System for knowledge instantiation and evidence synthesis through human-computer workflow orchestration and neuromorphic prompting

Abstract

The present invention discloses a modular AI-based system for knowledge instantiation and evidence synthesis, unifying five modules: Knowledge Instantiation, ingesting anchor knowledge via retrieval- augmented generation and neuromorphic prompting; Case-Based Agent Generation, orchestrating domain constraints, performance criteria, and sub-task decomposition; Secondary Epistemogenesis, spawning sub-agents with inherited heuristics; Workflow Orchestration and Evidence Synthesis, merging outputs, tracking metadata, and storing final artifacts; and Reinforcement Learning from Human Refinement, capturing feedback and edits. The method for output generation initiates by defining knowledge requirements, indexing public and proprietary knowledge, validating output structure, drafting content, and refining outputs via sub-agent spawning and human oversight. The system ensures domain alignment, concurrency control, and persistent versioning. By combining specialized knowledge sources, multi-agent concurrency, and iterative human feedback loops, it dynamically adapts to evolving knowledge requirements. This invention emphasizes reliability, traceability, and AI-driven outputs in regulated industries, delivering trustworthy outcomes anchored in expert knowledge.

Description

TITLE OF THE INVENTION: SYSTEM FOR KNOWLEDGE INSTANTIATION AND EVIDENCE SYNTHESIS THROUGH HUMAN-COMPUTER WORKFLOW ORCHESTRATION AND NEUROMORPHIC PROMPTINGFIELD OF INVENTION

[0001] The present invention relates generally to artificial intelligence (Al) and machine learning (ML) frameworks for knowledge instantiation and evidence synthesis in expert-driven domains. More particularly, it relates to a system utilizing specialized computing architectures, memory management modules, and neuromorphic prompting layers for instantiating expert knowledge, generating and orchestrating case-based Al agents (CBAs) through structured knowledge anchoring, neuromorphic prompting and secondary epistemogenesis.BACKGROUND OF INVENTION

[0002] The exponential growth of scientific knowledge in life sciences, and regulatory affairs has created an urgent demand for structured, Al-driven knowledge synthesis. Recently Scientific publications have been doubling approximately every 20 days (A. C. Chang, Intell. Based Med. 2020, 3, 100012). Despite this, only a fraction of this knowledge is effectively transformed into actionable insights.

[0003] Evidence-based knowledge is paramount in drug discovery, clinical research, and regulatory submissions, serving as the foundation for informed decision-making and ensuring patient safetyand treatment efficacy. Through rigorous scientific evaluation, evidence-based approaches provide confidence in the effectiveness and safety of new drugs, guiding researchers in the selection of promising candidates for further development. In clinical research, evidence-based knowledge informs study design, helping to establish robust methodologies and endpoints that accurately assess treatment outcomes. Moreover, regulatory submissions rely heavily on evidencebased data to demonstrate the benefits and risks of new therapies, facilitating approval processes and ensuring compliance with regulatory standards. Ultimately, evidence-based knowledge fosters trust and transparency in the pharmaceutical industry, leading to the development of safer and more effective treatments for patients worldwide.

[0004] Existing Al and machine learning (ML) systems have attempted to automate knowledge extraction and evidence synthesis across regulatory, clinical, research, and other high-risk domains, but they suffer from critical limitations, including but not limited to absence of expert cognitive heuristics-centered Al prompts, lack of domain logic in machine reasoning in Al agents and domain-agnostic Al agentic orchestrations. Large language models (LLMs), while effective in text generation, frequently lack domain-specific validation and often produce hallucinated outputs, undermining their reliability in high-stakes applications (Zhao et al., 2024, a / X / 2412.14191). Additionally, existing Al-based synthesis tools fail to maintain structured reasoning frameworks, making them unsuitable not only for regulatory and research applications, but also for other high-risk domains where decision-making requires structured validation and traceability.

[0005] A major limitation of Al-driven decision-support systems is their reliance on single-agent models, restricting cross-domain collaboration and adaptive knowledge retrieval. Current Al lacks structured multi-agent coordination, leading to inefficient validation and static reasoning. Studies show that integrating reasoning and action enhances problem-solving accuracy (Yao et al., 2022, arXi .2210.03629), but existing models fail to leverage this synergy, relying on fixed templates rather than dynamic expert-guided adaptation. This gap highlights the need for Al frameworks that enable real-time agent coordination and reinforcement learning from expert feedback.

[0006] Existing Al models lack a systematic approach to structured knowledge anchoring, where authoritative content or expert- defined cognitive heuristics guide Al-generated outputs. Current prompting techniques remain static, failing to dynamically adapt to domain-specific constraints and expert reasoning. While certain published frameworks introduce structured prompt optimization (Wang et al., 2023, a / * 7 2310.16427), they do not address the comprehensive integration of expert cognitive frameworks into Al workflows. This gap limits Al’s ability to generate context-aware, traceable, and domain- validated insights, making existing systems unreliable for high-risk applications.

[0007] Existing multi-agent architectures function as task-routing systems rather than epistemologically connected frameworks, lacking mechanisms for structured knowledge inheritance. While structured methods have been applied to multi-agent coordination, most approaches fail to establish a persistent epistemic structure whereagents inherit validated heuristics, leading to fragmented decisionmaking (Boy, Technology in Society, 2023). Some models incorporate structured policy iteration, but they remain limited in dynamic knowledge anchoring and adaptability (Lockhart et al., 2021 , arXi .2101.04237).

[0008] Reinforcement learning in Al systems remains fragmented and inefficient due to the absence of structured mechanisms for capturing and encoding real-time expert feedback. Current models fail to systematically track user modifications and incorporate them as training signals for iterative improvement (Stiennon et al., 2020, NeuriPS). Traditional reinforcement learning methods are designed for environment-driven adaptation, which is insufficient for Al workflows requiring continuous expert validation and refinement (Zhang et al., 2024, / 4C7L 3640543.3645163). Without structured reinforcement learning, Al-driven decision-making remains static, lacking the ability to evolve based on real-world expert inputs.

[0009] Patent WO2024249684A2, titled "Systems and Methods for Generating and Deploying Task-Specific Agents", discloses a method for building and deploying Al agents configured to perform specific tasks. The invention involves identifying a first agent type from a set of predefined agent types based on task requirements, where each agent type corresponds to a respective language model. The system further includes a model component and an implementation component that integrates with data sources, tools, and output modules. However, the disclosed system primarily focuses on taskspecific agent instantiation and lacks a structured framework for knowledge instantiation through expert-driven reasoning. The invention does not establish a robust mechanism for systematically capturing oradapting domain knowledge from expert sources. Additionally, the system relies on static agent deployment, failing to implement dynamic agent creation and instantiation based on the domain-specific context or or advanced orchestration among themselves, limiting their responsiveness to evolving demands.

[0010] Patent US 2018260239A1 , titled “Interface and Runtime Environment for Process Definition and Process Execution”, discloses a method for managing and executing structured workflows by dynamically routing task components through an orchestrated process. The described system optimizes task flow execution through preconfigured sequences but it does not facilitate recursively expanding or refining knowledge based on new inputs or expert feedback. Its workflow model is pre-configured and offers no adaptive logic that automatically updates or re-organizes tasks when the underlying domain information changes. Thus, it cannot support continuous improvement of agent decisions or knowledge structures.

[0011] Patent US 8275728B2, titled "Neuromorphic Computer," describes a hardware-based neuromorphic computing system that emulates neuronal and synaptic connections using variable resistance elements, focusing on electronic circuit design for neural processing. However, the invention lacks a software-implemented environment utilizing standard or specialized computing hardware to encode human expert-like domain-specific reasoning, executing goal-oriented, multistage, specialized tasks, that could guide Al’s domain-specific outputs.

[0012] Patent W02000067223A2 titled “Case-Based, Agent- Assisted Learning System and Method”, discusses case-basedreasoning (CBR) and agent-assisted learning, and its conceptual framework differs significantly from knowledge-instantiating case-based agents. The referenced patent focuses on traditional CBR, where agents retrieve, reuse, and adapt past cases to solve new problems based on similarity matching. However, it does not facilitate multilayered, expert-guided decision-making processes beyond classical CBR retrieval.

[0013] W02025010241 A2 titled “Agentic Artificial IntelligenceSystem for Educational Environments”, describes a system that utilizes prompting mechanisms combined with fuzzy logic for filtering content and interpreting responses based on contextual constraints. Its primary focus is dynamically adjusting search and recommendation outputs based on user interactions. However, it does not address high-stakes domains that require recursive, persistent and agentic learning from an evolving body of verified information beyond static content recommendations.

[0014] Patent CN1 18551751 B titled "Multi-Agent Cooperation Method, device, Equipment and Medium Based on Large Language Model", discloses a multi-agent framework for task decomposition, flow reasoning, and execution using a large language model (LLM). The patent introduces a hierarchical agent structure, including a main control agent, a task creation agent, an intention arrangement agent, and multiple domain-specific professional agents. These agents work collaboratively to process natural language inputs, perform flow decomposition, allocate subtasks, and iteratively refine processing flows through an error correction mechanism. However, the disclosed system does not describe a layered or cumulative method forintegrating newly validated data over time or systematically preserving contextual knowledge across the agents. It emphasizes task-routing and collaboration more than the underlying domain logic or complex feedback loops needed for robust knowledge-driven decision-making.

[0015] Patent US12124932B1 titled “Systems and Methods for Aligning Large Multimodal Models (LMMs) or Large Language Models (LLMs) with Domain-Specific Principles”, describes embedding domain principles into Al agents using predefined rules for structured decisionmaking. While it aligns agents with certain thematic or regulatory constraints, it uses static definitions that do not facilitate iterative adaptation. Because its embeddings remain relatively unchanged by user contributions or expert modifications, it does not support frequent, fine-grained updates as requirements evolve or additional insights arise, constraining the agents’ capacity for continuous learning.

[0016] Patent US2024303498A1 titled “Coordinating Reinforcement Learning (RL) for Multiple Agents in a Distributed System”, describes a system that employs reinforcement learning (RL) mechanisms to improve Al-driven decision-making through adaptive feedback loops. The invention utilizes reinforcement learning models that iteratively refine model performance by assessing past actions and outcomes in a structured Al workflow. However, the disclosed method does not establish a robust mechanism for systematically capturing and implementing continuous feedback loops from expert-driven heuristics. Further, the method does not leverage continuous monitoring of agentgenerated outputs, ensuring context-aware refinements across multiagent workflows. Furthermore, the reinforcement learning is not explicitly linked to a human-in-the-loop validation, obviating thegrounding of improvements for domain-specific considerations rather than statistical performance metrics alone.

[0017] Collectively, these references highlight efforts to modularize agent deployments, define structured workflows, and align Al outputs with domain principles. Yet, no existing solution discloses a unified technical system that provides a comprehensive approach that continually integrates verified domain data, captures expert-driven refinements, and fosters adaptive collaboration among multiple agents. High-stakes environments — such as regulatory, or research-intensive fields — require reliable knowledge extraction, transparent reasoning pathways, and iterative improvements guided by expert oversight. Without a method for steadily evolving agent logic in alignment with authoritative information, prior solutions fail to meet the demands of complex, rapidly changing domains. To bridge this gap, a technical advancement is required — one that harmonizes Al-driven knowledge transformation with human expertise through a structured, iterative process, ensuring that Al-generated insights remain traceable, domain- validated, and aligned with real-world decision-making needs. The integration of expert-driven cognitive frameworks, multi-agent workflows, and structured reinforcement learning is essential to address these performance challenges and establish scalable Al frameworks.OBJECTIVE OF INVENTION

[0018] It is an overarching objective of the present invention to provide a method and System for Knowledge Instantiation and Evidence Synthesis that mitigates the limitations of current Al-drivendecision-support, particularly those dealing with regulatory, clinical research, and other high-stakes knowledge-driven environments.

[0019] It is another objective of the present invention to provide a method for systematically instantiating knowledge into CBAs, allowing Al models to incorporate expert cognitive processes, to interpret and respond with expert-human-like reasoning, executing goal-oriented, multi-stage, specialized tasks, to function as semi-autonomous cognitive units, wherein each agent is instantiated based on expert- structured domain constraints and can perform specialized subtasks within a defined knowledge domain.

[0020] It is yet another objective of the present invention to develop a structured knowledge anchoring framework wherein an anchor knowledge source — such as a regulatory framework, submission template or expert heuristic rules — serve as the foundational reference for Al-driven knowledge instantiation.

[0021] It is another objective of the present invention to provide a system and method for generating neuromorphic Al prompts from the anchor knowledge source by structuring, refining, and validating knowledge iteratively through expert feedback, ensuring domain- aligned transformation and evidence synthesis.

[0022] It is yet another objective of the present invention to implement a multi-agent architecture, wherein epistemologically related agents are spawned from an anchor input source through a process termed ‘secondary epistemogenesis’, whereby structured knowledge anchoring and neuromorphic prompting iteratively generatehierarchically related prompts that enable the dynamic creation of specialized sub-agents that inherit and refine domain knowledge, ensuring contextual coherence, interrelated agentic workflows, and scalable knowledge-driven decision-making across a distributed Al ecosystem.

[0023] Another objective of the present invention is to provide a human-at-the-helm orchestration mechanism that allows for expert- guided refinement of Al-generated knowledge artifacts and human- driven agent orchestration, ensuring that expert validation and modifications are systematically integrated into Al-driven outputs, maintaining scientific rigor, compliance, and traceability.

[0024] Yet another objective of the present invention is to establish a reinforcement learning-based adaptive knowledge synthesis system, wherein user modifications to Al-generated outputs serve as feedback signals for iterative model improvement, enabling self-correcting, continuously improving Al workflows that align dynamically with evolving expert knowledge.

[0025] It is an objective of the present invention to unify these elements — knowledge instantiation, evidence synthesis, neuromorphic prompting, agent spawning, and reinforcement learning into a single inventive cohesive system.

[0026] It is an objective of the present invention to provide a scalable, modular framework for real-world deployment by integrating specialized computational modules, transient and persistent data storage interfaces, and networked communication system to ensureversion control, lineage tracking, and seamless integration with domainspecific platforms such as Clinical Trial Management Systems (CTMS), Regulatory Submission Systems, and Electronic Data Capture (EDC) systems, thereby facilitating domain compliance, reproducibility, energy & cost efficiency, and enhanced Al-driven outcomes.

[0027] These objectives collectively ensure that the system is a technological solution delivering technical benefits: reduced error, improved adaptability, enhanced traceability, and better resource utilization when orchestrating Al-driven tasks in high-stakes domains.SUMMARY OF THE INVENTION

[0028] The present invention discloses a comprehensive Al-driven System for Knowledge Instantiation and Evidence Synthesis (hereinafter referred to as “SKIES”) - engineered as a modular set of computational components. This system orchestrates knowledge instantiation, neuromorphic prompting, agent-based collaboration, secondary epistemogenesis, human-at-the-helm reinforcement, and output persistence. By leveraging these discrete yet interconnected modules, SKIES provides tangible technical advantages in generating and managing Al case-based agents for complex, domain-specific tasks. The system is designed to operate in conjunction with evolving Al methodologies, including but not limited to large language models (LLMs), pre-trained transformers, and hybrid neuro-symbolic Al architectures, ensuring compatibility with present and future Al frameworks. SKIES employs hardware-accelerated modules (e.g., vector databases, CPUs / GPUs / TPUs for parallel processing) thatreduce retrieval latency and improve throughput, resulting in measurable technical improvements in high-stakes applications.

[0029] A Knowledge Instantiation Module serves as the foundation for CBA instantiation and neuromorphic prompt generation. This module identifies and processes anchor knowledge sources, including structured regulatory documents, scientific literature, and related proprietary datasets. It generates neuromorphic prompts that encode expert cognitive reasoning, ensuring alignment with domain-specific knowledge structures. Additionally, the module leverages Retrieval- Augmented Generation (RAG) pipelines to generate additional prompts based on validated source information while integrating heuristic elements from human experts to prioritize knowledge segments and domain-specific refinements.

[0030] A CBA Generation Module utilizes neuromorphic prompts to create workflow-specific Al agents that define goal-oriented task structures, enabling the decomposition of complex workflows into modular sub-tasks. These agents generate task-specific Al workflows that dynamically adapt to evolving domain constraints. Furthermore, they validate knowledge synthesis outputs through expert human-at- the-helm feedback loops, ensuring Al-generated artifacts remain accurate, contextual, and regulatory-compliant and do not drift into generic or “hallucinated” outputs. This approach yields specialized, explainable Al prompts that effectively guide downstream agents.

[0031] A Secondary Epistemogenesis Module is the foundation for the agent-spawning mechanism from anchor knowledge sources. For complex tasks, a CBA may spawn one or more sub-agents, inheritingvalidated heuristics from the parent CBA’s knowledge structure. This approach enforces epistemic continuity, preventing contradictory logic or duplication of data across sub-agents. Over time, recursive agent spawning expands the system’s capacity to handle evolving tasks without fragmenting the domain intelligence and maintaining interrelated knowledge structures, allowing for multi-agent collaboration and contextual knowledge propagation. This approach optimizes Al- driven decision-making across multiple use cases, whilst ensuring agentic reasoning aligns with structured expert-derived frameworks.

[0032] A Workflow Orchestration and Evidence Synthesis Module stages the orchestration of multi-agent Al-generated knowledge outputs from input knowledge sources formatted into structured, machine- readable formats such as son, .xml, regulatory submission templates, as well as other data interchange formats, databases, and document structures known in the art. The Module ensures that Al-generated knowledge is persistently stored, version-controlled, and retrievable for future use. This module enables seamless third-party integrations, supporting platforms such as Clinical Trial Management Systems (CTMS), Regulatory Submission Systems, Electronic Data Capture (EDC) and other applications known in the art. Additionally, it implements continuous knowledge aggregation, allowing future agents to retrieve and reuse prior validated outputs without redundant processing.

[0033] A Reinforcement Learning from Human Refinement (hereinafter referred to “RLHR”) module continuously improves casebased agent and sub-agent performance through structured expert feedback. It captures user edits and validation actions, storingknowledge artifacts for iterative reinforcement learning. This module enhances agentic reasoning capabilities, ensuring that subsequent Al- generated outputs progressively align with expert expectations. It also implements adaptive reinforcement loops, where validated knowledge outputs recursively inform future Al-driven transformations.

[0034] By implementing these modular components as a unified architecture, the invention substantially improves the reliability, source traceability, and adaptability of Al-driven workflows. The system is designed for deployment on multi-core CPUs and / or GPU-accelerated architectures, and cloud-based environments, ensuring parallel processing and scalability. It supports containerized execution via frontend and back-end application containers known in the art, enabling seamless integration with enterprise Al stacks. Optimized for distributed computing, it ensures low-latency inference, dynamic resource allocation, and fault-tolerant execution. Al accelerators such as Tensor Processing Unit (TPUs) and Field Programmable Gate Array (FPGAs) enhance performance for large-scale, multi-agent workflows.SHORT DESCRIPTION OF FIGURES

[0035] Figure 1 illustrates the five main modules of the System for Knowledge Instantiation and Evidence Synthesis (SKIES), including their high-level interactions and data flows, as per a non-limiting embodiment of the invention.

[0036] Figure 2 presents a workflow for creating a regulatory submission under In-Vitro Diagnostic Regulation (IVDR) guidelines as per a non-limiting embodiment of the invention.

[0037] Figure 3 depicts a network and hardware architecture for an Al-driven application environment, as per a non-limiting embodiment of the invention.

[0038] Figures 4A to 4E shows various graphical user-interface (GUI) screens in an exemplary CE-IVDR submission workflow, as per a non-limiting embodiment of the invention.

[0039] Figure 4A illustrates a front-end screen GUI where a user begins by defining the knowledge asset requirement for a prospective CE-IVDR submission.

[0040] Figure 4B depicts a GUI through which public and proprietary documents are uploaded to the system.

[0041] Figure 4C shows GUI for a validation stage, where the frontend lays out an expected submission structure keyed to CE-IVDR guidelines.

[0042] Figure 4D shows GUI for the “Finalize Draft” stage for a specific chapter.

[0043] Figure 4E concludes the workflow by showing the GUI for “Review I Revise Final Draft” screen.DETAILED DESCRIPTION OF THE INVENTION

[0044] The following is a full description of a preferred embodiment of the invention. The embodiments are described in such a way that the disclosure is clearly communicated. The level of detail provided, on the other hand, is not meant to limit the expected variations of embodiments; rather, it is designed to include all modifications, equivalents, and alternatives that come within the spirit and scope of the current disclosure as defined by the attached claims. Unless the context indicates otherwise, the term “comprise”; and variants such as “comprises”; and “comprising” throughout the specification are to be read in an open, inclusive meaning, that is, “including, but not limited to”; When “embodiment” or “an embodiment” is used in this specification, it signifies that a particular feature, structure, or characteristic described in conjunction with the embodiment is present in at least one embodiment. As a result, the expressions “one embodiment” and “in an embodiment”; that appear throughout this specification do not necessarily refer to the same embodiment. Furthermore, in one or more embodiments, the specific features, structures, or qualities may be combined in any way that is appropriate. Unless the content clearly demands otherwise, the singular terms “a”, “an”, and “the”; include plural referents in this specification and the appended claims. Unless the content explicitly mandates differently, the term “or” is normally used in its broad definition, which includes “and / or”.

[0045] The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.

[0046] The headings and abstract of the invention provided herein are for convenience only and do not interpret the scope or meaning of the embodiments. All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.

[0047] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and / or patentability.

[0048] When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description that follows, and the embodiments described herein, is provided by way of illustration of an example or examples, of particular embodiments of the principles and aspects of the present disclosure. These examples are provided for the purposes of explanation, and not of limitation, of those principles and of the disclosure.

[0049] It should also be appreciated that the present invention can be implemented in numerous ways, including as a system, a method ora device. In this specification, these implementations, or any other form that the invention may take, may be referred to as processes. In general, the order of the steps of the disclosed processes may be altered within the scope of the invention. Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.

[0050] In the following, the systems and methods of this invention are described in the context of pharmaceutical, biomedical research & regulatory and clinical trial protocol documentation, post-market surveillance, rapid generation of regulatory documents, drug discovery. This invention is not so limited. Because SKIES is modular and scalable, it can be readily applied to numerous industries. It may also be advantageously configured to meet the requirements of the accounting / finance and legal sector by offering automated contract analysis, risk assessments, multi-jurisdictional compliance reviews. Likewise, SKIES is capable of being deployed to provide cybersecurity through real-time threat detection, knowledge inheritance among specialized detection agents, structured alert triaging. Furthermore, SKIES is also capable of being deployed for education & training by offering adaptive Al tutors grounded in validated educational frameworks and refined by teacher feedback. In each domain, SKIES offers a domain-aligned, continuously improving Al workflow that is auditable, traceable, and designed to meet stringent reliability standards.

[0051] The following glossary contains several terms frequently used in the Detailed Description of the invention. It is presented here as an aid in order to initially introduce important terms that are more fully described and explained in the following sections.A. “Knowledge Instantiation Module”Architecture & Function:• Anchor Knowledge Source Ingestion: This module ingests anchor knowledge sources — such as regulatory guidelines, scientific literature, proprietary datasets or expert heuristic records — through user uploads, Application Programming Interface (API) requests, or streaming data feeds.• Retrieval Augmented Generation (RAG): Implements natural language processing (NLP) RAG pipelines (e.g., tokenization, chunking, named-entity recognition, concept mapping) to produce structured representations of each anchor knowledge source.• Neuromorphic Prompt Generation: Encodes expert cognitive heuristics and domain-specific rules into neuromorphic prompts, which embed multi-step reasoning, compliance constraints, or domain logic. These prompts are then ready for consumption by the CBA Generation Module.Technical Impact:• Advanced Indexing & Retrieval: Maintains the processed anchor data in an internal structure (e.g., a vector store, relational DB, or knowledge graph) that permits low-latency lookups by downstream modules.• Minimized Redundancy & Accelerated Searches: Applies conceptbased chunking, hashing, or semantic embeddings to streamlineretrieval, reducing computational overhead and memory usage in large-scale deployments.• Anchor-Centric Reliability: By grounding all subsequent Al processes in validated, domain-specific anchor data (including neuromorphic prompts), the system lowers hallucination risks and ensures traceable Al outputs.B. “Case-Based Agent (CBA) Generation Module”Architecture & Function• Agent Initialization: Upon receiving neuromorphic prompts this module instantiates one or more CBAs. Each agent is configured with: o Goal Definition (e.g., summarizing a clinical research study, drafting a regulatory checklist), o Anchor Constraints (domain references, validated heuristics), o Performance Criteria (accuracy thresholds, time limits, resource usage). o Workflow Decomposition: Sub-divides complex tasks into manageable sub-tasks, assigning them to new or existing CBAs. o Expert Human-at-the-Helm Interface: Allows domain experts to preview or refine partial outputs generated by each CBA in real time.Technical Impact• Distributed or Multi-Threaded Execution: Each CBA can run as an isolated process, leveraging parallel computing (e.g., GPUs, multicore CPUs) for text analytics or specialized tasks.• Live Adaptation: If anchor data changes (e.g., new regulatory updates), the module can reconfigure an existing agent or spawn a fresh instance with updated constraints.• Concurrency & Load Balancing: Integrates scheduling and message queues to manage multiple CBAs concurrently, preventing resource conflicts and enhancing scalability.C. “Secondary Epistemogenesis Module”Architecture & Function• Recursive sub-agent Spawning: Allows a parent CBA to create subagents tasked with specialized or granular aspects of a larger problem.• Inherited Heuristics: Maintains an inheritance table or knowledge graph that codifies which domain rules and anchor references each sub-agent inherits from its parent.• Multi-Agent Collaboration: Provides mechanisms (e.g., message queues, shared memory) for sub-agents and parent agents to exchange data, partial analyses, or intermediate results.Technical Impact• Epistemic Continuity: Ensures consistency across sub-agents by passing validated heuristics downstream; avoids contradictory logic or data duplication.• Scalable Problem Decomposition: For large enterprises, each subagent can be distributed on separate hardware nodes, enabling parallel computations for different sub-tasks.• Reuse of Domain Knowledge: Minimizes overhead by reusing existing anchor constraints and agent-level validations, resulting in faster, more reliable expansions of the Al ecosystem.D. “Workflow Orchestration and Evidence Synthesis Module”Architecture & Function• Final Output Synthesis: Collects and consolidates the outputs synthesized by CBAs and their sub-agents, converting input knowledge sources into structured output knowledge artifacts (e.g., JSON, XML, domain-specific templates).• Metadata & Lineage Tracking: Associates each artifact with unique metadata, including the anchor source used, which CBAs contributed, and the version history of sub-agent interactions.• Persistent Output Repository: Stores generated artifacts in a redundant, fault-tolerant environment (e.g., cloud databases, onpremise clusters). Offers secure access to archived versions and ensures compliance for audit trails.Technical Impact• Regulatory & Audit Assurance: Facilitates end-to-end source traceability — especially crucial in healthcare, pharmaceutical, or finance domains — by cataloging how each output was derived.• Scalable Storage: Employs indexing strategies (e.g., time-based or entity-based indexing) to efficiently handle large volumes of output documents without significant performance degradation.• Version Control & Extensibility: New or updated sub-agents can retrieve prior validated outputs to prevent re-processing the same data, streamlining subsequent tasks.E. “Reinforcement Learning from Human Refinement (RLHR) Module”Architecture & Function• Continuous Feedback Tracking: Captures human corrections or validations from user interfaces (e.g., corrected trial end-points, flagged inaccuracies, compliance confirmations).• Structured Reinforcement: Transforms user edits into reward / penalty signals mapped to specific anchor segments or agent actions, ensuring domain-aligned improvements.• Recursive Update Mechanism: Feeds refined heuristics or newly learned constraints back into the Knowledge Instantiation Module and CBA Generation Module so that future agent spawns automatically inherit improved domain logic.Technical Impact• Continuous Learning: Each correction or expert note incrementally enhances the agent knowledge base, mitigating repeated mistakes.• Domain-Specific RL: Rather than arbitrary, black-box modifications of model weights, the system targets anchor-based knowledge elements, ensuring transparency and explainability. Source traceability and explainability is preferably achieved through tools known in the art including LIME and SHAP for Al decision transparency.• Adaptive Convergence: Over iterative cycles, the system converges more rapidly to domain-validated outputs, keeping pace with evolving regulatory standards or expert knowledge.

[0052] The invention is described in detail with reference to Figures 1 to 4 hereinbelow. Figure 1 illustrates the end-to-end architecture for the System for Knowledge Instantiation and Evidence Synthesis (800) as per one embodiment of the invention. This figure details the flow of information across five primary modules — Knowledge Instantiation(100), Case-Based Agent Generation (200), Secondary Epistemogenesis (300), Workflow Orchestration and Evidence Synthesis (400), and Reinforcement Learning from Human Refinement (500) — each of which is subdivided into function-specific elements. The interactions of these components to produce domain-validated and continuously improving Al outputs in regulated or complex knowledge domains is further described herein below.

[0053] Within the Knowledge Instantiation Module (100), Anchor Knowledge Sources are ingested at (105). At this stage, the system acquires documents such as regulatory guidelines, scientific papers, proprietary data feeds, expert-defined inputs, or any other resources pertinent to the target domain. The process at (105) rigorously converts these diverse inputs into a machine-readable format, often involving tokenization, entity extraction, metadata tagging, and other domain mapping procedures that align new incoming material with known taxonomies or ontological structures. These ingested sources are then securely cached or indexed to facilitate rapid downstream lookups. The system ensures that references to version numbers, provenance data, and user-defined metadata remain tightly linked to each document segment for auditability and traceability.

[0054] Following ingestion at (105), the system proceeds to a Retrieval-Augmented Generation (RAG) pipeline (110), which performs vector-based or semantic searches on the parsed content. In one embodiment, the pipeline (110) may employ chunking, i.e., splitting long documents into coherent segments, each enriched with domainrelevant metadata. These operations are executed by hardware- accelerated processing engines, and the resulting segments are storedin specialized data structures — CBA vector databases (685) — allowing the system to retrieve them efficiently based on their semantic similarity to user queries or agent requests. Chunking and vector indexing can leverage NLP methods, including transformer-based embeddings known in the art to ensure high-fidelity retrieval. The pipeline (110) can also incorporate domain heuristics, such as regulatory scoring or confidence thresholds, to prioritize critical segments over less essential ones. Once suitable segments are retrieved, they are optionally reranked according to domain-defined weights (for instance, compliance necessity or scientific impact), after which they are forwarded for prompt construction.

[0055] Having identified and extracted relevant knowledge segments in pipeline (110), the system generates a Neuromorphic Prompt (1 15). This generation is performed using a hardware- accelerated natural language processing engine (690) which maps the expert-derived heuristics onto physical data structures, such as a database, thereby reducing computational overhead and ensuring traceability. These prompts incorporate expert-derived logic flows — sometimes called “cognitive heuristics” or “domain reasoning protocols” — that ensure each Al operation is grounded in validated references rather than generic or hallucination-prone text generation. The neuromorphic prompt (115) may contain structured instructions such as step-wise reasoning, compliance thresholds, or even entire chains-of-thought consistent with best practices in the domain. By precisely encoding the anchor references obtained in (105) and (110), these prompts preserve contextual continuity between the original sources and any forthcoming Al-based agents or sub-agents. Furthermore, the neuromorphic prompts (115) may be tagged withmetadata and version markers to reflect the most up-to-date regulatory changes. The presence of these tags ensures that any agent relying on these prompts is aligned with the latest domain knowledge.

[0056] Once the neuromorphic prompt (115) has been created, control logically passes to the Case-Based Agent Generation Module (200). This module initializes one or more CBAs based on the domain objectives, constraints, and performance requirements distilled from the prompts. Specifically, the system configures Agent Initialization, Goal Definition, Anchor Constraints, and Performance Criteria (205), wherein each agent is assigned a specific target, for instance, drafting a regulatory checklist, compiling a scientific literature review, or generating a compliance risk assessment. The anchor knowledge sources identified in module (100) help the system bind domain rules, regulatory guidelines, or clinical research data with the agent’s reasoning parameters. This binding enforces that any agent operations or outputs adhere strictly to established domain constraints, thus reducing ungrounded speculation.

[0057] Upon configuring a CBA’s objectives and constraints at (205), the system triggers Workflow Decomposition (210). In one embodiment, this decomposition is executed on a distributed computing cluster, where containerized agents running on multi-core CPUs or GPUs in the back-end application decompose the task into parallel sub-tasks. In this step, the tasks required to fulfill the agent’s goal are analyzed and fragmented into smaller sub-tasks, each potentially requiring different domain knowledge or varying computational intensities. A single CBA can handle multiple sub-tasks if they remain within its capabilities. However, if certain aspects of the workflow demand highly specializedreasoning, the module (200) can communicate with the Secondary Epistemogenesis Module (300) to spawn new sub-agents. Workflow decomposition (210) thus ensures that no monolithic agent must handle overly broad or tangential tasks, effectively introducing concurrency and parallelization where applicable. It also adds to the system’s ability to effectively deploy computing resources to enable energy and cost efficiency.

[0058] Throughout this workflow, the Expert Human-at-the-Helm Interface (215) enables domain experts to inspect partial outputs, intermediate agent reasoning steps, and any integrated reference documents. Users can intervene directly, editing errors or clarifying ambiguous instructions. These interventions are transmitted via a feedback channel to the RLHR Module (500), thus creating a continuous improvement cycle. This real-time correction process is invaluable in regulated domains, where missed nuances or inaccurate references can significantly undermine the system’s credibility.

[0059] In situations where a task emerges that is beyond the immediate scope or domain coverage of an existing CBA, the Secondary Epistemogenesis Module (300) steps in to facilitate Recursive Sub-Agent Spawning (305). For instance, a single overarching agent may be tasked with generating a robust multi-region compliance document, requiring specialized knowledge of local regulations. The system can spawn child agents focusing specifically on region A, region B, etc., each armed with specialized anchor heuristics. These sub-agents inherit the validated heuristics from the parent agent through Inherited Heuristics (310), ensuring that fundamental domain truths and compliance standards remainconsistent across the hierarchy. This inheritance mechanism automatically reduces duplication of initial analysis and fosters an epistemically coherent environment, such that sub-agents do not conflict with the knowledge base established by the parent.

[0060] Because multiple sub-agents may coexist, the system leverages Multi-Agent Collaboration (315) to coordinate concurrency, track partial progress, and consolidate sub-results. This coordination is implemented via message queues and physical network interfaces linking containerized agents in the back-end application over a distributed computing environment. For example, if an agent working on region A compliance discovers a newly updated guideline, it can broadcast that information back to the parent or sibling agents via (315). This ensures that all relevant agents remain informed of important domain updates. Through these collaborative exchanges, the entire multi-agent ecosystem evolves in a synchronized, domain-aligned manner, avoiding fragmentation of knowledge across parallel subtasks.

[0061] After the system has produced partial deliverables across multiple CBAs and sub-agents, the Workflow Orchestration and Evidence Synthesis Module (400) plays the pivotal role of integrating, verifying, and finalizing output knowledge artifacts generated from input knowledge sources — proprietary, public, or a combination thereof. Within this module, Final Output Synthesis (405) aggregates agent outputs, checks them for completeness, and merges them into a coherent deliverable — be it a regulatory submission package, an executive report, or a structured data file. The system ensures that each portion of text or data in the final deliverable can be traced back to theassociated anchor knowledge source (105) segments or heuristics used by its originating agent.

[0062] To guarantee robust auditability, Metadata and Lineage Tracking (410) annotates each piece of the final deliverable with detailed provenance information. This metadata may indicate which anchor references were utilized, whether any sub-agents contributed specialized sections, and which user edits or compliance thresholds were invoked along the way. The system preserves this lineage in Persistent Output Repository (415), wherein outputs are time-stamped, version-controlled, and cryptographically signed if necessary. This repository architecture allows future tasks or newly spawned agents from modules (200) or (300) to retrieve validated content, ensuring that knowledge is not unnecessarily re-processed. Because regulated or safety-critical industries often require thorough traceability, storing these references and agent lineages in (415) reinforces provenance, domain compliance and fosters trust in the system’s outputs.

[0063] The RLHR Module (500) accumulates and processes user- driven feedback, forming a continuous improvement loop that transforms static Al models into dynamic learning agents. Whenever a domain expert intervenes at the Expert Human-at-the-Helm Interface (215) or the final output synthesis Human-at-the-Helm interface (405), the system logs that feedback in Continuous Feedback Tracking (505). This feedback may range from a minor textual correction, such as clarifying a dosage instruction, to a major conceptual pivot, like identifying an overlooked regulatory clause. Rather than applying a simplistic reward signal globally, the module engages in Structured Reinforcement (510), mapping corrections to the specific anchorreferences, agent states, or domain rules that need improvement. By focusing updates on the pertinent heuristics and prompt definitions, the system reduces the risk of overfitting or inadvertently degrading other well-understood knowledge areas.

[0064] Once the feedback has been characterized, the system executes a Recursive Update Mechanism (515) that synchronously suggests amendments to the anchor knowledge source, in case there are insights from evidence synthesis that warrant an update to the anchor knowledge source itself, or the neuromorphic prompts in module (100), as well as relevant agent configurations in module (200). In cases where sub-agents (see module (300)) were responsible for the flagged content, the system retroactively updates their inherited knowledge so that future sub-agents spawned under similar constraints are already informed of the corrected logic. Over time, the reinforcement loop in (500) accelerates the domain-alignment of all new or existing agents, resulting in outputs that increasingly match expert expectations without requiring repeated manual corrections for the same issues.

[0065] In operation, the SKIES architecture depicted in Figure 1 provides a closed-loop intelligence cycle. The Knowledge Instantiation Module (100) ensures that newly ingested anchor sources are transformed into meaningful representations and prompts. The Case- Based Agent Generation Module (200) creates specialized agents, which may, via the Secondary Epistemogenesis Module (300), spawn further sub-agents to handle highly specialized tasks in parallel. The Workflow Orchestration and Evidence Synthesis Module (400) collects partial and final results, merges them into coherent deliverables, and stores them alongside detailed lineage information for future retrieval.Meanwhile, the Reinforcement Learning from Human Refinement Module (500) captures corrections or suggestions from experts, converting them into domain-specific reinforcement signals that retroactively improve the anchor knowledge and agent logic. This synergy of ingestion, agent-based decomposition, sub-agent spawning, orchestrated synthesis, and structured RL-based refinement significantly elevates both the reliability and accuracy of system outputs in specialized, high-stakes domains.

[0066] As thus described, Figure 1 showcases a multi-layered, intelligent framework. By integrating advanced retrieval, neuromorphic prompting, hierarchical agent spawning, and continuous human reinforcement, SKIES (800) fosters a self-correcting ecosystem well- suited to regulated or complex domains. Each module contributes discrete technical features — such as concurrency management, domain-based chunking, or hierarchical knowledge inheritance — providing an architecture that delivers a robust, scalable solution that adapts dynamically to evolving user needs and domain constraints while preserving a complete audit trail of how and why each result was generated.

[0067] Figure 2 presents an exemplary workflow for creating a regulatory submission in accordance with In-Vitro Diagnostic Regulation (IVDR) guidelines under European regulatory frameworks (often referred to as a CE mark submission). This figure provides a more concrete illustration of how CBAs are leveraged, alongside anchor knowledge sources, vector databases, and user feedback loops, to generate a domain-compliant deliverable. Each step is identified by a reference numeral (600, 605, 610, etc.) and is described below in theorder of execution, enabling a person skilled in the art to understand how the system orchestrates knowledge retrieval, Al agent instantiation, sub-agent spawning, and iterative validation.

[0068] The process starts (600) when a user such as a regulatory affairs specialist initiates a new project to generate a CE-IVDR submission. The user proceeds to “Define Knowledge Asset Requirement” (605), where the intended scope and goals of the regulatory document are specified. This may include target diagnostic test information, details about the assay’s performance, and specific compliance requirements under IVDR guidelines. The system uses these requirements to determine which case-based agent will be relevant for the task.

[0069] In the next step “Define Public and Proprietary Sources of Knowledge” (610), the user uploads or searches for relevant material from both publicly available datasets such as European Medicines Agency guidelines, scientific literature, and proprietary sources including internal laboratory performance data. These sources are indexed and stored, if not already available, for quick reference by the Al pipeline.

[0070] Once the knowledge sources have been defined, the process continues to “Validate Output Structure” (615), which emphasizes preliminary checks of the desired submission format or the structural requirements mandated by IVDR. This validation may relate to how sections of the submission (analytical performance, risk management, labeling etc.) must be composed or how references and citations ought to be organized. Based on the correct CBA (640), the system ensuresit has the correct outline before content generation begins. However, final acceptance at this stage requires domain-expert input confirming data coverage and structural compliance.

[0071] In the next step “Create Draft CE-IVDR Submission” (620), the user instructs the Al-based workflow to begin drafting an initial version of the regulatory document. This includes pulling from all the relevant public and proprietary sources of knowledge stored in vector databases (see (635) below). During or after the draft creation, the user may revisit the overall objectives to ensure the submission adheres to the correct regulatory schema or classification.

[0072] The Case Based Agent (CBA) instantiation process begins with a “CBA Similarity Search” step (630), that is invoked at the time the Knowledge Asset is Defined (605). During the CBA Similarity Search (630), the system determines whether an existing CBA — stored or previously trained — already covers the scope of the CE-IVDR task. If the user or system finds a relevant agent, “CBA Available” (645), it can use that agent’s heuristics and domain knowledge to execute the drafting process. However, if the determination for “CBA Available” is “No” (645), indicating no suitable agent exists, the system proceeds to “Upload CBA Anchor Knowledge Source” (665), prompting the ingestion of new anchor data. Once new anchor data is uploaded, the system applies a RAG pipeline (670) to parse the newly uploaded documents, extract or chunk relevant segments, and store them in the vector database (685), embedding domain metadata, ensuring that the textual segments map efficiently to retrieval queries.

[0073] After retrieving the relevant knowledge embeddings the system conducts Neuromorphic Prompt Generation (675) using a hardware-accelerated natural language processing engine (690) which maps neuromorphic prompts on to physical data structures, such as a database, thereby reducing computational overhead and ensuring traceability. The generated neuromorphic prompts ensure compliance logic, domain heuristics, and any newly detected or user-defined constraints are consolidated into structured prompts collectively creating the parent CE-IVDR CBA (640). This approach significantly reduces the risk of hallucinated or factually incorrect outputs. Once the neuromorphic prompts for the parent CBA are ready, the system determines if “Sub-Agent Required” (680), whether the complexity of the tasks or the breadth of coverage, calls for agent subdivision. If subagent spawning is necessary, the process branches to Sub-Agent Spawning (650), which instantiates specialized sub-agents for separate regulatory sections, region-specific guidelines, or specialized assay data. These sub-agents along with the parent CBA store and retrieve domain logic in a Case-Based Agent Repository (660), ensuring continuity in the knowledge base and reusability for future tasks. On the other hand, if “Sub-Agent Required” (680), is answered “No,” meaning the single parent CBA can handle the entire IVDR workflow, sub-agent spawning is bypassed.

[0074] Once the user has validated the output structure (615) the data and generated a draft (620), the submission is advanced to “Review and Finalize CE-IVDR Submission” (625), wherein domain experts examine all sections for accuracy, regulatory compliance, and traceability. They can make in-line edits, commentaries, or issue approvals. These edits are tracked and integrated into the system’sknowledge base, potentially refining future agent spawns or updating the anchor references for improved accuracy. The near-final CE-IVDR submission is stored in the Persistent Output Repository (655) By maintaining a version-controlled Persistent Output Repository, the system ensures that all lineage information — such as references used, agent IDs, sub-agent contributions, and user edits — remain accessible for subsequent audits or improvements. The final output (695) for the workflow is delivered as a regulatory-grade CE-IVDR submission.

[0075] In parallel to the finalization and repository storage, any newly created or updated CBAs are also committed to the Case-Based Agent Repository (660), solidifying improvements to domain knowledge for future tasks. For instance, a newly derived sub-agent specialized in analyzing performance evaluation studies can be archived, enabling the system to jumpstart subsequent tasks involving similar datasets or compliance references.

[0076] In summary Figure 2 shows how the user’s initial definition of knowledge requirements seamlessly merges with agent reuse or new anchor ingestion to drive either the creation of a new agent or the reactivation of an existing one. Subsequent steps ensure the data is chunked, retrieved, structured via neuromorphic prompts, and possibly subdivided via sub-agent spawning. The entire process culminates in the production of a robust draft that is iteratively refined until a final CE- IVDR submission is ready and stored in both the Persistent Output Repository and the agent repository. This embodiment underscores how the SKIES (800) framework streamlines regulatory submission creation through advanced Al orchestration, domain knowledgereusability, and iterative expert feedback loops, achieving consistent alignment with IVDR or comparable domain-specific standards.

[0077] Refer now to Figure 3 which depicts network and hardware architecture for an Al-driven application environment (700) as per one embodiment of the invention. It shows how various system components — Front-End User Interface (705), Backend Application Server (710), Database Tier (715), Authentication (720), Region- Specific Compliant Cloud (725), Containerized Front-End and Back- End (730), Monitoring (735), and User Access (740) — interact to support a scalable, secure, and regulatory-compliant solution.

[0078] Initially, the Front-End User Interface (705) is presented to end users — such as domain experts or customers — via a web browser or a native client application. This interface may be built with modern frameworks (including but not limited to React, Angular, or Vue) and is served over secure HTTPS connections. It delivers dynamic visualizations, form inputs, or analytics dashboards, relying on real-time interactions with the Backend Application Server (710). The front-end issues API requests to the backend and receives processed responses or Al-driven insights in return. The invention is capable of being deployed on compute resources including High-performance CPUs (Intel Xeon, AMD EPYC), GPU Clusters (NVIDIA A100, H100, RTX 6000 Ada), TPUs (Google Cloud TPUs for optimized LLM inference), Edge Al (Jetson Xavier, Coral Edge TPU) for real-time processing. Any advancements in the art relating to User Interfaces can be advantageously integrated to work seamlessly with the system.

[0079] The Backend Application Server (710) is shown to communicate directly with the Database Tier (715) and the Authentication (720) components. This server may be implemented using frameworks like Django, Node.js, Flask, or other suitable web technologies, providing both RESTful and GraphQL endpoints for easy integration. In practice, the Backend Application Server (710) can incorporate logic that either directly hosts or interfaces with large language models (LLMs). For instance, the server might call external APIs for LLM inference — such as managed cloud-based LLM services — or locally hosted language models using GPU-accelerated environments. The server orchestrates workflow steps, caches partial results, and enforces application-level constraints such as domain rules or data validation before relaying outputs back to the front-end (705). Any advancements in the art relating to Backend Application Servers can be advantageously integrated to work seamlessly with the system and such modifications are within the scope of the instant invention.

[0080] The Database Tier (715) holds structured and unstructured data essential to the application, such as user profiles, historical agent outputs, domain reference documents, or indexed embeddings from neural models. While Figure 3 shows a single database icon, in practice the tier could comprise multiple databases — both SQL and NoSQL — deployed in a load-balanced cluster. The system can rely on relational engines (including but not limited to PostgreSQL, MySQL) or distributed data stores (including but not limited to Cassandra, MongoDB), depending on the volume of records and concurrency demands. The Database Tier (715) handles transactions, ensures data durability, and enforces referential integrity, which is crucial for compliance-driven scenarios (including but not limited to life sciences or finance). Anyadvancements in the art relating to Databases and their storage and management can be advantageously integrated to work seamlessly with the system and such modifications are within the scope of the instant invention.

[0081] The Authentication step (720) represents an independent service or module responsible for secure access control. This service can be implemented via OAuth, JWT tokens, or custom credential management systems, and may be externally hosted on a third-party identity provider or internally managed. The Database Tier (715) and Authentication (720) connection allow for the user or credential records to be persisted in a secure sub-database, ensuring consistent session handling. The connectivity of the Backend Application Server (710) to Authentication (720) facilitates Application Programming Interface (API) calls that validate tokens or sign in new users, thus maintaining session continuity. Any advancements in the art relating to Authentication can be advantageously integrated to work seamlessly with the system and such modifications are within the scope of the instant invention.

[0082] A Region-Specific Compliant Cloud (725) hosts or orchestrates the entire deployment, ensuring adherence to data residency and regulatory policies in different geopolitical zones. For instance, the solution might run in a European data center for compliance with General Data Protection Regulation (GDPR) or healthcare guidelines, or in an Asia / Pacific region for local sovereignty requirements. The Authentication (720) and Region-Specific Compliant Cloud (725) ensure region-specific compliance requirements — such as encryption, multi-region replication, or geofencing at the cloud infrastructure layer. Cloud-based compute instances or serverlessservices can be spun up or down dynamically to match workload demands, while guaranteeing data never leaves authorized regions. Any advancements in the art relating to Cloud storage including decentralised file storage systems can be advantageously integrated to work seamlessly with the system and such modifications are within the scope of the instant invention.

[0083] The Containerized Front-End and Back-End (730) denotes a layer where both the front-end user interface (705) and the backend application server (710) could be encapsulated within Docker containers, Kubernetes pods, or similar container orchestration environments. This layer allows the system to scale horizontally, performing rolling upgrades or quickly launching new instances in response to increased demand or redundancy needs. The Containerized Front-End and Back-End (730) run within the region- compliant cloud (725), leveraging the underlying virtualization or container orchestration services (e.g., Amazon ECS, Google Kubernetes Engine, or Azure Kubernetes Service), ensuring costefficient scaling and robust fault tolerance.

[0084] Monitoring (735) is connected to the Containerized Front-End and Back-End (730) as well as to the overall user access flow (740), ensuring the capture of real-time metrics and logs throughout the system. Monitoring (735) may be implemented via specialized tooling (e.g., Prometheus, Grafana, ELK stack, or cloud-provided monitoring solutions). This component tracks CPU usage, memory consumption, response times, error rates, or suspicious user activities. By analyzing these logs and metrics, administrators can proactively detect anomalies or performance bottlenecks. Monitoring (735) also aids complianceaudits by keeping a tamper-evident record of key transactions, which is especially valuable when the system deals with regulated data in life sciences, financial domains, or healthcare.

[0085] User Access (740) represents the human end-users who interface with the entire system through the Front-End User Interface (705), through a secure login or session-based mechanism that protects sensitive data at rest or in transit. User roles may vary: domain experts verifying Al-generated content, compliance officers reviewing logs or information lineage, or customers requesting real-time analytics. Whenever these users perform data entry, request a model inference, or update domain knowledge, the entire data flow is orchestrated across the application hardware and network architecture (700).

[0086] Figure 3 thereby demonstrates how each component — user interface, backend logic, persistence, security, containerization, monitoring, and region-compliant cloud deployment — works in synergy. The architecture remains open-ended, allowing for the integration of external large language model APIs (e.g., calling an NLP microservice that can be swapped with any LLM provider, where runtime requests dynamically route to the chosen model endpoint, enabling on-the-fly upgrades or replacements without altering the core orchestration logic) or an internal HPC cluster for CPU or GPU-accelerated training. By adhering to container standards and employing robust monitoring, the environment ensures both flexibility and reliability. The region-specific nature indicates that the same blueprint can be replicated across multiple geographic regions, each subject to local data governance policies. Overall, Figure 3 illustrates a practical approach to deploying a multi-layer Al application that remains compliant, modular, andscalable, leveraging the synergy of modern web frameworks, container orchestration, cloud-based resources, and user-centric interfaces. LLM Frameworks known in the art such as LLAMA Index, LangChain, Hugging Face Transformers and trained Models such as OpenAI GPT- 4 Turbo, Claude 3.5, Mistral 7B, LLaMA 3 70B may be conveniently deployed.

[0087] Figure 4 illustrates various front-end screens in an exemplary workflow for creating a CE-IVDR (In Vitro Diagnostic Regulation) submission. These graphical user-interface (GUI) screens provide a practical embodiment of how the system guides a regulatory specialist through defining knowledge assets, uploading sources, validating structure, finalizing a draft, and reviewing output for compliance. Each user interaction — such as document uploads or revisions — is immediately transmitted to a physical, cloud-based database and vector store, ensuring that the data transformation and storage occur in hardware and are fully traceable.

[0088] Figure 4A illustrates a front-end screen where a user begins by defining the knowledge asset requirement for a prospective CE- IVDR submission. Although the interface prominently displays fields such as the intended role, expected regulatory output, and IVD device classification, the essential concept is that the user is guiding the system to focus on a specific diagnostic test scenario. By populating relevant descriptors such as “miRNA based in-vitro diagnostic for skin tumor detection”, the system captures enough domain context to later shape retrieval-augmented prompting and decision logic. Beyond merely collecting textual details, Figure 4A exemplifies how the user’s indicated role, device class, and conformity assessment procedureinform the system’s subsequent prompts and compliance checks throughout the submission workflow. This ensures that each element of the final documentation aligns precisely with the risk category and regulatory posture implied by the user’s input.

[0089] Figure 4B depicts an interface through which public and proprietary documents are uploaded to the system. These screens allow users to combine open-access literature (such as scientific articles or guidelines) with confidential internal records (like performance data or clinical research study outcomes). Rather than reproducing every item listed, Figure 4B demonstrates how the user can seamlessly incorporate multiple data sources into a cohesive repository. The process includes parsing and indexing these files so that the system’s RAG pipeline has ready access to domain-relevant text segments. This approach further ensures that high-value references are quickly retrievable, minimizing duplication of effort or the risk of overlooking key findings in subsequent drafting steps.

[0090] Figure 4C shows a validation stage, where the front-end lays out an expected submission structure keyed to CE-IVDR guidelines. Each chapter or section aligns with recognized requirements, including post-market surveillance, risk management, analytical performance, and toxicity. The system prompts users to confirm that all relevant components are present, providing a clear, hierarchical view of what the final submission must address. By offering collapsible sections or clickable headers, the interface helps domain experts verify completeness. The significance here is that the system synthesizes the user’s earlier selections (from Figure 4A) and merges them with domain-driven templates to propose a consistent, high-level layout. Thisensures that the draft remains aligned with regulatory best practices while allowing for user overrides and contextual adjustments if new or unique sections are required.

[0091] Figure 4D presents an example of the “Finalize Draft” stage for a specific chapter. Though the screen references a q-MS PCR assay for miRNA biomarkers, the broader point is that the system has already aggregated relevant data, heuristics, and domain constraints from the ingestion pipeline and mapped them to the validated structure from Figure 4C. The user sees a pre-filled document containing accuracy, precision, sensitivity, and specificity narratives, each referencing domain evidence or anchor knowledge. The user can then enter free- text modifications or clarifications directly in-line, ensuring the final verbiage meets both scientific expectations and regulatory language requirements. This interface exemplifies how neuromorphic prompts and retrieval-based synthesis are operationalized, producing a draft that is not only comprehensive but also grounded in the anchor knowledge sources previously uploaded.

[0092] Figure 4E concludes the workflow by showing the “Review I Revise Final Draft” screen. Here, the user accesses labeling details, instructions for use, and warnings or precautions, all formatted to match the style and depth needed for a CE-IVDR submission. Any edits are integrated into the system’s knowledge structures, allowing updated references or new data to be captured for future tasks. Even though the interface might display tables with product information such as catalog numbers, expiration dates, the critical feature is that each entry remains traceable to the anchor knowledge or user input from earlier steps. When the user confirms the final output, the system can store it in aPersistent Output Repository for compliance auditing, iterative improvement, and seamless retrieval if a related submission or device upgrade arises. By weaving together the user’s domain expertise, the curated anchor data, and Al-driven drafting, these front-end screens facilitate an efficient yet rigorous creation of the final regulatory submission.

[0093] The invention described through the embodiment and with the help of Figures 1 to 4 provide a plurality of technical and feature advantages over the prior art. The SKIES (800) provides enhanced reliability & compliance by anchoring all Al processes in validated domain data (100) and embedding expert heuristics, SKIES (800) significantly reduces hallucinations and misalignment issues and makes it relevant for domain-specific reliable outputs in high-stakes domains such as biomedical research, regulatory affairs and life sciences.

[0094] Further, SKIES (800) enables scalable multi-agent collaboration by combining CBA Generation (200) and Secondary Epistemogenesis (300) allows for a knowledge-driven workflow-based expansion of tasks without fragmentation of domain logic. To achieve preprocessing and retrieval, tools known in the art such as FAISS, Milvus, Weaviate for vector-based knowledge retrieval are conveniently deployed. Natural Language processing is achieved by deploying NLP Pipelines such as SpaCy, NLTK, Transformers to achieve the desired text processing.

[0095] To achieve Multi- Agent collaboration, tools known in the art such as AutoGen, CrewAI, LangChain Agents are conveniently deployed. Furthermore, to achieve desired Regulatory & ClinicalWorkflows tools known in the art such as JSON / XML for structuring and CTD / eCTD, HL7 FHIR for health data are conveniently deployed.

[0096] Furthermore, continuous refinement through RLHR is achieved. The RLHR Module (500) ensures expert feedback is systematically incorporated, yielding domain-specific improvements that accelerate compliance with new guidelines. - For Multi-Agent Orchestration and Reinforcement Learning: RLHF (Human Feedback Loop), Proximal Policy Optimization (PPO) tools known in the art are conveniently deployed. To achieve formatted document synthesis & compliance, tools known in the art such as PyMuPDF, Pandas, LaTeX are conveniently deployed.

[0097] Likewise, traceability and auditable outputs are an essential feature of the Workflow Orchestration & Evidence Synthesis Module (400) that stores all results — along with lineage information — in a fault- tolerant repository, meeting stringent audit requirements. Tools known in the art such as LIME and SHAP for Al decision transparency are conveniently deployed for this purpose.

[0098] SKIES (800) is optimized for hardware utilization. Each module is capable of being containerized or deployed on specialized hardware (CPUs, GPUs, HPC clusters). Multi-agent workloads are balanced to minimize latency and maximize throughput.

[0099] This holistic integration — anchor-based knowledge ingestion, neuromorphic prompting, multi-agent spawning, domain-tailored RL feedback, and robust output versioning — significantly exceeds typicalAl frameworks that address only one or two of these elements in isolation.

[0100] While the foregoing describes various embodiments of the disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof. In particular, the inventive combination of hardware-accelerated modules for knowledge source anchoring and CBA generation, with a recursive multi-agent architecture linked to parent CBA, and structured reinforcement learning yields technical benefits to overcome known limitations in conventional Al systems by providing enhanced throughput, robustness, and traceability, which are achieved through a synergistic interplay of physical data storage, distributed processing, and real-time expert feedback. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions, or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

Claims

CLAIMS:

1. A computer-implemented method for knowledge instantiation and evidence synthesis, the method comprising: receiving, by a computing system comprising at least one processor and a memory, one or more anchor knowledge sources; processing the one or more anchor knowledge source using a retrieval-augmented generation (RAG) pipeline to generate structured representations for each of the one or more anchor knowledge sources, generating a neuromorphic prompt using a hardware- accelerated natural language processing engine, wherein the neuromorphic prompt comprises encoded expert cognitive heuristics, structured multi-step reasoning constraints, and compliance constraints derived from the anchor knowledge source; instantiating one or more case-based Al agents (CBA) configured to generate, against a specific task, context-aware knowledge artifacts in a structured format using the neuromorphic prompt, wherein the one or more CBAs are instantiated based on the domain objectives, constraints, and performance requirements extracted from the neuromorphic prompt;determining whether a sub-agent is required, and in response to a determination that a sub-agent is required, spawning a subagent, wherein the sub-agent inherits domain-specific heuristics from the parent agent, and synthesizes structured knowledge artifacts by merging outputs from multiple agents into a domain-specific format; validating, using a human-at-the-helm expert interface, the knowledge artifacts synthesized by each of the one or more CBAs and their sub-agents, based on a reinforcement learning pipeline and, consolidating the validated knowledge artifacts and persistently storing the validated knowledge artifacts in a version-controlled output repository.

2. The method of claim 1 , wherein the RAG pipeline tokenizes the input data, extracts named entities and domain-specific references, and stores processed knowledge segments in a hardware- implemented vector database for low-latency retrieval.

3. The method of claim 1 , wherein the sub-agent spawning further comprises managing sub-agent distribution across a plurality of nodes and / or containers in a computing cluster, optimizing parallel execution of specialized tasks while maintaining epistemic continuity through an inheritance table and / or knowledge graph.

4. The method of claim 1 , wherein the Al agent iteratively updates future knowledge artifacts based on expert refinements.

5. The method of claim 1 , wherein the neuromorphic prompts are tagged with metadata and version markers to reflect the most up- to-date regulatory and / or domain-specific changes.

6. The method of claim 1 , wherein the processor is operable to perform one of a; reconfigure an existing Al agent; and spawn a fresh instance of the Al agent with updated constraints in case of modifications to anchor knowledge sources.

7. The method of claim 1 , wherein the processor is operable to allow domain experts to preview and / or refine partial outputs generated by each CBA in real time using the expert human-at-the-helm interface.

8. The method of claim 1 , wherein the processor implements multiagent orchestration between two or more Al agents by exchanging data via a hardware-implemented message queue.

9. The method of claim 1 , wherein the processor associates each knowledge artifact with unique metadata, including the anchor source used, contributing CBAs, and the version history of subagent interactions.

10. The method of claim 1 , wherein the processor is configured for automatically spawning one or more future Al agents with inherited improved domain logic based on expert refinements.

11. A system for knowledge instantiation and evidence synthesis, the system comprising: at least one processor; at least one memory storing executable instructions; a Knowledge Instantiation Module configured to: receive one or more anchor knowledge sources; process the one or more anchor knowledge source using a retrieval-augmented generation (RAG) pipeline to generate structured representations for each of the one or more anchor knowledge sources, generate a neuromorphic prompt using a hardware- accelerated natural language processing engine, wherein the neuromorphic prompt comprises encoded expert cognitive heuristics, structured multi-step reasoning constraints, and compliance constraints derived from the anchor knowledge source; a Case-Based Agent Generation Module configured to: instantiate one or more case-based Al agents (CBA) configured to generate, against a specific task, context- aware knowledge artifacts in a structured format using the neuromorphic prompt, wherein the one or more CBAs are instantiated based on the domain objectives, constraints,and performance requirements extracted from the neuromorphic prompt; a Secondary Epistemogenesis Module configured to: determine whether a sub-agent is required, and in response to a determination that a sub-agent is required, spawning a sub-agent, wherein the sub-agent inherits domain-specific heuristics from the parent agent, and synthesizes structured knowledge artifacts by merging outputs from multiple agents into a domain-specific format; a Workflow Orchestration and Evidence Synthesis Module configured to: validate, using a human-at-the-helm expert interface, the knowledge artifacts synthesized by each of the one or more CBAs and their sub-agents, based on a reinforcement learning pipeline and, consolidate the validated knowledge artifacts and persistently store the validated knowledge artifacts in a version-controlled output repository. a Reinforcement Learning from Human Refinement Module configured to: accept user feedback through edits made to the validated knowledge artifacts and convert said feedback into structured reinforcement signals mapped to specificanchor knowledge source segments or agent decision nodes and, update the stored heuristics and / or domain representations in real time, ensuring subsequent agent spawns and outputs reflect the corrected domain logic.

12. The system of claim 11 , wherein the Knowledge Instantiation Module enables the RAG pipeline to tokenize the input data, extract named entities and domain-specific references, and store processed knowledge segments in a hardware-implemented vector database for low-latency retrieval.

13. The system of claim 11 , wherein the Secondary Epistemogenesis Module manages sub-agent distribution across a plurality of nodes and / or containers in a computing cluster and optimizes parallel execution of specialized tasks while maintaining epistemic continuity through an inheritance table and / or knowledge graph.

14. The system of claim 11 , wherein the Case-Based Agent Generation Module iteratively updates future knowledge artifacts based on expert refinements.

15. The system of claim 11 , wherein the Knowledge Instantiation Module tags the neuromorphic prompts with metadata and version markers to reflect the most up-to-date regulatory and / or domainspecific changes.

16. The system of claim 11 , wherein the Case-Based Agent Generation Module is operable to reconfigure an existing Al agent and / or spawn a fresh instance of the Al agent with updated constraints in case of modifications to anchor knowledge sources.

17. The system of claim 1 1 , wherein the Workflow Orchestration and Evidence Synthesis Module is operable to allow domain experts to preview and / or refine partial outputs generated by each CBA in real time using the expert human-at-the-helm interface.

18. The system of claim 11 , wherein the Case-Based Agent Generation Module implements multi-agent orchestration between two or more Al agents by exchanging data via a hardware-implemented message queue.

19. The system of claim 1 1 , wherein the Workflow Orchestration and Evidence Synthesis Module associates each knowledge artifact with unique metadata, including the anchor source used, contributing CBAs, and the version history of sub-agent interactions.

20. The system of claim 11 , wherein the Case-Based Agent Generation Module is configured for automatically spawning one or more future Al agents with inherited improved domain logic based on expert refinements.

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