Systems and methods for rubric driven interaction

Abstract

Embodiments described herein systems and methods for dynamically guiding a generative artificial intelligence model through structured, goal-oriented interactions using machine- generated rubrics for instructing operations of the generative Al model. The system selects, for each conversational turn, a first rubric defining response structure and a second rubric defining evaluation criteria. A generative model formulates prompts based on these rubrics and receives participant responses. An assessment module evaluates the responses against the second rubric to update the interaction context. A rubric orchestration module uses the updated context to select new rubrics for subsequent turns. This iterative process transforms a stateless generative model into a persistent, adaptive agent capable of conducting multi-turn interactions aligned with defined objectives.

Description

IXV01-PCT PATENTSYSTEMS AND METHODS FOR RUBRIC DRIVEN INTERACTIONCROSS REFERENCE TO RELATED PATENT APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application 63 / 688,198, filed August 28, 2024, U.S. Provisional Application 63 / 688,202, filed August 28, 2024, U.S. Provisional Application 63 / 688,204, filed August 28, 2024, U.S. Provisional Application 63 / 688,206, filed August 28, 2024, U.S. Provisional Application 63 / 691,272, filed September 5, 2024, and U.S. Provisional Application 63 / 812,676, filed May 27, 2025, each of which is incorporated herein by reference in its entirety for any and all purposes.BACKGROUND

[0002] Recent advances in generative artificial intelligence (Al) models, including large language models (LLMs), have enabled machines to produce natural-sounding, contextually relevant, and semantically coherent content across multiple modalities. These technologies, which can include transformer-based text generators, multimodal reasoning systems, code synthesis models, and task-oriented dialogue agents, have been used to generate human-like text, spoken responses, domain-specific analyses, and creative works that are stylistically and linguistically comparable to human output. LLMs and related generative Al architectures can process vast bodies of knowledge, emulate diverse personas, and deliver responses tailored to an input prompt, thereby transforming industries such as customer service, education, research, and entertainment.

[0003] Conventional artificial intelligence interaction systems, when simulating structured and executing goal-based engagements, have significant shortcomings despite these generative advances. Existing systems typically rely on static prompt engineering, fine-tuning for narrow tasks, or rigid scripted interaction flows. These approaches focus on immediate prompt-response pairing without providing the Al model with persistent, machine-readable definitions of the overarching purpose, structural rules, or measurable success criteria of the engagement. As a result, while a model can be instructed to “act as an interviewer” or “facilitate a meeting,” the model lacks a self-adaptable framework to identify what constitutes success, evaluate participant responses against consistent benchmarks, and adjust strategy in real time based on evolving context.

[0004] Furthermore, such systems often process interaction data in isolation, without integrating multi-dimensional assessment rubrics, dynamically updated procedural rules, orIXV01-PCT PATENT persistent state tracking across conversational turns. This prevents such systems from applying consistent evaluation logic, orchestrating multiple concurrent objectives, or pivoting intelligently when participants introduce new priorities or deviate from expected patterns. As a result, existing generative Al interaction solutions often fail to sustain coherent multi-turn dialogues, maintain alignment with strategic goals, or deliver verifiable, outcome-oriented results in complex applications such as competency assessment, structured negotiation, or adaptive learning facilitation.SUMMARY

[0005] Disclosed herein are systems and methods capable of addressing the abovedescribed shortcomings and may also provide any number of additional or alternative benefits and advantages. Implementations of the present disclosure relate to systems and methods for that can train and utilize generative artificial intelligence (Al) and / or machine-learning (ML) models (e.g., variational autoencoders (VAEs), neural networks, machine learning-based regression models, and / or any predictive learning models) and / or real-time monitoring (e.g., metrics of one or more processing units) and predictive analytics (e.g., predicted states) The embodiments described herein include computing systems and methods for orchestrating generative artificial intelligence models through structured, rubric-driven interactions.

[0006] A rubric includes a structured, machine-readable data model, including executable instructions and structured data or parameters for a machine-learning model, comprising one or more evaluation dimensions, each dimension associated with a defined set of criteria, scoring logic, and proficiency thresholds. A rubric may include procedural rules for interaction sequencing, structural constraints for prompt formatting, and success conditions for evaluating execution of processes and generated outputs. In some embodiments, a rubric may be encoded as a schema (e.g., JSON, XML) and used by a computer to guide the operation of a generative artificial intelligence model, such as a large language model (LLM), during multi-turn interactions. Rubrics may be categorized as interaction rubrics, which define the structure and flow of an engagement, or purpose rubrics, which define the goals and evaluation logic for assessing participant responses.

[0007] A computer can execute a rubric orchestration engine configured to guide a generative model across multiple conversational turns by selecting, modifying, and applyingIXV01-PCT PATENT machine-readable interaction rubrics and evaluation rubrics. Each rubric defines structural rules, procedural logic, and success criteria for a given interaction. The computer can generate prompts based on active rubrics, receive responses, evaluate those responses against rubric-defined criteria, and update the interaction context accordingly. The system can maintain persistent state across interactions, support concurrent rubric execution, and dynamically adjust evaluation logic based on real-time performance indicators, thereby enabling consistent, goal-oriented, and auditable AI- driven engagements.

[0008] Some implementations relate to a computer-implemented method for transforming a Large Language Model (LLM), the method including: receiving, by a computer, from a plurality of available rubrics, at least one first rubric including one or more structural rules and one or more procedural rules for generating an output during a first interaction; receiving, by the computer, from the plurality of available rubrics, at least one second rubric including one or more endpoints and one or more criteria corresponding to the first interaction; generating, by the computer executing the LLM, a prompt structured according to the at least one first rubric and the at least one second rubric; receiving, by the computer, a response to the prompt including interaction data corresponding to the at least one first rubric and the at least one second rubric; generating, by the computer, context data based on a comparison between the interaction data and the at least one second rubric, the context data indicating a likelihood of completion of the one or more endpoints and a measure of compliance with the one or more criteria; determining, by the computer, based on the context data, an updated at least one first rubric and an updated at least one second rubric for a subsequent interaction; and providing, by the computer executing the LLM, a second prompt generated according to the updated at least one first rubric and the updated at least one second rubric.

[0009] Some implementations relate to a method, wherein determining the updated first rubric includes selecting, by the computer, the updated first rubric by adding, removing, or modifying at least one structural rule or at least one procedural rule in the at least one first rubric, and wherein determining the updated second rubric includes selecting the updated second rubric by adding, removing, or modifying at least one endpoint or at least one criterion in the at least one first second rubric.IXV01-PCT PATENT[OO1O] Some implementations relate to a method, further including generating, by the computer, the first rubric including one or more dimensions corresponding to a gracefulness criteria having one or more modulation parameters, wherein the computer updates the first rubric by executing an orchestrator using the context data and historical interaction data.

[0011] Some implementations relate to a method, further including: at a training phase: training, by the computer, the orchestrator for generating the updated at least one first rubric using a training corpus including a plurality of training labels and a corresponding plurality of training interaction records, each training interaction record including interaction content and interaction metadata, a training label indicating whether the corresponding training interaction record achieves at least one defined interaction goal; and updating, by the computer, one or more parameters of the orchestrator based on a comparison between a predicted outcome generated by the orchestrator and an actual outcome indicated in the context data for a plurality of prior interactions, the comparison performed using a loss function applied to each predicted outcome and each actual outcome.

[0012] Some implementations relate to a method, further including: determining, by the computer, at least two second rubrics from the plurality of rubrics to be active during a first interaction; and generating, by the computer, the prompt structured to satisfy criteria of the at least two second rubrics.

[0013] Some implementations relate to a method, further including: evaluating, by the computer, outputs of the at least two second rubrics according to predetermined or dynamically determined priority values; and resolving, by the computer, a conflict between the at least two second rubrics by modifying at least one of the two second rubrics based on the evaluated outputs.

[0014] Some implementations relate to a method, further including: maintaining, by the computer, an active pool including the plurality of first rubrics and the plurality of second rubrics selected for concurrent use across a plurality of conversational turns; and updating, by the computer, the active pool including the plurality of first rubrics and the plurality of second rubrics based on the context data.

[0015] Some implementations relate to a method, further including: generating, by the computer, the at least one second rubric including one or more dimensions corresponding to aIXV01-PCT PATENT competency and a plurality of scored proficiency levels; wherein generating the context data further includes: determining, by the computer, a proficiency score for at least one dimension based on the response; and storing, by the computer, a data structure including the proficiency score and corresponding data supporting the proficiency score.

[0016] Some implementations relate to a method, wherein generating the prompt includes modifying the prompt based on a gracefulness profile, the gracefulness profile including one or more modulation parameters corresponding to at least one of politeness weighting, tactfulness threshold, or stylistic tone vector.

[0017] Some implementations relate to a method, wherein generating the prompt includes selecting a prompt structure based on a rubric-aligned objective associated with a user interface interaction, and wherein receiving the response includes evaluating the response in accordance with an interview simulation rubric including a plurality of competency dimensions and scoring criteria.

[0018] Some implementations relate to a computer-implemented method for conducting a simulated interview session using a generative artificial intelligence model, the method including: receiving, by a computer, a configuration of a simulated interviewee persona, the configuration including one or more behavioral traits, domain-specific knowledge parameters, and response modulation instructions; receiving, by the computer, contextual data defining an interview scenario, the contextual data including role-specific attributes, organizational metadata, and interviewer profile information; executing, by the computer, a simulated interviewee engine configured to generate responses to interviewer prompts based on the persona configuration and contextual data; executing, by the computer, a simulated interviewer engine configured to generate prompts based on a competency rubric, the competency rubric including evaluation dimensions, scoring criteria, and proficiency thresholds; executing, by the computer, a multi-turn interaction sequence between the simulated interviewer engine and the simulated interviewee engine, wherein each prompt and response is processed to update an interaction state; evaluating, by the computer, each response from the simulated interviewee engine against the competency rubric to generate a proficiency score and a set of evaluation results; and updating, by the computer, prompt generation logic of the simulated interviewer engine based on the proficiency score and interaction state, theIXV01-PCT PATENT prompt generation logic including machine-executable instructions for the simulated interviewer engine corresponding to at least one evaluation dimension of the competency rubric .

[0019] Some implementations relate to a method, wherein executing the simulated interviewee engine includes selecting, by the computer, a generative model from a plurality of available models based on a domain-specific expertise parameter included in the persona configuration.

[0020] Some implementations relate to a method, wherein the simulated interviewee engine determines response tone, verbosity, and persona style based on a context vector derived from the contextual data.

[0021] Some implementations relate to a method, wherein the simulated interviewer engine selects prompts from a prompt library indexed by competency dimension and proficiency level.

[0022] Some implementations relate to a method, wherein evaluating each response includes executing, by the computer, a scoring engine configured to apply semantic similarity models and rubric-defined criteria to generate a proficiency score.

[0023] Some implementations relate to a method, wherein updating the prompt generation logic includes modifying, by the computer, prompt structure, topic selection, or delivery modality based on the interaction state and rubric satisfaction thresholds.

[0024] Some implementations relate to a method, further including generating, by the computer, a visual dashboard including the proficiency score, rubric satisfaction indicators, and evidence log for display on a user interface.

[0025] Some implementations relate to a method, further including storing, by the computer, a structured record of the interaction sequence, the persona configuration, the contextual data, and the set of evaluation results in a persistent data repository, wherein the structured record stored in the persistent data repository includes a timestamped sequence of prompts and responses, rubric evaluation metadata, and persona configuration identifiers.IXV01-PCT PATENT

[0026] Some implementations relate to a method, wherein the multi-turn interaction sequence includes branching logic defined by the competency rubric, the branching logic including conditional prompt selection based on prior response evaluation.

[0027] Some implementations relate to a method, wherein the computer executes a feedback loop configured to adjust the simulated interviewee persona configuration in response to detected performance gaps during the interaction sequence.

[0028] Some implementations relate to a computer-implemented method for conducting a rubric-driven competency assessment, the method including: retrieving, by a computer, a competency rubric including a plurality of evaluation dimensions, a plurality of assessment criteria, and a plurality of target competences, for each target competency the competency rubric indicates at least one evaluation dimension and one or more assessment criteria for generating a proficiency score of the target competency; generating, by the computer, a user-directed prompt based on the one or more assessment criteria corresponding to the target competency as indicated by the competency rubric; receiving, by the computer, a response to the user-directed prompt from a participant, the response indicative of the one or more evaluation dimensions corresponding to the one or more assessment criteria; generating, by the computer, the proficiency score of the target competency score based upon the one or more assessment criteria and the one or more dimensions corresponding to the target competency score; evaluating, by the computer, the response against the assessment criteria of the target competency indicated by the competency rubric to generate a proficiency score for the at least one evaluation dimension corresponding to the target competency; generating, by the computer, a competency map indicating the proficiency score of the target competency, for each target competency of the competency rubric;; identifying, by the computer, a next target competency of the competency rubric based upon the competency map; and generating, by the computer, a second user-directed prompt according to the next target competency of the competency rubric.

[0029] Some implementations relate to a method, wherein the competency rubric includes a machine-readable schema defining a plurality of dimensions, each dimension associated with a scoring scale and a set of evaluation rules.IXV01-PCT PATENT

[0030] Some implementations relate to a method, wherein generating the user-directed prompt includes selecting a prompt template from a prompt library indexed by competency domain and rubric dimension.

[0031] Some implementations relate to a method, wherein evaluating the response includes executing a scoring engine configured to apply semantic similarity models and rubric-defined criteria to generate the proficiency score.

[0032] Some implementations relate to a method, wherein the computer updates the competency map by modifying a proficiency vector associated with the participant.

[0033] Some implementations relate to a method, further including generating a feedback signal based on the proficiency score and transmitting the feedback signal to a prompt generation engine configured to select a follow-up prompt.

[0034] Some implementations relate to a method, wherein the computer retrieves the competency rubric from a rubric repository including role-specific evaluation frameworks.

[0035] Some implementations relate to a method, wherein the computer stores the updated competency map in association with a timestamp and a session identifier.

[0036] Some implementations relate to a method, wherein the computer modifies prompt generation logic based on historical proficiency scores associated with the participant.

[0037] Some implementations relate to a method, wherein the computer generates a visual dashboard including the proficiency score, rubric satisfaction indicators, and a summary of the competency map.

[0038] Some implementations relate to a computer-implemented method for context assessments using rubrics generated with generative artificial intelligence models, the method including: receiving, by a computer, input data including one or more of textual content, speech- derived content, or sensor-derived content; retrieving, by the computer, a context rubric including a plurality of evaluation dimensions, each dimension associated with a ranked scale and one or more context-specific criteria; generating, by the computer, a context map by executing a generative artificial intelligence model configured to extract one or more context attributes fromIXV01-PCT PATENT the input data and associate each attribute with a corresponding evaluation dimension, wherein the context map includes instructions for generation of a prompt including content elements based on the one or more associated context attributes with the corresponding evaluation dimension; evaluating, by the computer, the context attributes against the context-specific criteria of the context rubric to generate a ranked context profile; updating, by the computer, the context map based on the ranked context profile; and generating, by the computer, a prompt according to the context map and at least one evaluation dimension of the context rubric .

[0039] Some implementations relate to a method, wherein the evaluation dimensions include parameters corresponding to performance of a context-specific objective.

[0040] Some implementations relate to a method, wherein generating the context map includes executing the generative artificial intelligence model to extract named entities, sentiment indicators, and domain-specific keywords from the input data.

[0041] Some implementations relate to a method, wherein scoring the context attributes includes applying a weighting function to each evaluation dimension based on a priority parameter defined in the context rubric.

[0042] Some implementations relate to a method, wherein the computer updates the context map by modifying a vector representation of the context attributes and associating each vector element with a rubric-defined score.

[0043] Some implementations relate to a method, further including generating a strategy framework based on the ranked context profile, the strategy framework including one or more action recommendations aligned with rubric-defined goals.

[0044] Some implementations relate to a method, wherein the input data includes a combination of structured data retrieved from an enterprise system and unstructured data received from a user interface.

[0045] Some implementations relate to a method, wherein the computer stores the ranked context profile in association with a timestamp, a context identifier, and a rubric version identifier.IXV01-PCT PATENT

[0046] Some implementations relate to a method, wherein the generative artificial intelligence model includes a transformer-based language model configured to process multimodal input and generate structured output.

[0047] Some implementations relate to a method, wherein the computer generates a visual dashboard including the ranked context profile, rubric satisfaction indicators, and a summary of the context map.

[0048] Some implementations relate to a computer-implemented method for generating gracefulness-adjusted content using a generative artificial intelligence model, the method including: receiving, by a computer, a prompt including a content request and a contextual parameter set, the contextual parameter set including a persona identifier, a communication objective, and a tone specification; retrieving, by the computer, a gracefulness profile associated with the persona identifier, the gracefulness profile including one or more modulation parameters corresponding to at least one of politeness weighting, tactfulness threshold, and stylistic tone vector; generating, by the computer, a first candidate response using a generative model based on the prompt and the gracefulness profile; adjusting behavior of the generative model by: evaluating, by the computer, the first candidate response using a scoring engine configured to compute a gracefulness score based on the modulation parameters; adjusting, by the computer, the first candidate response to produce a second candidate response, wherein the adjustment is based on a deviation between the gracefulness score and a target score defined in the gracefulness profile; validating, by the computer, the second candidate response against a rubric including one or more thresholds, including structural constraints and tone compliance rules, wherein the validation indicates the second candidate response satisfies the one or more thresholds corresponding to the contextual parameter set; and outputting, by the computer, the second candidate response to a user interface for presentation.

[0049] Some implementations relate to a method, wherein the gracefulness profile includes a tone modulation vector for a persona, configured to adjust content generation parameters based on a detected communication context.IXV01-PCT PATENT

[0050] Some implementations relate to a method, wherein the scoring engine includes a trained sub-model configured to compute a gracefulness score using a weighted combination of politeness, empathy, and stylistic fluency metrics.

[0051] Some implementations relate to a method, wherein adjusting the first candidate response includes modifying, by the computer, at least one of lexical selections, sentence structure, or rhetorical framing for aligning with the target score.

[0052] Some implementations relate to a method, wherein the rubric includes a set of machine-readable rules encoded in a structured schema including at least one of JSON, XML, or YAML.

[0053] Some implementations relate to a method, wherein validating the second candidate response includes executing, by the computer, a compliance check against a tone threshold and a style constraint for a persona.

[0054] Some implementations relate to a method, further including: generating, by the computer, a feedback signal based on the gracefulness score; and transmitting, by the computer, the feedback signal to a model adjustment engine configured to update the generative model parameters.

[0055] Some implementations relate to a method, wherein the contextual parameter set further includes a cultural sensitivity indicator and a formality level specifier.

[0056] Some implementations relate to a method, wherein the computer selects the generative model from a plurality of available models based on a performance metric associated with gracefulness score convergence.

[0057] Some implementations relate to a method, wherein outputting the second candidate response includes transmitting, by the computer, the response to a multimodal interface configured to present the response as an audio format, visual format, or haptic feedback.

[0058] Some implementations relate to a computer-implemented method for generating rubric-linked user interface interactivity, the method including: receiving, by a computer, contextual input including one or more sensor signals, user interaction data, or environmentalIXV01-PCT PATENT parameters; retrieving, by the computer, a rubric including a plurality of evaluation dimensions, each dimension associated with one or more interaction criteria; selecting, by the computer, one or more interface actions based at least in part on the contextual input and the rubric, each interface action including a target interface, a content payload, and a rubric-aligned objective; generating, by the computer, orchestration data for the one or more interface actions, the orchestration data including timing or sequencing information; initiating, by the computer, execution of the one or more interface actions via one or more interfaces including at least one of a visual interface, an audio interface, or a physical interface; before executing the one or more interface actions, modifying, by the computer, the one or more interface actions based on updated contextual input or rubric satisfaction status, wherein the modifying includes adjusting at least one interface corresponding to the orchestration data .

[0059] Some implementations relate to a method, wherein the rubric includes a machine- readable schema defining a plurality of dimensions including parameters corresponding to execution of one or more interface actions for accessibility, responsiveness, engagement, and clarity.

[0060] Some implementations relate to a method, wherein selecting the one or more interface actions includes identifying a rubric-aligned objective associated with a threshold score for a dimension of the rubric.

[0061] Some implementations relate to a method, wherein the content payload includes a structured data object configured to modify a user interface element, including at least one of a visual layout, an audio output, or a haptic feedback signal.

[0062] Some implementations relate to a method, wherein generating the orchestration data includes assigning a priority value to each interface action based on a rubric-defined urgency parameter.

[0063] Some implementations relate to a method, wherein initiating execution of the one or more interface actions includes transmitting the content payload to a client device configured to render the interface.IXV01-PCT PATENT

[0064] Some implementations relate to a method, wherein modifying the one or more interface actions includes replacing a previously selected interface action with an alternative action selected based on updated rubric satisfaction status.

[0065] Some implementations relate to a method, wherein the contextual input includes sensor signals, include at least one of ambient light level, ambient temperature, audio volume, device orientation, or user proximity.

[0066] Some implementations relate to a method, wherein the computer stores the data in association with a session identifier, a timestamp, and a rubric version identifier.

[0067] Some implementations relate to a method, further including generating a visual dashboard including a representation of the rubric satisfaction status, the executed interface actions, and the contextual input. 61.

[0068] Some implementations relate to a system, including: a computing system having one or more processors coupled with memory, configured to perform the method.

[0069] Some implementations relate to a computer-readable storage medium including computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the method.BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The present disclosure can be better understood by referring to the following figures. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the disclosure. In the figures, reference numerals designate corresponding parts throughout the different views.

[0071] FIG. 1 is a block diagram of an example of a system, according to embodiments.

[0072] FIG. 2 is a flowchart showing operations of a computer-implemented method for rubric-driven interaction, according to embodiments.

[0073] FIG. 3 is a flow diagram of an example of a method for a rubric-driven interaction system, according to embodiments.IXV01-PCT PATENT

[0074] FIG. 4 is a flow diagram of an example of a method for a rubric-driven interaction, according to embodiments.

[0075] FIG. 5 is a flowchart showing operations of a computer-implemented method for conducting a simulated interview session using a generative artificial intelligence model and rubrics, according to embodiments.

[0076] FIG. 6 is a flow diagram of an example of a method for conducting a simulated environment, according to embodiments.

[0077] FIG. 7 is a block diagram of an example of a system for a simulated environment, according to embodiments.

[0078] FIG. 8 is a flow diagram of an example of a method for a simulation system, according to embodiments.

[0079] FIG. 9 is a flow diagram of an example process for pathway optimization, according to embodiments.

[0080] FIG. 10 is a flow diagram of an example process for position matching, according to embodiments.

[0081] FIG. 11 is a flowchart showing operations of a computer-implemented method for conducting a rubric-driven competency assessment, according to embodiments.

[0082] FIG. 12 is a flowchart showing operations of a computer-implemented method for context assessments using rubrics generated with generative artificial intelligence models, according to embodiments.

[0083] FIG. 13 is a flow diagram of an example process for context assessment, according to embodiments.

[0084] FIG. 14 is a flowchart showing operations of a computer-implemented method for generating gracefulness-adjusted content using a generative artificial intelligence model, according to embodiments.IXV01-PCT PATENT

[0085] FIG. 15 is a flow diagram of an example of a method for a simulated environment, according to embodiments.

[0086] FIG. 16 is a flowchart showing operations of a computer-implemented method for generating rubric-linked user interface interactivity, according to embodiments.

[0087] FIG. 17 is a flow diagram of an example of a method for interface interactivity, according to embodiments.DETAILED DESCRIPTION

[0088] Reference will now be made to the illustrative embodiments illustrated in the drawings, and specific language will be used here to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended. Alterations and further modifications of the inventive features illustrated here, and additional applications of the principles of the inventions as illustrated here, which would occur to a person skilled in the relevant art and having possession of this disclosure, are to be considered within the scope of the invention.OVERVIEW OF TECHNOLOGY

[0089] Generative artificial intelligence models, particularly large language models (LLMs), have advanced rapidly, enabling the production of machine-generated content that is contextually coherent, stylistically adaptable, and perceptually similar to human-authored language across a wide range of domains. These models are increasingly applied in enterprise workflows, education, talent management, customer engagement, and decision-support systems. However, their use in high-stakes, goal-oriented interactions — such as competency assessment, structured negotiations, or policy consultations — has been constrained by the models’ inherent limitations, including their stateless nature, lack of intrinsic evaluation mechanisms, and reliance on static prompt engineering.

[0090] Conventional Al-powered interaction systems typically produce responses based on the most recent prompt, without persistent awareness of overarching objectives, rule sets, or success criteria. These architectures are configured as next-token predictors optimized for conversational plausibility, but they do not maintain machine-readable definitions of interaction structure (e.g., format constraints, sequencing requirements) or purpose (e.g., multi-dimensionalIXV01-PCT PATENT skill assessment frameworks, compliance checklists). As a result, such systems exhibit reduced consistency, adaptability, and auditability when compared to human-facilitated processes designed around clearly defined rubrics and iterative evaluation against established benchmarks.

[0091] Embodiments described herein address these technical shortcomings by implementing a dynamic, meta-level orchestration system programmed and trained to guide a generative model through multi-turn, rubric-driven interactions. The system maintains a persistent library of machine-readable interaction rubrics (e.g., interaction models) and evaluation rubrics (e.g., purpose models) each encoded as structured schemas, such as JSON definitions, specifying structural rules, procedural rules, goals, metrics, and success thresholds. An orchestration engine executes at each turn of an interaction, selecting, modifying, or augmenting one or more active rubrics based on an analysis of the real-time interaction context, historical interaction data, participant performance indicators, and environmental parameters. In some implementations, a rubric can be implemented as a computational model configured to parse inputs, apply scoring logic, and output dimension-level evaluations in real-time, allowing the rubric to function not only as a static schema definition but also as an executable module (e.g., model, software engine) within the orchestration pipeline.

[0092] Technical improvements emerge from the embodiments described herein through multiple (e.g., interrelated) capabilities. In some embodiments, the system implements real-time orchestration and concurrent multi -model (e.g., rubric) execution, providing multiple evaluation goals to remain active in parallel, with dynamic weighting and conflict resolution applied as the interaction evolves. The architecture further provides a structured, machine-readable representation of interaction rules and objectives, supporting deterministic formatting, multi-system interoperability, and strict schema validation of generated content. Continuous feedback loops are employed, wherein participant responses are automatically evaluated against dimension-level criteria in active purpose rubrics to produce ongoing proficiency scores and associated evidence logs. Adaptive context re-selection allows the system to pivot interaction goals or formatting mid-conversation in response to detected performance gaps, changes in participant focus, or external triggers. In addition, persistent, audit-friendly state tracking is maintained, enabling reproducibility of assessments, automated report generation, and compliance verification across regulated workflows.IXV01-PCT PATENT

[0093] The embodiments described herein further provide for technical improvements over conventional systems by providing dynamic, tum-by-turn selection and updating of data structures (e.g., interaction rubrics and purpose rubrics) based on both current context data and historical interaction data, thereby allowing adaptive control of the generative Al model throughout multi-turn engagements. In certain embodiments, structural rules and procedural rules in interaction rubrics and endpoints or criteria definitions in purpose rubrics are maintained as machine-readable data, permitting deterministic prompt formatting and consistent application of evaluation logic. The system also supports automated evaluation of received responses against active purpose rubrics, including updating a score for at least one dimension in a multi-dimensional assessment framework and documenting textual evidence derived from the response to support the score. Concurrent activation of multiple purpose rubrics during a single interaction is supported, along with priority-based weighting and conflict resolution among active goals. Persistent context tracking and coordinated model orchestration are maintained across conversational turns, enabling sustained alignment of interactions with defined objectives, generation of auditable evaluation records, and seamless integration with downstream analytics or compliance systems without manual post-processing.

[0094] By incorporating these capabilities, the described embodiments transform a generative Al engine from a passive, prompt-driven content generator into a persistent, purpose-driven interaction system. This yields improved interaction consistency, evaluation accuracy, and adaptive responsiveness in high-value applications such as competency assessment, structured interviewing, guided discovery workflows, and adaptive coaching, while reducing the need for human oversight to preserve direction, quality, and measurability of outcomes.

[0095] The described embodiments also improve computational efficiency by reducing the total number of inference calls made to the generative Al model. Through the use of dynamic rubric orchestration, the system is able to refine prompt construction in real time, resulting in fewer conversational turns being required to achieve interaction goals. By selecting and updating only those interaction rubrics and purpose rubrics relevant to the current context, the system avoids unnecessary model invocations that would otherwise consume processing cycles, network bandwidth, and memory resources.IXV01-PCT PATENT

[0096] Additionally, by maintaining structured, machine-readable representations of interaction rules and evaluation criteria, the system can pre-validate prompt formats and eliminate trial-and-error prompt adjustments that are common in conventional prompt-engineering approaches. This pre-validation step reduces wasted generative cycles, lowers latency by producing model-ready prompts on the first attempt, and minimizes redundant parsing or reformatting of outputs.

[0097] The adaptive context re-selection process further contributes to resource optimization by terminating or bypassing evaluation branches that have already been satisfied or deemed irrelevant, thereby freeing computing resources for active objectives. In a multi -rubric deployment, the ability to dynamically weight or deactivate certain rubrics allows the system to allocate processor time and GPU utilization to the most impactful evaluation tasks without over-provisioning computational effort across all possible objectives.

[0098] Moreover, persistent state tracking eliminates the need to repeatedly reprocess historical conversation context through the generative model. Because prior interaction data and evaluation scores are retained in structured form, the system can resume or pivot an interaction without consuming resources to regenerate or reinterpret information already gathered, reducing both compute load and storage I / O overhead. In some implementations, this efficiency provides the deployment of the rubric-driven orchestration engine on lower-cost hardware, or within constrained environments such as embedded systems or edge devices, without sacrificing interaction quality or consistency.SYSTEMS AND METHODS FOR RUBRIC-DRIVEN INTERACTION ENGINE

[0099] FIG. 1 depicts an example of a system 100 for intermediating interactions between an environment and one or more generative engines 114. The system 100 includes at least one data processing system 101 including components of the present disclosure. The data processing system 101 can include at least one computing device, which is sometimes referred to as a computer without limiting effect. The data processing system 101 can include or interface with a user interface 102 to exchange information with an environment. The data processing system 101 can include or interface with a context provider 104 to orchestrate various control loops, as may relate to various corresponding data structures. The data processing system 101 can include or interfaceIXV01-PCT PATENT with a contextual input handler 106 to parse and extract content from textual or multi-modal content of an environment or from a generative engine 114. The data processing system 101 can include or interface with a scorer 108 to evaluate content received from a generative engine 114. The data processing system 101 can include or interface with a controller 110 to execute various of the operations provided herein. The data processing system 101 can include or interface with an interface engine 112 to couple with and communicate with at least one generative engine 114.

[0100] The data processing system 101 can include at least one data repository 120. The user interface 102, context provider 104, contextual input handler 106, scorer 108, or interface engine 112, can each include at least one processing unit or other logic device such as programmable logic array engine, or module (e.g., the controller 110) configured to communicate with the data repository 120 or database. The user interface 102, context provider 104, contextual input handler 106, scorer 108, or interface engine 112 can be separate components, a single component, or part of the data processing system 101. The data processing system 101 and its respective models, engines, and other components can include hardware elements, such as one or more processors, logic devices, or circuits of the controller, as well as software components.

[0101] The data repository 120 can include one or more local or distributed databases, and can include a database management system. The data repository 120 can include computer data storage or memory and can store one or more data structures, such as a data structure corresponding to various nested operations. For example, depicted examples include at least one outer loop data structure 122 and at least one inner loop data structure 124.

[0102] An outer loop data structure 122 may refer to or include a set of machine readable instructions related to a multi -turn interaction. For example, the outer loop data structure 122 can define an interaction model including structural or procedural rules of an interaction, or a purpose for the interaction. For example, the instructions can include instructions to generate a competency map. The outer loop data structure 122 can be implemented in text-based structured data form, such as JavaScript Object Notation (JSON) files, among others. The outer loop data structure 122 can be executed by the context provider 104 to cause the data processing system 101 to orchestrate one or more interactions between a generative engine 114 and an environment, as can include a physical or computational environment external to the generative engine 114.IXV01-PCT PATENT

[0103] The outer loop data structure 122 can include content of or linkages to context data. The context data can include various sources of information related to a knowledge domain. For example, the context data can include curated information, such as academic publications, objective data, technical articles, and the like. The context data can include textual or multi-modal data configured for ingestion by the generative engine 114 to generate a context vector 123 in the generative engine 114. The data processing system 101 can store the context data as the context vector 123, or otherwise (e.g., as textual content). Accordingly, references to a context vector 123 can refer to either of the context data configured for ingestion by the generative model 114 to generate the context vector 123, or the context vector 123 generated upon ingestion. Some instances of the outer loop data structure 122 can include multiple instances of context vectors 123. For example, the outer loop data structure 122 can include a first context vector 123 for a first subset of turns of an interaction and a second context vector 123 for a second subset of turns of an interaction.

[0104] The context vector 123 can include domain specific knowledge. For example, the context vector 123 can include data for an enterprise, such as an organizational chart, career history, resume, performance evaluation, or so forth. The generative engine 114 can, based on such a context vector 123, evaluate a coverage map for one or more aspects of an enterprise, or generate prompts to cause the generative engine 114 to induce receipt of further information related to the enterprise.

[0105] The outer loop data structure 122 can include machine-readable instructions (e.g., rubric) for the generative engine 114. For example, the instructions can include instructions configured to modulate outputs of the generative engine 114. These instructions can correspond to content or stylistic components of the outputs. For example, the instructions can include an instruction to operate according to one or more personas. An instruction to operate according to a persona of an expert in a field of interest can modulate content of the outputs, relative to a novice in the field of interest. Further instructions can modulate a brevity, writing style, or gracefulness of the response. The outer loop data structure 122 can include paired sets of context vectors 123 and personas. In this way, the instructions can aid the outputs of the generative engine 114 to align with an area or degree of expertise based on content of the content vector 123. For example, by providing a context vector 123 related to an esoteric compliance field and providing an instructionIXV01-PCT PATENT to communicate according to a persona having a defined level of expertise, the data processing system 101 can communicate according to an expert level, or according to further gradations of expertise (e.g., novice, competent, proficient).

[0106] The outer loop data structure 122 can define various interactions of a sequence. For example, the sequence can refer to an interview (e.g., a mock interview or information gathering interview), a revision pass of textual content 103 or other content 105 (e.g., a user interface 102), or an organizational inventory. Outer loop data structures can include various jump, branch, or other logical sequences. For example, based on a receipt or presentation of some data, or an evaluation of a threshold, other data may become obviated or relevant.

[0107] The outer loop data structure 122 can include instructions for one or more turns of the interaction. For example, the instructions can include instructions to execute an inner loop data structure 124, instructions to provide the generative engine 114 with a context vector 123, or selection of a generative engine 114 from various generative engines 114 coupled with the data processing system. Selection of the generative engine 114 can be provided according to an explicit predetermined instruction or based on an evaluation of selection criteria. For example, the outer loop data structure 122 can include instructions to substitute a generative engine 114 in response to non-satisfaction of a closure criteria.

[0108] The instructions of the inner loop data structure 124 can include instructions (e.g., rubric) to cause the generative engine to generate a response to a prompt, for presentation to a user. The instructions of the inner loop data structure 124 can include instructions provided as a prompt to the generative engine 114, configured to cause the generative engine 114 to generate outputs of queries to the user. User responses can be provided to the generative engine 114 to evaluate the response. The generative engines 114 performing various portions of the sequences can differ from one another. For example, the data processing system 101 can update a context state of a second generative model in response to determining that a first generative engine 114 is not aligned with a capability (e.g., provide any multimodal input or output to the second generative model).

[0109] The inner loop data structure 124 can include instructions to generate a map, such as a competency map. The competency map can map a competency of an individual or enterprise. For example, the inner loop data structure 124 can include a competency rubric including aIXV01-PCT PATENT mapping of one or more gradations of expertise to skill, behaviors, or other attributes associated with the gradations. The inner loop data structure 124 can include a sequence of phases to determine the levels of gradation.

[0110] At a first step, the instructions of the inner loop data structure 124 can include instructions to determine a breadth or depth of knowledge. For example, the instructions can include predefined queries configured to elicit a user response to provide an indication of the breadth or depth of knowledge, or instructions to cause the generative engine 114 to generate non- deterministic queries. The data processing system 101 can provide user responses to the queries to the generative engine 114 along with a prompt to determine the breadth or depth of the knowledge. For example, the instructions can include instructions to generate an indication of a level of expertise and a confidence score. The data processing system 101 can compare the confidence score to a threshold to determine closure of the first step, or determine closure based on a number of iterative loops. In response to a non-satisfaction of the threshold, the instructions can provide iterative prompts to elicit further detail until the threshold is satisfied. The threshold can include a predetermined threshold or dynamic threshold. For example, the threshold can lower based on a number of iterations to prevent non-closure of the iterative loops. The closure of the first step can provide an initial map (e.g., initial competency map).[oni] At a second step, the instructions of the inner loop data structure 124 can include instructions to adjust bounds of the initial map generated at the first step. For example, according to a map corresponding to personal or enterprise competence, the instruction can include instructions for the generative engine 114 to generate queries to further prompt a user to provide information used to determine the competency level. The questions of step two can depend on the initial map of step one. For example, the questions can be targeted to regions which are not clearly within an expected knowledge domain, or clearly beyond a knowledge domain. The data processing system 101 can identify a boundary of the map lacking confidence, and generate questions to determine a position of said boundary. As for step one, the instructions can include instructions to determine closure conditions of step one. For example, step two can determine closure based on a confidence of one or more boundaries, or based on a number of iterated steps. In some embodiments, upon a failure to reach closure, the instructions can return to step one to reestablish the initial map.IXV01-PCT PATENT

[0112] At a third step, the instructions of the inner loop data structure 124 can cause the generative engine 114 to generate questions to test boundary conditions. As described with regard to steps one and two, the third step can determine closure upon determining the boundary conditions of the map to a threshold confidence. However, where adjustments are not made at this stage, upon failing to affirmatively test the boundary conditions, the loop can iterate to steps one or two (e.g., according to a magnitude of difference in the failure to affirm the boundary condition). That is, the third step can be performed as a validation step without modification to the competency map.

[0113] According to an example of a mock interview, the outer loop data structure 122 can include sequence information for a mock interview. For example, the outer loop data structure 122 can provide instructions to perform the interview according to one or more personas, or separate outer loop data structures can be provided for the various personas. The outer loop data structure 122 can correspond to one or more inner loop data structures 124. For example, one or more interactions of the outer loop data structure 122 can map to an inner loop data structure 124. The interactions can include single query turns (e.g., tell me about yourself) or multi-prompt turns, such as multiple prompts related to the same subject matter (e.g., prompts related to gathering an indication of technical proficiency within a domain) or related to determining a number of proficient individuals across an enterprise. Moreover, the interactions can include multiple satisfaction thresholds which can be satisfied according to a variable number of questions (e.g., if information is provided in a response, a further question may be omitted; conversely, the interaction can include instructions to generate additional questions based on an inclusion of other information in a response).

[0114] According to an example of a turn sequence of a data structure, the sequence can include one or more operations to evaluate text to determine data content, flow, or other attributes thereof (e.g., a gracefulness metric). Such an outer loop data structure 122 can include sequenced operations to evaluate the text external to the generative engine 114 and provide the evaluated text and the evaluation to a generative engine 114 configured to update the textual content to modify the attributes in an iterative fashion, to converge towards a local optimum of a “best” metric.

[0115] According to an example turn sequence of a user interface, the outer loop data structure 122 can generate a user interface based on contextual data. For example, the outer loopIXV01-PCT PATENT data structure 122 can generate components of the user interface to include adjustment to radio volume, mobile phone notification settings, or lighting based on contextual information of a user playing an audiobook.

[0116] According to an example turn sequence of an enterprise analysis, the outer loop data structure 122 can generate an organizational inventory. For example, the outer loop data structure 122 can include one or more prompts to present to various users of an enterprise, to prompt the users to input information based on a content vector 123 including organizational information. The outer loop data structure 122 can further include operations to evaluate various cross-organizational metrics.

[0117] An inner loop data structure 124 may refer to or include one or more interactions of the one or more outer loop data structures 122. For example, the inner loop data structures 124 can include one or more prompts for an interaction (e.g., can include a list of information to be presented or retrieved, as can be executed through multiple prompts). For example, if one response is partially responsive to the list of information, the inner loop data structure 124 can include instructions to generate an updated prompt more specific to any still-pending data.

[0118] References to a loop should not be construed to require multiple executions of the instructions of a data structure. For example, according to some implementations or uses of the present disclosure, the data processing system 101 can achieve loop closure according to a single pass of the instructions of the outer or inner loop data structure 124. According to some implementations or uses of the present disclosure, the data processing systems 101 can execute multiple passes (or loops) of the instructions. Moreover, various data structures can be nested into each other (e.g., two layers deep, three layers deep, or so forth).

[0119] The user interface 102 can include sensors to detect aspects of a physical or logical environment, as can include, for example, cameras or microphones configured to detect speech or activities. The sensors can be coupled with processing circuits to extract textual content from audio or video data. For example, the information extracted from the sensors can include text provided by a voice to text model, or further information included in the video or audio content. For example, the further information can include verbal or nonverbal cues, cognitive load indicators, or aspects of an environment. The aspects of the environment can include a level of lighting,IXV01-PCT PATENT background noise, focus of view, or so forth. The user interface 102 can include various inputs. For example, the user interface 102 can be configured to detect trigger conditions from sensors for a state of an e-book, television, or other aspect of an environment of a user. Accordingly, the user interface 102 can capture a trigger based on sensor data from touch, sound, timer, key-phrase, or scent-based sensors. The user interface 102 can further include various devices configured to detect user entries from a user. For example, such sensors can include a keyboard, mouse, or touchscreen device, along with the camera or microphone described above.

[0120] The user interface 102 can include data connectors configured to receive data relevant to one or more contexts. For example, the data connectors can include an application programming interface (API) configured to retrieve information corresponding to one or more interactions. For example, data received via the data connector can be used to proceed through an inner or outer loop data structure 122. This data can be provided to a generative engine 114 to adjust a response, or provided to a user (e.g., can include components of a context vector 123). Instructions to retrieve this data can be included in various of the outer or inner loop data structures 124. The user interface 102 can further include transceivers to output data. For example, the outputs can include presentation of textual or other content to a user, adjustments to lights, thermostats, further computing devices, displays, and so forth.

[0121] The context provider 104 can manage execution of at least one outer loop data structure 122. For example, the context provider 104 can instantiate the outer loop data structure 122, retrieve content vectors 123 associated therewith, and instantiate at least one inner loop data structure 124. The context provider 104 can generate instructions for the interface engine 112 to couple with selected generative engines 114, or provide the interface engine 112 with prompts, content vectors 123, or other inputs to the generative model 114. The context provider 104 can select an outer loop data structure 122 based on the instantiation information received from the user interface 102. For example, the context provider 104 can receive an explicit selection of a sequence, such as a drop down menu, or can receive an unstructured request (as can include textual, video, or audio content) and select one or more outer loop data structures 122 corresponding to the unstructured request (e.g., can identify a trigger condition based on data received from the contextual input handler 106). The context provider 104 can determine whether criteria for a branch or jump operation is satisfied or determine whether a criterion for closure of an inner loopIXV01-PCT PATENT or portion thereof is satisfied (e.g., that the outer loop data structure 122 can proceed to a subsequent turn, or that the inner loop can proceed to a subsequent step). The context provider 104 can retrieve information related from the contextual input handler 106 or other components of the data processing system 101 to manage the execution of the data structures. The context provider 104 can identify any thresholds related to a determined confidence or number of iterations to manage the execution of the instructions of the outer loop data structure 122.

[0122] The contextual input handler 106 can map textual or other content with aspects of an interaction. For example, an interaction can include a structured list of information to be retrieved and presented. The contextual input handler 106 can receive unstructured content textual or other content to map to the structured list of information. For example, a structured list of information can include informational items such as a name, intent, or position. The contextual input handler 106 can identify an indication of such a name, intent, or position from textual or other unstructured content according to various natural language processing techniques. For example, the contextual input handler 106 can identify one or more candidates for mapping to an informational item, and compare a confidence of a match to a confidence threshold. Based on the comparison, the contextual input handler 106 can map the data, omit the mapping, or cause the context provider 104 to repeat all or a portion of an interaction to increase a mapping confidence.

[0123] The contextual input handler 106 can identify aspects received from the sensors of the user interface 102. For example, the contextual input handler 106 can include or interface with a voice to text service, or can be configured to identify non-textual information including nonverbal cues present in audio or video data. In this way, the contextual input handler 106 can generate textual information indicating the presence of non-textual information that can be passed to the context provider 104 to pass to the generative engine 114 (e.g., where the generative engine 114 is not configured to receive the audio or video data, or is not configured to infer the non-textual information). The non-textual information provided by the contextual input handler 106 can further include various environmental aspects identified in sensor data of the sensors. For example, the contextual input handler 106 can include indications of lighting, identification of users, a state of one or more switches, and so forth, as may aid the generative engine 114 to generate responses based on the non-textual information.IXV01-PCT PATENT

[0124] The contextual input handler 106 can further interface with various data connectors to retrieve information therefrom. For example, the contextual input handler 106 can receive organizational charts, employee responsibilities, evaluations, or work product from an enterprise system, as may be provided to the context provider 104 for use in generation of a context vector 123

[0125] The inputs of the contextual input handler 106 can include inputs received from a physical or computing environment of the user interface (e.g., can include any of the sensor data), or can include inputs received from the generative engines 114. The context provider 104 can operate as a bidirectional bridge between the interface engine 112 and other components of the data processing system. The contextual input handler 106 can instantiate further components of the data processing system, such as the scorer 108.

[0126] The scorer 108 can determine one or more metrics associated with textual content. For example, the scorer 108 can generate metrics related to confidences, gracefulness, or brevity, among others. The scorer 108 can provide various scores, such as relevance scores (as can correspond to one or more information items), accuracy scores, readability scores (e.g., Flesch- Kincaid grade level) fluency scores (e.g., gracefulness metrics), length metrics (e.g., token or word counts), completeness, hallucination probability, or so forth. The scorer 108 can use various NLP techniques to determine the scores. For example, the scorer 108 can perform text normalization, tokenization, or segmentation of textual or other content provided to, or received from, the generative engine 114. The scorer 108 can apply statistical methods or other rule-based evaluations. The scorer 108 can use a semantic similarity model to derive relevance scores, factual verification models to determine accuracy assessments, and linguistic analysis routines to determine readability or fluency values. The resulting scores (sometimes referred to as metrics, without limiting effect) can be stored in the data repository. For example, the scorer 108 can store metrics locally for use according to the execution of other interactions of an outer loop data structure 122.

[0127] The controller 110 can include one or more processing circuits, as may be implemented according to hardware or software components, to aid in the operation of various of the components of the data processing system 101. The controller 110 can further include communications circuitry to interface between the various components of the data processingIXV01-PCT PATENT system 101. In some embodiments, the communications circuitry includes a network interface configured to communicate with one or more instances of a generative engine 114 in network communication with the data processing system 101. A network coupled with the network interface can include any number of other wired or wireless networks. For example, the components can be joined by an Ethernet, Wi-Fi, cellular, PCIe, AXI, or other network interfaces. The network can include various boundaries which are not depicted for clarity of the figures. For example, boundaries between devices, subnets, firewalled networks, and the like may be present.

[0128] The interface engine 112 can couple with one or more generative engines 114. For example, the interface engine can establish a logical connection with a generative engine 114 using the network interface. The interface engine 112 can provide prompts to the generative engines 114 and receive responses therefrom for use by other components of the data processing system. According to some implementations, the interface engine 112 can switch traffic (e.g., instructions to generate prompts) between various generative engines 114. For example, the interface engine 112 can route prompts according to an explicit instruction of the context provider 104, or according to received context. For example, the interface engine 112 can route textual content to a textual generative engine 114 and multi-modal content to a multi-modal generative engine 114.

[0129] In some embodiments, the interface engine 112 can perform preprocessing and postprocessing operations in connection with communications between the data processing system 101 and the generative engine 114. For example, the interface engine 112 can tokenize raw input text, serialize multi-modal assets (e.g., image embeddings, audio spectrograms) into a model-compatible format, and append structural control data (e.g., special tokens, persona specifiers, or routing tags) prior to transmission to the generative engine 114. The interface engine 112 can likewise decode structured outputs from the generative engine 114, converting such outputs into a normalized representation used by the data processing system 101. The interface engine 112 can monitor operational characteristics of generative engines 114, such as request latency, throughput capacity, or error rates, and select a target generative engine from multiple available instances based on performance, availability, or other criteria. The interface engine 112 can implement retry or fallback logic in the event of a failed generation attempt, or maintain contextual state across multiple request-response cycles (or across multiple generative engines 114)IXV01-PCT PATENT

[0130] The generative engine 114 can include one or more engines configured to generate outputs based on received prompts, context vectors 123, or other inputs. In some embodiments, the generative engine 114 comprises a transformer-based model, such as a large language model (LLM), multi-modal generative model, or other data-driven model that has been pre-trained on a corpus of textual, or other data. The generative engine 114 can operate remotely from the data processing system 101 (e.g., as a cloud-hosted service) or locally within the data processing system 101. The generative engine 114 can be configured to receive one or more context vectors 123 in conjunction with machine-readable instructions, and to synthesize corresponding outputs according to the structural or stylistic modulations defined by the instructions. Outputs generated by the generative engine 114 can include, without limitation, textual completions, structured data, code artifacts, visual renderings, or multi-modal content. The generative engine 114 can further provide metadata associated with generated outputs, such as per-token likelihoods, topical relevance scores, or model-reported confidence scores, which can be consumed by the scorer 108 or other components of the data processing system 101 to orchestrate subsequent operations.

[0131] FIG. 2 is a flow chart showing operations of a computer-implemented method 200 for transforming a LLM model (e.g., for rubric-driven interactions), in accordance with some implementations of the present disclosure. For ease of description and understanding, the operations of the computer-implemented method 200 are described as being performed by a computer, though embodiments may be implemented using any type of computing device and / or may be implemented using multiple computing devices. For example, various functions can be carried out using one or more processors executing instructions stored in one or more memories. The method can also be embodied as computer-usable instructions stored on computer storage media. The method can be provided by a standalone application, a service or hosted service (standalone or in combination with another hosted service), as a microservice via an application programming interface (API) or a plug-in to another product, to name a few. Embodiments may include additional or alternative operations and features, or omit certain operations and features, and still fall within the scope of this disclosure. The method 200 may be executed by a computer configured to perform rubric-driven interaction. The method may be implemented in whole or in part across distributed systems, cloud-based platforms, or embedded environments.IXV01-PCT PATENT

[0132] At operation 210, the computer obtains (e.g., receives, retrieves, and / or generates) at least one first rubric comprising one or more structure rules and one or more procedural rules for generating an output during a first interaction. For instance, the computer can retrieve at least one interaction rubric from a plurality of stored interaction rubrics maintained in association with the outer loop data structure 122 or inner loop data structure 124 of FIG. 1 for a given interaction type (e.g., interviewee simulation, guided assessment, or adaptive training session). In some implementations, the computer can generate or modify, based on received context data, a new interaction rubric (e.g., structure rules and / or procedural rules) for the interaction by instantiating a machine-readable schema that specifies the structure rules and procedural rules in alignment with the defined objectives of the context provider 104. As one example, the computer can generate an interaction rubric for a technical interview by retrieving relevant stored context vectors 123 containing role-specific competency data, and programmatically combining them with procedural templates that define evaluation sequences, scoring conditions, and transition logic. In some implementations, the computer can maintain an active pool comprising the plurality of interaction rubrics and a plurality of purpose rubrics selected for concurrent use across a plurality of conversational turns, allowing multiple evaluation and interaction objectives to remain active in parallel while dynamically adjusting weighting, priority, and configuration of each rubric during the course of the interaction. An interaction rubric can include a structured, machine-readable data structure that includes structure rules, which can define deterministic parameters such as output format constraints, ordering of prompts, persona configurations, or permissible input / output modalities, and procedural rules, which can define execution logic including branching conditions, closure criteria, iteration limits, evidence-capture rules, and evaluation triggers that dictate how each conversational turn or interaction step is conducted and processed. In some embodiments, the interaction rubric itself can be implemented not only as a static schema but also as an executable computational model configured to parse incoming participant inputs, apply the defined scoring or evaluation logic, and produce dimension-level outputs in real time for orchestration by the context provider 104 and / or execution by the generative engine 114.

[0133] In some implementations, an interaction rubric can further include multilingual and monolingual output directives that apply at a component-level granularity within the generated output. For example, the structure rules of the rubric can instruct that at least one component of the output, such as the conversational or interactive dialogue portion presented to the participant,IXV01-PCT PATENT be generated in a specified target language (e.g., Spanish, French, Mandarin) to facilitate accessibility, role-play authenticity, or localized engagement. At the same time, the procedural rules and / or evaluation criteria of the rubric can instruct monolingual output in another designated language, such as English or the primary language of the evaluation ecosystem, for all non-conversational components, including but not limited to rubric-driven evaluations, scoring metadata, state-tracking records, process control messages, and any structured outputs consumed by downstream automation or analytics systems. This partitioned multilingual / monolingual configuration can be encoded in the rubric as machine-readable constraints that apply per-component output filtering or pre- / post-processing, ensuring that the conversational layer remains flexible for linguistic variation while maintaining a consistent, standardized language for operational consistency, evaluation reproducibility, and reliable parsing by accompanying LLM orchestration, process evaluation components, and ancillary analytic subsystems.

[0134] In some implementations, generating and executing the interaction rubric can include defining at least one dimension as a gracefulness criterion, wherein the gracefulness criterion itself comprises one or more modulation parameters. The gracefulness criterion can be represented in the rubric as a quantitative or qualitative dimension that governs tone, politeness, tactfulness, stylistic quality, and / or other context-appropriate softening attributes to be applied to portions of the generated output. The modulation parameters can include, by way of example, politeness weighting values that bias lexical or syntactic selections toward more courteous phrasing, tactfulness thresholds that limit directness or confrontation in generated content, and stylistic tone vectors that influence sentence rhythm, formality level, or word choice alignment with a desired cultural or situational communication style. These modulation parameters can be encoded as machine-readable configuration values within the rubric’s structure and procedural rules, such that they are accessible to the generative engine during prompt execution. In operation, the modulation parameters of the gracefulness criterion can be applied in real time to generated content, allowing the system to adjust phrasing, structure, and semantic framing based on the active context vector, participant engagement state, and / or interaction objectives, thereby ensuring that the outputs meet both the functional performance requirements defined by the rubric and the desired interpersonal or cultural tone appropriate for the given scenario.IXV01-PCT PATENT

[0135] At operation 220, the computer obtains (e.g., receives, retrieves, and / or generates) at least one second rubric including one or more endpoints and one or more criteria corresponding to the first interaction. For instance, the computer can retrieve at least one purpose rubric from a plurality of stored interaction rubrics maintained in association with the outer loop data structure 122 or inner loop data structure 124 of FIG. 1 for a given interaction type. In some implementations, the computer can generate and / or modify (e.g., based on received context data), a new purpose rubric for the interaction by instantiating a machine-readable schema that specifies the endpoints and associated criteria in alignment with the defined objectives of the context provider 104. Endpoints (e.g., purposes) can represent discrete, measurable objectives or goals to be satisfied within an interaction (e.g., demonstrating competency in a specific skill domain, producing an artifact meeting defined quality thresholds, or completing a required decision step). Criteria can define the quantitative and / or qualitative conditions that must be satisfied for a corresponding endpoint to be considered achieved (e.g., minimum proficiency scores, conformance to formatting or procedural rules, inclusion of mandatory evidentiary elements, or performance benchmarks derived from historical data). An purpose rubric can include a machine-readable file or data object including a definition or schema used for defining structured data objects. Non-limiting examples of the types of data used for defining rubrics may include JSON, XML, and YAML, among others. The structured data of the rubric may include executable software routines (e.g., rules), data fields, and / or values of the data fields, among other types of information. For instance, the rubric may include data fields and values for mapping each endpoint to one or more criteria associated with the rubric. In some implementations, the computer can retrieve more than one purpose rubric to reflect an intent scenario for multiple desired endpoints for a given interaction. In further implementations, the computer can generate the at least one purpose rubric as a multi-dimensional assessment framework configured to define a competency across a plurality of scored proficiency levels. Such a framework can assign discrete score ranges to proficiency gradations (e.g., novice, developing, proficient, expert) for each competency dimension, and can include weightings or scaling factors for combining dimension-level scores into an overall competency index. The structured rubric can further associate each scored proficiency level with descriptive performance indicators and evidence requirements, enabling automated evaluation and consistent scoring across multiple interactions and participants.IXV01-PCT PATENT

[0136] At operation 230, the computer generates, by executing an LLM, a prompt structured according to the at least one interaction rubric and the at least one purpose rubric. For example, the computer can assemble the prompt from machine-readable schema elements that define structure rules, procedural rules, endpoints, and criteria. This assembly can involve concatenating fixed instruction segments, dynamically inserting context-specific data, and embedding control tokens or metadata tags that direct the LLM to produce outputs in compliance with the defined evaluation requirements. The prompt can incorporate placeholders for participant input, markers for evidence capture, modality specifications, and formatting constraints to provide deterministic parsing during evaluation. In some implementations, the prompt is structured to satisfy criteria of at least two purpose rubrics concurrently by merging their respective requirements into a single unified prompt definition. That is, the computer executing the LLM can apply dynamic weighting to evaluate and / or otherwise prioritize certain criteria, resolve conflicts between rubric rules through procedural precedence logic, and sequence instructions so that the generated output addresses all targeted endpoints in the required order. For example, in a skills-assessment scenario, criterion for a technical competency rubric can be merged with criterion for a communication-quality rubric, resulting in a prompt that instructs the participant to solve a complex problem while also explaining their reasoning with clarity and adherence to style and format guidelines.

[0137] In some implementations, generating the prompt can include modifying the prompt based on a gracefulness profile, the gracefulness profile comprising one or more modulation parameters corresponding to politeness weighting, tactfulness threshold, stylistic tone vector, among other parameters. The gracefulness profile can be represented as a machine-readable parameter set that encodes qualitative communication attributes using scalar values, thresholds, and / or vector embeddings. The computer can retrieve or generate the gracefulness profile in alignment with contextual factors such as participant role, cultural norms, situational sensitivity, and intended interaction goal. In operation, the computer can apply the gracefulness profile during prompt assembly by adjusting weighting coefficients in instruction templates, inserting or substituting lexical choices to meet politeness and tactfulness thresholds, and appending stylistic markers that shift the output tone according to the stylistic tone vector. For example, a high politeness weighting can trigger the insertion of additional acknowledgement or appreciation phrases, while an elevated tactfulness threshold can suppress direct or confrontational phrasingsIXV01-PCT PATENT by substituting semantically softer alternatives. The stylistic tone vector can further modulate sentence rhythm, word selection, and expressive variance to align generative output with an intended communicative style while maintaining compliance with the structural and procedural rules of the active interaction rubric and purpose rubric.

[0138] In some implementations, generating the prompt can include selecting a prompt structure based on a rubric-aligned objective associated with a user interface interaction. The rubric-aligned objective can be determined by evaluating current interaction state, participant performance indicators, and configured goals in the active interaction rubric and purpose rubric. The selected prompt structure can correspond to a predefined schema that optimizes information elicitation, output validation, or performance assessment for that specific objective. For example, in an immersive reading scenario, the selected prompt structure can be chosen to configure environmental adjustments such as lighting intensity, color temperature, background audio level, and / or haptic feedback timing. Receiving the response can further comprise evaluating the response in accordance with an immersive-environment rubric comprising a plurality of environmental control dimensions, each associated with specific scoring criteria and evidence capture requirements. The computer can apply the scoring criteria to the received response by parsing and segmenting the response content (e.g., preferences expressed by the user for lighting warmth, brightness levels, or ambient sound settings), mapping relevant portions to the associated environmental control dimension, and computing a dimension-level score using rubric-defined metrics (e.g., matching target lux levels, maintaining user comfort thresholds, achieving desired ambiance parameters). These computed scores, along with supporting annotation data such as the exact device commands issued and sensor-verified environmental readings, can be stored in a structured result object that is used to drive subsequent prompt selection, context adjustment, and real-time orchestration decisions during the ongoing interaction.

[0139] At operation 240, the computer obtains (e.g., receives, retrieves) a response comprising interaction data corresponding to the at least one interaction rubric and the at least one purpose rubric. In some implementations, the computer can receive this interaction data via one or more sensors (e.g., sensors 818), which can include microphones, cameras, motion sensors, biometric sensors, and / or environmental sensors. For example, the interaction data can include textual input, voice input, facial expression data, gaze tracking data, body movement tracking data,IXV01-PCT PATENT physiological indicators (e.g., heart rate, skin temperature, galvanic skin response), and / or environmental context data such as lighting level, sound level, or proximity readings, among other sensor data. Sensor-derived data can be preprocessed locally or remotely to generate structured input signals, which may include transcribed speech, extracted non-verbal cues, segmented motion patterns, or normalized environmental readings. In some implementations, the interaction data received via the sensors can further include event metadata such as timestamps, confidence scores of detected features, and modality identifiers, allowing the computer to correlate received information with specific dimensions, evaluation criteria, and procedural steps defined in the active interaction rubric and purpose rubric.

[0140] At operation 250, the computer generates context data based on a comparison between the interaction data and the at least one purpose rubric. For example, the context data can indicate a likelihood of completion of the one or more endpoints and a measure of compliance with the one or more criteria. In some implementations, the computer can map extracted features from the interaction data to the defined endpoints and criteria of the purpose rubric. The mapping process can include applying text analysis, semantic similarity scoring, statistical pattern recognition, and / or rule-based validation to align the received interaction data with the quantitative and / or qualitative success conditions defined for each endpoint. The generated context data can include one or more computed indicators, such as a likelihood of completion of the one or more endpoints and a numerical or categorical measure of compliance with the one or more criteria. In some implementations (e.g., when the purpose rubric defines a multi-dimensional assessment framework), the computer can determine a proficiency score for at least one dimension by applying a scoring algorithm to the aligned interaction data, such as summing weighted sub-criteria scores, normalizing raw values to a fixed scoring scale, and mapping the resulting score to a defined proficiency level (e.g., novice, developing, proficient, expert). The computer can store a data structure representing the evaluation results, which can include the proficiency score for each evaluated dimension and corresponding supporting data (e.g., excerpts of the response matched to rubric criteria, intermediate calculation results, timestamped sensor metadata, and model-generated confidence values). This stored data structure can be used, for example, for subsequent processing, including adaptive adjustment of active rubrics, longitudinal tracking of participant performance, and / or generation of audit-ready evaluation reports.IXV01-PCT PATENT

[0141] At operation 260, the computer determines (e.g., modifies, generates, and / or retrieves) an updated at least one interaction rubric and updated at least one purpose rubric for a subsequent interaction. In some implementations, determining the updated interaction rubric can comprise selecting the updated interaction rubric by adding, removing, and / or modifying at least one structural rule (e.g., defined prompt formats, sequencing constraints, modality requirements and / or other structural rules) or at least one procedural rule (e.g., branching logic, closure conditions, evidence-capture procedures, or iteration limits) in the initial interaction rubric, define. Determining the updated purpose rubric can likewise comprise selecting the updated purpose rubric by adding, removing, and / or modifying at least one endpoint or at least one criterion in the first purpose rubric. Endpoints (e.g., purposes) can represent discrete objectives within the interaction, and criteria can define the measurable or evaluative conditions for achieving those objectives. In some embodiments, the computer can generate the updated at least one interaction rubric by executing an orchestrator that processes the context data derived from the most recent interaction as well as the historical interaction data accumulated over prior turns or sessions. The orchestrator can identify rule modifications, endpoint adjustments, and / or criteria updates that would improve alignment between the interaction flow and the defined objectives for the next conversational turn or subsequent session.

[0142] In some implementations, the computer can train the orchestrator for generating the updated at least one interaction rubric using a training corpus comprising a plurality of training labels and a corresponding plurality of training interaction records, each training interaction record comprising interaction content and interaction metadata. Each training label can indicate whether the corresponding training interaction record achieves at least one defined interaction goal. The computer can update one or more parameters of the orchestrator based on a comparison between a predicted outcome generated by the orchestrator and an actual outcome indicated in the context data for a plurality of prior interactions, with the comparison performed using a loss function applied to each predicted outcome and each actual outcome. In some implementations, the computer can update an active pool comprising the plurality of interaction rubrics and the plurality of purpose rubrics based on the newly acquired context data, such that the updated rubrics replace or augment prior active rubrics in the pool. This updating process can ensure that the interaction orchestration remains dynamically aligned with participant performance trends, evolving operational objectives, and real-time environmental conditions, providing for concurrent use andIXV01-PCT PATENT active refinement of multiple evaluation and interaction objectives across successive conversational turns.

[0143] At operation 270, the computer provides, by executing the LLM, a second prompt generated according to the updated at least one interaction rubric and the updated at least one purpose rubric. In some implementations, generating the second prompt can include re-assembling the prompt structure from the updated machine-readable schema elements, incorporating any added, removed, or modified structural rules, procedural rules, endpoints, or criteria determined during the rubric-updating process. The second prompt can embed revised control tokens, metadata tags, or instruction modifiers that explicitly reflect the orchestrator-selected adjustments, such as new sequencing logic, altered evidence-capture requirements, modified evaluation thresholds, or updated persona configurations. The assembly process can merge updated context vectors with retained contextual elements from the prior interaction to ensure continuity, while also integrating newly relevant environmental parameters, participant performance indicators, or historical interaction patterns. The prompt generation module can apply dynamic weighting logic to balance the updated evaluation priorities, resolve conflicts introduced by newly activated rubrics, and generate the prompt for concurrent satisfaction of multiple updated endpoints. Once generated, the second prompt can be formatted into a model-ready instruction package (e.g., as a serialized JSON-encoded directive with embedded context and control flags) and transmitted to the LLM execution environment for processing, thereby initiating the subsequent conversational turn or interaction phase in accordance with the updated orchestration objectives.

[0144] FIG. 3 shows a dataflow amongst components of a system (e.g., system 100 of FIG. 1) performing operations of a method for implementing a rubric-driven interaction engine (e.g., for transforming an LLM). The dataflow can be performed or implemented with, for example, system 100, including respective subsystems (e.g., system data processing system 101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structure 122, and / or outer loop data structure 124). As shown, the dataflow 300 can include user device 310, assessment system 320, context system 330, rubric system 340, and / or knowledge system 350. The user device 310, assessment system 320, context system 330, rubric system 340, and / or knowledge system 350IXV01-PCT PATENT can include one or more processing circuits, memory devices, and / or data repositories configured to perform the processes described herein.

[0145] In some implementations, as shown by reference label 360, the user device 310 (e.g., executing an application interface) can initiate an assessment sequence with the assessment system 320. The initiation can include the user device 310 transmitting assessment initiation data, such as interaction context data, rubric selection identifiers, and / or configuration parameters, to the assessment system 320 over a network connection. The assessment system 320 can include an orchestrator engine configured to select at least one interaction model (e.g., interaction rubric) and at least one endpoint model (e.g., purpose rubric) from stored model libraries, a semantic mapping engine configured to align unstructured input data to structured informational items within the active loop structures, an evaluation engine configured to execute scoring logic in accordance with active endpoint model criteria, and / or an interface engine configured to transmit prompts to a generative model and receive returned output. The assessment system 320 can also include a data repository for persistent storage of interaction history, scoring metrics, and execution state, allowing the orchestrator to control multi-turn interactions without redundant model calls. Upon initiation by the user device 310, the assessment system 320 can verify configuration data, load the relevant loop data structures, and begin execution of the first assessment turn based on the received context parameters.

[0146] In some implementations, as shown by reference label 362, the assessment system 320 can initiate an assessment by transmitting assessment initialization data, including interaction context parameters and / or model selection identifiers, to a context system 330. The context system 330 receive the assessment initialization data, retrieve stored contextual information, and analyze that information to determine relevant interaction attributes. For example, in an interviewee simulation scenario, the assessment system 320 can select and load an outer loop data structure defining a mock interview sequence, along with one or more inner loop data structures containing prompt sets for individual interview topics. The assessment system 320 can provide to the context system 330 candidate profile data, including simulated resume details, target role information, and / or industry domain parameters. The context system 330 can process the provided data to align with rubric-defined informational requirements, such as communication skills, technical proficiency, or cultural fit, and return structured context output to the assessment system 320.IXV01-PCT PATENT

[0147] In some implementations, as shown by reference label 364, the context system 330 can retrieve a framework. For example, the context system 330 can retrieve (e.g., obtain) a competency framework from knowledge system 350. In some implementations, the competency framework can include a structured, machine-readable set of evaluation dimensions, scoring criteria, and proficiency level definitions. In the context of the interviewee simulation scenario, the context system 330 can request from the knowledge system 350 a competency framework corresponding to the target role identified in the assessment initialization data, such as a “Senior Software Engineer” role. The knowledge system 350 can store one or more role-specific frameworks, each defining weighted assessment categories such as technical expertise, problemsolving approach, communication skills, and cultural alignment, along with dimension-level scoring rubrics for each category.

[0148] As shown by reference label 366, the knowledge system 350 can return a framework and / or rubric to the context system 330. Upon receiving the framework from the knowledge system 350, the context system 330 can associate the retrieved evaluation dimensions with active purpose models in the assessment, (e.g., to provide for subsequent prompts and scoring logic executed by the assessment system 320 adhere to the defined framework). For example, if the retrieved framework specifies that “system design proficiency” is to be assessed with a weight of 30%, the context system 330 can provide that specification to the assessment system 320 so that the orchestrator aligns prompts and evaluation criteria accordingly.

[0149] As shown by reference label 368, the context system 330 can generate, transmit, formulate, and / or otherwise provide a context-aware assessment plan to assessment system 320. For example, the context system 330 can use the competency framework obtained from the knowledge system 350 together with an interaction rubric retrieved from stored model libraries to generate a sequenced assessment plan that specifies prompt structures, branching rules, and scoring logic. In an interviewee simulation scenario, the competency framework can define weighted evaluation dimensions such as “system design proficiency,” “programming fluency,” and “collaboration skills,” while the interaction rubric can define structural and procedural rules for generating prompts, ordering topics, and applying satisfaction thresholds. The context system 330 can align each dimension of the competency framework to specific structural elements defined in the interaction rubric.IXV01-PCT PATENT

[0150] The assessment system 320 can be implemented as an Al-driven system that uses the provided context-aware assessment plan to select, instantiate, execute, and manage relevant outer loop and inner loop data structures during assessment execution. In this configuration, the outer loop data structure can define the overall sequence of the assessment, including persona configuration, topic ordering, and completion conditions, while the inner loop data structure can define one or more prompts and corresponding logic for gathering specific pieces of information for each assessment stage. The assessment plan provided by the context system 330 can determine which outer loop to activate, which inner loops to associate with each stage, and how the generative language model is invoked within those loops.

[0151] At this stage, the dataflow 300 may include a loop. For example, assessment system320 can generate a contextual question for the application operating on user device 310, as shown by reference label 370. That is the assessment system can generate a contextual question of “Can you describe a challenging system design problem you have solved and outline the reasoning that led to your solution?”, where the phrasing and structure of the question are defined by the active interaction model in the currently running inner loop data structure.” As shown by reference label 372, the user device 310 can provide a response to the assessment system 320. For example, the application can transmit a spoken or text-based answer describing the design of a distributed microservices architecture, including system requirements, proposed components, and trade-offs considered in the design process.

[0152] As shown by reference label 374, the assessment system 320 can provide context data extracted from the received response to the context system 330. The context data can include key technical terms, detected problem-solving steps, and other structured elements derived from the response. The context system 330 can analyze this context data against the expectations defined in the active assessment plan. For example, the context system 330 can determine whether the candidate addressed scalability considerations as required by the competency framework dimension for “System Design Proficiency.” As shown by reference label 376, the context system 330 can provide the rubric system with the analyzed context elements for validation against the active rubric. The rubric system can compare the extracted elements to rubric-defined response attributes, such as inclusion of architectural diagrams, explanation of trade-offs, and use of domain-specific terminology relevant to the target role.IXV01-PCT PATENT

[0153] As shown by reference label 378, the rubric system can update a score, for example, the score for the “System Design Proficiency” competency. For instance, the rubric system can calculate a score of 4 out of 5 based on strong technical details but limited discussion of cost considerations extracted from the response. Additionally, the rubric system can provide the updated score back to the assessment system 320 so it can be incorporated into the persistent state of the ongoing assessment. As shown by reference label 380, the assessment system 320 can generate a next question strategy based on the updated score and the state of the outer loop data structure. For example, if the score for “System Design Proficiency” is high but the score for “Collaboration Skills” is incomplete, the next generated question can focus on team communication: “How did you work with cross-functional teams during this project to ensure alignment on the design approach?”. This process can conclude the current loop iteration and can initiate the next, repeating until all targeted competency dimensions have been addressed or completion criteria defined in the outer loop data structure are satisfied.

[0154] As shown by reference label 382, the knowledge system 350 can store the assessment results. The assessment system 320 can generate the assessment results in a structured format containing competency dimension scores, associated evidence extracted from responses, completion metadata, and any notes produced during rubric validation. These results can be transmitted to the knowledge system 350 for archival, analytics, and / or integration with other organizational systems. Additionally, as shown by reference label 384, the assessment system 320 can provide a profile (e.g., a competency profile), to the user device 310. For example, the competency profile can be presented on the application interface of the user device 310 as a visual dashboard displaying each evaluated dimension (e.g., System Design Proficiency, Programming Fluency, Collaboration Skills) with corresponding scores, performance summaries, and recommendations for improvement. The display can include a breakdown view of each score, showing which rubric criteria were met and where gaps remain, allowing the candidate to understand specific areas to strengthen in preparation for future interactions or assessments.

[0155] FIG. 4 shows a dataflow 400 amongst components of a system (e.g., system 100 of FIG. 1) performing operations of a method for implementing a rubric-driven interaction engine (e.g., for transforming a LLM). The dataflow can be performed or implemented with, for example, system 100, including one or more respective subsystems (e.g., system data processing systemIXV01-PCT PATENT101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structure 122, and / or outer loop data structure 124). As shown, the dataflow 400 can include user device 410, context system 420, server application 430, interaction system 440, and / or support system 480. The user device 410, context system 420, server application 430, interaction system 440, and / or support system 480 can include one or more processing circuits, memory devices, and / or data repositories configured to perform the processes described herein.

[0156] In some implementations, as shown by reference label 460, the user device 410 (e.g., executing an application interface of system 100) can trigger an interaction with server application 430. The interaction can include, for example, interaction detected by one or more sensors through presence detection, touch input, sound detection, timer-based activation, phrase recognition, scent detection, and / or other sensor-based event types. The application executed on the user device 410 can be, for example, a WebRTC client configured to establish and manage real-time audio and / or video sessions, a robot API client configured to transmit control commands to a physical actuator or interaction device, a JavaScript API client configured to provide browser-based access to the features of the system 100, and / or another client application configured to communicate with the components of the system 100 shown in FIG. 1.

[0157] As shown by reference label 462, the server application 430 may provide the context system 420 with interaction event(s). The interaction events can refer to compiled information, incremental information, and / or a combination thereof. For example, the server application 430 can receive sensor and application data originating from the user device 410. Alternatively, the server application 430 can transmit individual incremental interaction data items directly to the context system 420 for compilation. In this configuration, the context system 420 can perform temporal correlation, feature extraction, and context alignment tasks to combine the incremental data into a structured interaction representation. Alternatively, the server application 430 can transmit individual incremental interaction data items directly to the context system 420 for compilation. In this configuration, the context system 420 can perform temporal correlation, feature extraction, and context alignment tasks to combine the incremental data into a structured interaction representation.IXV01-PCT PATENT

[0158] As shown by reference label 464, the context system 420 can determine a context of the interaction based on the interaction events. For example, in an interviewee simulation scenario, the context system 420 can process compiled and / or incremental interaction events containing audio segments of answers, detected keywords, facial expression data from video frames, and gesture or posture changes captured by the user device 410. Using this data, the context system 420 can identify the current focus area of the interview (e.g., whether the candidate is discussing technical system architecture, prior project experience, or cross-team collaboration). This context system 420 can include aligning the extracted information with specific stages defined in the active outer loop data structure and associating the content with one or more dimensions of a competency framework, such as “System Design Proficiency” or “Collaboration Skills.” Alternatively, the context system 420 can provide context data of the interaction to the interaction system 440. The context data can include a pre-programmed intention of the interaction (e.g., a predefined competency evaluation sequence) and / or a dynamically determined intention based on ongoing analysis of participant responses, environmental conditions, and detected performance gaps. That is, the interaction system can determine dynamic intent based on runtime evaluations (e.g., performed by the orchestrator engine, context provider 104, contextual input handler 106, and scorer 108 acting on sensor data from sensors). The interaction system 440 can use the provided context data to select or modify prompts, invoke validation operations, and update assessment logic in real time. The interaction system 440 can operate in conjunction with the components of FIG. 1, and / or can be further configured as described for incorporating metrics and configurations into workflows such as described in relation to FIG. 6 and FIG. 15.

[0159] As shown by reference label 466, the interaction system 440 can generate interaction instructions, as discussed herein. As shown by reference label 466, the interaction system 440 can generate interaction instructions as discussed herein. The interaction system 440 can generate these instructions by analyzing the current context data, the active outer loop data structure, the associated inner loop data structure, and one or more active rubrics or models. Using these elements, the interaction system 440 can determine how to manipulate the current interaction state and define parameters for the next interaction turn. For example, in an interviewee simulation scenario, the interaction system 440 can adjust the prompt sequence to shift from assessing “System Design Proficiency” to evaluating “Collaboration Skills” based on previously updated rubric scores, while specifying changes in prompt formatting, tone, and delivery method. TheIXV01-PCT PATENT interaction system 440 can use this analysis to generate an updated interaction definition, which specifies the structure, rules, and procedural steps to be applied during the next conversational turn. That is, the interaction system 440 can provide interaction instructions to the context system 420 in a structured data format such as a set of JSON fields (e.g., "messageAudioUrl", "instructions", "WebRTCConfig" : { "room": "...", "token": "..." }).

[0160] At this stage, the dataflow 400 may include a loop (e.g., for each interaction instruction component). For example, context system 420 can utilize an environment to satisfy instructions, as shown by reference label 470. The context system 420, in coordination with the server application 430, can execute the received interaction instructions by invoking the appropriate hardware, software, and / or network resources defined in the updated interaction definition. In one example, the context system 420 and server application 430 can satisfy the instructions by causing the user device 410 to play an audio file through a connected output device, where the audio contains the text of the next interview prompt generated by the interaction system 440 and rendered by a text-to-speech engine. In another example, the context system 420 and server application 430 can manipulate a webpage rendered on the user device 410 to alter the format of the user interface in real time (e.g., changing the interface from a text-based question-and-answer view to a video-based interview scenario using embedded HTML5 video elements, dynamically increasing font size and contrast for partially sighted users, and / or reflowing interface components to accommodate different accessibility modes defined by the active interaction model). In relation to FIG. 1, the context system 420 can select and control these environment manipulations through operational links to the interface engine 112 and downstream client-side applications such as a WebRTC client, a robot API client, or a JavaScript API handler on the user device 410. The server application 430 can manage the communication between the context system 420 and the relevant end-point, ensuring that the output is delivered according to the timing, format, and modality constraints specified in the outer loop data structure 122 and inner loop data structure 124.

[0161] As shown by reference label 470, the server application 430 can provide a multi-modal interaction on the user device 410. For example, the server application 430 can play an audio output through speakers integrated with or connected to the user device 410, where the audio corresponds to a system-generated prompt rendered from the interaction instructionsIXV01-PCT PATENT received from the context system 420. In another example, the server application 430 can transmit control signals through a robot API client to move a robot arm to a specified position n to simulate a physical gesture associated with the interaction. In another implementation, the server application 430 can deliver a visual cue on the user device 410 to signal a process transition, such as displaying a progress indicator, animating an icon, or presenting a color change in a designated interface region, based on the format and display timing defined in the relevant outer loop data structure 122 and inner loop data structure 124. For example, the orchestrator can coordinate these actions so that each modality (e.g., audio data, visual data) is synchronized within the defined conversational turn. By executing multi-modal interactions, the server application 430 can engage multiple sensory channels, accommodate participant preferences or accessibility requirements, and maintain a consistent rubric-aligned sequence for the ongoing assessment loop.

[0162] As shown by reference label 472, the context system 420 and the server application 430 can interact with the environment to carry out control operations defined by the interaction instructions (e.g., with associated outer loop data structure 122 and / or inner loop data structure 124). For example, the context system 420 and the server application 430 can mute and / or unmute the microphone of the user device 410 to manage when audio input is captured during an assessment turn. In another example, the context system 420 and the server application 430 can adjust environmental conditions (e.g., dimming connected lighting systems or increasing brightness, based on accessibility settings, interview stage requirements, or rubric-driven presentation rules).

[0163] As shown by reference labels 474 and 476, the server application 430, via interactions with the user device 410, can determine an interaction start trigger and / or an interaction end trigger. For example, the server application 430 can start listening for speech through a microphone attached to or integrated with the user device 410 when a voice-activity-detection (V D) process detects the presence of speech energy above a defined threshold and / or when another interaction event occurs, such as a user-activated control in the application interface. Similarly, the server application 430 can stop listening when the VAD process determines that a continuous period of silence (e.g., exceeding a configurable duration defined in the active inner loop data structure 124) has occurred and / or when an end interactionIXV01-PCT PATENT event is triggered, such as a participant confirming that a response is complete through a gesture or button press.

[0164] As shown by reference label 478, the support system 480 can operate as an optional supporting service in the loop. For example, the support system 480 can execute a speech-to-text service defined within the interaction instructions, converting live or recorded audio input from the user device 410 into structured text data for downstream processing. The support system 480 can provide this text output in real time or in batches, depending on timing parameters defined in the active outer loop data structure 122 and / or inner loop data structure 124.

[0165] In some implementations, as shown by reference label 482, the support system 480 can provide a mid-interaction relay of context through the server application 430 and the context system 420. For example, during an interviewee simulation, the support system 480 can stream interim speech-to-text results and associated metadata (e.g., recognized keywords, confidence scores, timing markers) to the context system 420 while the participant is still delivering the response. The context system 420 can analyze this partial context mid-turn to determine whether rubric criteria are trending toward satisfaction, whether follow-up probes may be needed, or whether environmental adjustments (e.g., UI changes, prompt clarification) should be triggered before the participant completes the turn.

[0166] As shown by reference label 484, the server application 430 can provide (e.g., transmit), one or more interaction events to the context system 420. The server application 430 can generate interaction events as compiled data sets, incremental updates, and / or a combination thereof. For example, the server application 430 can compile synchronized audio data from a microphone, video frames from a camera, and touch gesture data from a touchscreen of the user device 410 into a unified, time-aligned interaction event package. A compiled package can include metadata (e.g., timestamps, confidence scores from a speech-to-text service, or gesture classification labels) for the context system 420 to process all relevant sensory streams together in context. Alternatively, the server application 430 can transmit incremental interaction data items directly to the context system 420 as they are captured. For instance, the server application 430 can first send a partial speech-to-text transcript from audio input, followed by detected facial expression data from video frames, and then positional data from touch or motion sensors. The context system 420 can use the incremental approach to build the complete interactionIXV01-PCT PATENT representation over time (e.g., aligning each modality to the procedural stage defined in the active outer loop data structure 122 and inner loop data structure 124).

[0167] In some implementations, as shown by reference label 486, the context system 420 (e.g., via server application 430) can provide a pre-designed interaction to the user device 410. For example, the context system 420 can execute a pre-designed comfort interaction defined in the active inner loop data structure 124 by causing the server application 430 to play an audio tone through the speakers of the user device 410 to inform the participant of a process transition, as specified in the interaction instructions. Other types of comfort interactions can include displaying a visual transition effect on the user interface, animating an icon, or showing a message panel that confirms the current stage has been completed. These interactions can be triggered by procedural markers in the outer loop data structure 122 or by conditions met in the active rubric evaluation sequence.SYSTEMS AND METHODS FOR SIMULATED ASSESSMENTS

[0168] FIG. 5 is a flowchart showing operations of a computer-implemented method 500 for conducting a simulated interview session using a generative artificial intelligence model and rubrics. For ease of description and understanding, the operations of the computer-implemented method 500 are described as being performed by a computer, though embodiments may be implemented using any type of computing device and / or may be implemented using multiple computing devices. Embodiments may include additional or alternative operations and features, or omit certain operations and features, and still fall within the scope of this disclosure. The method 500 may be executed by a computer configured to simulate both interviewer and interviewee roles and to orchestrate multi-turn interactions governed by structured evaluation criteria. The method may be implemented in whole or in part across distributed systems, cloud-based platforms, or embedded environments. Variations in the sequence or inclusion of operations may be applied without departing from the scope of the disclosed embodiments.

[0169] At operation 510, the computer obtains (e.g., receives, retrieves) configuration data of a simulated interviewee persona, the configuration comprising one or more behavioral traits, domain-specific knowledge parameters, and response modulation instructions. For instance, the configuration includes a structured schema specifying behavioral traits (e.g., assertiveness,IXV01-PCT PATENT formality), domain-specific knowledge parameters (e.g., technical expertise in software engineering), and response modulation instructions (e.g., verbosity level, tone adaptation). The computer uses these parameters to instantiate and execute a generative model capable of producing contextually appropriate responses that reflect the defined persona throughout the simulated interview session.

[0170] At operation 520, the computer obtains (e.g., receives, retrieves) contextual data defining an interview scenario. The contextual data includes, for example, role-specific attributes (e.g., job title, required competencies), organizational metadata (e.g., company values, departmental structure), and interviewer profile information (e.g., preferred questioning style, evaluation focus). The computer uses the contextual data to generate a context vector that informs both prompt generation and response evaluation during the interaction.

[0171] At operation 530, the computer instantiates or executes a simulated interviewee engine configured to generate responses to interviewer prompts based on the persona configuration and contextual data. The simulated interviewee engine comprises a generative artificial intelligence model configured to synthesize responses to interviewer prompts based on the persona configuration and contextual data. The simulated interviewee engine includes software programming and logic for tone modulation, domain-specific language generation, and adaptive response formatting aligned with the interview scenario.

[0172] At operation 540, the computer instantiates or executes a simulated interviewer engine configured to generate prompts based on a competency rubric, the competency rubric comprising evaluation dimensions, scoring criteria, and proficiency thresholds. The simulated interviewer engine is configured to generate prompts using a competency rubric that defines evaluation dimensions (e.g., communication skills, problem-solving ability), scoring criteria (e.g., relevance, completeness), and proficiency thresholds (e.g., novice, intermediate, expert). The computer selects prompts from a structured prompt library indexed by rubric dimension and proficiency level and applies branching logic based on prior responses.

[0173] At operation 550, the computer executes a multi-turn interaction sequence between the simulated interviewer engine and the simulated interviewee engine. The computer processes each prompt and response to update an interaction state. For instance, the computer processes eachIXV01-PCT PATENT prompt-response pair to update an interaction state, which includes current rubric satisfaction status, response history, and performance indicators. The computer uses the interaction state to guide prompt selection, rubric activation, and evaluation logic in subsequent turns.

[0174] At operation 560, the computer generates a proficiency score and associated evidence log. The computer evaluates each response from the simulated interviewee engine against the competency rubric to generate the proficiency score and a set of evaluation results (e.g., associated evidence log) For instance, the computer evaluates each response from the simulated interviewee engine against an active competency rubric. The computer applies semantic similarity models, rubric-defined criteria, and contextual weighting to generate a proficiency score for one or more evaluation dimensions. The computer also generates an evidence log comprising extracted response features, scoring rationale, and rubric alignment metadata. A combined data structure can be used by downstream processes to trace each assigned score back to specific evaluation criteria and the supporting response attributes, thereby maintaining auditability and permitting automated performance trend analysis over time

[0175] At operation 570, the computer updates the prompt generation logic (sometimes referred to as a prompt generation engine) of the simulated (e.g., interviewer) engine based on the proficiency score and interaction state. The computer updates the prompt generation logic of the simulated interviewer engine based on the current interaction state and the proficiency score. The update includes modifying prompt structure, adjusting topic sequencing, or changing delivery modality (e.g., text, audio). The computer may also activate or deactivate rubric dimensions dynamically based on rubric satisfaction thresholds or detected performance gaps. The prompt generation logic can include instructions (e.g., machine-executable instructions) stored as parameterized templates or rulesets that the system parses during runtime to construct prompts, insert dimension-specific terminology, and adjust sequencing logic according to whether the targeted dimension’s satisfaction threshold has been met in the interaction state. For example, linking the prompt generation instructions directly to the evaluation dimensions provides updates in the proficiency score to influence the topical coverage, difficulty level, and delivery modality of subsequent prompts in a manner that remains aligned with the rubric-defined assessment objectives.IXV01-PCT PATENT

[0176] In some implementations, the computer stores a structured record of the interaction sequence, the persona configuration, the contextual data, and the evaluation results into non- transitory machine-readable storage memory of a persistent data repository. The stored record includes the full interaction sequence (e.g., prompts and responses), the persona configuration, the contextual data, and the evaluation results (e.g., proficiency scores, evidence logs). The computer indexes the record by session identifier and timestamp for use in downstream analytics, auditability, or training feedback.

[0177] FIG. 6 shows a dataflow 600 amongst components of a system (e.g., system 100 of FIG. 1) performing operations of a method for conducting a simulated environment (e.g., interviewee for interview practice and / or market simulation). The dataflow can be performed or implemented with, for example, system 100, including one or more respective subsystems (e.g., system data processing system 101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structure 122, and / or outer loop data structure 124). Additionally, the dataflow 600 may incorporate or be an extension of dataflow 400. The dataflow may include interaction system 610 (e.g., implemented as or with interaction system 440), model 620, and / or configuration system 630. The configuration system 630 can generate an interviewee personification based on setup parameters and context, as shown by reference label 652. For example, the configuration system 630 can select and maintain a persistent set of behavioral traits, speech patterns, and persona attributes (e.g., formality level, domain expertise, interpersonal style) that are consistently applied by the relevant generative models throughout the interview scenario. The configuration system 630 can update the personification dynamically in response to mid-interaction context changes, ensuring the simulated interviewee maintains coherence, personality consistency, and contextually appropriate adaptation to the evolving conversation

[0178] As discussed with respect to FIG. 4, the interaction system 610 may receive context data from a context system (e.g., context system 420). The interaction system 610 can include, for example, a configuration interface implemented using the interface engine 112 described in FIG. 1. The interaction system 610 can communicate with configuration system 630 to define a configuration definition, as shown by reference label 650. The configuration definition can include a structured, machine-readable schema specifying operational parameters, instruction sets, andIXV01-PCT PATENT environment control directives to be applied during execution of an interaction loop. Operational parameters can include, for example, target generative engine type (e.g., LLM, multimodal, domain-specialized), model version to be executed, and / or performance constraints such as throughput limits, latency budgets, and GPU allocation requirements. The configuration definition can further embed environmental configuration logic, including enabled modalities (e.g., audio, visual, haptic), accessibility presentation rules (e.g., high-contrast UI, captioning, font scaling), and timing thresholds for prompt presentation or participant response windows.

[0179] At reference label 654, the interaction system 610 can process the received context data and enrich it by loading and creating any necessary state or persistence for the interaction context. For example, the interaction system can generate or retrieve an interaction identifier, establish authentication and / or authorization credentials, select and / or instantiate an interaction template, associate one or more LLM identifiers, and / or load state-machine code for driving execution of the planned interaction flow. At reference label 656 At reference label 656, the interaction system 610 can instruct the generation of data structure or configuration indicating an interaction definition by providing a set of high-level orchestration requirements, active rubric data, procedural rules, and contextual constraints to the model 620. These instructions can specify the interaction’s evaluative objectives, the scope of content to be covered, branching logic rules, and modality preferences for both prompt delivery and participant input capture.

[0180] At reference label 658 the model 620 can generate low-level instructions to manipulate the context and cause one or more effects that drive the interaction toward its intended objective. For example, the model 620 can generate these instructions within both stochastic and deterministic parameters defined by the interaction design, taking into account the current and predicted state of the context as well as any real-time environmental or system metrics fed back from active components.

[0181] At reference label 660, the model 620 can define the interaction definition as machine-readable content sufficient to instruct one or more available interaction devices, systems, and / or processes within the active context. The interaction definition can include orchestration instructions for sequencing, conditional branching, concurrency control, and modulation of prompt delivery style in alignment with rubric-driven evaluation criteria. The configuration definition can define input acquisition parameters, including which sensor data streams to activate from the userIXV01-PCT PATENT interface 102, preprocessing rules for those streams (e.g., tokenization, multimodal serialization into embeddings, normalization for consistent scaling), and mapping directives for the contextual input handler 106 to align raw data with structured context maps. Additionally, it can specify output parameters, such as which delivery modalities to use (e.g., audio playback through speakers, visual rendering on a display, physical actuation of a device), how to sequence those outputs, and how to synchronize their execution with procedural stages defined in the outer loop data structure 122 and inner loop data structure 124.

[0182] At reference label 662, the interaction system 610 or the model 620 can perform any necessary preprocessing of individual components of the generated instructions before execution. Such preprocessing can include converting text content into audio output using a text-to- speech subsystem, invoking an API to persist collected data to a secure storage backend, interacting with a tertiary system for purposes such as real-time analytics, content filtering, or sending alerts, and / or invoking additional LLMs for enrichment, validation, or repair of specific aspects of the generated instruction set. These preprocessing steps prepare the orchestrated instructions for execution by downstream systems, ensuring that the interaction is delivered in a manner consistent with the performance, accessibility, and rubric-driven objectives defined in the configuration definition.

[0183] FIG. 7 shows an example system 700 for a skills architecture management system (e.g., implementing system 100 of FIG. 1). The system can be performed via, or implemented with, for example, system 100, including one or more respective subsystems (e.g., system data processing system 101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structure 122, and / or outer loop data structure 124). As shown, the system 700 can include assessment system 710, technical system 720, and / or deployment system 760. The assessment system 710, technical system 710, and / or deployment system 760 can include, interact with, implemented with, and / or be executed by one or more processing circuits, memory devices, and / or data repositories configured to perform the processes described herein. In some implementations, the system 700 as a whole, as well as one or more of its subsystems, may be associated with a named model (e.g., for descriptive clarity), but such labeling can be for illustrative only, and any such named modelIXV01-PCT PATENT can represent a same, or separate, underlying model instance or architecture capable of performing the processes and functions described herein.

[0184] In some implementations, the assessment system 710 may include one or more model(s) 715 for implementing an assessment framework, across multiple data modalities and evaluation methods. For example, one model 715 can implement an Al-driven conversational evaluation pipeline, in which natural language inputs from a participant are processed through automatic speech recognition (ASR) and / or direct text ingestion, embedded into vector representations using domain-tuned encoders, and analyzed by a turn-level context model to extract semantic features such as topic coverage, sentiment polarity, and presence of rubric-specified key concepts. Another model 715 can perform document analysis by parsing uploaded or streamed text, PDF, or structured data files using natural language processing (NLP) pipelines to extract named entities, detect factual claims, classify structural elements (e.g., headings, bullet points, tables), and align detected content to predefined rubric domains or compliance requirements. A further model 715 can implement testimonial validation, in which captured interview statements or written testimonials are parsed into claim units, cross-checked against reference databases or verified case records via API queries, scored for corroborative evidence matches, and flagged with confidence scores based on similarity metrics and data provenance analysis. Yet another model 715 can conduct real-time performance capture by interfacing with connected sensors and systems, retrieving telemetry such as facial expression embeddings from video frame analysis, gaze-tracking vectors, keystroke dynamics logs, user interface event timing, biometric sensor metrics (e.g., heart rate variability, GSR), and environmental condition readings, and mapping these technical signals to rubric-defined performance indicators.

[0185] In operation, the assessment system 710 can provide the output data points from each respective model 715 and provide the output, as structured data indicating technical features, to a model (e.g., model 620) or software system (e.g., technical system 720) for orchestration and integration into the active interaction definition. For example, the conversational evaluation model 715 can provide token-level confidence scores, normalized semantic similarity values, and turn classification labels; the document analysis model 715 can supply extracted metadata key-value pairs, section-to-rubric mapping vectors, and document complexity metrics; the testimonialIXV01-PCT PATENT validation model 715 can deliver claim verification hashes, cross-reference match counts, and external source trust ratings; and the performance capture model 715 can send synchronized multi-channel time-series datasets annotated with detected performance events and threshold exceedances. The technical system 720 can ingest these technical data points through defined input acquisition parameters and incorporate them into the context manipulation and orchestration logic described herein.

[0186] As shown, the technical system 720 includes various software engines, including an organizational system 730, proficiency system 740, and / or pathway system 750. The organizational system 730 can include an organizational system model 735 configured to generate and maintain organizational intelligence for integration into orchestration workflows. In some implementations, the organizational system model 735 includes software routines of a business structure mapper operation, programmed to determine hierarchical, functional, and / or crossfunctional relationships between entities within software components of a management system, and to position a management system in relation to a team optimization engine and with collaboration analytics. The organizational system can, for example, incorporate a business structure mapper to identify reporting lines, stakeholder clusters, or interdepartmental linkages within a corporate environment, and generate structured mapping data the team optimization engine to enhance role allocation, workload balancing, and communication routing. The collaboration analytics module can evaluate historical and real-time interaction datasets to derive collaboration efficiency metrics, engagement indices, and interdependency scores that can be fed back into the organizational model 735 for continuous refinement. The output of the organizational system 730 can be provided to a deployment system 760, which can include an internal model 762 configured to deploy, adjust, or scale operational teams or workflows based on the organizational intelligence generated by the organizational model 735.

[0187] In some implementations, the technical system 720 can include a proficiency system 740 configured to generate, maintain, and / or adapt a skills architecture framework. For example, the proficiency system 740 can include a proficiency model 745, which can be operatively coupled to the assessment system 710 to receive heterogeneous technical feature sets including conversational evaluation outputs (e.g., token-level confidence scores, turn classification labels, semantic similarity values), document analysis outputs (e.g., metadata key-value pairs,IXV01-PCT PATENT section-to-rubric mapping vectors, document complexity metrics), testimonial validation outputs (e.g., claim verification hashes, cross-reference match counts, trust ratings), and real-time performance capture outputs (e.g., synchronized multi-channel time series datasets annotated with detected events and threshold exceedances). The proficiency model 745 can aggregate and normalize these technical inputs to generate a skill definition framework 748 including skill identifiers, competency thresholds, dependency hierarchies, and proficiency progression metrics mapped to specific organizational roles or functional clusters.

[0188] In some implementations, the proficiency model 745 can execute skill demand analytics to determine current and projected skill requirements within an enterprise or industry vertical. For example, in a healthcare support organization, the skill demand analytics may detect an increased requirement for “real-time patient interaction triage” by analyzing conversational turnaround time patterns from the conversational evaluation model, cross-referencing compliance alignment from document analysis, validating the accuracy of patient-linked service claims from testimonial validation, and correlating these with measured high-performance windows from the performance capture model. The skill demand analytics can output a quantified skill demand vector including the skill label, urgency score, location tag, and contextual performance parameters, which can be dynamically incorporated into the skill definition framework.

[0189] The proficiency system 740 can operate a role composition engine configured to assemble role profiles from the skill definition framework, integrating both mandatory competencies and optional capability clusters based on projected need. That is, the proficiency system 740 can operate the role composition engine coupled to a job description generator to assemble roles into structured job description payloads with embedded technical specifications, performance benchmarks, and measurement protocols. These payloads can be communicated to the deployment system 760, including both an internal model 762 and an external model 766. The internal model 762 can use these inputs to align existing personnel to newly defined or adapted roles, orchestrating reallocation or training pathways. The external model 766 can apply the payloads to external recruitment markets, partner organizations, or on-demand talent networks to source candidates that meet the precise skill and performance criteria defined by the proficiency system 740.IXV01-PCT PATENT

[0190] In some implementations, the technical system 720 can include a pathway system 750. The pathway system 750 can include a pathway model 755 configured to operate as a progression model for defining, monitoring, and adapting advancement pathways across skills, roles, and organizational objectives. The pathway model 755 can perform recalculation routines in response to updated technical feature inputs from the assessment system 710, thereby dynamically adjusting progression milestones, sequencing orders, and dependency chains. The pathway model 755 can further execute predictive analysis functions by applying statistical forecasting and machine learning evaluations to historical progression datasets, role performance vectors, and skill demand analytics received indirectly from the proficiency system 740.

[0191] In one example, the pathway model 755 can conduct multi-dimensional advancement mapping that aligns vertical growth (e.g., deeper specialization in a certain technical skill), lateral mobility (e.g., cross-department role adaptability), and project-based assignments into an integrated pathway score matrix. This multi-dimensional framework provides a scenario simulation where the pathway model 755 predicts the impact of certain interventions, such as targeted training or reassignment, upon meeting enterprise-level KPIs (e.g., execution performance metrics or thresholds). The pathway model 755 can transmit structured progression payloads, which include advancement recommendations, estimated time-to-proficiency, required resource allocations, and projected performance deltas, to software programming of the external model 766 of the deployment system 760.

[0192] As shown, the deployment system 760 can include model(s) (e.g., model(s) 764, internal model 762, and / or external model 766. In some implementations, the internal model 762 can be configured to access and utilize an employee database containing role history, competency certifications, performance ratings, and availability data. The internal model 762 can further integrate performance metrics sourced from operational systems or sensors, correlating the metrics with defined competencies in the skills inventory, such as those generated by the proficiency system 740. Based on this integrated dataset, the internal model 762 can generate postings for an internal job board, automatically matching available roles with qualified personnel. In addition, the internal model 762 can execute a team formation process by selecting personnel with complementary skill sets, availability alignment, and prior collaboration efficiency scores. These internally generated deployment pathways or team constructs can be transmitted directly to anIXV01-PCT PATENT assessment system (e.g., assessment system 320, 710, etc.) to initiate optional pre-deployment evaluations or readiness assessments prior to final deployment execution.

[0193] In some implementations, the external model 766 can operate as a marketplace ecosystem model configured to interface with multiple external databases and / or service providers. The external model 766 can retrieve candidate profiles from professional network databases, resume repositories, or partner talent pools, and perform skill verification processes by crossreferencing validated credentials, public certifications, and third-party endorsements. In some implementations, in parallel, the external model 766 can retrieve or receive organizational listings from external employers and generate a public job board formatted to industry standards, with embedded metadata suitable for automated parsing by recruitment platforms. Both the skill verification subsystem and the public job board module can be orchestrated by a recruitment engine running within the external model 766.

[0194] In some implementations, an assessment contextual analyzer system can provide a pathway optimizer that links to both the team formation mechanism of the internal model 762 and the recruitment engine of the external model 766. The pathway optimizer can utilize pathway progression vectors generated by the pathway model 755 to suggest multi-stage team or candidate development plans. For instance, the optimizer can recommend pairing incoming external hires (identified via the recruitment engine) with internal mentors (selected by the team formation process) whose advancement trajectories align with the target project scope, thereby improving onboarding efficiency and long-term retention. In some implementations, the pairing and progression recommendations can be integrated with the rubric driven system (e.g., interviewee system to dynamically configure interview question sets, assessment simulations, or scenariobased evaluations tailored to the candidate’s target pathway).

[0195] FIG. 8 shows a dataflow 800 amongst components of a system (e.g., system 100 of FIG. 1) performing operations of a method for conducting a simulated environment (e.g., interviewee for interview practice, market simulation, user interface interactivity, etc.). The dataflow can be performed or implemented with, for example, system 100 or any of the other example systems described herein, including one or more respective subsystems (e.g., system data processing system 101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structureIXV01-PCT PATENT122, and / or outer loop data structure 124). As shown, the dataflow 800 can include application 810, sensor(s) 812, context system 814, model 816, display system 820, feedback system 822, and / or storage 824. The application 810, sensor(s) 812, context system 814, model 816, display system 820, feedback system 822, and / or storage 824 can include, interact with, implemented with, and / or be executed by one or more processing circuits, memory devices, and / or data repositories configured to perform the processes described herein.

[0196] In some implementations, a real-time adjustment process can be executed (e.g., in a simulated interview scenario, instruction scenario by a humanoid robot, etc.). For example, simulation (e.g., interview) has commenced, the application 810 can operate as a simulated interviewer to receive sensor data from one or more sensor(s) 812. For instance, the application 810 can issue an initial interview question via a connected microphone, observe candidate reactions via a camera, and monitor environmental factors via an environmental sensor. The collected sensor data can be transmitted at step 832 to the context system 814 (e.g., a contextual input handler) to process the aggregated inputs, normalize them, and forward them to the model 816 (e.g., a generative Al model) at step 834.

[0197] At step 836, the model 816 can generate an output signal to be transmitted to sensor(s) 818 (e.g., for example, generating a simulated verbal response that is output via a connected speaker) where the application 810 delivers this spoken response at step 838. In addition, the model 816 can update the display system 820 at step 840, causing the display of a reaction (e.g., facial expression, visual emphasis cues) on the user device operating the application 810. At step 842, the display system 820 can present simulation content (e.g., animated gestures, contextual overlays, or scenario-based prompts).

[0198] At step 844, the model 816 can further adjust device settings, including parameters of the user device running the application 810 or a peripheral display system device, based on ongoing evaluation. At step 846, the application 810 can implement these adjustments (e.g., modifying audio output gain, adjusting camera sensitivity, altering ambient lighting settings and / or ambient temperature for the simulation). As depicted at steps 848 and 850, the model 816 and the context system 814 can request and provide continuous updates to one another.IXV01-PCT PATENT

[0199] In some implementations, at this stage, the dataflow 800 may include a loop (e.g., implementing an outer loop or inner loop data structure as discussed in FIG. 1.). For example, with the updated context data the application 810, can provide a prompt at 860 (e.g., present a follow up question via microphone, provide additional observations via camera, and / or detects environmental changes via an environmental system). From this point, the system can operate the loop. From this point, the dataflow 800 can execute a loop sequence, wherein steps 860, 862, 864, 866, 868, 870, 872, and 874 operate in a corresponding manner to operations 830, 832, 834, 836, 838, 840, 842, and 844, respectively, as previously described.

[0200] At step 874, the model 816 can provide feedback (e.g., an updated real-time evaluation score or adjusted progression metric) to the feedback system 822. In one example, the feedback system 822 can calculate both instantaneous and aggregated candidate performance indices based on response quality, behavioral cues, and environmental adaptation metrics. This processed feedback can then be provided back to the model 816 to refine subsequent simulated interviewer interactions within the ongoing loop. Finally, the system can store the final simulation results (e.g., as complete interview transcripts, performance scores, skill gap analyses, or pathway alignment metrics in storage 824 for archival, reporting, or downstream processing within the recruitment or training modules of the deployment system).

[0201] In some implementations, the example described herein can be adapted so that the application 810 operates as a simulated interviewee rather than as an interviewer. In this configuration, the application 810 can receive interview questions or prompts from a human or simulated interviewer via microphone, text input, or other connected interfaces. The sensor(s) 812 can capture interviewer reactions, tone, and context, providing the data at step 832 to the context system 814 for processing. The model 816 can then generate interviewee responses (e.g., spoken answers, displayed text, or simulated gestures) at step 836, outputting them via sensor(s) 818 and display system 820 in steps 838 and 840. This configuration can be used for interviewer training, evaluation of questioning techniques, or simulation of specific candidate profiles for calibration of recruitment and assessment workflows, while still maintaining the continuous feedback loop via steps 848 and 850.

[0202] FIG. 9 depicts a flow diagram of an example process 900 for pathway optimization. The implementation can be performed or implemented with, for example, a computing device orIXV01-PCT PATENT software component of the system 100 or any of the other example computing device or software described herein, including one or more respective subsystems (e.g., system data processing system 101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structure 122, and / or outer loop data structure 124).

[0203] For example, at step 902, a computer of the system 100 can determine and / or retrieve a current competency profile for a given user or group by querying the data repository 120 and parsing structured skill records, past performance metrics, and certifications encoded in a competency data schema. The system 100 may normalize disparate data formats into a unified feature vector representing the current skill set, proficiency levels, and recency of experience.

[0204] At step 904, the computer of the system 100 can determine a target position analysis by accessing a structured role definition repository (e.g., derived from the proficiency system 740 or deployment system 760) and parsing formalized job requirement schemas to extract mandatory competencies, weight factors for each skill, and associated performance benchmarks for the role in question.

[0205] At step 906, the computer of the system 100 can identify gaps by executing a vector comparison algorithm between the competency profile vector from step 902 and the target role requirement vector from step 904. This process may include set-differencing operations to determine missing competencies, ranking them by priority weight, and generating structured gap descriptors, each linked to specific training or acquisition pathways.

[0206] At step 908, the computer of the system 100 can perform market trend analysis by ingesting real-time labor market datasets, industry postings, and skill demand feeds from external APIs. A statistical trend detection module can parse these datasets to produce weighted skill demand forecasts and identify emergent competencies relevant to the user’s target pathway.

[0207] At step 910, the computer of the system 100 can perform a learning preference assessment by executing a parser against stored user assessment files, simulation interaction logs (e.g., generated during interviewee or interviewer training scenarios), and device usage telemetry. The computer of the system 100 may apply clustering algorithms to categorize the user into aIXV01-PCT PATENT preferred learning modality group (e.g., simulation-heavy, document-driven, collaborative) mapped to training modules in the organization’s learning library.

[0208] At step 912, the computer of the system 100 can check for available resources by querying internal and external training resource registries, parsing metadata such as format type, duration, cost, and proficiency improvement expectancy. The resource matching engine can then align available options with the identified gaps from step 906, filtered by the user’s preferred learning modalities from step 910, and output a ranked resource-to-gap mapping for execution in the pathway plan.

[0209] At this stage, the computer of the system 100 (e.g., via a model or LLM) can, using the steps and data described above, generate a pathway, shown by 920. That is, the system 100 can determine a linear progression path 922, a lateral development path 924, a cross-functional path 926, and / or a specialized deepening path 928 based on the competency profile, gap analysis, market trends, learning preferences, and available resources identified in steps 902-912. For example, in generating the linear progression path 922, the model can match the existing competency profile to the next hierarchical role in the same functional domain, parsing the associated role definition metadata to determine the shortest training and certification sequence needed for advancement.

[0210] In generating the lateral development path 924, the model can query related role clusters within the organizational intelligence dataset (e.g., from the organizational system 730) and identify a role of equivalent seniority in a different department, then compute a competency delta requiring primarily horizontal skill transfer rather than hierarchical advancement. For the cross-functional path 926, the model can utilize relationship mappings between separate functional areas stored in the semantic mapper engine or contextual input handler 106 to design a pathway that blends competencies from multiple business units, such as integrating technical engineering skills with sales or project management capabilities.

[0211] In generating the specialized deepening path 928, the model can perform a sub-skill decomposition of a core competency from the user’s profile and identify advanced, niche-level skills or certifications in the same domain, linking them to high-demand or expert-tier roles indicated by the market trend analysis. In each case, the model can output a structured pathwayIXV01-PCT PATENT object containing ordered training steps, duration estimates, prerequisite sequences, and recommended evaluation points for continuous skill verification.

[0212] In some implementations, the computer of the system 100 can combine two or more of the pathway types (e.g., linear progression path 922, lateral development path 924, cross-functional path 926, and specialized deepening path 928) into an integrated timeline optimization 930. In such a configuration, the model can evaluate each pathway type as a sequence of discrete competency acquisition events, each with an associated estimated duration, prerequisite set, and projected impact score. The model can then apply a constraint-based scheduling algorithm to merge the sequences, eliminating redundancy (e.g., shared prerequisite courses), aligning learning modules with availability windows, and sequencing tasks for maximum competency retention based on the learning preference assessment from step 910.

[0213] For example, in a healthcare technology enterprise, an employee may be targeted for advancement toward a senior clinical systems specialist role. The model could combine a linear progression path 922 (advancing from current clinical application analyst to senior analyst), a lateral development path 924 (adding responsibilities in compliance documentation systems), and a specialized deepening path 928 (earning an advanced certification in telemedicine technology). The timeline optimization 930 could sequence these so that foundational modules from the linear path are completed first, overlapping with compliance documentation cross-training from the lateral path during low-workload months, and integrating the advanced telemedicine certification from the specialized path during the final quarter to align with anticipated technology rollouts. This combined path would be presented as an optimized timeline (e.g., generated as a data structure), reducing total training time and ensuring skill acquisitions occur at points in the workflow where immediate application is most likely, increasing retention and organizational impact.

[0214] At step 940, the computer of the system 100 may recalculate a career pathway based on the timeline optimization described above. Recalculation can include generating and monitoring trigger events as shown by 950. Trigger events, in this context, can include discrete, detectable occurrences captured through system telemetry, external data feeds, or manual inputs that meet predefined criteria for initiating a review or adjustment of an active pathway plan. Such trigger events may include, without limitation, completion of a scheduled competency acquisition,IXV01-PCT PATENT detection of a significant organizational restructuring, validation or revocation of a certification, performance evaluation updates, or identification of new skill requirements from market intelligence feeds.

[0215] By monitoring these trigger events, the computer of system 100 can update a profile at 952 (e.g., append the successful acquisition of a new skill into the competency profile vector), revise targets at 954 (e.g., re-align a pathway to a newly created leadership role following an organizational change), and adjust strategy at 956 (e.g., incorporate a newly high-demand skill uncovered through market trend analysis). These inputs can all be incorporated by the system 100 in recalculating a career pathway at 944, which can then be further used or output by the system for deployment, training alignment, or resource allocation.

[0216] For example, in the healthcare technology scenario described above for the timeline optimization 930, the computer of the system 100 may detect a trigger event 950 where the employee completes the advanced telemedicine certification ahead of schedule. This event, combined with a concurrent organizational announcement of a new cross-regional telehealth implementation initiative, could prompt the system to update the employee’ s profile at 952, change their target role at 954 to a regional telehealth program lead, and adjust strategy at 956 to incorporate cross-functional leadership training earlier in the sequence. For example, recalculated pathway at 944 could then be fed into the pathway model 755 for orchestration with the internal model 762 or external model 766 to immediately align staffing plans with the updated organizational objectives.

[0217] FIG. 10 depicts a flow diagram of an example process 1000 for an implementation of position matching (e.g., skill s-matching), as performed in an example embodiment of the system 100 of FIG. 1, including computing hardware devices (e.g., one or more computers) and software components of the system 100. In some implementations, a computer of the system 100 can retrieve an individual skill profile 1010 from a data repository (e.g., profile data curated by the proficiency system 740 or assessment system 610). In some implementations, the position matching process can be implemented as a rubric-based evaluation framework, as discussed herein. In this configuration, the position matching algorithm operates by mapping both the individual skill profile 1010 and the target position requirements to a rubric schema that defines competency categories, scoring scales, and weighting parameters. The rubric can formalize howIXV01-PCT PATENT each skill dimension is assessed and compared, ensuring consistent evaluation across candidates and roles. For example, each extracted competency can be assigned a rubric-defined score based on proficiency level, certification validity, and recency, which is then normalized and weighted according to rubric rules

[0218] The computer of the system 100 can then perform competency vector generation 1012 by parsing the profile’s structured representation of skills, certifications, performance indicators, and experience data, mapping each to a multi-dimensional feature vector space. At step 1014, the computer of the system 100 can apply contextual weighting, wherein the competency vector is adjusted by weight assignments derived from role-specific priorities, recent performance recency decay rates, and relevance scores computed from operational data. The computer of the system 100 can then perform position requirements analysis 1016 by retrieving structured role definitions from internal or external repositories, parsing these definitions into comparable feature vectors, and normalizing the competency dimensions for direct mathematical comparison. The system can pass the results of these operations (e.g., weighted competency vectors for the individual and comparable vectors for potential positions) into a multi-dimensional matching algorithm for further processing, as shown by 1030.

[0219] Additional input into the multi-dimensional matching algorithm 1030 can be derived from the retrieval or generation of a job description 1020. That is, at 1022, the computer of the system 100 apply entity recognition, ontology mapping, and semantic similarity models to identify explicit and implicit skill requirements described in the text. The computer of the system 100 can use the extracted skills are then used for requirement vector generation at 1024, where each skill is encoded into a structured multi-dimensional requirement vector. At step 1026, the computer of the system 100 can perform priority weighting assignment by ranking the requirement vector elements according to criticality indicators derived from the job description, historical success factors, and market demand trends.

[0220] The computer of the system 100 can implement the multi-dimensional matching algorithm at 1030 to perform similarity score calculation 1032, wherein vector similarity measures (e.g., cosine similarity, Mahalanobis distance, or weighted Euclidean metrics are applied to compare candidate competency vectors to position requirement vectors). Additionally, organization context 1034 (e.g., structure, team composition, strategic objectives), industry contextIXV01-PCT PATENT1036 (e.g., sector-specific regulatory requirements, competitive landscape), and cultural factors 1038 (e.g., language, values alignment, collaboration style) can be provided as auxiliary inputs into a context adjustment stage, (e.g., performed by a contextual handler or the context system 814) The context adjustment stage can modify the preliminary similarity score from 1032 by scaling or biasing dimensions in accordance with the contextual priorities, producing a final match score for ranking and selection within the deployment system 760.

[0221] As shown at step 1040, the system (e.g., via the contextual input handler) can use the outputs and the processed results of steps 1032 through 1038 as context adjustment factors in the overarching rubric-driven system, including for updating inner loop data structures and / or outer loop data structures. The computer of the system 100 can perform transferability analysis 1042, wherein the computer of the system 100 evaluates whether the competencies of a candidate can be adapted to alternate roles or positions. For example, the computer of the system 100 can determine that the competency set of a candidate for a software engineering role can also satisfy a significant portion of the competency requirements for a systems architecture role, subject to minimal additional training as determined from the rubric evaluation. Based on the combined results of similarity scoring, context adjustment, and transferability analysis, the computer of the system 100 can generate a match score at step 1046 for deployment, ranking, and / or recommendation operations during a simulation.

[0222] As shown at step 1050, the computer of the system 100 can perform a threshold check to determine whether a candidate satisfies qualification criteria for a position. In some implementations, the computer of the system 100 can perform the threshold check by executing a vector comparison between the final match score vector (e.g., generated at 1046) and a stored qualification threshold vector for the target role. This comparison can include applying distance measures, magnitude evaluations, and / or weighted overlap calculations across competency dimensions to determine whether the similarity values exceed minimum acceptance parameters.

[0223] If the result of the threshold check indicates that the candidate meets or exceeds the qualification threshold, as shown at 1052, the computer of the system 100 can proceed to generate a ranking and / or recommendation at 1054. For example, in a healthcare technology recruitment setting, a candidate with a competency similarity score of 0.92 on a scale of 0 to 1.00 against a threshold score of 0.85 can be automatically placed in the top tier of candidates for selection. TheIXV01-PCT PATENT computer of the system 100 can position the candidate appropriately in an ordered candidate list, and transmit that ranking to a downstream deployment module for further review, automated offer generation, and / or interview scheduling.

[0224] If the result of the threshold check indicates that the candidate falls below the qualification threshold, the computer of the system 100 can determine a development opportunity at 1056 rather than eliminating the candidate from consideration. This determination can include performing a skill gap analysis at 1058. In one example, the skill gap analysis can evaluate token-level competency vector structures for the candidate and identify specific missing or low-scoring token clusters that correspond to required skills in the rubric. For instance, a candidate for a cloud security engineering role may lack rubric-mapped tokens associated with “infrastructure as code compliance validation” or “multi-cloud IAM policy modeling.”

[0225] Based on the skill gap analysis, the computer of the system 100 can develop a pathway suggestion at 1060. That is, at 1060, the computer of the system 100 can define a sequence of targeted interventions, such as completion of specific training modules, acquisition of industry certifications, and / or practical project assignments, that are likely to elevate the candidate’s competency match score above the qualification threshold. For example, in the case of the cloud security engineering role, the pathway suggestion can include completion of a 12-hour hands-on training module for infrastructure as code compliance scanning, followed by an accredited multi-cloud access policy workshop. In some implementations, the pathway suggestion can be output to the candidate management interface of a deployment system (e.g., deployment system 760), stored in the candidate profile data structure, and associated with follow-up evaluation schedules for re-assessment.SYSTEMS AND METHODS FOR CONTEXT AND COMPETENCY ASSESSMENT

[0226] FIG. 11 is a flowchart showing operations of a computer-implemented method 1100 for conducting a rubric-driven competency assessment, according to embodiments. For ease of description and understanding, the operations of the computer-implemented method 1100 are described as being performed by a computer, though embodiments may be implemented using any type of computing device and / or may be implemented using multiple computing devices. Embodiments may include additional or alternative operations and features, or omit certainIXV01-PCT PATENT operations and features, and still fall within the scope of this disclosure. The method 1100 may be implemented in whole or in part across distributed systems, cloud-based platforms, or embedded environments. Variations in the sequence or inclusion of operations may be applied without departing from the scope of the disclosed embodiments. The method 1200 may be executed by a computer configured to evaluate participant responses against structured evaluation criteria defined in a competency rubric.

[0227] At operation 1110, the computer obtains (e.g., retrieves or receives) a competency rubric comprising a plurality of evaluation dimensions. The competency rubric comprises a plurality of evaluation dimensions, each dimension associated with a ranked proficiency scale and one or more assessment criteria. The rubric is encoded in a machine-readable data structure that includes prompt generation instructions for a generative LLM. The computer parses the rubric to identify the target competency and corresponding evaluation logic to be used in subsequent operations. That is, the rubric can include a plurality of assessment criteria, and a plurality of target competences, for each target competency the competency rubric indicates one or more evaluation dimensions and one or more assessment criteria for generating a proficiency score of the target competency.

[0228] At operation 1120, the computer generates a user-directed prompt using the generative LLM, based on the competency rubric and a target competency (e.g.., assessment criteria). The prompt is constructed based on the target competency and the structural and procedural rules defined in the competency rubric. The computer formats the prompt to elicit a response that can be evaluated against rubric-defined criteria, and may include contextual modifiers such as tone, verbosity, or domain-specific terminology.

[0229] At operation 1130, the computer receives a response to the user-directed prompt via a user interface of a participant. The response may be received from a client device operated by a participant or directly through a graphical user interface of the computer. The computer captures the response in structured form, optionally applying preprocessing operations such as tokenization, normalization, or semantic parsing to prepare the response for evaluation. In some implementations, the response is indicative of the one or more evaluation dimensions.IXV01-PCT PATENT

[0230] At operation 1140, the computer generates a proficiency score for the participant. The computer evaluates the response against the assessment criteria of the competency rubric to generate the proficiency score for at least one evaluation dimension. The computer executes a scoring engine configured to apply semantic similarity models, rubric-defined benchmarks, and contextual weighting to generate a proficiency score for at least one evaluation dimension. The computer may also generate supporting metadata, including evidence logs and scoring rationale.

[0231] At operation 1150, the computer evaluates the response against the assessment criteria of the target competency target competency indicated by the competency rubric to generate a proficiency score for the at least one evaluation dimension corresponding to the target competency. That is, the computer processes the response into a machine-readable representation, extracts features relevant to the target competency, and compares those features to the rubric-defined criteria for the associated evaluation dimension or dimensions. This comparison can involve applying semantic similarity models to match extracted key phrases with expected domain-specific terminology, executing rule-based logic to detect satisfaction or omission of required content elements, and performing statistical calculations to quantify the degree of alignment with proficiency thresholds defined in the rubric. The resulting score is computed according to a weighting schema in the rubric that accounts for the relative importance of sub-criteria within the evaluation dimension. In some configurations, the computer also generates supplementary evaluation data, such as a scoring rationale linked to each satisfied or unsatisfied criterion, an evidence log containing excerpts of the response aligned to rubric items, and a record of confidence metrics from the matching algorithms. The proficiency score and associated evaluation data can then be stored in a competency map that links the target competency and its evaluation dimensions to current and historical performance metrics, enabling longitudinal tracking, comparative analysis against other participants, and downstream generation of targeted improvement prompts.

[0232] At operation 1160, the computer generates a competency map. The competency map comprises the target competency and the proficiency score, generated by the computer (as in operation 1140). The computer modifies a vector representation of the participant’s skill profile to reflect the updated score and may annotate the map with timestamped evaluation metadata. The competency map can be implemented as a structured data model representing a relationshipIXV01-PCT PATENT between data elements describing competencies and performance in relation to at least one dimension of the competency rubric. The competency map can further include configuration or formatting suitable for direct presentation through an API to a connected client system, such as a learning management system, analytics dashboard, or role-matching platform, without requiring additional translation of the stored data. Each relationship within the competency map can link a competency identifier to one or more rubric-defined evaluation dimensions, the corresponding proficiency scores, and associated assessment metadata such as scoring rationale, evaluation criteria satisfied, and timestamps of scoring events. The vector representation of the participant skill profile can be stored as a set of indexed numerical values aligned with the evaluation dimensions, enabling computational comparison, aggregation, or filtering across multiple assessments.

[0233] At operation 1170, the computer can identify a next target competency of the competency rubric based upon the competency map. That is, the computer can parse the competency rubric to obtain the full set of rubric-defined competencies and their associated evaluation dimensions, scoring criteria, and proficiency thresholds. The computer can then access the competency map, which includes a vector representation of the participant skill profile, where each vector element corresponds to an evaluation dimension and stores a current proficiency score, timestamp of last assessment, and linked rubric criteria satisfied to date. By iterating through the vector and comparing each stored score to the corresponding proficiency thresholds in the rubric, the computer can determine which competencies remain incomplete or fall below a defined target performance range. The computer can then apply prioritization logic — such as selecting the competency with the greatest deficiency relative to its threshold, the longest elapsed time since prior assessment, or a dimension designated as mandatory in the rubric metadata — to identify the next target competency for evaluation.

[0234] At operation 1180, the computer can generate a second-user directed prompt according to the next target competency. That is, the computer can retrieve the prompt-generation instructions linked to those evaluation dimensions from the rubric, where each instruction can specify structural, topical, and stylistic parameters for eliciting a response relevant to the criteria of the dimension. The computer can then provide these parameters, along with any contextual data from the participant’s prior responses stored in the competency map, as input to the LLM or otherIXV01-PCT PATENT generative model. The generative model can assemble a prompt that conforms to the rubric-specified parameters for the dimension (e.g., including mandatory subject matter elements, framing the request in a defined interaction style, and sequencing question components in the order prescribed by the rubric).

[0235] In some implementations, the competency map can be stored and / or queried as a graph-based or network-linked data structure, where each competency node is linked to one or more rubric-defined evaluation dimension nodes, and each link stores associated proficiency scores, historical scoring events, and metadata such as evaluation criteria satisfied and time of last scoring. That is, the competency map can be used to support queries such as retrieving all competencies across multiple participants associated with a specific evaluation dimension and exceeding a given proficiency threshold. In some implementations, the competency map can be represented as a normalized relational data model or multidimensional array, providing for indexed lookups that directly return a target competency record and its linked score values for the associated rubric dimensions. In a some implementations, the competency map can be stored as a data lookup table in which each row contains a competency identifier, a rubric dimension identifier, the current score, and a set of mapped criteria labels, supporting rapid retrieval for straightforward reporting needs.

[0236] As a non-limiting example, such as in an enterprise software development team assessment, the competency map can include a “System Design Proficiency” record linked to the rubric dimension identifier “DIM- 102,” with a current score of 4.2 on a 1-5 scale, evidence entries referencing criteria of the rubric satisfied (“addresses scalability considerations” and “incorporates trade-off analysis”), and a timestamp of the last update. This record can be exposed through an API call to a talent-management application, where the response payload is formatted for immediate display in a participant skills dashboard, or accessed through a database query for aggregation into organizational skill-gap analytics. Because the competency map maintains explicit linkage between competencies, rubric dimensions, and performance data, it can be consumed seamlessly by downstream systems for visualization, automated career-path planning, or compliance reporting while preserving referential integrity to the governing rubric definitions.

[0237] In some implementations, the computer stores the user-directed prompt, the received response, the generated proficiency score, and the updated competency map in a non-IXV01-PCT PATENT transitory machine-readable storage medium. The stored data may be indexed by session identifier, rubric version, and evaluation timestamp, and may be used for downstream analytics, auditability, or longitudinal tracking of participant development.

[0238] FIG. 12 is a flowchart showing operations of a computer-implemented method 1200 for context assessments using rubrics generated with generative artificial intelligence models, according to embodiments. For ease of description and understanding, the operations of the computer-implemented method 1200 are described as being performed by a computer, though embodiments may be implemented using any type of computing device and / or may be implemented using multiple computing devices. Embodiments may include additional or alternative operations and features, or omit certain operations and features, and still fall within the scope of this disclosure. The method may be implemented in whole or in part across distributed systems, cloud-based platforms, or embedded environments. Variations in the sequence or inclusion of operations may be applied without departing from the scope of the disclosed embodiments. The method 1200 may be executed by a computer configured to process multimodal input data, apply rubric-based evaluation logic, and generate structured context profiles.

[0239] At operation 1210, the computer obtains (e.g., receives, retrieves) input data comprising one or more (or at least one) of textual content, speech-derived content, or sensor- derived content. The input data may originate from computing devices of enterprise systems, user interfaces, or environmental sensors, and may include structured or unstructured formats. The computer may apply preprocessing operations such as speech-to-text conversion, image recognition, or sensor normalization to prepare the input data for semantic analysis.

[0240] At operation 1220, the computer obtains (e.g., receives, retrieves) a context rubric comprising a plurality of evaluation dimensions. The computer may, for example, retrieve a context rubric from a rubric repository. Each evaluation dimension is associated with a ranked scale and one or more context-specific criteria. The rubric is encoded in a machine-readable schema and may include weighting parameters, threshold values, and domain-specific evaluation logic. The computer parses the rubric to identify the dimensions relevant to the input data and prepares the rubric for use in subsequent scoring operations. In some implementations, the evaluation dimensions can include parameters corresponding to performance of a context-specific objective, such as, for example, strategic alignment (e.g., conformance of actions or outputs to oneIXV01-PCT PATENT or more high-level organizational or mission goals), stakeholder relevance (e.g., degree to which the outcome addresses stakeholder needs, priorities, or expectations), regulatory compliance (e.g., adherence to applicable legal, policy, or standards-based requirements), and operational feasibility (e.g., practical executability within defined resource, time, or environmental constraints). These parameters can be represented within the rubric as structured fields or rules that are machine-interpretable, enabling automated comparison of context attributes extracted from input data against defined criteria, with scoring values determined according to the ranked scales and any associated weighting logic. The computer can parse the rubric to identify and activate only those evaluation dimensions relevant to the current input data, thereby preparing the rubric for use in subsequent scoring operations and ensuring that the scoring process is aligned with the specific performance objectives applicable to the context under analysis.

[0241] At operation 1230, the computer generates a context map by executing a generative Al model, such as an LLM, programmed and configured to extract one or more context attributes from the input data and associate each attribute with a corresponding evaluation dimension. For instance, the generative Al model may be programmed and trained to extract the context attributes from the input data and associate each attribute with a corresponding evaluation dimension defined in the context rubric. The context map comprises a structured representation of the extracted attributes, including named entities, sentiment indicators, domain-specific keywords, and other semantic features.

[0242] At operation 1240, the computer generates a ranked context profile by scoring the context attributes against the context-specific criteria defined in the context rubric. The computer scores the context attributes against the context-specific criteria of the context rubric to generate the ranked context profile. The computer applies the scoring engine, which include software programmed and configured to evaluate each attribute using, for example, semantic similarity models, rule-based logic, and rubric-defined weighting functions. The ranked context profile includes a score for each evaluation dimension and may include metadata such as scoring rationale, evidence logs, and rubric satisfaction indicators. In some implementations, the evaluation dimensions can include parameters corresponding to performance of a context-specific objective. Such performance parameters can define measurable conditions, thresholds, and / or qualitative indicators relevant to the objective. For example, the parameters can include strategic alignmentIXV01-PCT PATENT(e.g., numerical or categorical measures indicating conformance to predefined organizational or mission goals), stakeholder relevance (e.g., a proportion of identified requirements addressed during an interaction), regulatory compliance (e.g., binary or scaled measure indicating adherence to a specific legal, policy, or standards-based requirement), and operational feasibility (e.g., indicators that quantify ability to execute within defined resource, time, and / or environmental constraints). The context map can include machine-readable instructions for generation of a prompt comprising content elements based on the one or more associated context attributes with the corresponding evaluation dimension. The instructions can specify one or more of: mapping rules that define how an extracted attribute is embedded into a prompt template aligned to its evaluation dimension, weighting parameters that influence the prominence or ordering of multiple attributes in the generated prompt, and conditional branching logic that determines the inclusion or exclusion of specific elements based on the current evaluation score for the dimension. The computer can provide these instructions, together with the scored attributes, as input to a large language model or other generative engine, such that the generated prompt conforms to the structural and topical boundaries defined by the rubric for the associated evaluation dimension.

[0243] At operation 1250, the computer updates the context map based on the ranked context profile. The update may include, for example, modifying one or more vector representations of context attributes, annotating the map with rubric-aligned scores, and adjusting the structure of the map to reflect changes in context relevance or priority. The updated context map may be used to guide downstream decision-making, strategy generation, or interaction orchestration. In some implementations, the context map includes machine-readable instructions for generation of a prompt comprising content elements based on the one or more associated context attributes with the corresponding evaluation dimension. That is, the extracted context attributes can be stored as linked data elements tied to specific evaluation dimensions, and the linkages incorporate parameters that define how those attributes are to be used in assembling a subsequent prompt. The parameters can include, for example, selection rules for inserting the attribute into a prompt template, weighting values that adjust the emphasis given to the attribute in prompt construction, and conditional sequencing logic that determines when the attribute should be included relative to other attributes. For example, the generative artificial intelligence model can parse these stored instructions in the updated context map at runtime to generate prompts that are context-aligned, rubric-compliant, and dimension-specific.IXV01-PCT PATENT

[0244] In a non-limiting example, such as in a virtual sales-training simulation, the system can receive speech-derived input indicating that a trainee emphasized product pricing and sensor-derived input showing customer hesitation. The context map can link these extracted attributes to the “Negotiation Skills” and “Active Listening” dimensions of the context rubric and store instructions indicating that the next prompt should include a roleplay scenario where the trainee re-addresses customer concerns about value without repeating pricing details. When the next interaction turn begins, the prompt-generation module reads these linked instructions from the context map, applies the templated roleplay format, and incorporates the specified context elements in the order and tone dictated by the associated rubric dimensions.

[0245] At operation 1260, the computer generates a prompt according to the context map and at least one evaluation dimension of the context rubric. That is, the computer can identify within the context map the stored machine-readable instructions and associated context attributes linked to the selected evaluation dimension or dimensions, and apply those instructions to construct the prompt in a manner aligned with the rubric-defined criteria and thresholds for those dimensions. The prompt construction process can include retrieving the relevant prompt template specified for the dimension, embedding the linked context attributes into designated positions within the template according to the mapping rules, adjusting the prominence or ordering of those attributes based on weighting parameters, and applying any conditional sequencing logic that governs inclusion or exclusion of individual elements based on the most recent evaluation scores. The generated prompt is configured to elicit a response from which additional data relevant to the targeted evaluation dimension can be extracted, thereby supporting further rubric-aligned scoring and continuous updating of the context map. In some implementations, the large language model or other generative engine can synthesize the final prompt text by combining the template layout, the populated attribute data, and the stylistic and structural constraints defined in the rubric for that evaluation dimension, ensuring that the resulting prompt is context-aligned, domain-appropriate, and optimized to generate dimension-specific assessment content in the subsequent interaction turn.

[0246] In some implementations, the computer stores the input data, the context rubric, the context map, and the ranked context profile into a non-transitory, machine-readable storageIXV01-PCT PATENT memory. The stored data may be indexed by context identifier, rubric version, and timestamp, and may be used for auditability, analytics, or iterative refinement of context assessment operations.

[0247] FIG. 13 shows a dataflow 1300 amongst components of a system (e.g., system 100 of FIG. 1) performing operations for context assessments. The implementation can be performed or implemented with, for example, system 100 or any of the other example systems described herein, including one or more respective subsystems (e.g., system data processing system 101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structure 122, and / or outer loop data structure 124). As shown, the example dataflow 1300 can include context system 1310, assessment system 1320, generation system 1330, and / or activity system 1340. The context system 1310, assessment system 1320, generation system 1330, and / or activity system 1330 can include, interact with, implemented with, and / or be executed by one or more processing circuits, memory devices, and / or data repositories configured to perform the processes described herein.

[0248] Tracking contextual metrics and competencies is useful across corporate environments, public policy domains, and / or other enterprise-centric applications. In such domains, systems can monitor competencies, skills, and performance indicators associated with specific contexts, such as organizational roles with required and / or optional competencies, public policy topics with corresponding engagement positions, and / or purchasing contexts with targeted customer sentiment parameters. FIG. 13 illustrates an example in which the systems and methods described herein can receive and store a defined concept of a context, interrogate and assess scenarios by receiving structured and / or unstructured feedback through speech-based, visual, textual, and / or sensor-driven inputs, generate analytical insights by applying structured analysis frameworks such as SWOT and / or TOWS, and manage activity processes by generating and orchestrating guided actions. That is, context system 1310 can define and store a context model 1315, the assessment system 1320 at 1325 can evaluate the defined context using rubric-driven and / or iterative interrogation, the generation system 1330 at 1335 can produce strategy frameworks and structured action plans based on the evaluated context, and the activity system 1340 at 1345 can coordinate execution tasks, monitor status, and provide processed results as feedback into the overall process for iterative refinement.IXV01-PCT PATENT

[0249] FIG. 13 illustrates an example implementation in which the context system 1310, the assessment system 1320, the generation system 1330, and the activity system 1340 operate together in a structured workflow to process a defined context. At 1315, the context system 1310 can define, receive, and / or store a context for evaluation by retrieving and structuring relevant data into normalized feature vectors, which can include attributes such as role definitions, competency requirements, stakeholder parameters, regulatory data, market indicators, and historical performance metrics. At 1325, the assessment system 1320 can use an artificial intelligence model, as described herein, to interactively gather data from one or more live and / or simulated scenarios. The assessment system 1320 can interface through text-based, speech-based, and / or visual inputs and outputs, and / or integrate sensor-based and API-based data from relevant external systems. The assessment system 1320 can conduct iterative interrogations using a rubric-driven interaction engine (e.g., as discussed with respect to FIG. 1) and / or dynamic discovery processes to refine the evaluated context and determine key influencing factors or competency requirements. At 1335, the generation system 1330 can perform analytical and strategic insight generation by applying one or more of: strengths, weaknesses, opportunities, and threats (SWOT) analysis, threats, opportunities, weaknesses, and strengths (TOWS) strategy transformation, job-to-be-done (JTBD) isolation techniques, and / or targeted experiential planning. At 1345, the activity system 1340 can define quantifiable targets and outputs in an executable plan, including task assignments, integration steps, mentoring schedules, and measurable performance or adoption metrics. The activity system 1340 can then transmit these outputs to a deployment system 760 for automated scheduling and tracking, and record progress metrics for continuous feedback into the context management process (e.g., updating the assessment system 1320 for refinement).

[0250] In an example, at 1315, the context system 1310 can define the accountability as “employee retention within a 12-month operational cycle for a software engineering department” by retrieving historical retention data, role definitions, and skill requirement vectors. The assessment system 1320 can interface with human resource analytics via API connections to conduct rubric-driven interrogations identifying alignment between skills and retention outcomes, along with early attrition indicators. The generation system 1330 can apply SWOT and TOWS analyses and create an experiential learning plan with targeted training modules. The activity system 1340 can set retention improvement goals (e.g., from 78 percent to at least 90 percent),IXV01-PCT PATENT assign training tasks, plan departmental integration steps, and output the workflow for automated scheduling and continuous feedback.

[0251] In an example, at 1315, the context system 1310 can define the context as “senior SCADA engineer recruitment” by importing role definition vectors containing SCADA protocol expertise, industrial control experience, cybersecurity compliance knowledge, and real-time monitoring skills. The assessment system 1320 can integrate data from recruitment platforms and applicant tracking systems to conduct rubric-driven interviews measuring alarm response time, PLC programming accuracy, and documentation compliance. The generation system 1330 can apply SWOT / TOWS to organizational SCADA needs and create onboarding learning plans. The activity system 1340 can set recruitment goals (e.g., five qualified candidates with >90 percent similarity to the ideal profile), assign interview tasks, plan integration into live SCADA environments, and generate an executable recruitment workflow with progress tracking.

[0252] In an example, at 1315, the context system 1310 can define the accountability as “public engagement for a city-wide transportation policy update” by loading stored engagement vectors such as identified stakeholder groups, historical participation rates, sentiment indicators, and compliance parameters. The assessment system 1320 can receive inputs from in-person meeting loT systems, survey platforms, and social media monitoring APIs, and conduct rubric-driven discovery to identify known / unknown public topics, message propagation times, and behavioral impact measurements. The generation system 1330 can use JTBD isolation, insight discovery, and SWOT / TOWS to develop outreach strategies and experiential engagement cycles. The activity system 1340 can set participation goals (e.g., >60 percent stakeholder response within two weeks), assign outreach tasks, coordinate digital and physical meetings, and produce a tracked engagement workflow.

[0253] In another example, at 1315, the context system 1310 The context system 1310 can define the context as “development of an innovative ecosystem for distributed renewable energy technology” by retrieving mission vectors including stakeholder lists, sector parameters, regulatory requirements, adoption forecasts, and innovation maturity scores. The assessment system 1320 can integrate market, supply chain, and pilot program monitoring data via APIs, and perform rubric-driven interrogations to determine onboarding efforts, market clarity, and success potential metrics. The generation system 1330 can apply JTBD isolation, insight discovery,IXV01-PCT PATENTSWOT / TOWS, and create multi -phase execution plans with prototyping sprints and partner engagement workshops. The activity system 1340 can set onboarding goals (e.g., partnership agreements with three pilot collaborators in six months), assign integration tasks, schedule mentoring between established companies and entrants, and output an executable mission workflow with milestone tracking and feedback logging.SYSTEMS AND METHODS FOR INTERFACE GRACEFULNESS ASSESSMENT

[0254] FIG. 14 is a flowchart showing operations of a computer-implemented method 1400 for generating gracefulness-adjusted content using a generative artificial intelligence model, according to embodiments. For ease of description and understanding, the operations of the computer-implemented method 1400 are described as being performed by a computer, though embodiments may be implemented using any type of computing device and / or may be implemented using multiple computing devices. Embodiments may include additional or alternative operations and features, or omit certain operations and features, and still fall within the scope of this disclosure. The method 1400 may be implemented in whole or in part across distributed systems, cloud-based platforms, or embedded environments. Variations in the sequence or inclusion of operations may be applied without departing from the scope of the disclosed embodiments. The method 1400 may be executed by a computer configured to synthesize content in accordance with stylistic and tonal constraints defined by a gracefulness profile.

[0255] At operation 1410, the computer obtains (e.g., receives, retrieves) a prompt comprising a content request and a contextual parameter set. The contextual parameter set includes, for example, a persona identifier, a communication objective, and a tone specification. The computer parses the prompt to identify, for example, the intended audience, stylistic expectations, and communicative goals, and prepares the prompt for processing by a generative model.

[0256] At operation 1420, the computer obtains (e.g., receives, retrieves) a gracefulness profile associated with the persona identifier. The gracefulness profile comprises one or more modulation parameters corresponding to at least one of politeness, tone modulation, weighting, tactfulness threshold, and stylistic tone vector. For example, the politeness weighting parameter can define a scalar or categorical value that influences lexical choice, phrasing, and sentenceIXV01-PCT PATENT structure toward more (or less) courteous or deferential output. That is, a higher politeness weighting can prompt the generative model to prepend acknowledgments, use indirect request forms, or include expressions of appreciation, whereas a lower weighting can permit more direct or utilitarian language. The tactfulness threshold parameter can define a decision boundary or probability cutoff above which certain content is re-phrased or omitted to reduce bluntness, confrontation, or potentially sensitive wording. That is, the computer can replace absolute statements with softer, context-appropriate alternatives based on the tactfulness threshold. The stylistic tone vector can represent a multidimensional feature set encoding target stylistic attributes such as formality level, cadence, sentence complexity, lexical register, cultural vernacular alignment, and emotional warmth. The tone vector can be applied as a set of weighted values or embeddings that modulate output generation so that the produced language is consistent with the intended persona tone and communication style.

[0257] At operation 1430, the computer generates a first candidate response using a generative model based on the prompt and the gracefulness profile. For instance, the generative model, executed by the computer, synthesizes the response based on the prompt and the gracefulness profile, applying the modulation parameters to influence lexical selection, sentence structure, and rhetorical framing. The output may include, for example, textual content, multimodal types of data assets, or structured data corresponding to the configuration of the generative model.

[0258] At operation 1440, the computer adjusts the behavior of the generative model. For example, at operation 1442, the computer generates a gracefulness score. The computer evaluates the first candidate response using a scoring engine configured to compute the gracefulness score based on the modulation parameters. For instance, the scoring engine applies a weighted combination of metrics representing behavior sentiment (e.g., politeness metric, empathy metric, stylistic fluency metric). The computer compares the computed score against a target score defined in the gracefulness profile and stores the evaluation results in non-transitory machine-readable storage memory.

[0259] For example, at operation 1444, the computer adjusts the first candidate response to produce a second candidate response. The adjustment is based on a deviation between the computed gracefulness score and the target score. The computer modifies one or more aspects ofIXV01-PCT PATENT the response, such as tone, phrasing, or structure, such that the output corresponds to or aligns with the stylistic expectations encoded in the gracefulness profile.

[0260] For example, at operation 1446, the computer validates the second candidate response against a rubric comprising one or more thresholds corresponding the contextual parameter set, such as structural constraints and tone compliance constraints, among others. The rubric is encoded in a machine-readable schema and defines acceptable ranges or threshold scores for stylistic and structural attributes. The computer performs a compliance check to confirm that the adjusted response satisfies the rubric-defined constraints. That is, validation can indicate that the second candidate response satisfies the one or more thresholds corresponding to the contextual parameter set by meeting or exceeding the specific structural, tonal, and stylistic criteria linked to the persona identifier, communication objective, and tone specification. The validation process can include parsing the second candidate response into measurable features, scoring each feature against one or more evaluation metrics for the applicable parameter set, and verifying that all required threshold values are achieved. Once the validation confirms threshold satisfaction, the computer can record a validation pass status in association with the generated response and update the adjustment parameters of the generative model so that subsequent outputs are generated in alignment with these validated ranges, reducing the likelihood of non-conforming content in future interaction cycles., Validation can indicate adherence of the second candidate response to the contextual parameter set retrieved with the prompt, (e.g., including alignment to the persona identifier, the communication objective, and the tone specification). By enforcing adherence to the contextual parameter set, the system ensures that adjustments to the response preserve the intended purpose, style, and communicative goals originally defined for the generative model output. This validation can constrain the model to operate within explicit, machine-defined bounds, thereby reducing drift toward outputs that are misaligned with scenario context or rubric criteria. The validation outcome can be recorded as metadata alongside the generated content, and the results can be fed back to the gracefulness adjustment process so that, over successive interactions, the generative model behavior is influenced to converge toward producing outputs that meet rubric constraints without requiring post-generation modification.

[0261] At operation 1450, the computer outputs the second candidate response to a user interface for presentation. The output may be rendered in textual, audio, visual, or haptic formatIXV01-PCT PATENT depending on the configuration of the interface. The computer may also transmit metadata associated with the response, including the gracefulness score and rubric validation status, for use in downstream analytics or feedback loops.

[0262] FIG. 15 shows a dataflow 1500 amongst components of a system (e.g., system 100 of FIG. 1) performing operations of a method for gracefulness-adjusted content generation. The dataflow can be performed or implemented with, for example, system 100, including one or more respective subsystems (e.g., system data processing system 101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structure 122, and / or outer loop data structure 124). Additionally, the dataflow may incorporate or be an extension of dataflow 400, and / or operate in the same manner as discussed in FIG. 4. For example, the system discussed in relation to FIG. 15 may perform the same operations at 1554, 1556, 1558, 1560, and / or 1562 as FIG. 6 at 654, 656, 658, 660, and / or 662. The dataflow may include interaction system 1510 (e.g., implemented as or with interaction system 440), model 1520, and / or adjustment system 1530. The adjustment system 1530 can define and maintain a “gracefulness version,” which can include a specific combination of parameters such as politeness weighting, tactfulness thresholds, aesthetic quality scores, and / or other context-appropriate softening factors. The adjustment system 1530 can utilize a set of rules and / or a trained sub-model capable of scoring generated text on a gracefulness scale and providing the interaction system 1510 and / or the model 1520 with updated vector context and other technical results necessary for implementing gracefulness adjustments during content generation.

[0263] For example, benchmarks can include lexical politeness measures that define minimum frequency or presence thresholds for courteous markers such as “please,” “thank you,” and “I appreciate,” along with avoidance thresholds for abrupt imperative forms; tactfulness indicators that apply semantic similarity thresholds to known softening phrases, mitigate direct negative statements through conditional or hedged phrasing, and avoid keywords flagged as confrontational; syntactic fluidity metrics that constrain sentence length to ranges such as between 12 and 25 words, control clause complexity, and avoid fragment or run-on structures; stylistic tone vectors that align sentiment polarity and formality level to a target range defined in the gracefulness profile, measured using trained tone classification models; and aesthetic readability scores such as Flesch-Kincaid or Gunning Fog index values constrained to rubric-defined rangesIXV01-PCT PATENT for the intended communication context. Each generated output can be tokenized and evaluated against these benchmarks, producing parameter-level scores that are aggregated using weighting coefficients from the gracefulness profile. The aggregated score can then be compared to a target scoring range stored in association with the rubric and the active gracefulness version. If the computed score falls below the target range, the adjustment system 1530 can apply one or more modification rules such as synonym substitution, insertion of acknowledgement clauses, or reordering of sentences until the adjusted output satisfies the benchmark criteria. For example, in a diplomatic correspondence generation scenario, the gracefulness profile can require a politeness weighting of at least 0.85, no more than two direct imperatives per paragraph, and a tone classifier output within ±0.05 of “formal neutral,” ensuring that all generated communications meet a quantifiable and reproducible standard for interpersonal tact and presentation quality.

[0264] In another example, the context provider (e.g., interaction system 1510) can input details regarding a diplomatic matter and associated parties. The gracefulness metric provider can define standards for diplomatic tact, respectfulness thresholds, and phrasing control parameters. The model 1520 can generate language that conveys the required message while adhering to diplomatic etiquette in structure and tone. The feedback loop can evaluate the generated output against the target diplomatic gracefulness profile, adjust configuration variables within the adjustment system 1530, and update the interaction guidance configuration definition to maintain compliance with the defined diplomatic communication requirements throughout the ongoing interaction. The gracefulness aspects can encompass, individually or in any combination, language, cultural context, engagement state, intended engagement outcomes, generated engagement outputs, and / or environmental parameters relevant to the interaction.

[0265] For example, during an interaction sequence (e.g., implemented via system 100) the context provider can supply details about an employee performance review conversation in which the employee exhibits an apparent lack of awareness regarding defined expectations. The gracefulness metric provider can define parameters for generating a polite and empathetic response configured to advance the conversation relative to extended context factors, such as the employee’s vernacular, cultural background, and industry-specific norms. The model 1520 can generate a response that addresses the detected misunderstanding while maintaining empathetic and polite tone metrics consistent with the employee’s profile. The feedback loop can evaluate the generatedIXV01-PCT PATENT output against the defined gracefulness metric, produce updated scoring and parameter adjustments, and modify the interaction guidance configuration definition to ensure alignment with organizational employee engagement standards.

[0266] In another example, the context provider (e.g., interaction system 1510) can input details regarding a diplomatic matter and associated parties. The gracefulness metric provider can define standards for diplomatic tact, respectfulness thresholds, and phrasing control parameters. The model 1520 can generate language that conveys the required message while adhering to diplomatic etiquette in structure and tone. The feedback loop can evaluate the generated output against the target diplomatic gracefulness profile, adjust configuration variables within the adjustment system 1530, and update the interaction guidance configuration definition to maintain compliance with the defined diplomatic communication requirements throughout the ongoing interaction.SYSTEMS AND METHODS FOR INTERFACE ACTIVITY GENERATION

[0267] FIG. 16 is a flowchart showing operations of a computer-implemented method 1600 for generating rubric-linked user interface interactivity, according to embodiments. For ease of description and understanding, the operations of the computer-implemented method 1600 are described as being performed by a computer, though embodiments may be implemented using any type of computing device and / or may be implemented using multiple computing devices. Embodiments may include additional or alternative operations and features, or omit certain operations and features, and still fall within the scope of this disclosure. The method 1600 may be implemented in whole or in part across distributed systems, embedded platforms, or cloud-based environments. Variations in the sequence or inclusion of operations may be applied without departing from the scope of the disclosed embodiments. The method 1600 may be executed by a computer configured to interpret contextual input data, apply rubric-based logic, and orchestrate interface actions across various types of data in multiple modalities or data formats.

[0268] At operation 1610, the computer obtains (e.g., receives, retrieves) contextual input data. The contextual input data includes, for example, one or more (or at least one) of sensor signals (e.g., ambient light level, ambient temperature level, audio volume, device orientation), user interaction data (e.g., touch gestures, voice commands), or environmental parameters (e.g.,IXV01-PCT PATENT temperature, proximity). The computer may preprocess the input using normalization, tokenization, or multimodal serialization to prepare the data for rubric alignment.

[0269] At operation 1620, the computer obtains (e.g., receives, retrieves) a rubric comprising a plurality of evaluation dimensions. Each evaluation dimension is associated with one or more interaction criteria, such as accessibility, responsiveness, engagement, or clarity. The rubric is encoded in a machine-readable schema and may include threshold values, priority weights, and modality-specific constraints. The computer parses the rubric to identify active dimensions relevant to the contextual input. In some implementations, the rubric includes a machine-readable schema defining a plurality of dimensions comprising parameters corresponding to execution of one or more interface actions for accessibility, responsiveness, engagement, and clarity. The accessibility dimension can include parameters such as font size scaling factors, color contrast ratios, alternate text requirements, multi-modal content availability (e.g., text, speech, gesture), and input device compatibility. The responsiveness dimension can include parameters defining permissible latency thresholds, frame or refresh rate requirements, content adaptation rules for different bandwidth or device capabilities, and response-time targets for user actions. The engagement dimension can include parameters such as interaction pacing intervals, adaptive content personalization rules, stimulus-response variability targets, and gamification triggers. The clarity dimension can include parameters controlling content readability metrics, layout simplicity thresholds, instructional step granularity, and error-message specificity rules. The computer (e.g., executing the LLM) can parse this schema to identify which dimensions and parameters are active based on the current contextual input and then map those active parameters directly to execution settings for the relevant interface actions, ensuring that each executed action conforms to the rubric’s defined performance, presentation, and interaction standards.

[0270] At operation 1630, the computer selects one or more interface actions based at least in part on the contextual input and the rubric. Each interface action comprises a target interface (e.g., visual, audio, haptic), a content payload (e.g., text, image, sound file), and a rubric-aligned objective (e.g., increase engagement, reduce cognitive load). The computer may apply rule-based logic or model-driven inference to determine which actions satisfy rubric-defined criteria.

[0271] At operation 1640, the computer generates orchestration data for the one or more interface actions. The orchestration data includes timing parameters, sequencing logic, andIXV01-PCT PATENT concurrency rules. The computer may assign priority values to each action based on rubric-defined urgency levels and generate a structured execution plan that governs how and when each action is delivered to the user interface.

[0272] At operation 1650, the computer initiates execution of the interface actions via one or more interfaces. The interfaces may include a visual interface (e.g., display screen), an audio interface (e.g., speaker), or a physical interface (e.g., haptic actuator). The computer transmits the content payload to the appropriate interface and coordinates delivery according to the orchestration data previously generated by the computer (as in operation 1640).

[0273] At operation 1660, the computer modifies the one or more interface actions based on updated contextual input or rubric satisfaction status. That is, before initiating delivery of the interface actions according to the orchestration data, the computer can re-evaluate the execution plan and apply changes so that the actions remain synchronized with the most recent context state and continue to satisfy the rubric-aligned objectives. The computer may replace a previously selected action with an alternative action, adjust timing parameters, or deactivate actions that no longer align with rubric objectives, allowing the interface to respond in real time to changes in user behavior or environmental conditions. The modification can include adjusting at least one interface parameter defined in the orchestration data (e.g., altering a timing value to advance or delay an action, resequencing the order of multiple actions, and / or changing concurrency rules) so that specific actions are presented sequentially instead of in parallel. The computer can also replace a planned action with an alternative action that maintains compliance with the active rubric constraints while better matching updated context conditions.

[0274] In some implementations, the adjustments are directly mapped to active rubric dimensions so that any change in the orchestration data preserves conformity to the rubric’s performance, presentation, and interaction standards. In a nonlimiting example, such as in an adaptive fitness-training application, the orchestration data may define a sequence where a visual cue on a display is followed one second later by a haptic vibration on a wearable device. If updated motion-sensor input indicates that the participant has already completed the targeted movement, the computer can resequence the actions by canceling the haptic feedback, advancing the next visual cue in the sequence by two seconds, and adjusting the timing in the orchestration data soIXV01-PCT PATENT the flow of interface actions remains aligned to the “engagement” and “clarity” dimensions of the rubric.

[0275] In some implementations, the computer stores data representing the contextual input, the rubric, and the executed interface actions in a non-transitory machine-readable storage medium. The stored data may be indexed by session identifier, timestamp, and rubric version, and may be used for auditability, analytics, or iterative refinement of interface orchestration logic.

[0276] FIG. 17 depicts a dataflow amongst components of a system (e.g., system 100 of FIG. 1) performing operations of a method for real-time adjustment in a simulated scenario. The implementation can be performed or implemented with, for example, system 100 or any of the other example systems described herein, including one or more respective subsystems (e.g., system data processing system 101, sensors, user interface 102, context provider 104, contextual input handler 106, scorer 108, controller 110, interface engine 112, data repository 120, outer loop data structure 122, and / or outer loop data structure 124). As shown, the dataflow 1700 can include device 1710, sensor(s) 1720, context system 1730, model 1740, and / or display system 1750. The application device 1710, sensor(s) 1720, context system 1730, model 1740, and / or display system 1750 can include, interact with, implemented with, and / or be executed by one or more processing circuits, memory devices, and / or data repositories configured to perform the processes described herein.

[0277] In some implementations, a real-time interface interactivity and adjustment process can be executed (e.g., in a simulated interview scenario, instruction scenario by a humanoid robot, and / or other media consumption interface). For example, once the interaction has commenced, the device 1710 can operate via an application interface that is configured for the scenario type. The sensor(s) 1720 can collect sensor data at steps 1760 and 1764, (e.g., dance movement data from motion sensors, verbal feedback captured via a microphone, and / or current lighting and sound level parameters from connected environmental systems). The context system 1730 (e.g., a contextual input handler) can capture context data as received from the sensor(s) 1720, such as motion capture readings at step 1762 and / or voice commands at step 1766, and can also retrieve additional context data, such as audiobook metadata and / or stored user preferences. At step 1768, the context data can be sent to the model 1740, which can operate within an inner loop and / or outer loop rubric-driven process to generate output instructions (e.g., directing a humanoid robot toIXV01-PCT PATENT adjust hand placement for a dance lesson, dynamically changing room lighting to create an immersive environment, lowering background audio levels, and / or activating mobile device haptic pulses in synchronization with content events). The model 1740 can provide the generated instructions to the display system 1750 at steps 1772 and 1774 for presentation and execution on the user device.

[0278] In some implementations, the process can transition into a continuous interaction loop. In this loop, the sensor(s) 1720 can capture ongoing data at step 1780, such as updated motion tracking readings and / or additional verbal feedback, and provide this to the context system 1730 at step 1782. The updated context data can be sent from the context system 1730 to the model 1740 at step 1784, where the model 1740 can generate updated instructions based on the most recent context information at step 1786. These updated instructions can then be provided to the device 1710 and / or display system 1750 for execution, allowing the interface to adjust in real-time to changes in user performance, environmental conditions, and / or interaction flow.EXAMPLE LANGUAGE MODELS

[0279] In at least some implementations, language models, such as large language models (LLMs), vision language models (VLMs), multi-modal language models (MMLMs), and / or other types of generative artificial intelligence (Al) can be implemented. Generally, the language models can process operational data (e.g., metrics such as thermal conditions, workload distribution, energy consumption) to generate outputs that assist in determining predicted operational states and updating system parameters in real-time or near real-time. These models can be capable of understanding, summarizing, translating, and / or otherwise generating text (e.g., natural language text, code, etc.), images, video, computer aided design (CAD) assets, OMNIVERSE and / or METAVERSE file information (e.g., in USD format, such as OpenUSD), and / or the like, based on the context provided in input prompts or queries. These language models can be considered “large,” in implementations, based on the models being trained on massive datasets and having architectures with large number of learnable network parameters (weights and biases)-such as millions or billions of parameters. The LLMs / VLMs / MMLMs / etc. can be implemented for summarizing textual data, analyzing and extracting insights from data (e.g., textual, image, video, etc.), and generating new text / image / video / etc. in user-specified styles, tones, and / or formats. The LLMs / VLMs / MMLMs / etc. of the present disclosure can be used exclusively for text processing,IXV01-PCT PATENT in implementations, whereas in other implementations, multi-modal LLMs can be implemented to accept, understand, and / or generate text and / or other types of content like images, audio, 2D and / or 3D data (e.g., in USD formats), and / or video. For example, vision language models (VLMs), or more generally multi-modal language models (MMLMs), can be implemented to accept image, video, audio, textual, 3D design (e.g., CAD), and / or other inputs data types and / or to generate or output image, video, audio, textual, 3D design, and / or other output data types.

[0280] Various types of LLMs / VLMs / MMLMs / etc. architectures can be implemented in various implementations. For example, different architectures can be implemented that use different techniques for understanding and generating outputs-such as text, audio, video, image, 2D and / or 3D design or asset data, etc. In some implementations, LLMs / VLMs / MMLMs / etc. architectures such as recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) can be used, while in other implementations transformer-based architectures-such as those that rely on self-attention and / or cross-attention (e.g., between contextual data and textual data) mechanisms-can be used to understand and recognize relationships between words or tokens and / or contextual data (e.g., other text, video, image, design data, USD, etc.). One or more generative processing pipelines that include LLMs / VLMs / MMLMs / etc. can also include one or more diffusion block(s) (e.g., denoisers). The LLMs / VLMs / MMLMs / etc. of the present disclosure can include encoder and / or decoder block(s). For example, discriminative or encoder-only models like BERT (Bidirectional Encoder Representations from Transformers) can be implemented for tasks that involve language comprehension such as classification, sentiment analysis, question answering, and named entity recognition. As another example, generative or decoder-only models like GPT (Generative Pretrained Transformer) can be implemented for tasks that involve language and content generation such as text completion, story generation, and dialogue generation. LLMs / VLMs / MMLMs / etc. that include both encoder and decoder components like T5 (Text-to- Text Transformer) can be implemented to understand and generate content, such as for translation and summarization. These examples are not intended to be limiting, and any architecture typeincluding but not limited to those described herein-can be implemented depending on the particular implementation and the task(s) being performed using the LLMs / VLMs / MMLMs / etc.

[0281] In various implementations, the LLMs / VLMs / MMLMs / etc. can be trained using unsupervised learning, in which an LLMs / VLMs / MMLMs / etc. learns patterns from large amountsIXV01-PCT PATENT of unlabeled text / audio / video / image / design / USD / etc. data. Due to the extensive training, in implementations, the models cannot require task-specific or domain-specific training. LLMs / VLMs / MMLMs / etc. that have undergone extensive pre-training on vast amounts of unlabeled data can be referred to as foundation models and can be adept at a variety of tasks like question-answering, summarization, filling in missing information, translation, image / video / design / USD / data generation. Some LLMs / VLMs / MMLMs / etc. can be tailored for a specific use case using techniques like prompt tuning, fine-tuning, retrieval augmented generation (RAG), adding adapters (e.g., customized neural networks, and / or neural network layers, that tune or adjust prompts or tokens to bias the language model toward a particular task or domain), and / or using other fine-tuning or tailoring techniques that optimize the models for use on particular tasks and / or within particular domains.

[0282] In some implementations, the LLMs / VLMs / MMLMs / etc. of the present disclosure can be implemented using various model alignment techniques. For example, in some implementations, guardrails can be implemented to identify improper or undesired inputs (e.g., prompts) and / or outputs of the models. In doing so, the system can use the guardrails and / or other model alignment techniques to either prevent a particular undesired input from being processed using the LLMs / VLMs / MMLMs / etc., and / or preventing the output or presentation (e.g., display, audio output, etc.) of information generating using the LLMs / VLMs / MMLMs / etc. In some implementations, one or more additional models-or layers thereof-can be implemented to identify issues with inputs and / or outputs of the models. For example, these “safeguard” models can be trained to identify inputs and / or outputs that are “safe” or otherwise okay or desired and / or that are “unsafe” or are otherwise undesired for the particular application / implementation. As a result, the LLMs / VLMs / MMLMs / etc. of the present disclosure can be less likely to output language / text / audio / video / design data / USD data / etc. that can be offensive, vulgar, improper, unsafe, out of domain, and / or otherwise undesired for the particular application / implementation.

[0283] In some implementations, the LLMs / VLMs / etc. can be configured to or capable of accessing or using one or more plug-ins, application programming interfaces (APIs), databases, data stores, repositories, etc. For example, for certain tasks or operations that the model is not ideally suited for, the model can have instructions (e.g., as a result of training, and / or based on instructions in a given prompt) to access one or more plug-ins (e.g., 3rd party plugins) for help inIXV01-PCT PATENT processing the current input. In such an example, where at least part of a prompt is related to restaurants or weather, the model can access one or more restaurant or weather plug-ins (e.g., via one or more APIs) to retrieve the relevant information. As another example, where at least part of a response requires a mathematical computation, the model can access one or more math plug-ins or APIs for help in solving the problem(s), and can then use the response from the plug-in and / or API in the output from the model. This process can be repeated-e.g., recursively-for any number of iterations and using any number of plug-ins and / or APIs until a response to the input prompt can be generated that addresses each ask / question / request / process / operation / etc. As such, the model(s) can not only rely on its own knowledge from training on a large dataset(s), but also on the expertise or optimized nature of one or more external resources-such as APIs, plug-ins, and / or the like.

[0284] In some implementations, multiple language models (e.g., LLMs / VLMs / MMLMs / etc., multiple instances of the same language model, and / or multiple prompts provided to the same language model or instance of the same language model can be implemented, executed, or accessed (e.g., using one or more plug-ins, user interfaces, APIs, databases, data stores, repositories, etc.) to provide output responsive to the same query, or responsive to separate portions of a query. In at least one implementation, multiple language models e.g., language models with different architectures, language models trained on different (e.g. updated) corpuses of data can be provided with the same input query and prompt (e.g., set of constraints, conditioners, etc.). In one or more implementations, the language models can be different versions of the same foundation model. In one or more implementations, at least one language model can be instantiated as multiple agents, such that more than one prompt can be provided to constrain, direct, or otherwise influence a style, a content, or a character, etc., of the output provided. In one or more example, non-limiting implementations, the same language model can be asked to provide output corresponding to a different role, perspective, character, or having a different base of knowledge, etc. -as defined by a supplied prompt.

[0285] In any one of such implementations, the output of two or more (e.g., each) language models, two or more versions of at least one language model, two or more instanced agents of at least one language model, and / or two more prompts provided to at least one language model can be further processed, e.g., aggregated, compared or filtered against, or used to determine (andIXV01-PCT PATENT provide) a consensus response. In one or more implementations, the output from one language model-or version, instance, or agent-can be provided as input to another language model for further processing and / or validation. In one or more implementations, a language model can be asked to generate or otherwise obtain an output with respect to an input source material, with the output being associated with the input source material. Such an association can include, for example, the generation of a caption or portion of text that is embedded (e.g., as metadata) with an input source text or image. In one or more implementations, an output of a language model can be used to determine the validity of an input source material for further processing, or inclusion in a dataset. For example, a language model can be used to assess the presence (or absence) of a target word in a portion of text or an object in an image, with the text or image being annotated to note such presence (or lack thereof). Alternatively, the determination from the language model can be used to determine whether the source material should be included in a curated dataset, for example and without limitation.EXAMPLE NETWORK ENVIRONMENTS

[0286] Network environments suitable for use in implementing implementations of the disclosure can include one or more client devices, servers, network attached storage (NAS), other backend devices, and / or other device types. The client devices (e.g., user device), servers, and / or other device types (e.g., each device) can be implemented on one or more instances of the computing device(s) described here e.g., each device can include similar components, features, and / or functionality of the computing device(s) described herein.

[0287] Components of a network environment can communicate with each other via a network(s), which can be wired, wireless, or both. The network can include multiple networks, or a network of networks. By way of example, the network can include one or more Wide Area Networks (WANs), one or more Local Area Networks (LANs), one or more public networks such as the Internet and / or a public switched telephone network (PSTN), and / or one or more private networks. Where the network includes a wireless telecommunications network, components such as a base station, a communications tower, or even access points (as well as other components) can provide wireless connectivity.IXV01-PCT PATENT

[0288] Compatible network environments can include one or more peer-to-peer network environments-in which case a server cannot be included in a network environment-and one or more client-server network environments-in which case one or more servers can be included in a network environment. In peer-to-peer network environments, functionality described herein with respect to a server(s) can be implemented on any number of client devices.

[0289] In at least one implementation, a network environment can include one or more cloud-based network environments, a distributed computing environment, a combination thereof, etc. A cloud-based network environment can include a framework layer, a job scheduler, a resource manager, and a distributed file system implemented on one or more of servers, which can include one or more core network servers and / or edge servers. A framework layer can include a framework to support software of a software layer and / or one or more application(s) of an application layer. The software or application(s) can respectively include web-based service software or applications. In implementations, one or more of the client devices can use the web-based service software or applications (e.g., by accessing the service software and / or applications via one or more application programming interfaces (APIs)). The framework layer can be, but is not limited to, a type of free and open-source software web application framework such as that can use a distributed file system for large-scale data processing (e.g., “big data”).

[0290] A cloud-based network environment can provide cloud computing and / or cloud storage that carries out any combination of computing and / or data storage functions described herein (or one or more portions thereof). Any of these various functions can be distributed over multiple locations from central or core servers (e.g., of one or more data centers that can be distributed across a state, a region, a country, the globe, etc.). If a connection to a user (e.g., a client device) is relatively close to an edge server(s), a core server(s) can designate at least a portion of the functionality to the edge server(s). A cloud-based network environment can be private (e.g., limited to a single organization), can be public (e.g., available to many organizations), and / or a combination thereof (e.g., a hybrid cloud environment).

[0291] The client device(s) can include at least some of the components, features, and functionality of the example computing device(s) described herein. By way of example and not limitation, a client device can be embodied as a Personal Computer (PC), a laptop computer, a mobile device, a smartphone, a tablet computer, a smart watch, a wearable computer, a Personal Digital Assistant (PDA), an MP3 player, a virtual reality headset, a Global Positioning System (GPS) or device, a video player, a video camera, a surveillance device or system, a vehicle, a boat,IXV01-PCT PATENT a flying vessel, a virtual machine, a drone, a robot, a handheld communications device, a hospital device, a gaming device or system, an entertainment system, a vehicle computer system, an embedded system controller, a remote control, an appliance, a consumer electronic device, a workstation, an edge device, any combination of these delineated devices, or any other suitable device.

[0292] The disclosure can be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc., refer to code that perform particular tasks or implement particular abstract data types. The disclosure can be practiced in a variety of system configurations, including handheld devices, consumer electronics, general-purpose computers, more specialty computing devices, etc. The disclosure can also be practiced in distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network.

[0293] As used herein, a recitation of “and / or” with respect to two or more elements should be interpreted to mean only one element, or a combination of elements. For example, “element A, element B, and / or element C” can include only element A, only element B, only element C, element A and element B, element A and element C, element B and element C, or elements A, B, and C. In addition, “at least one of element A or element B” can include at least one of element A, at least one of element B, or at least one of element A and at least one of element B. Further, “at least one of element A and element B” can include at least one of element A, at least one of element B, or at least one of element A and at least one of element B.

[0294] The subject matter of the present disclosure is described with specificity herein to meet statutory requirements. However, the description itself is not intended to limit the scope of this disclosure. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described in this document, in conjunction with other present or future technologies. Moreover, although the terms “step” and / or “block” can be used herein to connote different elements of methods employed, the terms should not be interpreted as implying any particular order among or between various steps herein disclosed unless and except when the order of individual steps is explicitly described.

Claims

IXV01-PCT PATENTCLAIMSWhat is claimed is:

1. A computer-implemented method for transforming a Large Language Model (LLM), the method comprising: receiving, by a computer, from a plurality of available rubrics, at least one first rubric comprising one or more structural rules and one or more procedural rules for generating an output during a first interaction; receiving, by the computer, from the plurality of available rubrics, at least one second rubric comprising one or more endpoints and one or more criteria corresponding to the first interaction; generating, by the computer executing the LLM, a prompt structured according to the at least one first rubric and the at least one second rubric; receiving, by the computer, a response to the prompt comprising interaction data corresponding to the at least one first rubric and the at least one second rubric; generating, by the computer, context data based on a comparison between the interaction data and the at least one second rubric, the context data indicating a likelihood of completion of the one or more endpoints and a measure of compliance with the one or more criteria; determining, by the computer, based on the context data, an updated at least one first rubric and an updated at least one second rubric for a subsequent interaction; and providing, by the computer executing the LLM, a second prompt generated according to the updated at least one first rubric and the updated at least one second rubric.

2. The method of claim 1, wherein determining the updated first rubric comprises selecting, by the computer, the updated first rubric by adding, removing, or modifying at least one structural rule or at least one procedural rule in the at least one first rubric, and wherein determining the updated second rubric comprises selecting the updated second rubric by adding, removing, or modifying at least one endpoint or at least one criterion in the at least one first second rubric.

3. The method of claim 1, further comprising generating, by the computer, the first rubric comprising one or more dimensions corresponding to a gracefulness criteria having one or moreIXV01-PCT PATENT modulation parameters, wherein the computer updates the first rubric by executing an orchestrator using the context data and historical interaction data.

4. The method of claim 3, further comprising: at a training phase: training, by the computer, the orchestrator for generating the updated at least one first rubric using a training corpus comprising a plurality of training labels and a corresponding plurality of training interaction records, each training interaction record comprising interaction content and interaction metadata, a training label indicating whether the corresponding training interaction record achieves at least one defined interaction goal; and updating, by the computer, one or more parameters of the orchestrator based on a comparison between a predicted outcome generated by the orchestrator and an actual outcome indicated in the context data for a plurality of prior interactions, the comparison performed using a loss function applied to each predicted outcome and each actual outcome.

5. The method of claim 1, further comprising: determining, by the computer, at least two second rubrics from the plurality of rubrics to be active during a first interaction; and generating, by the computer, the prompt structured to satisfy criteria of the at least two second rubrics.

6. The method of claim 5, further comprising: evaluating, by the computer, outputs of the at least two second rubrics according to predetermined or dynamically determined priority values; and resolving, by the computer, a conflict between the at least two second rubrics by modifying at least one of the two second rubrics based on the evaluated outputs.

7. The method of claim 1, further comprising: maintaining, by the computer, an active pool comprising the plurality of first rubrics and the plurality of second rubrics selected for concurrent use across a plurality of conversational turns; and updating, by the computer, the active pool comprising the plurality of first rubrics and the plurality of second rubrics based on the context data.IXV01-PCT PATENT8. The method of claim 1, further comprising: generating, by the computer, the at least one second rubric comprising one or more dimensions corresponding to a competency and a plurality of scored proficiency levels; wherein generating the context data further comprises: determining, by the computer, a proficiency score for at least one dimension based on the response; and storing, by the computer, a data structure comprising the proficiency score and corresponding data supporting the proficiency score.

9. The method of claim 1, wherein generating the prompt comprises modifying the prompt based on a gracefulness profile, the gracefulness profile comprising one or more modulation parameters corresponding to at least one of politeness weighting, tactfulness threshold, or stylistic tone vector.

10. The method of claim 1, wherein generating the prompt comprises selecting a prompt structure based on a rubric-aligned objective associated with a user interface interaction, and wherein receiving the response comprises evaluating the response in accordance with an interview simulation rubric comprising a plurality of competency dimensions and scoring criteria.

11. A computer-implemented method for conducting a simulated interview session using a generative artificial intelligence model, the method comprising: receiving, by a computer, a configuration of a simulated interviewee persona, the configuration comprising one or more behavioral traits, domain-specific knowledge parameters, and response modulation instructions; receiving, by the computer, contextual data defining an interview scenario, the contextual data comprising role-specific attributes, organizational metadata, and interviewer profile information; executing, by the computer, a simulated interviewee engine configured to generate responses to interviewer prompts based on the persona configuration and contextual data; executing, by the computer, a simulated interviewer engine configured to generate prompts based on a competency rubric, the competency rubric comprising evaluation dimensions, scoring criteria, and proficiency thresholds;IXV01-PCT PATENT executing, by the computer, a multi-turn interaction sequence between the simulated interviewer engine and the simulated interviewee engine, wherein each prompt and response is processed to update an interaction state; evaluating, by the computer, each response from the simulated interviewee engine against the competency rubric to generate a proficiency score and a set of evaluation results; and updating, by the computer, prompt generation logic of the simulated interviewer engine based on the proficiency score and interaction state, the prompt generation logic comprising machine-executable instructions for the simulated interviewer engine corresponding to at least one evaluation dimension of the competency rubric.

12. The method of claim 11, wherein executing the simulated interviewee engine comprises selecting, by the computer, a generative model from a plurality of available models based on a domain-specific expertise parameter included in the persona configuration.

13. The method of claim 11, wherein the simulated interviewee engine determines response tone, verbosity, and persona style based on a context vector derived from the contextual data.

14. The method of claim 11, wherein the simulated interviewer engine selects prompts from a prompt library indexed by competency dimension and proficiency level.

15. The method of claim 11, wherein evaluating each response comprises executing, by the computer, a scoring engine configured to apply semantic similarity models and rubric-defined criteria to generate a proficiency score.

16. The method of claim 11, wherein updating the prompt generation logic comprises modifying, by the computer, prompt structure, topic selection, or delivery modality based on the interaction state and rubric satisfaction thresholds.

17. The method of claim 11, further comprising generating, by the computer, a visual dashboard comprising the proficiency score, rubric satisfaction indicators, and evidence log for display on a user interface.

18. The method of claim 11, further comprising storing, by the computer, a structured record of the interaction sequence, the persona configuration, the contextual data, and the set of evaluationIXV01-PCT PATENT results in a persistent data repository, wherein the structured record stored in the persistent data repository comprises a timestamped sequence of prompts and responses, rubric evaluation metadata, and persona configuration identifiers.

19. The method of claim 11, wherein the multi -turn interaction sequence includes branching logic defined by the competency rubric, the branching logic comprising conditional prompt selection based on prior response evaluation.

20. The method of claim 11, wherein the computer executes a feedback loop configured to adjust the simulated interviewee persona configuration in response to detected performance gaps during the interaction sequence.

21. A computer-implemented method for conducting a rubric-driven competency assessment, the method comprising: retrieving, by a computer, a competency rubric comprising a plurality of evaluation dimensions, a plurality of assessment criteria, and a plurality of target competences, for each target competency the competency rubric indicates at least one evaluation dimension and one or more assessment criteria for generating a proficiency score of the target competency; generating, by the computer, a user-directed prompt based on the one or more assessment criteria corresponding to the target competency as indicated by the competency rubric; receiving, by the computer, a response to the user-directed prompt from a participant, the response indicative of the one or more evaluation dimensions corresponding to the one or more assessment criteria; generating, by the computer, the proficiency score of the target competency score based upon the one or more assessment criteria and the one or more dimensions corresponding to the target competency score; evaluating, by the computer, the response against the assessment criteria of the target competency indicated by the competency rubric to generate a proficiency score for the at least one evaluation dimension corresponding to the target competency; generating, by the computer, a competency map indicating the proficiency score of the target competency, for each target competency of the competency rubric;IXV01-PCT PATENT identifying, by the computer, a next target competency of the competency rubric based upon the competency map; and generating, by the computer, a second user-directed prompt according to the next target competency of the competency rubric.

22. The method of claim 21, wherein the competency rubric comprises a machine-readable schema defining a plurality of dimensions, each dimension associated with a scoring scale and a set of evaluation rules.

23. The method of claim 21, wherein generating the user-directed prompt comprises selecting a prompt template from a prompt library indexed by competency domain and rubric dimension.

24. The method of claim 21, wherein evaluating the response comprises executing a scoring engine configured to apply semantic similarity models and rubric-defined criteria to generate the proficiency score.

25. The method of claim 21, wherein the computer updates the competency map by modifying a proficiency vector associated with the participant.

26. The method of claim 21, further comprising generating a feedback signal based on the proficiency score and transmitting the feedback signal to a prompt generation engine configured to select a follow-up prompt.

27. The method of claim 21, wherein the computer retrieves the competency rubric from a rubric repository comprising role-specific evaluation frameworks.

28. The method of claim 21, wherein the computer stores the updated competency map in association with a timestamp and a session identifier.

29. The method of claim 21, wherein the computer modifies prompt generation logic based on historical proficiency scores associated with the participant.

30. The method of claim 21, wherein the computer generates a visual dashboard comprising the proficiency score, rubric satisfaction indicators, and a summary of the competency map.IXV01-PCT PATENT31. A computer-implemented method for context assessments using rubrics generated with generative artificial intelligence models, the method comprising: receiving, by a computer, input data comprising one or more of textual content, speech- derived content, or sensor-derived content; retrieving, by the computer, a context rubric comprising a plurality of evaluation dimensions, each dimension associated with a ranked scale and one or more context-specific criteria; generating, by the computer, a context map by executing a generative artificial intelligence model configured to extract one or more context attributes from the input data and associate each attribute with a corresponding evaluation dimension, wherein the context map comprises instructions for generation of a prompt comprising content elements based on the one or more associated context attributes with the corresponding evaluation dimension; evaluating, by the computer, the context attributes against the context-specific criteria of the context rubric to generate a ranked context profile; updating, by the computer, the context map based on the ranked context profile; and generating, by the computer, a prompt according to the context map and at least one evaluation dimension of the context rubric.

32. The method of claim 31, wherein the evaluation dimensions comprise parameters corresponding to performance of a context-specific objective.

33. The method of claim 31, wherein generating the context map comprises executing the generative artificial intelligence model to extract named entities, sentiment indicators, and domainspecific keywords from the input data.

34. The method of claim 31, wherein scoring the context attributes comprises applying a weighting function to each evaluation dimension based on a priority parameter defined in the context rubric.

35. The method of claim 31, wherein the computer updates the context map by modifying a vector representation of the context attributes and associating each vector element with a rubric- defined score.IXV01-PCT PATENT36. The method of claim 31, further comprising generating a strategy framework based on the ranked context profile, the strategy framework comprising one or more action recommendations aligned with rubric-defined goals.

37. The method of claim 31, wherein the input data comprises a combination of structured data retrieved from an enterprise system and unstructured data received from a user interface.

38. The method of claim 31, wherein the computer stores the ranked context profile in association with a timestamp, a context identifier, and a rubric version identifier.

39. The method of claim 31, wherein the generative artificial intelligence model comprises a transformer-based language model configured to process multi-modal input and generate structured output.

40. The method of claim 31, wherein the computer generates a visual dashboard comprising the ranked context profile, rubric satisfaction indicators, and a summary of the context map.

41. A computer-implemented method for generating gracefulness-adjusted content using a generative artificial intelligence model, the method comprising: receiving, by a computer, a prompt comprising a content request and a contextual parameter set, the contextual parameter set comprising a persona identifier, a communication objective, and a tone specification; retrieving, by the computer, a gracefulness profile associated with the persona identifier, the gracefulness profile comprising one or more modulation parameters corresponding to at least one of politeness weighting, tactfulness threshold, and stylistic tone vector; generating, by the computer, a first candidate response using a generative model based on the prompt and the gracefulness profile; adjusting behavior of the generative model by: evaluating, by the computer, the first candidate response using a scoring engine configured to compute a gracefulness score based on the modulation parameters; adjusting, by the computer, the first candidate response to produce a second candidate response, wherein the adjustment is based on a deviation between the gracefulness score and a target score defined in the gracefulness profile;IXV01-PCT PATENT validating, by the computer, the second candidate response against a rubric comprising one or more thresholds, including structural constraints and tone compliance rules, wherein the validation indicates the second candidate response satisfies the one or more thresholds corresponding to the contextual parameter set; and outputting, by the computer, the second candidate response to a user interface for presentation.

42. The method of claim 41, wherein the gracefulness profile comprises a tone modulation vector for a persona, configured to adjust content generation parameters based on a detected communication context.

43. The method of claim 41, wherein the scoring engine comprises a trained sub-model configured to compute a gracefulness score using a weighted combination of politeness, empathy, and stylistic fluency metrics.

44. The method of claim 41, wherein adjusting the first candidate response comprises modifying, by the computer, at least one of lexical selections, sentence structure, or rhetorical framing for aligning with the target score.

45. The method of claim 41, wherein the rubric comprises a set of machine-readable rules encoded in a structured schema including at least one of JSON, XML, or YAML.

46. The method of claim 41, wherein validating the second candidate response comprises executing, by the computer, a compliance check against a tone threshold and a style constraint for a persona.

47. The method of claim 41, further comprising: generating, by the computer, a feedback signal based on the gracefulness score; and transmitting, by the computer, the feedback signal to a model adjustment engine configured to update the generative model parameters.

48. The method of claim 41, wherein the contextual parameter set further comprises a cultural sensitivity indicator and a formality level specifier.IXV01-PCT PATENT49. The method of claim 41, wherein the computer selects the generative model from a plurality of available models based on a performance metric associated with gracefulness score convergence.

50. The method of claim 41, wherein outputting the second candidate response comprises transmitting, by the computer, the response to a multimodal interface configured to present the response as an audio format, visual format, or haptic feedback.

51. A computer-implemented method for generating rubric-linked user interface interactivity, the method comprising: receiving, by a computer, contextual input comprising one or more sensor signals, user interaction data, or environmental parameters; retrieving, by the computer, a rubric comprising a plurality of evaluation dimensions, each dimension associated with one or more interaction criteria; selecting, by the computer, one or more interface actions based at least in part on the contextual input and the rubric, each interface action comprising a target interface, a content payload, and a rubric-aligned objective; generating, by the computer, orchestration data for the one or more interface actions, the orchestration data comprising timing or sequencing information; initiating, by the computer, execution of the one or more interface actions via one or more interfaces comprising at least one of a visual interface, an audio interface, or a physical interface; before executing the one or more interface actions, modifying, by the computer, the one or more interface actions based on updated contextual input or rubric satisfaction status, wherein the modifying comprises adjusting at least one interface corresponding to the orchestration data.

52. The method of claim 51, wherein the rubric comprises a machine-readable schema defining a plurality of dimensions comprising parameters corresponding to execution of one or more interface actions for accessibility, responsiveness, engagement, and clarity.

53. The method of claim 51, wherein selecting the one or more interface actions comprises identifying a rubric-aligned objective associated with a threshold score for a dimension of the rubric.IXV01-PCT PATENT54. The method of claim 51, wherein the content payload comprises a structured data object configured to modify a user interface element, including at least one of a visual layout, an audio output, or a haptic feedback signal.

55. The method of claim 51, wherein generating the orchestration data comprises assigning a priority value to each interface action based on a rubric-defined urgency parameter.

56. The method of claim 51, wherein initiating execution of the one or more interface actions comprises transmitting the content payload to a client device configured to render the interface.

57. The method of claim 51, wherein modifying the one or more interface actions comprises replacing a previously selected interface action with an alternative action selected based on updated rubric satisfaction status.

58. The method of claim 51, wherein the contextual input comprises sensor signals, include at least one of ambient light level, ambient temperature, audio volume, device orientation, or user proximity.

59. The method of claim 51, wherein the computer stores the data in association with a session identifier, a timestamp, and a rubric version identifier.

60. The method of claim 51, further comprising generating a visual dashboard comprising a representation of the rubric satisfaction status, the executed interface actions, and the contextual input.

61. A system, comprising: a computing system having one or more processors coupled with memory, configured to perform the method of any of claims 1-60.

62. A computer-readable storage medium comprising computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the method of any preceding claim.

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