics.
2. Transformation into a Generative Execution Environment
The system transforms:
historical scored interactions
into
dynamically generated LLM prompt structures
into
a real-time simulation engine.
The analysis is not the end result; it is the input to a generative AI execution loop. This satisfies the "practical application" requirement.
Additionally:
Dependent Claims 3 and 4 recite:
temperature and top_p hyperparameters, and conversational memory.
Claim 8 further specifies:
the use of a GPT-based model.
These limitations impose meaningful constraints on how the system operates, defining:
how outputs are probabilistically controlled, how prior interactions are incorporated, and
how model selection is concretely implemented.
Such features go beyond mere automation of a business process and instead define a particular technical mechanism for generating adaptive, context-aware outputs.
Moreover, the system's use of:
structured prompt templates (Fig. 3), and a defined orchestration pipeline (Fig. 1), ensures that the claimed invention is tied to a concrete technological implementation, not a generalized idea”
The Examiner respectfully disagrees. The Applicant’s claims are not considered a “Practical Application,” because the claims do not provide any of the following:
An improvement in the functioning of a computer, or an improvement to other technology or technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
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Furthermore, there are also several factors that reasonably explain that the Applicant’s claims are not indicative of integration into a practical application, which include:
Merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
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Here, the Applicant’s claims are not providing any technological advancement as described in the first five bulleted factors and, as described above in the rejection, the Applicant’s claims are merely claimed to use a computer as a tool to perform an abstract idea and to generally link the use of a judicial exception to a particular technological environment or field of use. As such, the argument is not persuasive.
Step 2B --- The Claims Recite Significantly More Than Any Alleged Abstract Idea
The Applicant respectfully argues “Applicant submits that the claims recite a specific technical architecture for LLM orchestration that provides significantly more than an abstract idea. The claims require: (1) dynamically injecting structured database outputs (recurring improvement areas) into multi- variable prompt templates containing evaluation_form_definition, evaluation_questions, simulation_objective, and simulation_example, as shown in Figures 10A-10D; (2) bounding the model with specific hyperparameters including temperature and top_p to control randomness and narrow the computational search space; and (3) utilizing conversational memory buffers to maintain stateful simulation context. The specification explains that "ConversationSummaryBufferMemory" is used "so that if the earlier chat messages exceed the token limit, they could be summarized, and more recent chat messages could be buffered." As-Filed Specification, paragraph [0047]. This is a direct technical solution to manage memory and token overhead. Furthermore, the "top_p hyperparameter" forces the model to "sample from a narrower selection of words," which "actively reduces the computational search space the model must process." As-Filed Specification, paragraph [0048]. These computational efficiency optimizations represent concrete improvements to computer functioning.
Thus, the claims recite an inventive concept that transforms any alleged abstract idea into patent-eligible subject matter.
Specifically, the invention provides:
a non-conventional LLM orchestration framework, including:
model selection logic,
hyperparameter tuning,
conversational memory integration, and
structured prompting;
a system capable of simultaneously simulating multiple roles (coach and customer);
a mechanism for iterative performance improvement driven by recurring behavioral
insights.
These features, taken together, represent a non-routine and non-conventional arrangement of elements that:
improves the functioning of AI systems, and
enables a new form of automated coaching not achievable using generic or off-the-shelf
components.
Notably, the Examiner has already conceded that the claims contain allowable subject matter over the prior art, finding that the closest prior art "does not explicitly teach" the claimed combination of elements. Office Action, page 7. This acknowledgment demonstrates the unconventional nature of the claimed combination. Because these specific steps are novel, they represent an unconventional improvement to contact center technology, which inherently provides the "significantly more" required by 35 U.S.C. § 101.
For at least the foregoing reasons, Applicant respectfully requests withdrawal of the rejection of claims 1-20 under 35 U.S.C. § 101.”
The Examiner respectfully disagrees. The Applicant has an abstract idea of “simulating a coaching session,” and fails to provide any sufficient structure or software improvement to existing structure, other than applying a new data set to an existing technology in order to demonstrate an improvement in a generic computer system that can reasonably be considered “significantly more than the abstract idea itself.” As such, the argument is not persuasive. Therefore, the rejections under 35 U.S.C. § 101 are not withdrawn.
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 extension fee 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 date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ROBERT P BULLINGTON whose telephone number is (313)446-4841. The examiner can normally be reached on Mon.-Fri. 8:00-4:00. 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, Peter Vasat, can be reached on (571) 270-7625. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Robert P Bullington, Esq./
Primary Examiner, Art Unit 3715
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