System and method for orchestrating multi-agent operations using language models

Abstract

In described embodiments, there is provided a multi-agent system for processing information, comprising a data processing agent configured to ingest and standardize raw data input to produce standardized data, and a standard integration agent configured to integrate the standardized data with the standard integration agent. The standard integration agent is configured to apply a reporting standard to the standardized data to generate an integrated reporting standard. The system also includes a performance alignment agent configured to align performance metrics based on the standardized data and the integrated reporting criteria, and an information synthesis agent configured to process narrative information from the standardized data and the integrated reporting criteria. An orchestration framework is also provided that is configured to manage operation of the data processing agent, the standard integration agent, and the performance alignment agent to generate regulatory reports that comply with regulatory requirements. The orchestration framework may also be executed by a large language model.

Description

TECHNICAL FIELD

[0001] The present application relates generally to artificial intelligence and machine learning systems, and in particular to systems and methods for orchestrating multi-agent operations using language models. BACKGROUND

[0002] In the current data-driven landscape, organizations across industries are facing increasing challenges in managing, securing, and deriving actionable insights from their extensive and rapidly expanding analytical data assets. Traditional approaches to data governance, access control, and exploration are insufficient to handle the volume, diversity, and complexity of today’s big data environments. These systems struggle with manual and non-scalable data feature discovery and cataloging, and lack the granularity of data access control mechanisms required for effective management in complex, distributed ecosystems. As a result, organizations face higher risks of data breaches, unauthorized access, and underutilization of data assets.

[0003] Furthermore, traditional keyword-based search methods fail to capture complex semantic relationships and nuanced meanings in large datasets, hindering users from formulating queries that accurately reflect their intent. This results in subpar data exploration and analysis outcomes. Challenges also extend to maintaining data integrity and provenance as data undergoes extensive transformations and moves across various platforms. Establishing robust, tamper-proof audit trails is increasingly challenging, eroding trust in analytical insights and increasing the risk of non-compliance with regulatory standards. Moreover, the vast scale, distributed nature, and complexity of modern data ecosystems often overwhelm traditional systems, which struggle to provide the necessary performance, scalability, and operational resiliency to support real-time, high-concurrency analysis workloads across hybrid and multi-cloud environments.

[0004] These systemic deficiencies necessitate a fundamental overhaul of existing practices. Outdated conventional manual metadata management techniques, rule-based access control models, and keyword-based search methods often yield incomplete or irrelevant results and fail to ensure data security and integrity throughout its lifecycle. It is increasingly important for systems to adapt to the scale, distribution, and heterogeneity of today’s data landscape.

[0005] Accordingly, it is desirable to provide systems and methods that leverage the capabilities of large language models (LLMs), generative AI (GenAI), quantum computing, and advanced machine learning techniques to address the shortcomings or limitations of existing technologies, or at least to provide the public with a useful alternative. SUMMARY

[0006] Embodiments herein provide new and useful systems and methods for orchestrating multi-agent operations using language models in an artificial intelligence environment.

[0007] Broadly speaking, the present disclosure proposes a multi-agent system for processing information, including a data processing agent configured to ingest and standardize raw data inputs to produce standardized data, and a standard integration agent configured to apply reporting standards to the standardized data to generate integrated reporting standards. The system further includes a performance alignment agent configured to align performance metrics based on the standardized data and the integrated reporting standards. The system further includes an information synthesis agent configured to process narrative information from the standardized data and the integrated reporting standards. The system further includes an orchestration framework configured to manage operations of the data processing agent, the standard integration agent, the performance alignment agent, and the information synthesis agent to produce regulatory reports that comply with regulatory requirements, wherein the orchestration framework is executable by a large language model.

[0008] In embodiments, the data processing agent further includes a data cleaning module capable of removing errors and inconsistencies from the raw data inputs to improve accuracy of the standardized data.

[0009] In embodiments, the system further includes an importance alignment agent configured to assess and align importance and boundaries based on the standardized data and the integrated reporting standards.

[0010] In embodiments, the system further includes a compliance alignment agent configured to align assurance processes based on the standardized data and the integrated reporting standards.

[0011] In embodiments, the performance alignment agent uses machine learning to dynamically adapt performance metrics based on updates to the reporting standards and corresponding real-time data.

[0012] In embodiments, the system further includes a report aggregation agent configured to employ a data integration platform to consolidate and align output data from the data processing agent, the standard integration agent, the performance alignment agent, and the information synthesis agent.

[0013] In embodiments, the orchestration framework further includes a scheduling module that adjusts the order and priority of tasks based on real-time assessments of data processing needs and agent capacities.

[0014] In embodiments, the information synthesis agent further includes a context analysis module configured to integrate contextual cues from the standardized data and the integrated reporting standards.

[0015] The present disclosure also proposes a system for managing metadata, comprising: an ingestion module configured to pre-process data inputs according to metadata standards; an extraction module configured to employ language processing techniques to extract and normalize metadata from the pre-processed data inputs; an abstraction module configured to convert the extracted and normalized metadata into structured schema; a mapping module configured to translate the converted metadata based on taxonomies using artificial intelligence techniques; and a storage module configured to index and enable search on the translated metadata.

[0016] In embodiments, the mapping module is further configured to utilize ontology-based reasoning and semantic similarity measures to establish mappings between metadata elements from different taxonomies.

[0017] In embodiments, the mapping module is further configured to employ neural networks and transfer learning to refine and enhance the conversion of metadata between taxonomies.

[0018] In embodiments, the storage module comprises a vector database configured to store the translated metadata, an indexing module configured to generate vector embeddings corresponding to the translated metadata, and a search engine module configured to perform searches based on contextual relevance and semantic similarity using the vector embeddings.

[0019] In embodiments, the system for managing metadata further comprises a data provenance and provenance tracking module for capturing audit trails of metadata management processes in the system to comply with regulatory requirements.

[0020] In embodiments, the ingestion module is further configured to interface directly with external data sources to automatically retrieve data inputs.

[0021] The present disclosure also proposes a system for extracting and normalizing metadata, comprising an input interface configured to receive pre-processed data inputs conforming to metadata standards, a processing module configured to apply natural language processing techniques to extract metadata from the received pre-processed data inputs, a normalization engine configured to normalize the extracted metadata according to predetermined metadata standards, a refinement module configured to enhance the normalized metadata with additional information derived from external sources to generate refined metadata, and an output interface configured to output the refined metadata for additional processing within a metadata management system.

[0022] The present disclosure also presents a system for integrating data patterns into a data representation, comprising a data input interface configured to receive data from a plurality of sources, a feature extraction module configured to extract features within the received data, a security module configured to encrypt the extracted features to generate a secure data representation, a data integration module configured to integrate the encrypted features corresponding to the secure data representation into a unified data representation, and a validation module configured to validate the unified data representation against predefined standards or regulations.

[0023] In an embodiment, the security module employs a cryptographic hash function to verify the authenticity and integrity of the secure data representation.

[0024] In an embodiment, the feature extraction module is configured to extract features from the received data using natural language processing and machine learning algorithms.

[0025] In an embodiment, the system for integrating data patterns comprises an access management module configured to convert at least one of user roles, user requirements, or data access patterns into a unified mathematical representation.

[0026] In an embodiment, the access management module is further configured to encode user roles and access permissions into unique numerical codes using natural language processing and machine learning.

[0027] In an embodiment, the access management module comprises a control engine that functions as an authority for enforcing access controls based on the unified security access model.

[0028] In an embodiment, the access management module is configured to automatically synchronize its access control rules with external compliance monitoring systems to maintain adherence to regulatory changes.

[0029] The present disclosure further presents a method for processing information in a multi-agent system, comprising ingesting raw data inputs via a data processing agent and normalizing the ingested raw data using the data processing agent to produce standardized data. The method further comprises applying reporting standards to the standardized data using a standard integration agent to generate integrated reporting standards, aligning performance metrics based on the standardized data and the integrated reporting standards using a performance alignment agent, and processing narrative information from the standardized data and the integrated reporting standards using an information synthesis agent. The method further comprises orchestrating operations of the data processing agent, the standard integration agent, the performance alignment agent, and the information synthesis agent through an orchestration framework to produce regulatory reports that comply with regulatory requirements, wherein the orchestration framework is executable by a large language model.

[0030] The above description is provided as an overview of some embodiments of the present disclosure. Those embodiments and further descriptions of other embodiments are described in more detail below. BRIEF DESCRIPTION OF DRAWINGS

[0031] Embodiments of the present application will now be explained with reference to the following drawings for the purpose of illustration only, in which:

[0032] Figure 1 is a functional block diagram illustrating an example service and functionality within a multi-agent artificial intelligence framework according to embodiments herein.

[0033] Figure 2 is a functional block diagram illustrating an example of a reporting integration system within a multi-agent artificial intelligence framework according to embodiments herein.

[0034] Figure 3 is a functional block diagram illustrating an example of a metadata management system within a multi-agent artificial intelligence framework according to embodiments herein.

[0035] Figure 4 is a functional block diagram illustrating an example of a data management and access control system within a multi-agent artificial intelligence framework according to embodiments herein

[0036] Figure 5 is a block diagram illustrating an example computer system that can be configured to implement systems and methods as disclosed herein. DETAILED DESCRIPTION

[0037] Embodiments will now be discussed with reference to the drawings, which depict one or more example embodiments. The embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments, and it is understood that mechanical, logical, and other changes can be made without departing from the scope of the embodiments. The embodiments can thus be implemented in many different forms and should not be construed as limited to the embodiments set forth herein as presented in the attached drawings and / or described below.

[0038] As used in this disclosure, the terms “component,” “module,” “system,” “device,” “interface,” “agent,” and the like are generally intended to refer to computer-related entities, either hardware, a combination of hardware and software, software, or software in execution. For example, a component or module can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and / or a computer. By way of illustration, both an application running on a controller and the controller can be a component or module. One or more components / modules can reside within a process and / or thread of execution, and a component can be localized, partially localized, and / or distributed across two or more computers.

[0039] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, which are commonly used, should also be interpreted as having a meaning consistent with their meaning in the relevant art.

[0040] Disclosed herein is a system and method for implementing a neural symbolic knowledge representation called GenFoundry (Generative Foundation for Metadata Management, Report Alignment, and Data Access Control). In embodiments, the GenFoundry system and method combines the strengths of neural networks and symbolic reasoning to represent knowledge. It mirrors the brain’s ability to encode and retrieve distributed representations of concepts. GenFoundry forms the foundation of COGNIGEN-AX, a neuro morphic reasoning module inspired by the brain’s distributed representations of concepts. GenFoundry can use the distributed semantics and compositional capabilities of large language models (LLMs) to encode and retrieve contextualized knowledge snippets as high-dimensional vector embeddings. This integration of neural and symbolic methods captures nuanced relationships and affordances in data, much like the brain’s ability to build rich situational models.

[0041] In embodiments, the GenFoundry system can be part of a neuro morphic architecture that aims to replicate the complex cognitive capabilities of the human brain and nervous system. This architecture, referred to as brain-inspired computing, combines large language models (LLMs), quantum computing, and an advanced neuro symbolic knowledge representation framework. Together, these elements overcome the limitations of classical computing architectures in dealing with complex unstructured data.

[0042] In embodiments, GenFoundry can combine the computational strength of neural networks with the precision of symbolic reasoning, similar to how the brain encodes and retrieves distributed representations of concepts. GenFoundry, as a foundational neuro symbolic knowledge base, can encode contextual knowledge as high-dimensional vector embeddings. It can leverage the distributed semantics and compositional capabilities of LLMs to capture nuanced relationships and build situational models, similar to the brain’s conceptual processing. GenFoundry has the ability to capture and encode complex information in structured frameworks, including the rich meanings, relationships, and affordances present in data. GenFoundry can support other components, such as COGNIGEN-AX, which utilizes the neuro symbolic output provided by GenFoundry for dynamic, meta-cognitive processing, and episodic narrative construction.

[0043] Integration with other neuromorphic components:

[0044] Neuro morphic reasoning core (COGNIGEN-AX): This component complements GenFoundry by modeling the brain’s meta-cognitive processes through the utilization of GenFoundry’s neuro symbolic output. It weaves multi-modal sensory data into coherent narratives, mirroring the neural processes of the prefrontal cortex.

[0045] Quantum Neural Network (Quantum Computing Optimization Framework): This framework enhances the capabilities of GenFoundry's output by processing narratives in a quantum-enhanced format. It simultaneously explores multiple interpretation pathways, revealing deeper semantic associations that classical methods cannot achieve.

[0046] In embodiments, GenFoundry is a multi-agent neural architecture designed for automated metadata management, dynamic data access control, intelligent semantic search, and comprehensive data governance across analytical datasets. GenFoundry seamlessly integrates and coordinates advanced AI agents such as MEGAN (Metadata Extraction, Generation, and Alignment Network), SEPHYR (Self-Evolving Pattern Harmonization for Unified Reporting), and supporting modules.

[0047] GenFoundry's containerized cloud-native architecture seamlessly coordinates various components such as LLM agents, generative AI agents, metadata management, quantum encryption, controlled semantic query processing, and encryption auditing. A Petri net-based neural architecture coordinates interactions between these agents. The system dynamically adapts based on data changes, user feedback, rules, and new information sources.

[0048] This unified application of AI, quantum computing, and distributed systems creates a flexible framework for governing analytical datasets. It provides powerful querying and data exploration capabilities aligned with enterprise data policies, user permissions, and regulatory requirements. Coordinated neural orchestration combines automated metadata, quantum encryption, query interpretation, and encryption integrity auditing into a trusted solution for scalable data-driven analysis.

[0049] LLM-driven automated metadata discovery and enrichment: GenFoundry can use large language models and deep learning to automate the discovery, extraction, normalization, and semantic enrichment of metadata from various data sources. It generates ISO 11179-compliant metadata repositories without human intervention or predefined taxonomies, overcoming the limitations of traditional metadata management approaches.

[0050] Mathematical encoding of metadata within a unified feature space: GenFoundry introduces a unique method of encoding metadata features and their relationships using dynamic numerical sequences. This encoding enables efficient indexing, mapping, and semantic similarity searches and serves as a foundation for integrating access control policies and query processing within the metadata feature space.

[0051] Fusion of semantic-aware query processing and policy enforcement: GenFoundry introduces a new intelligent query processing method. It utilizes LLMs and generative AI to interpret natural language queries based on the semantic context and domain knowledge encoded in the metadata feature space. It then combines this semantic understanding with mathematically encoded security policies and user privileges to filter and retrieve authorized data insights that align with the user's true intent. Figure 1

[0052] Coordinated multi-agent neural architecture: GenFoundry establishes a groundbreaking multi-agent neural architecture that coordinates and orchestrates various components such as LLM agents, generative AI models, quantum computing components, encryption engines, semantic query processors, and audit log modules. This integrated approach brings together AI, quantum information, data security, and distributed system principles within a cohesive and scalable architecture for trustworthy data-driven analysis.

[0053] Continuous AI model refinement and system adaptation: The Petri net-based neural architecture in GenFoundry combines neural architecture search, automatic model tuning, and reinforcement learning. This allows the system to continuously expand, refine, and optimize AI agents, query interpretation models, security policy enforcement, and overall system capabilities based on evolving data landscapes, user feedback, expanded training data, and changes to governance or regulatory requirements. This self-improving and self-regulating AI structure sets Genfoundry systems and methods apart from static approaches.

[0054] GenFoundry is a multi-agent neural architecture that integrates and coordinates advanced AI modules to achieve automated metadata management, dynamic data access control, intelligent semantic search, and comprehensive data governance across diverse analytical datasets. At the core of GenFoundry is the coordination of the following key components:

[0055] ​ALFINI (Alignment, Language Models, Financial and Non-Financial Reporting Integration): ALFINI employs a synergistic integration of AI agents to align financial and non-financial reporting practices according to the ISO 5116-3:20 21 standard. These agents utilize natural language processing and machine learning to analyze, evaluate, and iteratively enhance the coordination of reporting information.

[0056] SEPHYR (Self-Evolving Pattern Harmonization for Unified Reporting): SEPHYR harmonizes disparate data patterns and features into a unified, cryptographically verifiable representation. Its agents utilize mathematical algorithms to encode features for efficient cataloging and searching. SEPHYR ensures data provenance, traceability, and trustworthiness through digital native mathematical representations, consensus mechanisms, and cryptographic verifiability.

[0057] GAMA (Governance-Aware Multilevel Access Management Architecture): GAMA orchestrates the mathematical encoding of user roles, permissions, and regulatory policies into an integrated access control model. It employs agents like RoleIdentifier, RoleEncoder, AccessClassifier, and RegulationConsensus to implement fine-grained, context-aware access policies that dynamically adapt to evolving organizational structures, data sensitivity levels, and compliance requirements.

[0058] MEGAN (Metadata Extraction, Generation, and Alignment Network): MEGAN automatically generates metadata registries compliant with ISO 11179 and the United Nations / Center for Administrative Electronic Commerce. It utilizes advanced natural language processing agents to extract, standardize, enrich, and align metadata from diverse data sources. MEGAN also abstracts the DataVault2.0 schema and supports cross-taxonomy mapping and translation to facilitate data interoperability.

[0059] QSEM (Quantum Security and Encryption Module): QSEM provides robust data security using quantum encryption algorithms, quantum key distribution, and post-quantum cryptography. It employs quantum random number generation and quantum state mapping techniques to enhance encryption strength. Additionally, QSEM ensures data integrity through quantum resilient digital signatures.

[0060] CATM (Cryptographic Audit Trail Module): CATM generates immutable audit trails that capture the provenance of data elements throughout their entire lifecycle in an encrypted manner, from the original source to the analytical consumption. It leverages quantum computing technology for large-scale efficient source verification and integrates with distributed ledger technology for transparent, multi-party governance of audit trails.

[0061] NODM (Neural Orchestration and DevOps Module): NODM provides a cloud-native architecture for deploying and orchestrating GenFoundry modules across hybrid multi-cloud environments. It utilizes Petri net-based orchestration workflows to coordinate complex interactions and data flows between modules. NODM also automates continuous delivery pipelines, enabling seamless updates and scaling of modules.

[0062] In embodiments, the LLM provides central intelligence in GENFOUNDARY (ALFINI, MEGAN, SEPHYR, etc.) dynamically invoking, implementing, and coordinating various agents to process prompts, perform specific tasks, and interact with COGNIGEN-AX. Its role involves interpreting input prompts, understanding their context and requirements, and activating relevant agents to generate appropriate outputs. In embodiments, the LLM constructs and adapts Petri net models at runtime based on the specific requirements of each prompt, its responses, and the neutral state of the LLM. It utilizes its understanding of the prompts and available agents to determine the necessary processing steps, the order of agent invocations, and the data flow between them. The LLM designs the Petri net by defining places, transitions, and arcs representing the data flow or control between places and transitions. It assigns appropriate agents to transitions, specifying their required inputs and produced outputs. As prompts are processed, the LLM dynamically executes the Petri net by triggering transitions (invoking agents) based on the availability of required input data and the satisfaction of necessary conditions. Tokens representing data or control flow move through the Petri net, triggering agents to execute and enabling information flow between them. The LLM can continuously monitor Petri net execution, track agent progress, handle exceptions or errors, and adapt the Petri net structure as needed based on intermediate results and changing prompt requirements. This dynamic adaptation optimizes the processing flow, efficiently allocates resources, and ensures timely and accurate output generation. Throughout the processing, the LLM can utilize formal properties of Petri nets (such as reachability, boundedness, and liveness) to verify the correctness and efficiency of agent interactions and detect / resolve potential deadlocks or performance bottlenecks. Ongoing research involves modeling and integrating neuroregulatory effects into the orchestration model and network data, including developing neuro-morphic data structures to capture the LLM's thought patterns.

[0063] In embodiments, there can be a single master LLM agent that serves as a central coordinator and orchestrator for various specialized agents (ALFINI, MEGAN, SEPHYR, etc.). This master LLM agent typically operates on the most neutral and powerful model (such as Opus) to handle the overall orchestration and management of the neural state. It interprets input prompts, understands their context and requirements, and dynamically creates and executes Petri net models to coordinate the interactions and workflows of specialized agents. The master LLM agent assigns appropriate agents to transitions in the Petri network and monitors their execution, adapting as needed based on intermediate results and changing requirements of the prompts. However, for specific structured tasks that require limiting or constraining creativity, the master LLM agent can invoke “student” agents through a platform like AWS Bedrock. These student agents operate on lower models, such as Sonnet, which are better suited for focused specific tasks that require less creativity and more structured output. The master LLM agent determines when to invoke these student agents based on the nature of the task and the desired level of creativity or constraints. It communicates with the student agents, providing necessary input data and instructions, and integrates their output into the overall workflow coordinated by the Petri net model. While the student agents execute their specific tasks using the lower models, the overall orchestration and management of the neural state remains under the control of the master LLM agent operating on the most powerful model. The master LLM agent ensures the coherence and consistency of the neural state across different agents and models, thereby maintaining a unified and contextualized representation of the ongoing tasks and their progress. By leveraging this layered architecture, where the master LLM agent coordinates student agents on lower models for specific structured tasks, the system can balance creativity and constraints, optimize the allocation of computational resources, and ensure the generation of accurate and contextually relevant outputs.

[0064] Figure 1 is a functional block diagram 100 illustrating example services and functions within a multi-agent artificial intelligence framework according to embodiments herein. The multi-agent artificial intelligence framework corresponds to embodiments of the GenFoundry neural architecture.

[0065] As Figure 1Regulation 102 can set standards and guidelines that affect the entire system, directly and indirectly affecting various services to ensure compliance and governance within the framework. ALFINI 104 can utilize natural language processing and machine learning agents to align and coordinate financial and non-financial reporting practices in accordance with the ISO 5116-3:20 21 standard. ALFINI 104 can interact directly with MEGAN 108 and SEPHYR 106 to integrate the unified reporting practices into the broader metadata management system. SEPHYR 106 can harmonize different data schemas and features into a unified, cryptographically verifiable representation through mathematical algorithms and consensus mechanisms. SEPHYR 106 facilitates efficient cataloging, searching, and metadata management and feeds into the GAMA 114 system for further processing of access control models. MEGAN 108 can automatically generate metadata registries compliant with ISO 11179 and UN / CEFACT. MEGAN 108 processes the extraction, normalization, enrichment, and alignment of metadata from various sources, aiding in the creation of structured and interoperable data sets. GAMA 114 orchestrates the mathematical encoding of user roles, permissions, and regulatory policies into integrated access control models. It employs fine-grained, context-aware policy enforcement agents to manage access and security protocols within the system. QSEM 116 can provide quantum encryption, key distribution, and post-quantum cryptography mechanisms. QSEM 116 protects data throughout the system, supplemented by quantum random number generation and state mapping techniques to ensure the integrity and security of data transmission and storage. CATM 112 generates encrypted immutable audit trails that capture the provenance of data throughout its lifecycle. CATM 112 leverages quantum computing for large-scale verification and integrates with distributed ledger technology to enhance the reliability and traceability of data transactions. NODM 110 handles Petri net orchestration for the deployment and orchestration of modules across LLM agents. NODM 110 integrates automated DevOps pipelines to streamline operations and maintain system efficiency and scalability.

[0066] Relationship between ALFINI 104 and SEPHYR 106:

[0067] ALFINI 104 leverages the cryptographically verifiable data representations of SEPHYR 106. ALFINI employs a synergistic integration of AI agents to analyze, evaluate, and iteratively enhance the alignment of reporting information based on the ISO 5116-3:20 21 standard. These agents leverage natural language processing and machine learning techniques to align financial and non-financial reporting practices. On the other hand, SEPHYR 106 coordinates different data patterns and features into a unified, cryptographically verifiable representation. Using mathematical algorithms, it leverages agents to encode features. This enables efficient cataloging, searching, and metadata management while ensuring data provenance, traceability, and trustworthiness through consensus mechanisms and cryptographic verifiability. The association between ALFINI 104 and SEPHYR 106 represents ALFINI 104 leveraging or utilizing the cryptographically verifiable data representations and mathematical algorithms provided by SEPHYR 106 to enhance the alignment and coordination of reporting information. The capabilities of SEPHYR 106 in encoding data patterns, establishing consensus, and ensuring cryptographic verifiability can complement and support ALFINI 104's efforts in aligning financial and non-financial reporting practices. While ALFINI 104 focuses on the reporting alignment aspect using AI agents, it can benefit from the associated coordination of data representations, consensus mechanisms, and cryptographic techniques of SEPHYR 106 to enhance the overall reporting integration process.

[0068] Relationship between ALFINI 104 and MEGAN 108:

[0069] MEGAN 108 and ALFINI 104 are associated because they both handle the extraction, alignment, and coordination of metadata and reporting information in different contexts and using various technologies. MEGAN 108 automatically extracts, standardizes, enriches, and aligns metadata from different sources to generate ISO 11179 / UN / CEFACT-compliant metadata registries and facilitate data interoperability. ALFINI 104 employs AI agents that leverage natural language processing and machine learning to analyze and align financial and non-financial reporting information based on the ISO 5116-3:20 21 standard. While MEGAN 108 focuses on metadata extraction and alignment, and ALFINI 104 focuses on reporting alignment, both involve the coordination of data from different sources using advanced technologies. Their aligned metadata registries and unified reporting outputs can be complementary.

[0070] Relationship between MEGAN 108 and GAMA 114 of SEPHYR:

[0071] MEGAN 108 provides metadata input and data models that influence GAMA's access control models and policies. MEGAN 108 can generate ISO 11179 and UN / CEFACT compliant metadata registries, DataVault 2.0 schemas, and cross-taxonomy mappings through intelligent agents. These outputs capture the structure, relationships, and semantics of data elements managed within the GenFoundry system. GAMA 114 leverages this detailed metadata to translate user roles, permissions, and regulatory policies into robust, integrated access control models. The access control models and security policies implemented by GAMA 114 are directly formed from the metadata, schemas, and data models provided by MEGAN 108, ensuring that GAMA 114's access controls are both intelligent and securely applied across various data landscapes.

[0072] Relationship between GAMA 114 and QSEM 116:

[0073] GAMA 114's access control models rely on QSEM 116 for encryption and key management, which is critical for ensuring data security and compliance. GAMA 114, or the management-aware, multi-level access management architecture, orchestrates the mathematical encoding of user roles, permissions, and regulatory policies into sophisticated access control models. It leverages intelligent agents, such as RoleIdentifier, RoleEncoder, AccessClassifier, and RegulationConsensus, with fine-grained, context-aware policies. These capabilities are protected by QSEM 116, which provides quantum encryption algorithms, key distribution, and post-quantum cryptography mechanisms. QSEM 116's quantum-safe solutions are crucial for GAMA 114 to protect its access control models, maintain the confidentiality of user roles, and ensure the integrity of sensitive data elements. By relying on QSEM 116's advanced security mechanisms, GAMA 114 achieves a high level of security and resilience, which is essential for defending against potential quantum computing threats and ensuring long-term data security.

[0074] Relationship between MEGAN 108 and CATM 112:

[0075] MEGAN (Metadata Extraction, Generation, and Alignment Network) provides the necessary metadata inputs, which are used by CATM (Cryptographic Audit Trail Module) to generate audit trails. MEGAN automatically generates ISO 11179 and UN / CEFACT compliant metadata registries, abstracts DataVault 2.0 schema, and facilitates cross-taxonomy mapping and translation. The process involves extraction, normalization, enrichment, and alignment of metadata from different sources through intelligent agents. The key output of MEGAN is metadata and data provenance information, particularly captured by DLPTA (Data Lineage and Provenance Tracking Agent), which tracks the origin, transformation, and evolution of data elements. CATM relies on this comprehensive metadata and data provenance information to create immutable audit trails that cryptographically record the entire lifecycle of data elements, effectively fulfilling its role in audit trail generation and data provenance tracking.

[0076] Relationship between CATM 112 and NODM 110:

[0077] The audit requirements specified by CATM, such as data provenance tracking, cryptographic verification, and multi-party governance, significantly influence the deployment and orchestration strategies adopted by NODM. These requirements guide NODM in managing the deployment, configuration, and maintenance of systems across hybrid cloud infrastructures. The CATM's requirements for audit trail generation, verification, and governance act as constraints and drivers, determining how NODM provisions resources, manages configurations, and automates the delivery pipeline for GenFoundry modules, ensuring that the audit functionality of CATM is effectively integrated and maintained.

[0078] NODM orchestration:

[0079] NODM coordinates the deployment and integration of various modules across the infrastructure, ensuring seamless interaction and operational consistency between ALFINI, SEPHYR, GAMA, MEGAN, and QSEM. This role involves careful management of the setup and ongoing operation of these modules to support the overall functionality and performance of the system, aligning the specific capabilities and requirements of each module with the strategic objectives of the GenFoundry system.

[0080] Use case / Petri net example:

[0081] The Financial Stability Board (FSB) has collaborated with the International Organization for Standardization (ISO) to develop a new regulation aimed at enhancing the transparency, consistency, and reliability of financial and non-financial reporting by organizations. This regulation calls for the use of advanced technologies such as artificial intelligence (AI) and quantum computing to automate and streamline the reporting process while ensuring compliance with internationally recognized standards, such as ISO / IEC 15909-1:20 19 for Petri net modeling.

[0082] The regulation emphasizes the integration of financial and non-financial data, aligning key performance indicators (KPIs), narrative information, significance assessments, and assurance procedures to provide a comprehensive and consistent view of an organization's performance and compliance status. The ultimate goal is to improve the quality and credibility of regulatory reporting, enabling better decision-making by stakeholders and more effective oversight by regulatory bodies.

[0083] In the design, GENFOUNDRY, COGNIGEN-AX, and Quantum are used: To meet the requirements of this new regulation, organizations can leverage the power of GENFOUNDRY, a multi-agent AI framework, in conjunction with COGNIGEN-AX, an advanced reasoning core, and quantum computing technologies. Large Language Models (LLMs) can be used to orchestrate these technologies to create an intelligent, automated, and compliant regulatory reporting system.

[0084] LLMs are trained on vast amounts of financial and regulatory data, enabling them to understand the complex requirements of regulation and design Petri net architectures that best utilize GENFOUNDRY agents and COGNIGEN-AX to achieve desired outcomes. Petri nets that adhere to the ISO / IEC 15909-1:20 19 standard provide a formal mathematical foundation for modeling the reporting process, ensuring consistency and reliability.

[0085] In the Petri net, GENFOUNDRY's ALFINI agents, such as the Data Harmonization Agent (DHA), the Reporting Standards KnowledgeBase Agent (RSKBA), and the Narrative Information Alignment Agent (NIAA), work together to ingest, harmonize, and align various types of data required for regulatory reporting. These agents utilize advanced AI techniques, such as natural language processing (NLP), machine learning, and knowledge representation, to extract insights and ensure data quality and consistency.

[0086] The meta-cognitive reasoning core (COGNIGEN-AX) is crucial in evaluating the comprehensive regulatory reports generated by the ALFINI agents. It uses its advanced reasoning capabilities to check the reports against encoded regulatory requirements, identifies areas for improvement, and iteratively optimizes the reports until they meet all necessary compliance standards. The "think about thinking" ability of COGNIGEN-AX allows it to adapt and learn from each iteration, continuously enhancing the quality and efficiency of the reporting process.

[0087] Quantum computing technology can be integrated into the design to accelerate complex computations, such as risk assessment, scenario analysis, and optimization tasks. Quantum algorithms can help identify patterns and anomalies in data more efficiently, enabling faster and more accurate detection of potential compliance issues. Integrating quantum computing with the GENFOUNDRY framework and COGNIGEN-AX creates a powerful, future-oriented solution for regulatory reporting.

[0088] The LLM coordinates the entire process, from the initial design of the Petri net to monitoring and optimizing the system's performance over time. It can interpret the requirements of regulations, understand the organization's specific needs and constraints, and adjust the design accordingly. When new regulations emerge or existing ones change, the LLM can quickly modify the Petri net and redeploy an updated system, ensuring ongoing compliance.

[0089] Petri net design / agent orchestration

[0090] The Petri net automates and streamlines the process of generating compliant regulatory reports using GENFOUNDRY's AI agents, ALFINI and COGNIGEN-AX. It begins with the ingestion of raw financial and non-financial data from various sources within the organization (P1).

[0091] The first transformation, T1, activates a Data Harmonization Agent (DHA) to clean, normalize, and consolidate raw data into a consistent format for further processing. The harmonized data is then stored in P2.

[0092] Data flows from P2 to several parallel transformations:

[0093] T2: The Reporting Standards Knowledge Base Agent (RSKBA) combines relevant reporting standards and regulatory requirements from its knowledge base (P3) with the harmonized data.

[0094] T3: The Key Performance Indicator Alignment Agent (KPIAA) aligns relevant KPIs from the harmonized data (P2) and reporting standards (P3), storing the aligned KPIs in P4.

[0095] T4: The Narrative Information Alignment Agent (NIAA) processes and aligns narrative information from the harmonized data (P2) and reporting standards (P3), storing the aligned narrative information in P5.

[0096] T5: The Materiality and Boundary Alignment Agent (MBAA) assesses and aligns materiality and reporting boundaries based on the harmonized data (P2) and reporting standards (P3), storing the aligned materiality assessments and boundaries in P6.

[0097] T6: The Assurance Alignment Agent (AAA) aligns assurance processes based on the harmonized data (P2) and reporting standards (P3), storing the aligned assurance processes in P7.

[0098] Outputs from P4, P5, P6, and P7 flow into transformation T7, where the Reporting Integration Agent (RIA) merges all aligned information into an integrated regulatory report stored in P8.

[0099] The COGNIGEN-AX reasoning core, represented by transformation T8, evaluates the integrated report against encoded regulatory requirements. This is an iterative process, represented by a self-loop arc from P8 to T8 and back to P8. COGNIGEN-AX utilizes its meta-cognitive capabilities to critically and optimize the report, ensuring compliance with all necessary standards.

[0100] Once COGNIGEN-AX determines that the report is fully compliant and consistent, it approves the final version. The Petri net then moves to the final transition T9, whose output is the approved regulatory report.

[0101] Throughout the process, the GENFOUNDRY framework maintains end-to-end traceability and machine-readable semantic encoding, ensuring data provenance and efficient data management and analysis.

[0102] The Petri net architecture follows the standard elements and graphical symbols specified in ISO / IEC 15909-1:2019, ensuring compatibility and interoperability with other systems and tools that support the standard. The architecture uses high-level AI agents and formal Petri net models to automate and integrate the different stages of the regulatory reporting process. It demonstrates how GENFOUNDRY can significantly improve the efficiency, accuracy, and compliance of regulatory reporting while reducing manual work and error risks.

[0103] Petri net: ALFINI regulatory reporting system

[0104] Places (P):

[0105] P1: Raw financial and non-financial data

[0106] P2: Unified financial and non-financial data (output of DHA agent)

[0107] P3: Integrated reporting standards knowledge base (output of RSKBA agent)

[0108] P4: Aligned key performance indicators (output of KPIAA agent)

[0109] P5: Aligned narrative information (output of NIAA agent)

[0110] P6: Aligned materiality assessment and boundaries (output of MBAA agent)

[0111] P7: Aligned assurance processes (output of AAA agent)

[0112] P8: Integrated regulatory report (output of RIA agent)

[0113] Transitions (T):

[0114] T1: Ingest and harmonize raw financial and non-financial data (DHA agent)

[0115] T2: Integrate reporting standards knowledge base (RSKBA agent)

[0116] T3: Align Key Performance Indicators (KPIs) (AA Agent)

[0117] T4: Align Narrative Information (NIA) (AA Agent)

[0118] T5: Align Importance Assessment and Reporting Boundaries (MBAA Agent)

[0119] T6: Align Assurance Processes (AAA Agent)

[0120] T7: Integrate Aligned Information into Regulatory Reports (RIA Agent)

[0121] T8: COGNIGEN-AX Reasoning Core Evaluates Report Against Regulatory Requirements

[0122] T9: COGNIGEN-AX Optimizes and Approves Final Regulatory Report

[0123] Arcs (Arcs):

[0124] P1 to T1, T1 to P2

[0125] P2 to T2, T2 to P3

[0126] P2 to T3, P3 to T3, T3 to P4

[0127] P2 to T4, P3 to T4, T4 to P5

[0128] P2 to T5, P3 to T5, T5 to P6

[0129] P2 to T6, P3 to T6, T6 to P7

[0130] P4 to T7, P5 to T7, P6 to T7, P7 to T7, T7 to P8

[0131] P8 to T8, T8 to P8 (Self Loop)

[0132] P8 to T9

[0133] The Petri net utilizes the ALFINI agents in the orchestration workflow to ingest raw financial and non-financial data, align it to reporting standards, assess importance, integrate narrative information, align KPIs and assurance processes, and generate an integrated regulatory report.

[0134] The COGNIGEN-AX reasoning core then evaluates this preliminary report against encoded regulatory requirements in an iterative loop, using its meta-cognitive abilities to assess and optimize the report until it is fully compliant and coherent.

[0135] Finally, a certified report is generated, and the GENFOUNDRY framework maintains end-to-end traceability and machine-readable semantic encoding throughout the process. Petri nets are defined using standard elements and graphical symbols as specified in ISO / IEC 15909-1:20 19.

[0136] Financial reporting use case

[0137] GenFoundry has the potential to revolutionize the way banks implement and comply with financial regulations by leveraging advanced AI, quantum computing, and distributed system technologies. The methods described in this patent can help banks reduce costs, speed up time-to-market, improve quality, and mitigate operational risks associated with regulatory compliance. Here's how:

[0138] Automated metadata management and data governance: MEGAN can automatically generate ISO 11179 and UN / CEFACT-compliant metadata registries, abstract DataVault2.0 schemas, and enable cross-taxonomy mapping. This simplifies the regulatory reporting process across jurisdictions and data standards, reduces manual work, and ensures consistency. As a result, banks can significantly reduce costs and operational risks associated with data governance and regulatory reporting.

[0139] Dynamic access control and data security: GAMA translates user roles, permissions, and regulatory policies into an integrated access control model, ensuring granular, context-aware data access control. This reduces the risk of data breaches and unauthorized access. Additionally, QSEM's integration of quantum encryption algorithms and post-quantum cryptography provides robust data security, ensuring long-term protection against emerging threats from quantum computing.

[0140] Intelligent semantic search and query processing: SEPHYR harmonizes different data schemas and features into a unified, cryptographically verifiable representation. This enables efficient cataloging, searching, and metadata management. Furthermore, it enables intelligent, semantically aware query processing, allowing banks to quickly retrieve relevant data insights. This reduces the time and effort required for regulatory reporting and compliance tasks.

[0141] Comprehensive data provenance and traceability tracking: CATM generates immutable audit trails and cryptographically captures data provenance. QUASAR's quantum computing technology optimizes provenance verification, ensuring end-to-end traceability and transparency. This comprehensive data provenance and traceability tracking significantly enhances regulatory compliance, reduces operational risks, and facilitates audits and investigations.

[0142] Scalability and Elastic Deployment: HYDRA automates cloud provisioning, configuration management, and container orchestration. This ensures the scalability and elastic deployment of GenFoundry modules across hybrid cloud environments. Additionally, NEMO's Petri net-based workflow generation and optimization, combined with OASIS's automated continuous delivery, enables efficient and adaptable orchestration of regulatory compliance processes. GenFoundry provides powerful capabilities to simplify compliance with various financial reporting standards and regulations. With its advanced metadata management, data governance, access control, and intelligent query processing, GenFoundry becomes a valuable tool for achieving compatibility. Here are some examples of how GenFoundry facilitates compliance with key financial reporting standards and regulations:

[0143] Basel III Capital and Liquidity Standards: Leveraging MEGAN's capabilities to generate ISO 11179-compliant metadata registries and abstract DataVault2.0 schemas, banks can establish consistent and unified data models for risk data aggregation and reporting. This is crucial for meeting the requirements set by the Basel III Effective Risk Data Aggregation and Reporting Principles (BCBS 239). SEPHYR's harmonization of different data schemas and GAMA's dynamic access control functionalities ensure that risk data is consistently cataloged, searchable, and accessible only by authorized personnel, thereby reducing operational risk.

[0144] International Financial Reporting Standards (IFRS): ALFINI aligns financial and non-financial reporting practices, combined with MEGAN's cross-classification mapping capabilities, helps banks harmonize and integrate accounting data across multiple IFRS jurisdictions and reporting classifications (e.g., IFRS classification, US GAAP classification). CATM's immutable audit trail and QUASAR's quantum-optimized provenance verification provide tamper-resistant records of data transformations and calculations, thereby enhancing the transparency and audibility of IFRS-compliant financial statements.

[0145] Solvency II (Insurance Regulation): MEGAN's ability to extract and normalize metadata from different sources helps insurance companies establish a consistent data foundation for Solvency II Pillar 3 reporting requirements. These requirements involve complex calculations and disclosures related to capital adequacy, risk management, and governance. SEPHYR's unified data representation and GAMA's role-based access control ensure secure and controlled retrieval of data required for Solvency II reporting, thereby reducing operational risk and ensuring compliance with data privacy regulations.

[0146] Market Instruction for Financial Instruments (MiFID II): ALFINI's alignment of key performance indicators and narrative information helps banks and investment firms comply with MiFID II's requirements for comprehensive reporting on best execution, transaction cost analysis, and product management. CATM's cryptographic audit trail and QUASAR's post-quantum security mechanisms provide secure and tamper-proof records of transaction execution, ensuring compliance with MiFID II's audit trail and record-keeping requirements.

[0147] Foreign Account Tax Compliance Act (FATCA): MEGAN's ability to extract and normalize customer data from various sources can help financial institutions establish a consistent and up-to-date repository of customer tax information. This, in turn, facilitates compliance with FATCA reporting requirements. Additionally, GAMA's dynamic access control and QSEM's quantum encryption provide secure handling and protection of sensitive customer tax data, reducing the risk of data breaches and non-compliance.

[0148] By leveraging GenFoundry's advanced capabilities in metadata management, data governance, access control, intelligent query processing, and quantum-resilient security, financial institutions can simplify their compliance efforts across various reporting standards and regulations. This results in reduced operational costs, mitigated risks, and enhanced quality and applicability of regulatory reporting processes.

[0149] Financial crime use case

[0150] GenFoundry can provide a comprehensive horizontal capability for combating financial crime. Its core component, COGNIGEN-AX, encompasses multiple domains, including Anti-Money Laundering (AML), fraud detection, and regulatory compliance. GenFoundry's different components contribute to this horizontal capability in various ways.

[0151] COGNIGEN-AX is an AI-driven agent that generates detailed narratives about potential financial crime scenarios. It analyzes various types of data, such as transaction records, customer profiles, watchlist entries, and open-source intelligence. The tool's meta-cognitive capabilities allow it to continuously adapt its knowledge models and reasoning strategies. This ensures the accuracy and relevance of the generated narratives as new information emerges or patterns evolve.

[0152] MEGAN: Automated Metadata Management and DataVault2.0 Modeling: MEGAN enables the generation of metadata registries and abstract DataVault2.0 schemas compliant with ISO 11179 standards, establishing a consistent and unified data foundation for financial crime data. It encompasses various sources, including transaction monitoring systems, customer due diligence databases, and external watch lists. The DataVault2.0 modeling approach facilitates data historicization and audit tracking by preserving the chronological order of events and data transformations, promoting the reconstruction of financial crime narratives.

[0153] SEPHYR: Coordinated Data Representation and Cryptographically Verifiable: SEPHYR harmonizes different data schemas and features into a unified, cryptographically verifiable representation. This enables efficient cataloging, searching, and analysis of financial crime data, facilitating cross-channel monitoring and overall risk assessment. The cryptographic verifiability provided by SEPHYR ensures the integrity and immutability of financial crime data, supporting robust investigations, audits, and regulatory compliance.

[0154] GAMA: Dynamic Access Control and Regulatory Compliance: GAMA translates user roles, permissions, and regulatory policies into an integrated access control model. This ensures that financial crime data is accessible only to authorized personnel, mitigating the risk of data breaches and unauthorized access. By enforcing fine-grained, context-aware access policies, GAMA supports separation of duties and maintains the confidentiality of sensitive financial crime investigations while facilitating cross-functional collaboration.

[0155] QSEM and CATM: Quantum Resilient Security and Auditable Data Provenance: QSEM integrates quantum encryption algorithms and post-quantum cryptography to protect the confidentiality and integrity of financial crime data. This ensures long-term security against emerging threats from quantum computing. CATM generates immutable audit trails and encrypts captured data provenance. This provides a tamper-resistant record of financial crime data, supporting robust investigations, regulatory audits, and forensic analysis.

[0156] By integrating these components, GenFoundry creates a horizontal capability to combat financial crime. These functionalities span data governance, narrative intelligence, access control, security, and audit. This holistic practice enables financial institutions to more effectively investigate, investigate, and report financial crime. It also ensures robust data integrity, regulatory compliance, and operational resilience.

[0157] ALFINI

[0158] ALFINI (Alignment, Language Models, Financial and Non-Financial Reporting, Integration, NLP and AI, ISO 5116-3:20 21) is an advanced system and method for effectively aligning financial and non-financial reporting through ISO 5116-3:20 21. At its core is a synergistic whole composed of specialized agencies and methods that, through continuous optimization cycles, collaboratively analyze, assess, and strengthen the coordination of reporting practices.

[0159] Key advantages of ALFINI:

[0160] Diverse implementation approaches: Adaptation to large language models (LLM), quantum computing, data grid architecture, and graph and vector data storage for cross-computational context adaptability.

[0161] Synergistic agent system and method: ALFINI integrates agents such as DHA, RSKBA, KPIAA, NIAA, MBAA, AAA, and RIA with unique capabilities for unified reporting practices.

[0162] Coordinated platoon agreement: Strictly assess and iteratively enhance the alignment of financial and non-financial reporting through integrated multi-agent capabilities to improve transparency, reliability, and decision usefulness.

[0163] Adaptive optimization: ALFINI uses advanced technologies and architectures to facilitate system adaptation to derive improvement strategies and fine-tune the alignment process.

[0164] Agents:

[0165] DHA: Analyzes the quality and consistency of financial and non-financial data to ensure accuracy, completeness, and timeliness.

[0166] RSKBA: Integrates principles and guidelines from various reporting frameworks and standards to provide a comprehensive knowledge base for coordination efforts.

[0167] KPIAA: Utilizes machine learning techniques to identify and align key performance indicators, ensuring consistency and comparability across reporting periods and entities.

[0168] NIAA: Aligns narrative information such as management commentary and sustainability reports using natural language processing (NLP), ensuring consistency and coherence.

[0169] MBAA: Applies machine learning algorithms to align importance assessments and reporting boundaries, enhancing the relevance and comparability of reported information.

[0170] AAA coordinates with assurance providers to align assurance processes, facilitate consistent and reliable assurance opinions, and enhance credibility and trust.

[0171] RIA: Integrates aligned information into consolidated reports following connectivity, consistency, and accessibility principles to meet the needs of different stakeholders.

[0172] Evaluation and Validation:

[0173] Ground truth data and evaluation metrics are carefully tuned to align with the specific functions and objectives of each component of ALFINI:

[0174] DHA (Data Harmonization Agent): The performance of DHA is evaluated using real data from established financial and non-financial databases, report templates, and expert annotations. Data accuracy, completeness, and timeliness are used to assess the effectiveness of DHA in ensuring the quality and consistency of data used for alignment.

[0175] Report Standard Knowledge Base Agent: The evaluation of the Report Standard Knowledge Base Agent relies on real data provided by internationally recognized reporting frameworks, standards, and best practices. Metrics such as knowledge base coverage, update frequency, and query response accuracy measure the ability of RSKBA to provide comprehensive and up-to-date guidance for aligned reporting practices.

[0176] KPIAA (Key Performance Indicator Alignment Agent): The performance of KPIAA is evaluated using real data from industry benchmarks, historical KPI data, and expert validation of alignment. Metrics such as KPI alignment accuracy, consistency score, and comparability index are used to assess the effectiveness of KPIAA in identifying and aligning financial and non-financial KPIs across reporting periods and entities.

[0177] NIAA (Narrative Information Alignment Agent): NIAA is evaluated using real data from manually aligned narrative reports, linguistic resources, and domain-specific ontologies. Metrics such as thematic consistency, risk and opportunity identification accuracy, and consistency score are used to assess the ability of NIAA to effectively align narrative information, ensuring consistency and coherence between reports.

[0178] MBAA (Materiality and Boundaries Alignment Agent): The performance of MBAA is evaluated using ground truth data from stakeholder surveys, industry materiality maps, and expert-defined boundary scenarios. Metrics such as materiality alignment accuracy, boundary consistency, and thematic priority relevance are used to measure the effectiveness of MBAA in aligning materiality assessments and reporting boundaries.

[0179] AAA (Assurance Alignment Agent): The evaluation of AAA relies on real data from alignment cases coming from assurance standards, historical assurance opinions, and expert reviews. Metrics such as assurance consistency, opinion reliability, and consistency adherence assess AAA's ability to effectively coordinate with assurance providers and facilitate consistent and reliable assurance processes.

[0180] RIA (Report Integration Agent): The performance of RIA is evaluated using ground truth data from integrated reporting frameworks, stakeholder feedback, and expert validated report samples. Metrics such as information connectivity, consistency, accessibility, and stakeholder satisfaction measure RIA's effectiveness in integrating aligned information into comprehensive reports that meet the needs of different stakeholders.

[0181] Agents:

[0182] The ALFINI Agent Architecture comprises several sub-agents, each with specific roles and responsibilities, working together to ensure compliance with the provisions and guidelines of ISO 5116-3:20 21:

[0183] The Data Harmonization Agent (DHA) aligns with Clause 6, ensuring data quality and consistency.

[0184] The Report Standards Knowledge Base Agent (RSKBA) aligns with Clause 5, providing aligned principles and guidelines.

[0185] The Key Performance Indicator Alignment Agent (KPIAA) aligns with Clause 7, identifying and aligning KPIs.

[0186] The Narrative Information Alignment Agent (NIAA) aligns with Clause 8, aligning narrative information using natural language processing techniques.

[0187] The Importance and Boundaries Alignment Agent (MBAA) aligns with Clause 9, aligning importance assessments and reporting boundaries using machine learning.

[0188] The Assurance Alignment Agent (AAA) aligns with Clause 10, coordinating with assurance providers to align assurance processes.

[0189] The Report Integration Agent (RIA) aligns with Clause 11, integrating aligned information into comprehensive reports.

[0190] The sub-agents are orchestrated using Petri net models, which ensure the coordinated and efficient execution of alignment and integration tasks through the principles outlined in Clause 4 of ISO 5116-3:20 21. Petri nets model dependencies and interactions between sub-agents, ensuring that each clause is addressed in the appropriate order and with the necessary inputs and outputs.

[0191] For example, the Petri net ensures that the DHA (Clause 6) provides quality-assured data to the KPIAA (Clause 7) and the NIAA (Clause 8) for their respective alignment tasks. The Petri net then triggers the MBAA (Clause 9) to align the importance assessment and reporting boundaries based on the outputs of the KPIAA and the NIAA. Once all alignment tasks are complete, the Petri net initiates the RIA (Clause 11) to integrate the aligned information into the final report. Finally, the Petri net triggers the AAA (Clause 10) to independently ensure the alignment and integration processes.

[0192] Figure 2 is a functional block diagram 200 illustrating an example of a reporting integration system within a multi-agent artificial intelligence framework according to embodiments herein. The reporting integration system corresponds to an embodiment of the ALFINI agent framework.

[0193] DHA (Data Harmony Agent) 206:

[0194] The DHA 206 is a data processing agent configured to ingest and normalize raw data inputs to produce standardized data. Additionally, the data processing agent includes a data cleansing module that enhances its ability to remove errors and inconsistencies, thereby improving the accuracy of the standardized data. This agent also serves as the core of the reporting aggregation agent, using a data integration platform to consolidate data outputs.

[0195] The DHA 206 further analyzes and coordinates financial and non-financial data to ensure quality, consistency, and accuracy. The DHA 206 also serves the KPIAA 214, the NIAA 212, and the MBAA 202 by providing coordinated data as input to these agents, thereby facilitating their respective functions.

[0196] RSKBA Report Standards Knowledge Base Agent 210:

[0197] The RSKBA 210 is a standard integration agent that maintains reporting standards and guidelines from various frameworks. The standard integration agent applies the reporting standards to the standardized data, thereby generating integrated reporting standards. This integration ensures that the data aligns with regulatory and compliance requirements, thereby reflecting the functions of the compliance alignment agent. The RSKBA 210 forms associations with the KPIAA 214, the NIAA 212, the MBAA 202, and the AAA 204 to inform alignment activities based on standards, thereby ensuring compliance across all reports.

[0198] KPIAA Key Performance Indicator Alignment Agent 214:

[0199] KPIAA 214 is a performance alignment agent that employs NLP and machine learning to identify and align KPIs across reports. The performance alignment agent can use machine learning to dynamically adapt performance metrics based on standardized data and integrated reporting standards. KPIAA 214 provides aligned KPIs as input to RIA 208, supports comprehensive reporting integration, and leverages coordinated data from DHA 206 and standards from RSKBA 210 to enhance the accuracy of performance measures.

[0200] NIAA (Narrative Information Alignment Agent) 212:

[0201] NIAA 212 is an information synthesis agent that leverages NLP to align narrative information such as risks and opportunities. This agent processes narrative information in standardized data and integrated reporting standards. It includes a contextual analysis module that integrates contextual cues from data to enhance narrative accuracy and relevance.

[0202] NIAA 212 can receive aligned narratives from RIA 208 to integrate into a comprehensive report and correlate with data from DHA 206 and standards from RSKBA 210, ensuring that narratives are accurate and compatible in context.

[0203] MBAA (Importance and Boundary Alignment Agent) 202:

[0204] MBAA 202 is a materiality alignment agent and applies machine learning to align materiality and reporting boundaries. This agent assesses and adjusts materiality and boundaries according to standardized data and integrated reporting standards, ensuring that reports comply with specific materiality thresholds and boundary definitions of global standard regulations.

[0205] MBAA can receive aligned materiality and boundary inputs to RIA 208, which is crucial for structuring reports and correlates with data and standards from DHA 206, RSKBA 210, KPIAA 214, and NIAA 212, ensuring a comprehensive approach to boundary definition.

[0206] AAA (Assurance Alignment Agent) 204:

[0207] AAA is a compliance alignment agent that coordinates with assurance providers to align assurance processes according to standardized data and integrated reporting standards, ensuring that reports are compliant and verified.

[0208] AAA correlates with RSKBA's 210 standards and RIA 208's processes to ensure that reports are not only compatible but also validated, and further ensures that assurance processes are consistent with integrated reports produced by RIA 208.

[0209] RIA (Report Integration Agent) 208:

[0210] The RIA is the orchestration framework that integrates aligned information from various agents into a comprehensive report. The orchestration framework acts as a central hub for receiving inputs from agents such as KPIAA 214, NIAA 212, and MBAA 202, merging them into a final report. The RIA establishes connections with other ALFINI agents, leverages and synthesizes their outputs, producing coherent and comprehensive reporting outputs for ALFINI.

[0211] Figure 2 The relationships shown in the middle summarize:

[0212] The DHA 206 provides a unified data foundation, which is critical for all data-dependent processes. The RSKBA 210 provides guidance on standards, which is crucial for maintaining compliance across functions. The KPIAA 214, NIAA 212, and MBAA 202 perform specific alignments using data and standards provided by the DHA 206 and RSKBA 210. The AAA 204 coordinates assurance processes, ensuring that reports comply with external and internal assurance standards. The RIA 208 synthesizes the inputs from all agents, producing a final comprehensive report that reflects the overall collaborative effort of the ALFINI system.

[0213] ALFINI achieves the following objectives and capabilities:

[0214] Integrating multi-modal data for comprehensive alignment:

[0215] ALFINI has mechanisms to collect, process, and merge data from various sources such as financial statements, sustainability reports, performance metrics, and external frameworks. Considering different data modalities, the system and method provide AI agents with an overall view of the reporting landscape and enable them to identify patterns, correlations, and insights that may not be apparent from individual data sources. Furthermore, ALFINI has techniques to weight and prioritize different data types based on their relevance, reliability, and importance, ensuring that

[0216] Facilitating collaboration between AI agents and human experts:

[0217] ALFINI enables smooth collaboration between AI agents and human experts, such as financial professionals, sustainability specialists, and auditors. The system provides interfaces and protocols for experts to input their knowledge, guidelines, and feedback into the alignment process. AI agents can incorporate this human expertise into their decision-making and output generation. Additionally, ALFINI includes mechanisms for AI agents to explain their alignment decisions and recommendations to human experts, promoting transparency and trust in the collaboration.

[0218] Capabilities:

[0219] Automatic alignment of financial and non-financial information:

[0220] ALFINI enables AI agents to automatically align financial and non-financial information from different sources, ensuring consistency and comparability across reporting periods and entities. The system utilizes advanced NLP techniques such as named entity recognition, coreference resolution, and semantic similarity to identify and link related concepts across different reports. Additionally, ALFINI employs ontology alignment techniques to map and reconcile terms and classifications used in various reporting frameworks and standards.

[0221] Continuous learning and adaptation to changing reporting requirements:

[0222] ALFINI enables AI agents to continuously learn and adapt to changes in reporting requirements, frameworks, and best practices. The system includes mechanisms for monitoring and incorporating updates to relevant standards (e.g., ISO 5116-3:20 21) and adjusting the alignment process accordingly. Furthermore, ALFINI employs machine learning techniques such as reinforcement learning and transfer learning to allow AI agents to improve their alignment performance over time based on feedback and experience.

[0223] Explainable and auditable alignment decisions:

[0224] ALFINI ensures that alignment decisions made by AI agents are explainable and auditable. The system includes techniques for generating human-readable explanations of the reasoning behind specific alignments, including the data sources considered, rules and guidelines applied, and confidence levels of the output. Additionally, ALFINI maintains detailed logs of all alignment activities, including data inputs, intermediate results, and final outputs, enabling thorough auditing and verification of the alignment process.

[0225] Data harmonization agent (DHA):

[0226] Method

[0227] The DHA employs advanced data validation, transformation, and harmonization techniques to ensure the quality, consistency, and completeness of financial and non-financial data, both within and across systems and processes.

[0228] Data validation:

[0229] The DHA conducts comprehensive data validation to assess accuracy, completeness, and timeliness. It employs rule-based validation, constraint checking, and cross-field validation to identify and resolve data inconsistencies, missing values, or anomalies. The validation process follows a multi-step approach:

[0230] Data Profiling: DHA analyzes the structure, content, and statistical properties of data to identify potential issues such as outliers, data type mismatches, or violations of business rules.

[0231] Rule-Based Validation: Data is subject to a set of predefined validation rules that cover data format, value ranges, and logical constraints.

[0232] Cross-Field Validation: DHA checks for consistency between related data fields, ensuring that interdependent values align with business rules and data integrity constraints.

[0233] Exception Handling: Identified data issues are flagged and appropriate remediation measures are taken, such as data cleaning, imputation, or manual intervention.

[0234] The time complexity of the data validation process is O(n), where n is the number of data records, as each record needs to be processed and validated against defined rules and constraints.

[0235] Data Transformation:

[0236] DHA coordinates data by applying transformation techniques to ensure consistent data representation across systems and processes. It uses data mapping, standardization, and normalization methods to align data formats, units, and semantics. The transformation process involves the following steps:

[0237] Data Mapping: DHA maps data elements from source systems to a standardized data model, addressing structural and semantic differences.

[0238] Data Standardization: Adopting consistent data formats, units, and coding schemes to ensure consistency between systems and processes.

[0239] Data Normalization: DHA normalizes data values to standard scales or ranges, enabling meaningful comparisons and analysis across different data sources.

[0240] Data Enrichment: Additional data attributes or derived values can be added to the reconciled data to enhance its usability and analytical capabilities.

[0241] The time complexity of the data transformation process is O(n log n), where n is the number of data records, as sorting and merging operations may be required during the transformation steps.

[0242] DHA's data validation and transformation techniques aim to ensure compliance with clause 6.2.2 of the standard, which outlines data accuracy, completeness, and timeliness requirements. By applying these methods, DHA helps improve the quality and consistency of financial and non-financial data, enabling reliable decision-making and regulatory compliance.

[0243] Business Layer

[0244] Process:

[0245] Data Ingestion and Preprocessing: This process involves collecting and preprocessing data from various sources, ensuring data quality and consistency through validation and transformation techniques.

[0246] Data Coordination and Integration: This process focuses on coordinating and integrating data from multiple sources, addressing structural and semantic discrepancies to create a consistent and unified data representation.

[0247] Data Quality Monitoring and Reporting: This process involves continuously monitoring the quality and consistency of unified data, identifying and addressing issues, and generating reports for stakeholders.

[0248] Functionality:

[0249] Improved Data Quality: By validating and transforming data, DHA ensures the accuracy, completeness, and timeliness of financial and non-financial data, enabling reliable decision-making and regulatory compliance.

[0250] Consistent Data Representation: DHA coordinates data from multiple sources, aligning formats, units, and semantics to create a consistent and unified data representation across systems and processes.

[0251] Data Integrity and Traceability: DHA maintains data integrity by addressing inconsistencies, resolving missing values, and providing traceability through data lineage and audit trails.

[0252] Improved Data Governance: DHA supports data governance initiatives, enforces data quality standards, promotes consistency, and enables effective data management practices.

[0253] Application Layer

[0254] DHA comprises the following key components:

[0255] DataValidator: This component performs data validation tasks, including rule-based validation, constraint checking, and cross-field validation.

[0256] DataTransformer: This component handles data transformation tasks such as mapping, standardization, normalization, and enrichment to ensure consistent data representation.

[0257] DataIntegrator: This component integrates and coordinates data from multiple sources, addressing structural and semantic discrepancies to create a unified data model.

[0258] DataQualityMonitor: This component continuously monitors the quality and consistency of unified data, identifying and reporting issues or deviations from established standards.

[0259] Services:

[0260] DataValidationService: Provides methods for validating data against predefined rules, constraints, and business requirements.

[0261] DataTransformationService: Offers services for transforming data, including data mapping, standardization, normalization, and enrichment.

[0262] DataIntegrationService: This service helps integrate and harmonize data from multiple sources, creating a consistent and unified data representation.

[0263] DataQualityMonitoringService: Allows continuous monitoring and reporting of data quality and consistency, ensuring adherence to established standards.

[0264] Interfaces:

[0265] DataValidatorInterface: Defines methods and parameters for performing data validation tasks.

[0266] DataTransformerInterface: Specifies methods and input / output formats for data transformation operations.

[0267] DataIntegratorInterface: Describes methods and data models for integrating and harmonizing data from multiple sources.

[0268] DataQualityMonitorInterface: Provides methods for monitoring data quality, setting thresholds, and generating reports.

[0269] Implementation

[0270] Data Ingestion and Preprocessing: This process involves collecting and preprocessing data from various sources, ensuring data quality and consistency through validation and transformation techniques.

[0271] Data Harmonization and Integration: This process focuses on harmonizing and integrating data from multiple sources, addressing structural and semantic discrepancies to create a consistent and unified data representation.

[0272] Data Quality Monitoring and Reporting: This process involves continuously monitoring the quality and consistency of unified data, identifying and resolving issues, and generating reports for stakeholders.

[0273] Reporting Standards Knowledge Base Agent (RSKBA):

[0274] Method

[0275] RSKBA maintains an integrated knowledge base of internationally recognized reporting frameworks and standards, enabling financial and non-financial reporting practices to align with the provisions of ISO 5116-3:2021, clause 5.

[0276] Knowledge Base Management:

[0277] RSKBA employs advanced knowledge management techniques to store, update, and retrieve relevant reporting standards and guidelines. The knowledge base utilizes ontology and semantic web technologies for efficient querying and reasoning. The knowledge base management process includes the following steps:

[0278] Knowledge Acquisition: RSKBA acquires and processes various reporting standards, frameworks, and guidelines from authoritative sources such as International Financial Reporting Standards (IFRS), Global Reporting Initiative (GRI), and Task Force on Climate-related Financial Disclosures (TCFD).

[0279] Knowledge Representation: The acquired knowledge is represented using ontology and semantic web languages such as Web Ontology Language (OWL) and Resource Description Framework (RDF). This enables explicit representation of concepts, relationships, and rules within the reporting domain.

[0280] Knowledge Enrichment: RSKBA applies Natural Language Processing (NLP) techniques and machine learning algorithms to utilize additional contextual information such as synonyms, related concepts, and inferred relationships to enrich the knowledge base.

[0281] Knowledge Updating: RSKBA continuously monitors updates or changes to reporting standards and frameworks by authoritative sources and automatically updates the knowledge base accordingly.

[0282] The time complexity of the knowledge base management process depends on the size of the knowledge base and the complexity of the ingested standards and frameworks. The size of the ontology and associated metadata determine the spatial complexity.

[0283] Reporting Guidelines Retrieval:

[0284] RSKBA provides a query interface that enables stakeholders to access and interpret relevant reporting guidelines, consistent with clause 5.3 of the standards. The interface supports natural language queries and employs semantic search techniques to retrieve the most relevant information from the knowledge base. The retrieval process involves the following steps:

[0285] Query Processing: RSKBA preprocesses and parses natural language queries, using NLP techniques to identify key concepts, entities, and relationships.

[0286] Semantic Matching: RSKBA maps the extracted query components to ontological concepts and relationships within the knowledge base, enabling semantic matching and reasoning.

[0287] Result Ranking: RSKBA considers concept similarity, relationship strength, and contextual information to rank the retrieved results based on their relevance to the query.

[0288] Result Presentation: RSKBA presents relevant reporting guidelines to users, providing explanations, examples, and interpretations for easy understanding and application.

[0289] The time complexity of the reporting guideline retrieval process depends on the size of the knowledge base and the complexity of the query. The space complexity is determined by the size of the ontology and any intermediate data structures used during query processing and result ranking. By maintaining a comprehensive reporting standards knowledge base and providing an intuitive query interface, reporting standards support financial and non-financial reporting practices align with internationally recognized frameworks, as outlined in Clause 5 of the ISO 5116-3:20 21 standard.

[0290] Business Layer

[0291] Process:

[0292] Reporting Standards Acquisition: This process involves identifying and acquiring relevant reporting standards, frameworks, and guidelines from authoritative sources, ensuring the comprehensiveness and universality of the knowledge base.

[0293] Knowledge Base Management: This process focuses on the structuring, enrichment, and maintenance of the knowledge base, ensuring efficient storage, retrieval, and updating of reporting standards and guidelines.

[0294] Reporting Guidelines Dissemination: This process involves making relevant reporting guidelines easily accessible to stakeholders, enabling reporting practices to align with recognized standards and frameworks.

[0295] Function:

[0296] Reporting Coordination: RSKBA supports the alignment of financial and non-financial reporting practices with internationally recognized standards and frameworks, promoting consistency and comparability across organizations.

[0297] Regulatory Compliance: By maintaining a comprehensive reporting standards knowledge base and providing guidance, RSKBA facilitates compliance with regulatory requirements and industry best practices.

[0298] Decision Support: RSKBA's query interface enables stakeholders to access relevant reporting guidelines, supporting informed decision-making and effective implementation of reporting practices.

[0299] Knowledge Sharing: RSKBA promotes knowledge sharing and dissemination within organizations, fostering a culture of continuous learning and improvement in reporting practices.

[0300] Application Layer

[0301] KnowledgeBaseManager: This component is responsible for acquiring, building, enriching, and maintaining the knowledge base of reporting standards and frameworks.

[0302] QueryProcessor: This component processes natural language queries from stakeholders, employing NLP techniques and semantic matching to retrieve relevant reporting guidelines from the knowledge base.

[0303] ResultRanker: This component ranks and prioritizes the retrieved reporting guidelines based on relevance, taking into account concept similarity, relationship strength, and contextual information.

[0304] GuidancePresenter: This component presents relevant reporting guidelines to stakeholders in a transparent and interpretable manner, providing explanations, examples, and interpretations to facilitate understanding and application.

[0305] Services:

[0306] KnowledgeBaseManagementService: Provides methods for acquiring, updating, and maintaining the knowledge base of reporting standards and frameworks.

[0307] ReportingGuidanceQueryService: Facilitates the submission of natural language queries and retrieval of relevant reporting guidelines for stakeholders.

[0308] ResultRankingService: Enables the ranking and prioritization of retrieved reporting guidelines based on defined relevance criteria.

[0309] GuidancePresentationService: Promotes the transparent and interpretable presentation of reporting guidelines, including explanations, examples, and interpretations.

[0310] Interfaces:

[0311] KnowledgeBaseManagerInterface: Defines methods and parameters for managing the knowledge base, including knowledge acquisition, enrichment, and updates.

[0312] QueryProcessorInterface: Specifies input and output formats for natural language queries and methods for query processing and semantic matching.

[0313] ResultRankerInterface: Describes methods and criteria for ranking and prioritizing retrieved reporting guidelines based on relevance.

[0314] GuidancePresenterInterface: Provides methods for presenting report guidelines in various formats, including text, visualizations, and interactive explanations.

[0315] Key Performance Indicator Alignment Agent (KPIAA):

[0316] Method

[0317] The KPIAA ensures consistency of financial and non-financial Key Performance Indicators (KPIs) across reporting periods and entities, as per Clause 7 of ISO 5116-3:2021, facilitating consistency and comparability.

[0318] KPI Identification and Consistency:

[0319] The KPIAA employs Natural Language Processing (NLP) and machine learning techniques to identify and align relevant KPIs from various data sources, including financial statements, operational reports, and industry-specific benchmarks. The KPI alignment process involves the following steps:

[0320] Data Ingestion: The KPIAA ingests structured and unstructured data sources containing KPI-related information, such as financial reports, management comments, and industry guidelines.

[0321] KPI Extraction: The KPIAA uses NLP techniques, such as named entity recognition and semantic parsing, to identify and extract potential KPIs from ingested data sources.

[0322] KPI Normalization: Extracted KPIs are normalized into standardized representations, addressing inconsistencies in naming conventions, measurement units, and calculation methods.

[0323] KPI Alignment: The KPIAA aligns standardized KPIs with industry-specific taxonomies and reporting frameworks, ensuring consistency and comparability across entities and reporting periods.

[0324] KPI Validation: Aligned KPIs are validated against predefined rules, constraints, and business logic to ensure accuracy and relevance.

[0325] The time complexity of the KPI alignment process depends on the size and complexity of ingested data sources and the number of KPIs to be processed. The size of intermediate data structures and KPI alignment models determines the space complexity.

[0326] KPI Discrepancy Detection and Resolution:

[0327] The KPIAA employs machine learning techniques to detect and resolve discrepancies in KPI definitions, calculations, and reporting practices, as encouraged by Clause 7.3 of the standard. The discrepancy detection and resolution process involves the following steps:

[0328] Baseline Establishment: KPIAA establishes a baseline of expected KPI values and trends based on historical data, industry benchmarks, and domain-specific rules.

[0329] Anomaly Detection: KPIAA uses anomaly detection algorithms and predictive modeling techniques to identify deviations or discrepancies between reported KPIs and expected baselines.

[0330] Root Cause Analysis: KPIAA analyzes the identified discrepancies to determine their root causes, which can include calculation errors, data quality issues, or changes in reporting practices.

[0331] Discrepancy Resolution: Based on root cause analysis, KPIAA suggests corrective measures or adjustments to address discrepancies, such as recalculating KPIs, updating formulas, or adjusting reporting practices.

[0332] Continuous Monitoring: KPIAA monitors KPI reporting practices and updates baselines and models as new data becomes available, ensuring ongoing consistency and comparability.

[0333] The time complexity of the discrepancy detection and resolution process depends on the number of KPIs, the size of historical data, and the complexity of anomaly detection and predictive modeling algorithms. The size of KPI data and discrepancy detection models determine the space complexity.

[0334] By aligning KPIs and detecting and resolving discrepancies, KPIAA promotes consistency, comparability, and accuracy in KPI reporting, aligning with the principles outlined in Clause 7 of ISO 5116-3:2021 standard.

[0335] Business Layer

[0336] Process:

[0337] KPI Data Ingestion: This process involves collecting and ingesting structured and unstructured data sources containing KPI-related information from various internal and external sources.

[0338] KPI Alignment: This process focuses on identifying, extracting, standardizing KPIs, and adjusting them to industry-specific classifications and reporting frameworks, ensuring consistency and comparability across reporting periods and entities.

[0339] KPI Discrepancy Management: This process involves detecting and resolving discrepancies in KPI definitions, calculations, and reporting practices using machine learning techniques and root cause analysis.

[0340] Functionality:

[0341] Consistent KPI Reporting: KPIAA ensures consistent and comparable KPI reporting within an organization, enabling stakeholders to make informed decisions based on reliable and consistent performance metrics.

[0342] Regulatory Compliance: KPIAA facilitates compliance with regulatory requirements and industry best practices by aligning KPIs with industry-specific reporting frameworks and standards.

[0343] Performance Monitoring: Consistent and validated KPIs of KPIAA enable effective performance monitoring and benchmarking, supporting data-driven decision-making and continuous improvement initiatives.

[0344] Automated Disparity Detection: Machine learning capabilities of KPIAA enable automated detection of KPI disparities, reducing manual effort and improving efficiency in the reporting process.

[0345] Application Layer

[0346] KPIAA comprises the following key components:

[0347] KPIExtractor: This component is responsible for ingesting data sources and employing NLP techniques to identify and extract potential KPIs.

[0348] KPINormalizer: This component normalizes the extracted KPIs into standardized representations, addressing inconsistencies in naming conventions, units of measurement, and calculation methods.

[0349] KPIAligner: This component aligns the normalized KPIs with industry-specific taxonomies and reporting frameworks, ensuring consistency and comparability across entities and reporting periods.

[0350] DisperpancyDetector: This component employs machine learning techniques to detect disparities in KPI definitions, calculations, and reporting practices using anomaly detection algorithms and predictive modeling.

[0351] DisperpancyResolver: This component analyzes identified disparities, determines their root causes, and suggests corrective measures or adjustments to address these issues.

[0352] Services:

[0353] KPIExtractionService: Provides methods for ingesting data sources and extracting potential KPIs using NLP techniques.

[0354] KPINormalizationService: Offers services for normalizing extracted KPIs into standardized representations, addressing inconsistencies in naming conventions, units, and calculation methods.

[0355] KPIAlignmentService: Aligns normalized KPIs with industry-specific taxonomies and reporting frameworks, ensuring consistency and comparability.

[0356] DisperpancyDetectionService: This service facilitates the use of machine learning techniques, anomaly detection algorithms, and predictive modeling to detect KPI discrepancies.

[0357] DisperpancyResolutionService: Provides services for analyzing identified discrepancies, determining root causes, and recommending corrective actions or adjustments.

[0358] Interface:

[0359] KPIExtractorInterface: Defines methods and parameters for ingesting data sources and extracting potential KPIs using NLP techniques.

[0360] KPINormalizerInterface: This interface specifies input and output formats for KPI normalization and methods for resolving inconsistencies in naming conventions, units, and calculation methods.

[0361] KPIAlignerInterface: Describes methods and standards for aligning normalized KPIs with industry-specific classifications and reporting frameworks.

[0362] DisperpancyDetectorInterface: Provides methods for detecting KPI discrepancies using machine learning techniques, anomaly detection algorithms, and predictive modeling.

[0363] DisperpancyResolverInterface: This interface defines methods and output formats for analyzing identified discrepancies, determining root causes, and suggesting corrective actions or adjustments.

[0364] Narrative Information Alignment Agent (NIAA):

[0365] Method

[0366] The NIAA ensures alignment of narrative information in financial and non-financial reports through clause 8 of the ISO 5116-3:20 21 standard, promoting consistency, coherence, and decision usefulness.

[0367] Narrative Information Extraction and Coordination:

[0368] The NIAA employs advanced Natural Language Processing (NLP) techniques to extract and coordinate key themes, risks, and opportunities from narrative information across different reports and sources. The narrative alignment process includes the following steps:

[0369] Data Ingestion: The NIAA ingests unstructured narrative information from management comments, sustainability reports, and regulatory filings.

[0370] Text preprocessing: The ingested textual data is preprocessed using tokenization, stemming, and stop-word removal to prepare it for further analysis.

[0371] Topic modeling: NIAA applies topic modeling algorithms such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) to identify and extract key themes, risks, and opportunities from narrative information.

[0372] Sentiment analysis: NIAA performs sentiment analysis on the extracted themes, risks, and opportunities to determine their polarity (positive, negative, or neutral) and potential impact.

[0373] Cross-report coordination: The extracted and analyzed themes, risks, and opportunities are coordinated across different reports and sources, resolving inconsistencies and aligning their presentation and interpretation.

[0374] The time complexity of the narrative alignment process depends on the volume of narrative information and the complexity of the employed NLP and topic modeling algorithms. The spatial complexity is determined by the size of the textual data and the intermediate data structures required for processing.

[0375] Narrative clarity and conciseness:

[0376] NIAA follows the principles of balanced, clear, and concise narrative alignment as required by standard clause 8.3. It uses text summarization, language generation, and readability analysis to enhance the understandability and decision usefulness of the aligned narrative information. The narrative clarity and conciseness process includes the following steps:

[0377] Key information extraction: NIAA identifies and extracts the most relevant and important information from the coordinated narrative themes, risks, and opportunities.

[0378] Text overview: A text overview algorithm is used to summarize the extracted key information, providing a concise yet comprehensive representation of the narrative content.

[0379] Language generation: NIAA generates clear and coherent narrative statements using natural language generation techniques, ensuring consistent tone, style, and level of detail across reports.

[0380] Readability assessment: The generated narrative statements are assessed for readability using metrics such as Flesch-Kincaid Grade Level or Gunning Fog Index, ensuring that the intended audience can quickly understand the narrative information.

[0381] Refinement and iteration: Based on the readability assessment, NIAA iteratively refines the generated narrative statements to improve clarity and conciseness while maintaining the accuracy and completeness of the information.

[0382] The time complexity of narrating clear and concise processes depends on the amount of narrative information and the complexity of the employed text summarization, language generation, and readability assessment algorithms. The spatial complexity is determined by the size of the narrative data and the intermediate data structures required for processing.

[0383] Business Layer

[0384] Process:

[0385] Narrative Information Ingestion: This process involves collecting and ingesting unstructured narrative information from various sources such as management reviews, sustainability reports, and regulatory filings.

[0386] Narrative Extraction and Coordination: This process focuses on extracting key themes, risks, and opportunities from the narrative information and coordinating their presentation and interpretation across different reports and sources.

[0387] Narrative Clarity and Conciseness: This process involves using text summarization, language generation, and readability assessment techniques to enhance the clarity and conciseness of aligned narrative information.

[0388] Functionality:

[0389] Consistent Narrative Reporting: NIAA ensures consistent and coherent narrative reporting across the organization, enabling stakeholders to gain a comprehensive understanding of the organization's performance, risks, and opportunities.

[0390] Regulatory Compliance: NIAA facilitates compliance with regulatory requirements and industry best practices by aligning narrative information with specific industry reporting frameworks and standards.

[0391] Enhanced Decision Utility: NIAA's ability to extract and coordinate key themes, risks, and opportunities from narrative information while ensuring clarity and conciseness enhances the decision utility of reported information for stakeholders.

[0392] Automated Narrative Processing: NIAA's natural language processing and text processing capabilities enable automated extraction, coordination, and enhancement of narrative information, reducing manual effort and improving the efficiency of the reporting process.

[0393] Application Layer

[0394] NIAA comprises the following key components:

[0395] TextPreprocessor: This component is responsible for ingesting and preprocessing unstructured narrative information, performing tasks such as tokenization, stem extraction, and stop-word removal.

[0396] ThemeExtractor: This component uses topic modeling algorithms to identify and extract key themes, risks, and opportunities from pre-processed narrative information.

[0397] SentimentAnalyzer: This component performs sentiment analysis on the extracted themes, risks, and opportunities to determine their polarity and potential impact.

[0398] NarrativeHarmonizer: This component harmonizes the extracted and analyzed themes, risks, and opportunities across different reports and sources, resolving inconsistencies and making them consistent in presentation and explanation.

[0399] NarrativeEnhancer: This component enhances the clarity and conciseness of aligned narrative information using text summarization, language generation, and readability assessment techniques.

[0400] Services:

[0401] TextPreprocessingService: Provides methods for ingesting and pre-processing unstructured narrative information, performing tasks such as tokenization, stem extraction, and stop word removal.

[0402] ThemeExtractionService: This service provides services for identifying and extracting key themes, risks, and opportunities from pre-processed narrative information using topic modeling algorithms.

[0403] SentimentAnalysisServices: Enables the analysis of sentiment polarity and potential impact on extracted themes, risks, and opportunities.

[0404] NarrativeHarmonizationService: Facilitates the harmonization of extracted and analyzed themes, risks, and opportunities across different reports and sources, resolving inconsistencies and making them consistent in presentation and explanation.

[0405] NarrativeEnhancementService: Provides services to enhance the clarity and conciseness of aligned narrative information using text summarization, language generation, and readability assessment techniques.

[0406] Interfaces:

[0407] TextPreprocessorInterface: This interface defines methods and parameters for ingesting and pre-processing unstructured narrative information, including tokenization, stem extraction, and stop word removal.

[0408] ThemeExtractorInterface: This interface specifies the input and output formats for extracting key themes, risks, and opportunities from pre-processed narrative information using theme modeling algorithms.

[0409] SentimentAnalyzerInterface: This interface describes the methods and output formats for analyzing sentiment polarity and extracting the potential impact of themes, risks, and opportunities.

[0410] NarrativeHarmonizerInterface: This interface provides methods and parameters for harmonizing the extracted and analyzed themes, risks, and opportunities across different reports and sources, resolving inconsistencies, and making them consistent in presentation and interpretation.

[0411] NarrativeEnhancerInterface: This interface defines methods and output formats for enhancing the clarity and conciseness of aligned narrative information using text summarization, language generation, and readability assessment techniques.

[0412] Significance and Boundary Alignment Agent (MBAA):

[0413] Method

[0414] The MBAA ensures the alignment of significance assessment and reporting boundaries for financial and non-financial reports according to Clause 9 of ISO 5116-3:20 21, thereby enhancing the relevance and comparability of reported information.

[0415] Significance Assessment and Prioritization:

[0416] The MBAA applies machine learning algorithms to identify and prioritize significance themes based on stakeholders' concerns and business impacts, as outlined in Clause 9.2. The process of significance assessment and prioritization includes the following steps:

[0417] Data Ingestion: The MBAA ingests data from various sources, including stakeholder surveys, social media sentiment analysis, industry reports, and internal risk assessments.

[0418] Theme Modeling: The MBAA employs theme modeling algorithms, such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF), to identify latent significance themes from ingested data.

[0419] Feature Engineering: The MBAA extracts relevant features from data, such as stakeholder sentiment scores, risk impact assessments, and industry trends, to use as inputs for machine learning models.

[0420] Importance Scoring: The MBAA applies supervised or unsupervised machine learning algorithms, such as logistic regression, decision trees, or clustering algorithms, to score and prioritize the identified themes based on their importance.

[0421] Theme Validation: The prioritized importance themes are validated and refined through expert review, stakeholder consultation, and iterative model training.

[0422] The time complexity of the importance assessment and prioritization process depends on the volume of input data, the complexity of the employed topic modeling and machine learning algorithms, and the number of iterations required for model training and validation. The space complexity is determined by the size of the input data and the intermediate data structures required for processing.

[0423] Boundary Alignment:

[0424] The MBAA ensures consistent application of the importance and boundary principles across financial and non-financial reporting as required by Clause 9.3. The boundary alignment process involves the following steps:

[0425] Reporting Analysis: The MBAA analyzes existing financial and non-financial reports to determine the current reporting boundaries and their underlying assumptions.

[0426] Importance Mapping: The prioritized importance themes are mapped to the identified reporting boundaries to determine potential gaps, overlaps, or inconsistencies in applying the importance principles.

[0427] Boundary Alignment: The MBAA recommends aligning the reporting boundaries based on the importance mapping to ensure consistent coverage of important themes across financial and non-financial reporting.

[0428] Stakeholder Consultation: Through stakeholder consultation, the proposed boundary alignment is reviewed and confirmed to ensure alignment with stakeholders' expectations and concerns.

[0429] Final Determination of Boundaries: The final determined reporting boundaries are documented and communicated to relevant stakeholders, providing a consistent and transparent basis for reporting on importance themes.

[0430] The time complexity of the boundary alignment process depends on the number of reports analyzed, the complexity of the importance mapping, and the extent of stakeholder consultation required. The space complexity is determined by the size of the report data and the intermediate data structures required for processing.

[0431] By applying machine learning algorithms for importance assessment and prioritization, and ensuring consistent application of the importance and boundary principles, the MBAA promotes the relevance and comparability of reported information between financial and non-financial reporting, aligning with the principles outlined in Clause 9 of the ISO 5116-3:20 21 standard.

[0432] Business Layer

[0433] Process:

[0434] Data Ingestion and Preprocessing: This process involves collecting and preprocessing data from various sources such as stakeholder surveys, social media, industry reports, and internal risk assessments for use as inputs for significance assessment and prioritization.

[0435] Significance Assessment and Prioritization: This process considers stakeholder concerns and business impacts, using machine learning algorithms to identify and prioritize significant topics.

[0436] Boundary Alignment: This process ensures that financial and non-financial reporting uniformly applies significance and boundary principles, adjusting reporting boundaries to provide comprehensive and comparable significance topic reporting.

[0437] Functionality:

[0438] Stakeholder-focused Reporting: The MBAA is able to determine significant topics based on stakeholder concerns and determine their priority, ensuring that the reporting addresses the most relevant and impactful issues for stakeholders.

[0439] Risk-aligned Reporting: The MBAA facilitates alignment of reporting information with the organization's risk management strategies and priorities by considering business impacts and internal risk assessments.

[0440] Consistent Reporting: The MBAA ensures consistent application of significance and boundary principles across financial and non-financial reporting, enhancing the comparability and decision usefulness of reporting information for stakeholders.

[0441] Automated Significance Assessment: The MBAA's machine learning capabilities enable automated identification and prioritization of significant topics, reducing manual effort and increasing efficiency in the significance assessment process.

[0442] Application Layer

[0443] The MBAA includes the following key components:

[0444] Data Ingester: This component is responsible for acquiring and preprocessing data from various sources such as stakeholder surveys, social media, industry reports, and internal risk assessments.

[0445] Topic Modeler: This component employs modeling algorithms to identify potential significant topics from ingested data.

[0446] FeatureExtractor: This component extracts relevant features from the data, such as stakeholder sentiment scores, risk impact assessments, and industry trends, to be used as input for machine learning models.

[0447] MaterialityScorer: This component applies supervised or unsupervised machine learning algorithms to score and prioritize the identified topics based on their materiality.

[0448] BoundaryAligner: This component maps the prioritized materiality topics to existing reporting boundaries, identifies gaps or inconsistencies, and suggests boundary adjustments to ensure consistent coverage of materiality topics across reports.

[0449] The M B A discloses the following application services and interfaces:

[0450] Services:

[0451] DataIngestionService: Provides methods for ingesting and pre-processing data from various sources for use as input in the materiality assessment and prioritization process.

[0452] TopicModelingService: Provides a service for identifying potential materiality topics from ingested data using topic modeling algorithms.

[0453] FeatureExtractionService: This service enables the extraction of relevant features from data, such as stakeholder sentiment scores, risk impact assessments, and industry trends, to be used as input for machine learning models.

[0454] MaterialityScoringService: Using supervised or unsupervised machine learning algorithms, this service facilitates scoring and prioritization of identified topics based on their materiality.

[0455] BoundaryAlignmentService: This service maps prioritized materiality topics to existing reporting boundaries, identifies gaps or inconsistencies, and suggests boundary alignment to ensure consistent coverage across reports.

[0456] Interfaces:

[0457] DataIngesterInterface: This interface defines methods and parameters for ingesting and pre-processing data from various sources, such as stakeholder surveys, social media, industry reports, and internal risk assessments.

[0458] TopicModelerInterface: Specifies input and output formats for using topic modeling algorithms to identify potentially material topics from ingested data.

[0459] FeatureExtractorInterface: Describes methods and output formats for extracting relevant features from data used as input for machine learning models.

[0460] MaterialityScorerInterface provides methods and parameters for scoring and prioritizing identified topics based on their materiality using supervised or unsupervised machine learning algorithms.

[0461] BoundaryAlignerInterface: This interface defines methods and output formats for mapping prioritized materiality topics to existing reporting boundaries, identifying gaps or inconsistencies, and suggesting boundary alignments to ensure consistent coverage of materiality topics in reports.

[0462] Assurance Alignment Agent (AAA):

[0463] Method

[0464] The AAA coordinates with internal and external assurance providers to ensure consistent and reliable assurance opinions on financial and non-financial reports in accordance with ISO 5116-3:20 21 standard clause 10, thereby enhancing the credibility and trustworthiness of reported information.

[0465] Assurance Provider Coordination:

[0466] The AAA facilitates coordination and collaboration between internal and external assurance providers, ensuring consistent application of assurance standards and methodologies across financial and non-financial reporting domains. The assurance provider coordination process involves the following steps:

[0467] Assurance Provider Identification: The AAA identifies and maintains a register of internal and external assurance providers, including their areas of expertise, methodologies, and certifications.

[0468] Assurance Scope Alignment: The AAA collaborates with assurance providers to align the scope and objectives of assurance engagements, ensuring comprehensive coverage of materiality topics and reporting boundaries.

[0469] Method Coordination: The AAA collaborates with assurance providers to coordinate assurance methodologies, sampling techniques, and evidence collection procedures, promoting consistency in assurance practices.

[0470] Assurance Resource Allocation: The AAA optimizes the allocation of assurance resources based on risk assessments, materiality considerations, and stakeholder expectations, ensuring efficient and effective assurance processes.

[0471] Timeline synchronization: AAA coordinates and synchronizes the assurance timelines across different reporting domains, enabling timely and integrated assurance opinions.

[0472] The time complexity of the assurance provider coordination process depends on the number of assurance providers involved, the complexity of assurance agreements, and the extent of coordination required. The spatial complexity is determined by the size of the assurance provider registry and associated metadata.

[0473] Assurance investigation results communication:

[0474] The AAA communicates assurance investigation results and recommendations to the Reporting Integration Agent (RIA) for transparent disclosure as required by clause 10.3. The assurance investigation results communication process includes the following steps:

[0475] Assurance report consolidation: AAA consolidates assurance reports and opinions from different assurance providers, ensuring consistency in reporting format and categorization.

[0476] Results analysis: AAA analyzes assurance investigation results to identify areas of concern, opportunities for improvement, and potential discrepancies in reporting areas.

[0477] Recommendation formulation: AAA formulates recommendations based on investigation results analysis for improving the reliability, transparency, and credibility of reporting information.

[0478] Communication with RIA: AAA communicates comprehensive assurance investigation results and recommendations to the RIA, ensuring transparent disclosure and integration into the final reporting output.

[0479] Stakeholder engagement: AAA facilitates stakeholder engagement and communication on assurance investigation results, addressing concerns, and providing additional context or clarification.

[0480] The time complexity of the assurance investigation results communication process depends on the number of assurance reports, the complexity of investigation results analysis, and the extent of stakeholder engagement required. The size of assurance reports and associated metadata determines the spatial complexity. AAA enhances the credibility and trustworthiness of reporting information by coordinating with assurance providers and communicating assurance investigation results and recommendations, aligning with the principles outlined in clause 10 of ISO 5116-3:20 21 standard.

[0481] Business layer:

[0482] Process:

[0483] Assurance provider management: This process involves identifying, registering, and coordinating internal and external assurance providers, ensuring alignment of their assurance objectives, methods, and timelines.

[0484] Assurance Business Planning: This process focuses on defining the scope and objectives of assurance engagements, allocating assurance resources based on risk assessment and materiality considerations, and synchronizing assurance schedules across reporting domains.

[0485] Assurance Investigation Results Integration: This process involves integrating assurance reports and opinions from different assurance providers, analyzing findings, developing recommendations, and communicating them to the Reporting Integration Agent (RIA) for transparent disclosure.

[0486] Functionality:

[0487] Consistent Assurance Practices: AAA ensures consistent application of assurance standards and methodologies across financial and non-financial reporting domains, promoting comparability and reliability of assurance opinions.

[0488] Efficient Assurance Resource Allocation: By optimizing the allocation of assurance resources based on risk assessment and materiality considerations, AAA helps improve the efficiency and cost-effectiveness of the assurance process.

[0489] Transparent Assurance Disclosure: AAA promotes transparent disclosure of assurance investigation results and recommendations, enhancing stakeholders' trust and confidence in reported information.

[0490] Collaborative Assurance: AAA fosters collaboration and coordination among internal and external assurance providers, cultivating an integrated and consistent assurance practice culture within the organization.

[0491] Application Layer

[0492] AAA comprises the following key components:

[0493] Assurance Provider Registry: This component maintains a registry of internal and external assurance providers, including their areas of expertise, methodologies, and certifications.

[0494] Assurance Engagement Planner: This component defines the scope and objectives of assurance engagements, allocates assurance resources based on risk assessment and materiality considerations, and synchronizes assurance schedules across reporting domains.

[0495] Assurance Report Consolidator: This component consolidates assurance reports and opinions from different assurance providers, ensuring consistency in reporting formats and classifications.

[0496] Findings Analyzer: This component analyzes consolidated assurance investigation results, identifying areas of focus, opportunities for improvement, and potential discrepancies across reporting domains.

[0497] RecommendationGenerator: This component formulates recommendations based on the findings analysis to improve the reliability, transparency, and credibility of the report information.

[0498] Services:

[0499] AssuranceProviderRegistryService: Provides a registry for managing internal and external assurance providers, including registration, update, and retrieval functions.

[0500] AssuranceEngagementPlanningService: Provides services for defining the scope and objectives of assurance engagements, allocating assurance resources based on risk assessment and importance considerations, and synchronizing assurance timelines across reporting domains.

[0501] AssuranceReportConsolidationService: Allows consolidation of assurance reports and opinions from various assurance providers, ensuring consistency in report format and categorization.

[0502] FindingsAnalysisServices: Facilitates analysis of consolidated assurance findings, identifying areas of concern, opportunities for improvement, and potential discrepancies in reporting domains.

[0503] RecommendationGenerationService: Provides services for formulating recommendations to improve the reliability, transparency, and credibility of the report information based on findings analysis.

[0504] Interfaces:

[0505] AssuranceProviderRegistryInterface: This interface defines methods and parameters for managing the registration of internal and external assurance providers, including registration, update, and retrieval functions.

[0506] AssuranceEngagementPlannerInterface: This interface specifies input and output formats for defining the scope and objectives of assurance engagements, allocating assurance resources based on risk assessment and importance considerations, and synchronizing assurance timelines across reporting domains.

[0507] AssuranceReportConsolidatorInterface: Describes methods and input / output formats for consolidating assurance reports and opinions from different assurance providers, ensuring consistency in report format and categorization.

[0508] FindingsAnalyzerInterface: Provides methods and parameters for analyzing consolidated assurance findings, identifying areas of concern, improvement opportunities, and potential discrepancies across reporting domains.

[0509] RecommendationGeneratorInterface: Defines methods and output formats for generating recommendations to enhance the reliability, transparency, and credibility of reporting information based on findings analysis.

[0510] Reporting Integration Agent (RIA):

[0511] Method

[0512] The RIA follows the principles of connectivity, consistency, and accessibility outlined in clause 11.2 of the ISO 5116-3:20 21 standard, integrating aligned financial and non-financial information into comprehensive reports.

[0513] Information Integration and Connectivity:

[0514] The RIA employs advanced data integration and visualization techniques to combine information from various sources, ensuring connectivity and traceability across financial and non-financial domains. The information integration and connectivity process involves the following steps:

[0515] Data Ingestion: The RIA ingests aligned data from the Data Harmonization Agent (DHA), Key Performance Indicator Alignment Agent (KPIAA), Narrative Information Alignment Agent (NIAA), and Importance and Boundary Alignment Agent (MBAA).

[0516] Data Mapping and Transformation: The RIA maps aligned data to a standard data model, ensuring consistent representation and enabling cross-domain connections and traceability.

[0517] Data Linking and Relationship Modeling: The RIA establishes relationships and links between relevant data elements in financial and non-financial domains, facilitating comprehensive reporting and analysis.

[0518] Data Visualization and Storytelling: The RIA employs data visualization techniques and narrative storytelling methods to present comprehensive information coherently and meaningfully, highlighting cross-domain connections and interdependencies.

[0519] Report Generation: The RIA generates comprehensive reports that seamlessly integrate financial and non-financial information, enabling stakeholders to gain a holistic understanding of an organization's performance, risks, and opportunities.

[0520] The time complexity of the information integration and connectivity process depends on the volume and complexity of the aligned data and the complexity of the data mapping, transformation, and visualization techniques employed. The spatial complexity is determined by the size of the aligned data and the intermediate data structures required for processing.

[0521] Report formatting and accessibility:

[0522] The RIA generates comprehensive reports in various formats as specified in Clause 11.3 to cater to the diverse needs and preferences of stakeholders. The report formatting and accessibility process involves the following steps:

[0523] Stakeholder needs analysis: The RIA analyzes the specific reporting needs and preferences of different stakeholder groups, including preferred formats, accessibility requirements, and distribution channels.

[0524] Report template design: The RIA designs report templates that adhere to industry standards and best practices, ensuring uniform and accessible presentation of integrated information.

[0525] Content adaptation: The RIA adapts integrated information into specific report formats, optimizing content layout, typography, and visual elements for each format.

[0526] Accessibility enhancements: The RIA incorporates accessibility features such as alternative text descriptions, screen reader compatibility, and color contrast adjustments to ensure equal access to information for all stakeholders.

[0527] Report distribution: The RIA distributes generated reports through various channels such as print, digital platforms, or interactive web-based interfaces to cater to the preferences of stakeholders and ensure widespread dissemination of integrated information.

[0528] The time complexity of the report formatting and accessibility process depends on the number of required report formats, the complexity of content adaptation, and the extent of accessibility enhancements needed. The spatial complexity is determined by the size of the report templates and the generated reports. By integrating adjusted information and generating comprehensive reports in various accessible formats, the RIA facilitates connectivity, consistency, and accessibility in the presentation and communication of financial and non-financial information, aligning with the principles outlined in Clause 11 of the ISO 5116-3:20 21 standard.

[0529] Business layer:

[0530] Process:

[0531] Aligned information ingestion: This process involves ingesting aligned financial and non-financial information from various sources, including DHA, KPIAA, NIAA, and MBAA.

[0532] Information Integration and Connectivity: This process focuses on integrating aligned information, establishing cross-domain connections and traceability, and generating comprehensive reports that seamlessly combine financial and non-financial information.

[0533] Report Formatting and Distribution: This process involves formatting comprehensive reports in various available formats to meet the needs and preferences of different stakeholders and ensure widespread distribution through appropriate distribution channels.

[0534] Functionality:

[0535] Overall Reporting: RIA is capable of generating comprehensive reports that integrate financial and non-financial information, providing stakeholders with a holistic understanding of the organization's performance, risks, and opportunities.

[0536] Cross-Domain Connectivity: RIA facilitates a more comprehensive understanding of the organization's overall performance by establishing connectivity and traceability across financial and non-financial domains, thereby promoting integrated analysis and decision-making.

[0537] Stakeholder-Centric Reporting: RIA ensures equal access to information and promotes greater transparency and accountability by generating reports in various accessible formats, catering to the needs and preferences of different stakeholders.

[0538] Efficient Report Generation: RIA simplifies the report generation process by automating data integration, formatting, and distribution tasks, reducing manual effort, and improving the efficiency of the reporting process.

[0539] Application Layer:

[0540] RIA comprises the following key components:

[0541] DataIntegrator: This component ingests aligned data from various sources and performs data mapping, transformation, and linking to establish cross-domain connections and traceability.

[0542] ReportGenerator: This component generates comprehensive reports by integrating financial and non-financial information, utilizing data visualization and narrative storytelling techniques.

[0543] FormatAdapter: This component adapts integrated information to specific report formats, optimizing content layout, typography, and visual elements for each format.

[0544] AccessibilityEnhancer: This component includes accessibility features such as alternative text descriptions, screen reader compatibility, and color contrast adjustments to ensure equal access to information for all stakeholders.

[0545] ReportDistributer: This component manages the distribution of generated reports through various channels, such as printing, digital platforms, or web-based interactive interfaces, to cater to the preferences of stakeholders.

[0546] Services:

[0547] DataIntegrationService: Provides methods for ingesting aligned data from various sources, performing data mapping, transformation, and linking to establish cross-domain connections and traceability.

[0548] ReportGenerationService: Offers services for generating comprehensive reports by combining integrated financial and non-financial information, utilizing data visualization and narrative storytelling techniques.

[0549] FormatAdaptationService: This service enables integrated information to adapt to specific report formats, optimizing content layout, typography, and visual elements for each format.

[0550] AccessibilityEnhancementService: Facilitates the incorporation of accessibility features, such as alternative text descriptions, screen reader compatibility, and color contrast adjustments, to ensure equal access to information for all stakeholders.

[0551] ReportDistributionService: Provides services for managing the distribution of generated reports through various channels, such as printing, digital platforms, or web-based interactive interfaces, to cater to the preferences of stakeholders.

[0552] Interfaces:

[0553] DataIntegratorInterface: Defines methods and parameters for ingesting aligned data from various sources, performing data mapping, transformation, and linking to establish cross-domain connections and traceability.

[0554] ReportGeneratorInterface: Specifies input and output formats for generating comprehensive reports by combining integrated financial and non-financial information, utilizing data visualization and narrative storytelling techniques.

[0555] FormatAdapterInterface: Describes methods and input / output formats for adapting integrated information to specific report formats, optimizing content layout, typography, and visual elements for each format.

[0556] AccessibilityEnhancerInterface: Provides methods and parameters for incorporating accessibility features such as alternative text descriptions, screen reader compatibility, and color contrast adjustments to ensure equal access to information for all stakeholders.

[0557] ReportDistributorInterface: This interface defines methods and parameters for managing the distribution of generated reports through various channels such as printing, digital platforms, or interactive web-based interfaces to cater to the preferences of stakeholders.

[0558] ALFINI Petri net agent

[0559] Method

[0560] The ALFINI Petri net agent coordinates the orchestration and execution of various ALFINI sub-agents, ensuring seamless and effective alignment of financial and non-financial reporting processes through the principles outlined in clause 4 of ISO 5116-3:20 21 standard.

[0561] Petri net modeling and execution:

[0562] The ALFINI Petri net agent employs Petri net modeling techniques to define the interactions, dependencies, and execution sequences of ALFINI sub-agents. The Petri net modeling and execution process involves the following steps:

[0563] Agent interaction modeling: The ALFINI Petri net agent models the interactions and dependencies between ALFINI sub-agents (such as DHA, RSKBA, KPIAA, NIAA, MBAA, AAA, and RIA) using Petri net constructs (such as places, transitions, and arcs).

[0564] Execution sequence definition: The ALFINI Petri net agent defines the execution sequences and control flows of ALFINI sub-agents, ensuring that each agent is triggered at the appropriate time with the necessary inputs and outputs, adhering to the principles outlined in clause 4 of ISO 5116-3:20 21 standard.

[0565] Petri net verification: The ALFINI Petri net agent verifies the constructed Petri net model to ensure its correctness, completeness, and adherence to relevant Petri net standards, such as ISO / IEC 15909-1:20 04 (Petri net markup language) and ISO / IEC 15909-2:20 11 (Transport format for Petri nets).

[0566] Petri net execution and monitoring: ALFINI Petri net agents execute Petri net models, monitor the progress of ALFINI sub-agents, and ensure that aligned and integrated tasks are performed in the proper order and necessary coordination.

[0567] Dynamic adaptation: ALFINI Petri net agents support dynamic adaptation of Petri net models, allowing adjustments and modifications of execution sequences and agent interactions based on feedback, monitoring data, or changes in reporting requirements.

[0568] The time complexity of the Petri net modeling and execution process depends on the complexity of the Petri net model, the number of ALFINI sub-agents involved, and the amount of data and interactions between sub-agents. The space complexity is determined by the size of the Petri net model and the related data structures required for execution and monitoring.

[0569] Petri net visualization and analysis:

[0570] ALFINI Petri net agents provide visualization and analysis capabilities to monitor, troubleshoot, and optimize the coordination and execution of ALFINI sub-agents. The Petri net visualization and analysis process involves the following steps:

[0571] Petri net rendering: ALFINI Petri net agents render a visual representation of the Petri net model, including places, transitions, arcs, and token markings, adhering to Petri net visualization standards such as ISO / IEC 15909-4:2017 (Graphical Representation of Petri Nets).

[0572] Execution tracing: ALFINI Petri net agents trace the execution of the Petri net model, highlighting active transitions, token movements, and sub-agent interactions. This enables stakeholders to monitor the progress of aligned and integrated tasks.

[0573] Performance analysis: ALFINI Petri net agents analyze the performance of Petri net execution, collecting metrics such as execution time, resource utilization, and bottlenecks, and providing insights for optimization and improvement.

[0574] Deadlock and liveness analysis: ALFINI Petri net agents perform deadlock and liveness analysis on the Petri net model, identifying potential deadlocks, livelocks, or other execution issues that may hinder the completion of aligned and integrated tasks.

[0575] Reporting and visualization: ALFINI Petri net agents generate reports and visualizations summarizing execution status, performance metrics, and analysis results, enabling stakeholders to effectively monitor and optimize the coordination and execution of ALFINI sub-agents.

[0576] The time complexity of the Petri net visualization and analysis process depends on the size of the Petri net model, the complexity of the rendering and analysis algorithms employed, and the volume of execution data to be processed. The size of the Petri net model determines the spatial complexity, related execution data, and intermediate data structures required for rendering and analysis. By employing Petri net modeling and execution techniques, the ALFINI Petri net Agent ensures the coordinated and efficient execution of ALFINI sub-agents, adhering to the principles outlined in clause 4 of ISO 5116-3:20 21 standard and following the relevant Petri net ISO standards.

[0577] Business Layer:

[0578] Process:

[0579] ALFINI Sub-Agent Coordination: This process involves modeling the interactions and dependencies between ALFINI sub-agents, defining their execution sequences, and orchestrating their coordinated execution to adjust financial and non-financial reporting processes.

[0580] Execution Monitoring and Analysis: This process focuses on monitoring the execution of the Petri net model, tracking the progress of ALFINI sub-agents, and analyzing performance metrics, deadlocks, and liveliness to identify optimization opportunities and potential issues.

[0581] Reporting and Visualization: This process involves generating reports and visualizations that summarize the execution status, performance metrics, and analysis results, enabling stakeholders to effectively monitor and optimize ALFINI sub-agent coordination and execution.

[0582] Functionality:

[0583] Efficient Alignment and Integration: The ALFINI Petri net Agent ensures the efficient and coordinated execution of ALFINI sub-agents, resulting in effective alignment and integration of financial and non-financial reporting processes.

[0584] Compliance with ISO Standards: By adhering to relevant Petri net ISO standards, the ALFINI Petri net Agent promotes compliance with industry best practices and ensures interoperability with other Petri net-based systems and tools.

[0585] Transparency and Monitoring: The ALFINI Petri net Agent provides transparency into the execution of ALFINI sub-agents, enabling stakeholders to monitor progress, identify bottlenecks, and optimize adjustment and integration processes.

[0586] Adaptability and Scalability: ALFINI Petri Net Agents support dynamic adaptation of Petri Net models, allowing adjustments and modifications to accommodate changes in reporting requirements or the introduction of new ALFINI sub-agents.

[0587] Application Layer:

[0588] ALFINI Petri Net Agents include the following key components:

[0589] PetriNetModeler: This component is responsible for modeling the interactions and dependencies between ALFINI sub-agents using Petri Net constructs, adhering to relevant Petri Net ISO standards.

[0590] PetriNetExecutor: This component executes the Petri Net models, orchestrating the coordinated execution of ALFINI sub-agents and monitoring their progress and interactions.

[0591] PetriNetAnalyzer: This component performs various analyses on the Petri Net models and their executions, including performance analysis, deadlock and liveliness analysis, and identification of optimization opportunities.

[0592] PetriNetVisualizer: This component presents visual representations of the Petri Net models and their executions, adhering to Petri Net visualization standards, and enables stakeholders to monitor the progress of aligned and integrated tasks.

[0593] ReportingEngine: This component generates reports and visualizations summarizing the execution status, performance metrics, and analysis results, enabling stakeholders to effectively monitor and optimize the coordination and execution of ALFINI sub-agents.

[0594] Services:

[0595] PetriNetModelingService: Provides methods for modeling the interactions and dependencies between ALFINI sub-agents using Petri Net constructs, adhering to relevant Petri Net ISO standards.

[0596] PetrineTexCutionService: Provides services for executing Petri Net models, orchestrating the coordinated execution of ALFINI sub-agents, and monitoring their progress and interactions.

[0597] PetriNetAnalysisService: Implements various analyses on the Petri Net models and their executions, including performance analysis, deadlock and liveliness analysis, and identification of optimization opportunities.

[0598] PetriNetVisualizationService: Facilitates the rendering of visual representations of Petri net models and their execution, adhering to Petri net visualization standards, and enables stakeholders to monitor the progress of alignment and integration tasks.

[0599] ReportingService: Provides services for generating reports and visualizations summarizing execution status, performance metrics, and analysis results, enabling stakeholders to effectively monitor and optimize ALFINI sub-agent coordination and execution.

[0600] Interface:

[0601] PetriNetModelerInterface: This interface defines methods and parameters for modeling interactions and dependencies between ALFINI sub-agents using Petri net constructs, adhering to relevant Petri net ISO standards.

[0602] PetriNetExecutorInterface: Specifies methods and parameters for executing Petri net models, orchestrating the coordinated execution of ALFINI sub-agents, and monitoring their progress and interactions.

[0603] PetriNetAnalyzerInterface: Describes methods and parameters for various analyses of Petri net models and their execution, including performance analysis, deadlock and liveliness analysis, and identification of optimization opportunities.

[0604] PetriNetVisualizerInterface: Provides methods and parameters for rendering visual representations of Petri net models and their execution, adhering to Petri net visualization standards, and enabling stakeholders to monitor the progress of alignment and integration tasks.

[0605] Reporting Interface: This interface defines methods and parameters for generating reports and visualizations that summarize execution status, performance metrics, and analysis results. It enables stakeholders to effectively monitor and optimize ALFINI sub-agent coordination and execution.

[0606] MEGAN

[0607] MEGAN (Metadata Extraction, Generation, and Alignment Network) is an advanced agent-based module of GenFoundary that automatically generates ISO 11179 and UN / CEFACT compliant metadata registries using the CoLLEGe framework to abstract DataVault2.0 schemas and perform cross-taxonomy mapping and translation based on varying data requirements. At its core, MEGAN employs a synergistic integration of specialized agents collaborating through continuous optimization loops supervised by Petri net choreography models.

[0608] Key advantages of MEGAN:

[0609] Automatic metadata generation: Utilizes generative AI models and instant engineering techniques to automatically generate rich, context-aware, and standards-compliant metadata from varying data requirements.

[0610] DataVault2.0 schema abstraction: Employs the CoLLEGe framework to abstract key business concepts and relationships from ISO 11179 metadata, enabling the automatic generation of DataVault2.0 schemas.

[0611] Cross-taxonomy mapping and translation: Involves the use of AI-driven techniques to automatically map and translate metadata elements across different reporting frameworks, jurisdictions, and data standards.

[0612] Continuous learning and adaptation: Incorporates user feedback, new data sources, and evolving requirements to improve the accuracy and relevance of generated metadata and DataVault2.0 schemas over time.

[0613] Agents

[0614] The MEGAN agent architecture comprises several sub-agents, each with specific roles and responsibilities, working together to ensure the automatic generation and adaptation of metadata registries and DataVault2.0 schemas:

[0615] Data Requirement Ingestion Agent (DRIA): This agent ingests data requirements from various sources and formats, pre-processes and normalizes the data to ensure compliance with ISO 11179 and ISO 20022 principles.

[0616] Metadata Extraction and Normalization Agent (MENA): This agent employs advanced NLP techniques to extract relevant metadata elements from ingested data and normalizes them into a consistent format compliant with ISO 11179 and ISO 20022.

[0617] DataVault 2.0 Schema Abstraction Agent (DSAA): This agent applies the CoLLEGe framework to abstract key business concepts and relationships from the generated metadata, producing DataVault 2.0 schemas that conform to ISO 11179 and ISO 20022.

[0618] Cross-Taxonomy Mapping and Alignment Agent (CTMAA): Utilizes AI and NLP techniques to automatically map and translate metadata elements across different reporting frameworks, jurisdictions, and data standards.

[0619] Vector Database Agent (VDA): Stores and indexes the generated metadata, DataVault 2.0 schemas, and cross-taxonomy mappings in high-performance vector databases, enabling efficient similarity-based search and knowledge discovery.

[0620] Data Lineage and Provenance Tracking Agent (DLPTA): Captures and maintains a complete audit trail of the metadata management, schema generation, and mapping processes, ensuring end-to-end traceability and compliance.

[0621] Ontology-Based Reasoning Agent (OBRA): This agent utilizes domain knowledge represented in ontologies to infer relationships and align concepts across different taxonomies, enabling more accurate and context-aware metadata mapping and integration.

[0622] Sub-agents are orchestrated using Petri net models to ensure coordinated and efficient execution of metadata generation, schema abstraction, and cross-taxonomy mapping tasks. Petri nets model dependencies and interactions between sub-agents, ensuring each task is executed in the proper order with the necessary inputs and outputs.

[0623] Evaluation and Validation

[0624] Ground truth data and evaluation metrics are carefully tuned to align with the specific functionality and goals of each MEGAN component:

[0625] Data Requirements Ingestion Agent (DRIA): The performance of DRIA is evaluated using ground truth data from the constructed regulatory schemas, data dictionaries, and expert-validated data requirements. Metrics such as ingestion accuracy, standardization consistency, and ISO 11179 / ISO 20022 conformance are employed to assess the effectiveness of DRIA in pre-processing and normalizing diverse data requirements.

[0626] Metadata Extraction and Normalization Agent (MENA): The evaluation of MENA relies on ground truth data from manually annotated metadata, ISO 11179 / ISO 20022 metadata registry, and expert validated extracted real data. Metrics such as extraction accuracy, normalization consistency, and ISO compliance are used to measure the ability of MENA to extract and normalize relevant metadata elements.

[0627] DataVault 2.0 Schema Abstraction Agent (DSAA): The performance of DSAA is evaluated using ground truth data from manually designed DataVault 2.0 schema, CoLLEGe framework guidelines, and expert validated abstracted real data. Metrics such as schema correctness, business concept consistency, and ISO compatibility are employed to evaluate the effectiveness of DSAA in abstracting DataVault 2.0 schema from ISO 11179 metadata.

[0628] Cross Taxonomy Mapping and Alignment Agent (CTMAA): CTMAA is evaluated using ground truth data from manually mapped metadata elements, cross-jurisdictional reporting standards, and expert validated aligned real data. Metrics such as mapping accuracy, translation consistency, and interoperability are used to evaluate the ability of CTMAA to automatically map and translate metadata elements across different taxonomies and frameworks.

[0629] Vector Database Agent (VDA): The performance of VDA is evaluated using real data from established metadata repositories, schema catalogs, and expert curated knowledge bases. Metrics such as indexing efficiency, search relevance, and knowledge discovery accuracy are employed to measure the effectiveness of VDA in storing, indexing, and retrieving metadata, schema, and mappings.

[0630] Ontology-Based Reasoning Agent (OBRA): The performance of OBRA is evaluated using real data from established domain ontologies, expert curated concept alignments, and manually validated inferred real data. Metrics such as reasoning accuracy, concept alignment precision, and inferred completeness are employed to evaluate the effectiveness of OBRA in utilizing ontologies to infer relationships and align concepts across taxonomies.

[0631] Data Lineage and Provenance Tracking Agent (DLPTA): The evaluation of DLPTA relies on ground truth data from manually maintained audit trails, regulatory compliance requirements, and expert validated provenance records. Metrics such as lineage completeness, source accuracy, and compliance adherence are used to evaluate the ability of DLPTA to capture and maintain comprehensive and reliable audit trails of metadata management processes.

[0632] Figure 3 is a functional block diagram 300 illustrating an example of a metadata management system within a multi-agent artificial intelligence framework according to embodiments herein. The metadata management system corresponds to an embodiment of the MEGAN agent architecture.

[0633] DRIA (Data Demand Ingestion Agent) 304:

[0634] DRIA is an ingestion module that ingests and pre-processes different data requirements into ISO 11179 / 20022 format. DRIA can directly interface with external data sources to automatically retrieve data inputs, ensuring that metadata extraction starts from a standardized baseline. DRIA can serve MENA 306 by providing pre-processed ISO compliant data, ensuring that metadata extraction starts from a standardized baseline. DRIA is associated with external data sources that receive raw data inputs that are critical for initial data processing steps.

[0635] MENA (Metadata Extraction and Normalization Agent) 306:

[0636] MENA is a metadata extraction and normalization agent that employs natural language processing (NLP) to extract, standardize, and enrich metadata from the output provided by DRIA 304.

[0637] MENA can serve DSAA 308 by providing extracted and normalized metadata that is essential for subsequent abstraction into DataVault2.0 schema.

[0638] MENA, in combination with the output of DRIA and external knowledge sources, ensures a comprehensive approach to metadata processing and augmentation.

[0639] DSAA (DataVault 2.0 Schema Abstraction Agent) 308:

[0640] DSAA is an abstraction module that abstracts ISO 11179 / 20022 metadata from MENA into DataVault2.0 schema, creating structured data models suitable for different applications.

[0641] DSAA serves CTMAA 312 by providing abstracted database schema, which is used for further cross-taxonomy mapping.

[0642] Maintaining close association with MENA 306, DSAA receives metadata inputs that are critical for schema abstraction.

[0643] CTMAA (Cross Taxonomy Mapping and Alignment Agent) 312:

[0644] CTMAA is a mapping module that utilizes advanced AI techniques to map and translate metadata across different taxonomies and standards.

[0645] CTMAA serves VDA 314 by providing cross-taxonomy mappings, which are essential for enhancing the semantic search capabilities of databases.

[0646] CTMAA is associated with the schema and external ontologies of DSAA (308), integrating these resources to perform accurate and efficient classification mapping.

[0647] VDA (Vector Database Agent) 314:

[0648] VDA is a storage module that stores, indexes, and allows semantic search of metadata received from CTMAA 312 for efficient data retrieval and utilization.

[0649] VDA maintains association with CTMAA 312, receiving and managing mapping metadata for enhanced search functionality.

[0650] DLPTA (Data Lineage and Provenance Tracking Agent) 310:

[0651] Audit trails track capture data provenance and trace all processes within MEGAN.

[0652] Integrates and liaises with other MEGAN agents, ensuring comprehensive tracking and recording of data flow and transformations, which is critical for audit and compliance purposes.

[0653] Figure 3 The relationships outlined in the diagram are summarized as follows:

[0654] DRIA 304 acts as an entry point for raw data, processing it into a form suitable for MENA 306, which then extracts and normalizes metadata. MENA 306 passes this refined metadata to DSAA 308, which abstracts it into the DataAvault2.0 schema, which is further mapped by CTMAA 312. VDA 314 utilizes the output of CTMAA 312 for storage and indexing, ensuring that metadata is easily accessible and searchable. DLPTA 310 covers the process by tracking data provenance and associations for all agents, enhancing the system's integrity and traceability.

[0655] DRIA (Data Demand Ingestion Agent):

[0656] The Data Requirements Ingestion Agent (DRIA) is a highly advanced component of MEGAN that leverages the neural capabilities of Large Language Models (LLMs) such as Claude-3 to automate the ingestion, preprocessing, normalization, and validation of data requirements from various sources and formats. By utilizing the capabilities of LLMs, DRIA can understand and process complex data structures such as ISO 11179-compliant metadata registries, data dictionaries, conceptual data models, and ISO 20022 financial message schemas and business process definitions.

[0657] DRIA employs the natural language understanding and generation capabilities of LLM to preprocess and normalize ingested data, ensuring compliance with ISO 11179 and ISO 20022 principles and structures. This involves extracting relevant metadata elements such as data element concepts, data elements, value domains, classification schemes, message components, data types, and business rules as defined by these international standards.

[0658] Additionally, DRIA utilizes the inferential capabilities of LLM to perform syntactic and semantic validation of ingested data against ISO 11179 and ISO 20022 specifications. This ensures the quality and consistency of data requirements, identifying and flagging any discrepancies or non-compliant elements.

[0659] Once data requirements are ingested, preprocessed, normalized, and validated, DRIA sends the ISO 11179 and ISO 20022-aligned data to the metadata extraction and normalization agent for further processing. The use of LLM enables DRIA to handle the complexity and variability of data requirements from different sources and domains, adapting to new formats and structures as needed.

[0660] By leveraging the neural capabilities of LLM, DRIA achieves a high level of automation, accuracy, and efficiency in managing data requirements. It reduces manual effort and ensures the quality and interoperability of metadata across MEGAN systems. This ultimately contributes to the overall effectiveness of systems in generating ISO 11179 and UN / CEFACT-compliant metadata registries, abstract DataVault2.0 schemas, and implementing cross-taxonomy mapping and translation.

[0661] DRIA Methodology:

[0662] DRIA employs advanced data ingestion, preprocessing, standardization, and validation techniques to ensure the quality, consistency, and compliance of data requirements with ISO 11179 and ISO 20022 standards.

[0663] Data Ingestion and Preprocessing:

[0664] DRIA ingests data requirements from various sources and formats, including ISO 11179-compliant metadata registries, data dictionaries, conceptual data models, and ISO 20022 financial message schemas and business process definitions. The ingestion process involves the following steps:

[0665] Source Identification: DRIA identifies and connects to relevant data sources containing data requirements, such as databases, file systems, or APIs.

[0666] Data Retrieval: DRIA retrieves data requirements from the identified sources using appropriate protocols and methods, such as SQL queries, file parsing, or API calls.

[0667] Format detection: DRIA uses intelligent format detection algorithms to automatically detect the format of ingested data, such as XML, JSON, CSV, or proprietary formats.

[0668] Data cleansing: DRIA applies data cleansing techniques to remove any irrelevant, inconsistent, or corrupted data from the ingested data requirements, ensuring a clean and reliable dataset for further processing.

[0669] The time complexity of the data ingestion and pre-processing process depends on the volume and variety of data sources and the complexity of the employed data cleansing and format detection algorithms. The size of the ingested data and any intermediate data structures used during pre-processing determine the space complexity.

[0670] Data normalization and alignment:

[0671] DRIA pre-processes and normalizes the ingested data to ensure compliance with ISO 11179 and ISO 20022 principles and structures. The normalization and alignment process involves the following steps:

[0672] Metadata element extraction: DRIA extracts relevant metadata elements from the ingested data requirements, such as data element concepts, data elements, value domains, classification schemes, message components, data types, and business rules, as defined by ISO 11179 and ISO 20022.

[0673] Syntax normalization: DRIA normalizes the extracted metadata elements to comply with the syntax rules and conventions prescribed by ISO 11179 and ISO 20022, such as naming conventions, data type representations, and cardinality constraints.

[0674] Semantic alignment: DRIA aligns the normalized metadata elements with the semantic concepts and relationships defined in the ISO 11179 and ISO 20022 meta-models, ensuring consistent and interoperable metadata representation across different data sources and domains.

[0675] Cross-referencing: DRIA establishes cross-references and mappings between related metadata elements, such as data elements and value domains, or message components and business processes, to facilitate data integration and traceability.

[0676] The time complexity of the data normalization and alignment process depends on the number and complexity of the metadata elements and the efficiency of the employed extraction, normalization, and alignment algorithms. The space complexity is determined by the size of the extracted metadata and any auxiliary data structures employed during the process.

[0677] Data validation and quality assurance:

[0678] To ensure data quality and consistency, DRIA performs syntax and semantic validation of ingested data against ISO 11179 and ISO 20022 specifications. The validation process includes the following steps:

[0679] Syntax validation: DRIA validates ingested data against syntax rules and constraints defined in ISO 11179 and ISO 20022, such as data type compatibility, mandatory field presence, and format compliance.

[0680] Semantic validation: DRIA validates ingested data against semantic rules and relationships specified in ISO 11179 and ISO 20022 meta-models, such as data element concept bindings, value domain consistency, and business rule adherence.

[0681] Quality checks: DRIA applies additional quality checks on ingested data, such as completeness, uniqueness, and referential integrity, to identify and flag any data quality issues or anomalies.

[0682] Error handling and reporting: DRIA handles validation and quality issues by generating detailed error reports, logging problems, and triggering appropriate error resolution workflows or notifying data administrators and risk takers.

[0683] The time complexity of the data validation and quality assurance process depends on the volume and complexity of ingested data and the number and complexity of applied validation rules and quality checks. The space complexity is determined by the size of ingested data and any error logs or reports generated during the process.

[0684] Business layer:

[0685] Process:

[0686] Data source identification and connection: This process involves identifying relevant data sources containing data requirements and establishing secure connections to retrieve data.

[0687] Data ingestion and preprocessing: This process focuses on ingesting data requirements from various sources and formats, detecting data formats, and applying data cleaning techniques to ensure a clean and reliable dataset for further processing.

[0688] Metadata extraction and normalization: This process involves extracting relevant metadata elements from ingested data requirements and normalizing them to comply with ISO 11179 and ISO 20022 syntax rules and practices.

[0689] Data alignment and cross-referencing: This process involves aligning normalized metadata elements with ISO 11179 and ISO 20022 semantic concepts and relationships and establishing cross-references and mappings between relevant metadata elements.

[0690] Data validation and quality assurance: This process focuses on performing syntactic and semantic validation on the ingested data in accordance with ISO 11179 and ISO 20022 standards, applying quality checks, and handling validation errors and quality issues.

[0691] Function:

[0692] Automated Data Ingestion: DRIA allows for the automatic ingestion of data requirements from different sources and formats, thereby reducing manual work and improving efficiency.

[0693] Data format detection and cleaning: DRIA automatically detects the format of the ingested data and applies data cleaning techniques to ensure data reliability and consistency.

[0694] Compliant with ISO 11179 and ISO 20022: DRIA ensures that the data requirements for ingestion are preprocessed, normalized, and consistent with the principles and structure of ISO 11179 and ISO 20022, thereby promoting data interoperability and standardization.

[0695] Metadata element extraction and mapping: DRIA extracts relevant metadata elements from the ingested data and establishes cross-references and mappings between relevant elements, thereby promoting data integration and traceability.

[0696] Data Quality and Validation: DRIA performs rigorous syntactic and semantic validation on the ingested data, applies quality checks, and handles validation errors and quality issues to ensure data accuracy and consistency.

[0697] Application layer

[0698] DRIA includes the following key components:

[0699] DataSourceConnector: This component identifies and establishes a secure connection to the relevant data source containing the data requirements.

[0700] DataIngester: This component focuses on ingesting data requirements from various sources and formats, detecting data formats, and applying data cleaning techniques to ensure data reliability.

[0701] MetadataExtractor: This component extracts relevant metadata elements from the ingested data requirements and normalizes them to conform to ISO 11179 and ISO 20022 syntax rules and practices.

[0702] DataAligner: This component aligns normalized metadata elements with the semantic concepts and relationships of ISO 11179 and ISO 20022, and establishes cross-references and mappings between related metadata elements.

[0703] DataValidator: This component performs syntax and semantic validation of ingested data according to ISO 11179 and ISO 20022 specifications, applies quality checks, and handles validation errors and quality issues.

[0704] Services:

[0705] DataSourceConnectionService: Provides methods for identifying and establishing secure connections with relevant data sources containing data requirements.

[0706] DataIngestionService: Provides services for ingesting data requirements from various sources and formats, detecting data formats, and applying data cleansing techniques.

[0707] MetadataExtractionService: This service enables the extraction of relevant metadata elements from ingested data requirements and their normalization to comply with ISO 11179 and ISO 20022 syntax rules and conventions.

[0708] DataAlignmentService: Facilitates the alignment of normalized metadata elements with ISO 11179 and ISO 20022 semantic concepts and relationships and the establishment of cross-references and mappings between relevant metadata elements.

[0709] DataValidationService: Provides services for performing syntax and semantic validation of ingested data according to ISO 11179 and ISO 20022 specifications, applying quality checks, and handling validation errors and quality issues.

[0710] Interfaces:

[0711] DataSourceConnectorInterface: Defines methods and parameters for identifying and establishing secure connections with relevant data sources containing data requirements.

[0712] DataIngesterInterface: Specifies methods and input / output formats for ingesting data requirements from various sources and formats, detecting data formats, and applying data cleansing techniques.

[0713] MetadataExtractorInterface: Describes methods and input / output formats for extracting relevant metadata elements from ingested data requirements and normalizing them to comply with ISO 11179 and ISO 20022 syntax rules and practices.

[0714] DataAlignerInterface: This interface defines methods and parameters for aligning normalized metadata elements with ISO 11179 and ISO 20022 semantic concepts and relationships, and establishing cross-references and mappings between related metadata elements.

[0715] DataValidatorInterface: Specifies methods and input / output formats for performing syntax and semantic validation of ingested data against ISO 11179 and ISO 20022 specifications, applying quality checks, and handling validation errors and quality issues.

[0716] Metadata Extraction and Normalization Agent (MENA):

[0717] The Metadata Extraction and Normalization Agent (MENA) is a highly advanced component of MEGAN that leverages the neural capabilities of large language models (LLMs) similar to Claude-3 to automate the extraction, normalization, and semantic enrichment of metadata from data requirements ingested by the Data Requirements Ingestion Agent (DRIA). By utilizing the power of LLMs, MENA can understand and process complex metadata structures, such as those defined by ISO 11179 and ISO 20022 standards.

[0718] MENA employs the natural language understanding and generation capabilities of LLMs to extract relevant metadata elements, such as data element concepts, data elements, value domains, classification schemes, message components, data types, and business rules, from pre-processed and normalized data requirements. It then applies semantic enrichment techniques, such as entity linking, synonym identification, and context analysis, to augment the extracted metadata with additional semantic information and relationships.

[0719] Furthermore, MENA utilizes the reasoning capabilities of LLMs to perform semantic validation and consistency checks on the extracted and enriched metadata, ensuring compliance with ISO 11179 and ISO 20022 meta-models and ontologies. This involves identifying and resolving semantic conflicts, ambiguities, and inconsistencies in the metadata, such as duplicate data element concepts, inconsistent value domain definitions, or conflicting business rules.

[0720] Once the metadata is extracted, normalized, and semantically enriched, MENA sends the ISO 11179 and ISO 20022-compliant metadata to the Metadata Registry Generation Agent for further processing and integration into the target metadata registry and data models. The use of LLMs enables MENA to handle the complexity and variability of metadata across different domains and use cases, adapting to new semantic requirements and evolving standards as needed.

[0721] By leveraging the neural capabilities of LLMs, MENA achieves a high level of automation, accuracy, and semantic richness in metadata extraction and normalization. It reduces manual effort and ensures metadata quality, consistency, and interoperability across MEGA N systems. This ultimately contributes to the overall success of the system in generating metadata registries compliant with ISO 11179 and UN / CEFACT, abstract DataVault 2.0 schemas, and implementing cross-taxonomy mapping and translation.

[0722] Method

[0723] MENA employs advanced metadata extraction, normalization, semantic enrichment, and validation techniques to ensure metadata quality, consistency, and compliance with ISO 11179 and ISO 20022 standards.

[0724] Metadata Extraction: MENA extracts relevant metadata elements from the pre-processed and normalized data requirements ingested by DRIA. The extraction process includes the following steps:

[0725] Metadata Element Identification: MENA identifies key metadata elements, such as data element concepts, data elements, value domains, classification schemes, message components, data types, and business rules, as defined by ISO 11179 and ISO 20022, within the ingested data requirements.

[0726] Syntax Parsing: MENA applies syntax parsing techniques, such as regular expressions, grammar-based parsing, or machine learning-based named entity recognition, to extract the identified metadata elements from the source data.

[0727] Metadata Element Structuring: MENA organizes the extracted metadata elements into a structured representation, such as JSON, XML, or object-oriented models, based on the ISO 11179 and ISO 20022 meta-models.

[0728] Metadata Cleaning and Normalization: MENA applies data cleaning and normalization techniques, such as removing duplicates, standardizing formats, and resolving inconsistencies, to the extracted metadata elements to ensure data quality and consistency.

[0729] The time complexity of the metadata extraction process depends on the quantity and complexity of the ingested data requirements and the efficiency of the employed identification, parsing, structuring, and cleaning algorithms. The space complexity is determined by the size of the extracted metadata and any intermediate data structures used during the extraction process.

[0730] Semantic Enrichment: MENA applies semantic enrichment techniques to augment the extracted metadata elements with additional semantic information and relationships. The semantic enrichment process involves the following steps:

[0731] Entity linking: MENA links the extracted metadata elements to related concepts, entities, or resources in external knowledge bases, taxonomies, or ontologies, such as industry-specific vocabularies, ISO 11179 or ISO 20022 reference models, or linked open data sources.

[0732] Synonym and variant identification: MENA uses LLM-based techniques (such as word embeddings, semantic similarity, or contextual analysis) to identify synonyms, abbreviations, and variant terms for the extracted metadata elements, enhancing semantic interoperability.

[0733] Semantic relationship extraction: MENA uses LLM-based techniques (such as dependency parsing, co-reference resolution, or knowledge graph embeddings) to extract semantic relationships between the extracted metadata elements, such as hierarchies, associations, or mappings.

[0734] Contextual metadata enrichment: MENA enriches the extracted metadata elements with contextual information (such as data provenance, traceability, usage, or quality metrics) by analyzing surrounding text, data streams, or system logs using LLM-based techniques (such as sentiment analysis, topic modeling, or sequence labeling).

[0735] The time complexity of the semantic enrichment process depends on the number and complexity of the extracted metadata elements, the size and diversity of external knowledge sources, and the efficiency of the employed entity linking, synonym identification, relationship extraction, and contextual enrichment algorithms. The size of the enriched metadata determines the spatial complexity, external knowledge sources, and any intermediate data structures used during the enrichment process.

[0736] Semantic validation and consistency checks: MENA performs semantic validation and consistency checks on the extracted and enriched metadata to ensure compliance with ISO 11179 and ISO 20022 meta-models and ontologies. The validation process includes the following steps:

[0737] Semantic constraint validation: MENA validates the extracted and enriched metadata against semantic constraints (such as domain and range restrictions, cardinality constraints, or logical axioms, defined in ISO 11179 and ISO 20022 meta-models and ontologies) using LLM-based reasoning techniques (such as description logic, rule-based reasoning, or graph-based constraint checking).

[0738] Consistency and completeness checks: MENA checks the consistency and completeness of the extracted and enriched metadata by identifying and resolving semantic conflicts, ambiguities, or gaps (such as duplicate or missing metadata elements, inconsistent value domain definitions, or conflicting business rules) using LLM-based techniques (such as anomaly detection, clustering, or knowledge graph completion).

[0739] Semantic mapping and alignment: MENA uses LLM-based techniques such as ontology matching, pattern matching, or semantic similarity to map and align the extracted and enriched metadata to ISO 11179 and ISO 20022 meta-models and ontologies, ensuring semantic interoperability and compatibility.

[0740] Semantic error handling and reporting: MENA handles semantic validation and consistency issues by generating detailed error reports, logging problems, and triggering appropriate error resolution workflows or notifying data administrators and stakeholders.

[0741] The time complexity of the semantic validation and consistency checking process depends on the amount and complexity of the extracted and enriched metadata, the size and expressiveness of the ISO 11179 and ISO 20022 meta-models and ontologies, and the efficiency of the employed constraint validation, consistency checking, mapping, and error handling algorithms. The space complexity is determined by the size of the validated metadata, meta-models, and ontologies, and any error logs or reports generated during the process.

[0742] Business layer

[0743] Process:

[0744] Metadata extraction and structuring: This process involves identifying and extracting relevant metadata elements from pre-processed and normalized data requirements and organizing them into a structured representation based on ISO 11179 and ISO 20022 meta-models.

[0745] Semantic enrichment and linking: This process focuses on enhancing the extracted metadata elements with additional semantic information such as synonyms, related concepts, or contextual metadata and linking them to relevant external knowledge sources and reference models.

[0746] Semantic validation and consistency checking: This process involves validating the extracted and enriched metadata against semantic constraints and consistency rules defined in ISO 11179 and ISO 20022 meta-models and ontologies and handling any semantic errors or inconsistencies.

[0747] Metadata mapping and alignment: This process maps and aligns the validated and consistent metadata to target ISO 11179 and ISO 20022 meta-models and ontologies, ensuring semantic interoperability and compatibility.

[0748] Metadata delivery and integration: This process involves delivering the extracted, enriched, validated, and aligned metadata to a metadata registry generation agent for further processing and integration into target metadata registries and data models.

[0749] Functionality:

[0750] Automatic Metadata Extraction: MENA enables the automatic extraction of relevant metadata elements from diverse data requirements, reducing manual effort and increasing efficiency.

[0751] Semantic Enrichment and Linking: MENA enhances the extracted metadata with additional semantic information and relationships, utilizing external knowledge sources and reference models to improve semantic interoperability and richness.

[0752] ISO 11179 and ISO 20022 Compliance: MENA ensures that the extracted and enriched metadata is validated and aligned with ISO 11179 and ISO 20022 meta-models and ontologies, promoting standardization and consistency.

[0753] Semantic Validation and Error Handling: MENA performs rigorous semantic validation and consistency checks on the extracted and enriched metadata, identifying and resolving semantic errors and inconsistencies, and generating detailed error reports and notifications.

[0754] Metadata Integration and Delivery: MENA seamlessly integrates the extracted, enriched, validated, and aligned metadata with MEGAN's metadata registry for intelligent agents and other components, enabling efficient metadata delivery and consumption.

[0755] Application Layer

[0756] MENA comprises the following key components:

[0757] Metadata Extractor: This component identifies and extracts relevant metadata elements from pre-processed and normalized data requirements and organizes them into structured representations based on ISO 11179 and ISO 20022 meta-models.

[0758] Semantic Enricher: This component enhances the extracted metadata elements with additional semantic information, such as synonyms, related concepts, or contextual metadata, and links them to relevant external knowledge sources and reference models.

[0759] Semantic Validator: This component validates the extracted and enriched metadata against semantic constraints and consistency rules defined in ISO 11179 and ISO 20022 meta-models and ontologies and handles any semantic errors or inconsistencies.

[0760] Metadata Aligner: This component maps and aligns the validated and consistent metadata to target ISO 11179 and ISO 20022 meta-models and ontologies to ensure semantic interoperability and compliance.

[0761] Metadata Deliverer: This component delivers the extracted, enriched, validated, and aligned metadata to the metadata registry generation agent for further processing and integration into the target metadata registry and data model.

[0762] Services:

[0763] Metadata Extraction Service: Provides methods for identifying and extracting relevant metadata elements from pre-processed and normalized data requirements and organizing them into structured representations.

[0764] Semantic Enrichment Service: Provides services for enhancing extracted metadata elements with additional semantic information and linking them to relevant external knowledge sources and reference models.

[0765] Semantic Validation Service: Enables validation of extracted and enriched metadata against semantic constraints and consistency rules defined in ISO 11179 and ISO 20022 meta-models and ontologies, as well as handling semantic errors and inconsistencies.

[0766] Metadata Alignment Service: Facilitates mapping and alignment of validated and consistent metadata to target ISO 11179 and ISO 20022 meta-models and ontologies.

[0767] Metadata Delivery Service: Provides services for delivering extracted, enriched, validated, and aligned metadata to the metadata registry generation agent and other components of MEGAN.

[0768] Interfaces:

[0769] Metadata Extractor Interface: This interface defines methods and parameters for identifying and extracting relevant metadata elements from pre-processed and normalized data requirements and organizing them into structured representations.

[0770] Semantic Enricher Interface: Specifies methods and input / output formats for enhancing extracted metadata elements with additional semantic information and linking them to relevant external knowledge sources and reference models.

[0771] SemanticValidatorInterface: describes methods and input / output formats for validating the extracted and enriched metadata against semantic constraints and consistency rules defined in ISO 11179 and ISO 20022 meta-models and ontologies, as well as handling semantic errors and inconsistencies.

[0772] MetadataAlignerInterface: defines methods and parameters for mapping and aligning the validated and consistent metadata to target ISO 11179 and ISO 20022 meta-models and ontologies.

[0773] MetadataDelivererInterface: specifies methods and input / output formats for delivering the extracted, enriched, validated, and aligned metadata to other components of the metadata registry generation agent and MEGAN.

[0774] In embodiments, the metadata extraction and normalization agent (MENA), as part of its integration within the larger MEGAN system, can handle complex transformations of metadata to meet specific standards such as ISO 11179. For example, MENA can be used to convert existing JSON schemas to fully compliant JSON schemas with ISO 11179, ensuring that each metadata element is properly extracted, defined, and structured. In embodiments, the conversion process can include the following steps:

[0775] Metadata extraction and structuring: This step involves extracting all necessary data from the original JSON schema. For each element identified, the script assigns a unique identifier, maps the description to a standardized definition, and categorizes each element according to specified properties and mappings related to FIBO (Financial Industry Business Ontology) and FIRE standards.

[0776] Data element formation: Each element from the data is assigned a unique ID and structured into a new array. This includes mapping names, defining elements according to the previous descriptions, and adjusting data types according to ISO standards.

[0777] Categorization and membership assignment: Elements are categorized into a categorization scheme to logically organize the metadata. A membership array is then populated to associate elements with their respective categories.

[0778] Relationships and usage guidelines: Define relationships between data elements and provide comprehensive guidelines for using, customizing, and integrating the new schema.

[0779] Documentation: Generate user documentation to help understand, implement, and effectively utilize the new schema.

[0780] Neuromorphic data structure

[0781] The data structure presented in the JSON schema combines neural computing principles and properties within the schema elements, making it unique and neuromorphic. This design enables artificial neural networks and graph-based machine learning algorithms to process and utilize the schema more effectively.

[0782] Key neuromorphic features include:

[0783] Activation functions: Each node (e.g., concept, data element, column, row) in the graph structure of the schema is assigned an activation function, such as ReLU (Rectified Linear Unit), Sigmoid, or Tanh. These activation functions introduce non-linearity, allowing the nodes to learn and represent complex patterns and relationships in the data.

[0784] Embedding sizes: Schema elements are assigned embedding sizes, which determine the dimensionality of their vector representations. These embeddings allow the nodes to be represented in a continuous vector space, capturing their semantic and structural properties. Embedding sizes can be adjusted based on the complexity and granularity of the data.

[0785] Weights: The relationships (edges) between schema elements are assigned weights, indicating the strength or importance of the connections. These weights can be learned and optimized during the training process of machine learning algorithms, enabling the discovery of meaningful patterns and associations within the data.

[0786] These neuromorphic properties make the schema more compatible with neural network architectures and graph-based learning algorithms. Activation functions, embedding sizes, and weights allow the data to be processed and analyzed to mimic the behavior of biological neural networks, enabling the extraction of complex patterns, relationships, and insights from the data.

[0787] Furthermore, the neuromorphic design facilitates the integration of the schema with other neuro-symbolic systems, such as knowledge graphs, ontologies, and reasoning engines. The semantic annotations and ontology mappings provided in the schema (e.g., using SKOS properties) can be utilized to establish connections and perform inferences across different data sources and domains.

[0788] The neuromorphic features also enhance the adaptability and scalability of the schema. As new data is added or the schema evolves, the activation functions, embedding sizes, and weights can be fine-tuned and optimized to accommodate changes and maintain the effectiveness of the schema in representing and processing data.

[0789] DataVault2.0 Schema Abstraction Agent (DSAA):

[0790] DataVault 2.0 Schema Abstraction Agent (DSAA) is a highly advanced component of MEGAN that leverages the neural capabilities of large language models (LLMs) such as Claude-3 to automatically generate DataVault 2.0 schemas from ISO 11179 / ISO 20022 compliant metadata. By utilizing the capabilities of LLMs, DSAA can understand and process complex semantic structures and relationships defined in metadata and apply domain-specific knowledge to abstract key business concepts, processes, and relationships.

[0791] DSAA employs the natural language understanding and generation capabilities of LLMs to analyze ISO 11179 / ISO 20022 compliant metadata received from a generative AI model agent (GAMA) and applies the Concept Embedding for Large Language Models (CoLLEGe) framework to abstract the metadata into a format suitable for DataVault 2.0 modeling. The CoLLEGe framework enables DSAA to identify and extract key business concepts, processes, and relationships from metadata while maintaining semantic integrity, traceability, and adherence to ISO 11179 and ISO 20022 standards.

[0792] Furthermore, DSAA utilizes the reasoning capabilities of LLMs to generate semantically rich and well-organized DataVault 2.0 schemas, including hubs (HUBs), links (LINKs), and satellites (SATELLITES), ensuring alignment with ISO 11179 and ISO 20022 principles, the CoLLEGe framework, and database modeling best practices. The ability of LLMs to understand and generate complex data structures allows DSAA to create schemas that accurately represent the underlying business domain and are tailored to financial data management and analysis requirements.

[0793] DSAA also maintains bidirectional traceability and provenance between ISO 11179 / ISO 20022 metadata elements and corresponding DataAvault 2.0 schema components by leveraging the reasoning and memory capabilities of LLMs. This enables DSAA to track relationships between source metadata and generated schemas, facilitating impact analysis, change management, and regulatory compliance.

[0794] To ensure the quality and consistency of generated DataVault 2.0 schemas, DSAA uses the ability of LLMs to understand and apply complex validation rules against ISO 11179 and ISO 20022 consistency rules and industry-specific data quality checks. This allows DSAA to identify and flag any issues or inconsistencies in the schemas, ensuring their accuracy and reliability.

[0795] Finally, the DSAA sends the generated ISO 11179 / ISO 20022-aligned DataVault2.0 schema to the Cross- Taxonomy Mapping and Alignment Agent for further processing and integration into the overall MEGAN. The use of LLMs enables the DSAA to handle the complexity and variability of schema abstraction tasks, adapting to new standards and domain-specific requirements as needed.

[0796] By leveraging the neural capabilities of LLMs, the DSAA achieves a high level of automation, accuracy, and efficiency in generating DataVault2.0 schemas from ISO 11179 / ISO 20022-compliant metadata. This reduces manual effort, ensures the quality and semantic integrity of the schemas, and facilitates the overall effectiveness of MEGAN in generating ISO 11179- and UN / CEFACT-compliant metadata registries, abstracting DataVault2.0 schemas, and enabling cross-taxonomy mapping and translation.

[0797] Method

[0798] The DSAA employs advanced metadata abstraction, schema generation, traceability management, and validation techniques to ensure the quality, consistency, and compliance of the generated DataVault2.0 schemas with ISO 11179 and ISO 20022 standards.

[0799] Metadata Abstraction using CoLLEGe Framework: The DSAA applies the Concept Embedding Generation for Large Language Models (CoLLEGe) framework to abstract ISO 11179 / ISO 20022-compliant metadata into a format suitable for DataVault2.0 modeling. The abstraction process involves the following steps:

[0800] Semantic Analysis: The DSAA leverages the natural language understanding capabilities of LLMs to analyze the semantic structures and relationships defined in the ISO 11179 / ISO 20022 metadata, identifying key business concepts, processes, and relationships.

[0801] Concept Embedding Generation: The DSAA uses LLM-based techniques (such as word embeddings, sentence embeddings, or graph embeddings) to generate concept embeddings for the identified business concepts, processes, and relationships, capturing their semantic meanings and contexts.

[0802] Metadata abstraction: DSAA abstracts ISO 11179 / ISO 20022 metadata into a format suitable for DataVault 2.0 modeling by embedding the generated concepts into corresponding DataVault 2.0 schema components (such as hubs, links, and satellites) while preserving semantic integrity and traceability.

[0803] Domain-specific enrichment: DSAA enriches the abstracted metadata with additional attributes and relationships specific to the financial industry, leveraging domain knowledge embedded in the LLM to tailor the resulting schema to financial data management and analysis requirements.

[0804] The time complexity of the metadata abstraction process depends on the volume and complexity of ISO 11179 / ISO 20022 metadata, the size and diversity of the LLM knowledge base, and the efficiency of the employed semantic analysis, concept embedding generation, and abstraction algorithms. The space complexity is determined by the size of the abstracted metadata, concept embeddings, and any intermediate data structures used during the abstraction process.

[0805] DataVault 2.0 schema generation: DSAA generates semantic-rich and well-organized DataVault 2.0 schemas from the abstracted metadata, ensuring alignment with ISO 11179 and ISO 20022 principles, CoLLEGe framework, and database modeling best practices. The schema generation process involves the following steps:

[0806] HUB generation: DSAA uses the LLM's ability to understand and develop complex data structures to define HUB attributes, keys, and constraints, generating HUBs for key business concepts identified during the metadata abstraction process.

[0807] LINK generation: DSAA generates links to represent relationships between HUBs, leveraging the LLM's understanding of semantic relationships defined in the abstracted metadata to create link attributes, keys, and cardinality constraints.

[0808] SATELLITE generation: DSAA generates satellites to capture time-varying attributes and historical changes associated with HUBs and LINKs, using the LLM's ability to understand temporal aspects and generate appropriate satellite structures and attributes.

[0809] Schema optimization: DSAA optimizes the generated DataAvault 2.0 schemas by applying database modeling best practices, such as denormalization, historicization, and consistency with naming conventions. It leverages the LLM's knowledge of these practices to ensure that the schemas are efficient, maintainable, and scalable.

[0810] The time complexity of the schema generation process depends on the size and complexity of the abstract metadata, the number and diversity of business concepts, relationships, and attributes, and the efficiency of the employed HUB, LINK, SATELLITE generation and optimization algorithms. The space complexity is determined by the size of the generated DataVault2.0 schema and any intermediate data structures used during the generation process.

[0811] Traceability and provenance management: DSAA uses the reasoning and memory capabilities of LLM to maintain bidirectional traceability and provenance between ISO 11179 / ISO 20022 metadata elements and corresponding DataVault2.0 schema components. The traceability management process includes the following steps:

[0812] Metadata-schema mapping: DSAA establishes a mapping between ISO 11179 / ISO 20022 metadata elements and corresponding DataVault2.0 schema components during the schema generation process, leveraging the ability of LLM to track these relationships.

[0813] Provenance capture: DSAA uses LLM-based techniques such as knowledge graphs, provenance modeling, or version control to capture provenance information for each mapping, such as source metadata elements, transformation rules, and target schema components.

[0814] Impact analysis: DSAA utilizes the reasoning capabilities of LLM to analyze the impact of changes to ISO 11179 / ISO 20022 metadata or DataVault2.0 schema on mappings and provenance, thereby identifying affected components and propagating changes accordingly.

[0815] Traceability reporting: DSAA uses the natural language generation capabilities of LLM to generate traceability reports and visualizations such as provenance graphs, impact analysis matrices, or audit trails to communicate traceability and provenance information to users and stakeholders.

[0816] The time complexity of the traceability management process depends on the number and complexity of metadata-schema mappings, the frequency and scope of changes, and the efficiency of the employed provenance capture, impact analysis, and reporting algorithms. The space complexity is determined by the traceability and provenance metadata size, knowledge graphs or version control systems, and any intermediate data structures used during the management process.

[0817] DataVault2.0 schema validation: DSAA validates the generated DataVault2.0 schema against ISO 11179 and ISO 20022 conformance rules and industry-specific data quality checks to ensure its accuracy, consistency, and reliability. The validation process includes the following steps:

[0818] Consistency rule validation: DSAA validates the generated DataVault 2.0 schema against the consistency rules and constraints defined in ISO 11179 and ISO 20022 standards, leveraging the LLM's ability to understand and apply complex validation rules to identify any violations or inconsistencies.

[0819] Data quality checks: DSAA uses the LLM's domain knowledge and reasoning capabilities to identify any data quality issues or anomalies, applying industry-specific data quality checks (e.g., data type consistency, referential integrity, or business rule compliance) to the generated DataVault 2.0 schema.

[0820] Schema consistency verification: DSAA verifies the internal consistency and coherence of the generated DataVault 2.0 schema, checking for issues such as naming conflicts, circular references, or structural inconsistencies. It leverages the LLM's ability to analyze and reason about complex data structures.

[0821] Verification report and solutions: DSAA generates a verification report and notifications highlighting any consistency, data quality, or coherence issues in the generated DataVault 2.0 schema, and uses the LLM's natural language generation and problem-solving capabilities to suggest possible solutions or remediation steps.

[0822] The time complexity of the schema verification process depends on the size and complexity of the generated DataVault 2.0 schema, the number and diversity of consistency rules, data quality checks, coherence constraints, and the efficiency of the verification algorithms employed. The size of the metadata determines the spatial complexity, the rule base or knowledge graph used, and any intermediate data structures used during the verification process.

[0823] Business layer

[0824] Process:

[0825] ISO 11179 / ISO 20022 metadata acquisition: This process involves acquiring ISO 11179 / ISO 20022 compliant metadata from the Generative AI Model Agent (GAMA) and preparing it for the metadata abstraction process.

[0826] Metadata abstraction and concept embedding generation: This process focuses on applying the CoLLEGe framework to abstract the ISO 11179 / ISO 20022 metadata into a format suitable for DataAvault 2.0 modeling, generating concept embeddings for key business concepts, processes, and relationships.

[0827] DataVault 2.0 schema generation and optimization: This process involves generating semantically rich and well-organized DataVault 2.0 schemas, including HUB, LINK, and SATELLITE, from abstracted metadata and optimizing them based on data vault modeling best practices.

[0828] Traceability and lineage management: This process maintains bidirectional traceability and lineage between ISO 11179 / ISO 20022 metadata elements and corresponding DataVault 2.0 schema components, capturing lineage information and performing impact analysis.

[0829] DataVault 2.0 schema validation and problem resolution: This process involves validating the generated DataVault 2.0 schemas against ISO 11179 and ISO 20022 consistency rules and industry-specific data quality checks, identifying issues or inconsistencies, and suggesting solutions or remediation steps.

[0830] Features:

[0831] Automatic metadata abstraction: DSAA enables automatic abstraction of ISO 11179 / ISO 20022 metadata into a format suitable for DataVault 2.0 modeling, reducing manual effort and increasing efficiency.

[0832] Semantic enrichment and domain-specific tailoring: DSAA leverages additional attributes and relationships specific to the financial industry to enrich the abstracted metadata, utilizing the domain knowledge of LLM to customize the resulting schemas for financial data management and analysis requirements.

[0833] Intelligent schema generation: DSAA generates semantically rich and well-organized DataVault 2.0 schemas, ensuring alignment with ISO 11179 and ISO 20022 principles, CoLLEGe framework, and database modeling best practices.

[0834] Comprehensive traceability and lineage: DSAA maintains bidirectional traceability and lineage between metadata elements and schema components, facilitating impact analysis, change management, and regulatory compliance.

[0835] Strict validation and quality assurance: DSAA validates the generated schemas against consistency rules and data quality checks, identifies issues, and suggests solutions, ensuring accuracy, consistency, and reliability.

[0836] Application layer

[0837] DSAA includes the following key components:

[0838] MetadataAcquisitionManager: This component acquires ISO 11179 / ISO 20022 compliant metadata from Generative AI Model Agents (GAMA) and prepares it for the metadata abstraction process.

[0839] MetadataAbstractionEngine: This component applies the CoLLEGe framework to abstract ISO 11179 / ISO 20022 metadata into a format suitable for DataVault 2.0 modeling, generating concept embeddings for key business concepts, processes, and relationships.

[0840] SchemaGenerationOptimizer: This component generates semantically rich and well-organized DataVault 2.0 schemas, including HUBs, LINKs, and SATELLITES, from abstracted metadata and optimizes them based on data vault modeling best practices.

[0841] TraceabilityIneageManager: This component maintains bidirectional traceability and lineage between ISO 11179 / ISO 20022 metadata elements and corresponding DataVault 2.0 schema components, capturing lineage information and performing impact analysis.

[0842] SchemaValidationResolverAgent: This component validates generated DataVault 2.0 schemas against ISO 11179 and ISO 20022 conformance rules and industry-specific data quality checks, identifies issues or inconsistencies, and suggests solutions or remediation steps.

[0843] Services:

[0844] MetadataAcquisitionService: Provides methods for acquiring ISO 11179 / ISO 20022 compliant metadata from Generative AI Model Agents (GAMA) and preparing it for the metadata abstraction process.

[0845] MetadataAbstractionService: Provides services for applying the CoLLEGe framework to abstract ISO 11179 / ISO 20022 metadata into a format suitable for DataVault 2.0 modeling, generating concept embeddings for key business concepts, processes, and relationships.

[0846] SchemaGenerationService: This service allows the generation of semantically rich and well-organized DataVault2.0 schemas from abstracted metadata, including HUBs, LINKs, and SATELLITES, and optimizes them based on data vault modeling best practices.

[0847] TraceabilityIneageService: Facilitates the maintenance of bidirectional traceability and lineage between ISO 11179 / ISO 20022 metadata elements and corresponding DataVault2.0 schema components, capturing lineage information and performing impact analysis.

[0848] SchemaValidationService: Provides services for validating generated DataVault2.0 schemas against ISO 11179 and ISO 20022 conformance rules and industry-specific data quality checks, identifying issues or inconsistencies, and suggesting solutions or remediation steps.

[0849] Interfaces:

[0850] MetadataAcquisitionInterface: Defines methods and parameters for acquiring ISO 11179 / ISO 20022 compliant metadata from generative AI model agents (GAMA) and preparing it for the metadata abstraction process.

[0851] MetadataAbstractionInterface: Specifies methods and input / output formats for applying the CoLLEGe framework to abstract ISO 11179 / ISO 20022 metadata into a format suitable for DataVault2.0 modeling, generating concept embeddings for key business concepts, processes, and relationships.

[0852] SchemaGenerationInterface: Describes methods and input / output formats for generating semantically rich and well-organized DataVault2.0 schemas (including HUBs, LINKs, and SATELLITES) from abstracted metadata and optimizing them based on data vault modeling best practices.

[0853] TraceabilityIneageInterface: Defines methods and parameters for maintaining bidirectional traceability and lineage between ISO 11179 / ISO 20022 metadata elements and corresponding DataVault2.0 schema components, capturing lineage information, and performing impact analysis.

[0854] SchemaValidationInterface: Specifies methods and input / output formats for validating generated DataVault 2.0 schemas against ISO 11179 and ISO 20022 conformance rules and industry-specific data quality checks, identifying issues or inconsistencies, and suggesting solutions or remediation steps.

[0855] In embodiments, a DataVault 2.0 Schema Abstraction Agent (DSAA) can leverage the capabilities of LLMs to automate the generation and management of DataVault 2.0 schemas from metadata that conforms to ISO 11179 and ISO 20022 standards. For example, this operation begins with a DSAA that employs the neural capabilities of an LLM, such as Claude-3, to perform a deep semantic analysis of the metadata. This analysis focuses on understanding the complex semantic structures and relationships embedded within the metadata. The DSAA utilizes the Concept Embedding Generation (CoLLEGe) framework of large language models to identify key business concepts, processes, and relationships. This framework facilitates the generation of concept embeddings that effectively capture the semantic essence and contextual relevance of these elements. Once the key concepts are identified and their embeddings are generated, the DSAA abstracts this metadata into a structured format suitable for DataAvault 2.0 modeling. This abstraction includes mapping the generated concept embeddings to the corresponding schema components—HUBs, LINKs, and SATELLITES—while meticulously maintaining semantic integrity and ensuring traceability. This step is crucial as it transforms the raw metadata into structured schemas that accurately reflect the underlying processes and relationships of the business.

[0856] Following the abstraction of metadata, the DSAA proceeds to generate DataVault 2.0 schemas. This phase leverages the inferential capabilities of LLMs to create schema components that are semantically rich and well-organized. The DSAA constructs HUBs to represent core business entities, constructs LINKs to depict relationships between these entities, and constructs SATELLITES to capture detailed time-varying attributes associated with both HUBs and LINKs. Each component is meticulously designed to ensure that it aligns with established database modeling best practices and the semantic framework provided by ISO standards. The DSAA applies data warehouse modeling best practices, such as denormalization and historicalization, to tailor the schema for enhanced data retrieval efficiency and scalability. This optimization ensures that the schema is not only technically sound but also consistent with the operational and analytical needs of the business.

[0857] The operation of the DSAA is characterized by maintaining bidirectional traceability and provenance. The process involves tracking relationships between original ISO metadata elements and newly generated DataAvault 2.0 schema components. This traceability is critical for effective impact analysis and change management, allowing business departments to understand how changes to metadata affect the schema and vice versa. Additionally, the DSAA thoroughly validates the generated schema against ISO 11179 and ISO 20022 conformance rules and performs industry-specific data quality checks. This validation ensures that the schema not only conforms to international standards but also meets high data quality benchmarks. Finally, once validated, the DataVault 2.0 schema is forwarded to the Cross- Taxonomy Mapping and Alignment Agent for further processing. This step integrates the schema into the broader MEGAN system, enabling comprehensive data management and facilitating advanced data analytics capabilities.

[0858] Cross-Taxonomy Mapping and Alignment Agent (CTMAA):

[0859] The Cross-Taxonomy Mapping and Alignment Agent (CTMAA) is a highly advanced component of MEGAN that leverages the neural capabilities of large language models (LLMs), such as Claude-3, to automate the mapping and translation of metadata across different taxonomies, jurisdictions, and financial messaging standards. By harnessing the power of LLMs, the CTMAA can understand and process complex semantic relationships, conceptual data modeling principles defined in ISO 11179 and ISO 20022 standards, and industry-specific ontologies like the Financial Industry Business Ontology (FIBO).

[0860] The CTMAA employs the natural language understanding and reasoning capabilities of LLMs to analyze ISO 11179 / ISO 20022 compliant metadata and DataVault 2.0 schemas received from the DataVault 2.0 Schema Abstraction Agent (DSAA) and applies state-of-the-art AI and NLP techniques, such as ontology-based reasoning, semantic similarity measures, graph neural networks, and transfer learning, to establish mappings and translations between metadata elements from different taxonomies and standards. These techniques enable the CTMAA to identify and extract meaningful relationships and equivalences between metadata elements, ensuring consistent and context-aware mappings.

[0861] Additionally, the CTMAA utilizes the reasoning capabilities of LLMs to process ISO 11179 classification schemes, value domains, ISO 20022 business process catalogs, message component dictionaries, and data dictionaries to establish interoperability and semantic alignment across regulatory and enterprise contexts and different financial messaging domains. The ability of LLMs to understand and process these complex semantic structures allows the CTMAA to create accurate and comprehensive mappings, facilitating seamless data exchange and integration between agents and domains.

[0862] CTMAA also generates cross-references, mapping specifications, and transformation rules that comply with ISO 11179 and ISO 20022 by leveraging the natural language generation capabilities of the LLM. These artifacts provide a transparent and traceable record of the mappings and alignments, enabling users to understand and validate the relationships between metadata elements across different contexts.

[0863] To ensure the quality and consistency of the generated mappings and alignments, CTMAA uses the LLM's ability to understand and apply complex validation rules against ISO 11179 and ISO 20022 semantic constraints, data quality rules, and industry-specific coordination and validation frameworks. This allows CTMAA to identify and flag any issues or inconsistencies in the mappings, ensuring their accuracy and reliability.

[0864] Finally, CTMAA sends the ISO 11179 / ISO 20022-aligned metadata, DataVault2.0 schemas, cross-taxonomy mappings, and semantic representations to the vector database agent and large language model agent for further processing and integration into MEGAN. The use of LLMs enables CTMAA to handle the complexity and variability of cross-taxonomy mapping and alignment tasks, adapting to new standards and domain-specific requirements as needed.

[0865] By leveraging the neural capabilities of LLMs, CTMAA achieves a high level of automation, accuracy, and efficiency in mapping and translating metadata across different taxonomies, jurisdictions, and financial messaging standards. This reduces manual effort, ensures the quality and semantic integrity of the mappings, and facilitates MEGAN's overall effectiveness in generating ISO 11179 and UN / CEFACT-compliant metadata registries, abstract DataVault2.0 schemas, and implementing cross-taxonomy mappings and translations.

[0866] Method

[0867] CTMAA employs advanced AI and NLP techniques, semantic reasoning, and validation methods to ensure the quality, consistency, and compliance of cross-taxonomy mappings and their alignment with ISO 11179 and ISO 20022 standards.

[0868] Ontology-based reasoning and semantic similarity:

[0869] CTMAA utilizes ontology-based reasoning and semantic similarity techniques to establish mappings and transformations between metadata elements from different taxonomies and standards. The mapping process involves the following steps:

[0870] Ontology alignment: CTMAA uses LLM-based techniques such as ontology matching, concept embedding, or graph alignment to align ISO 11179 and ISO 20022 ontologies with industry-specific ontologies such as FIBO to establish a common semantic framework for mapping metadata elements across different classifications and standards.

[0871] Semantic similarity computation: CTMAA uses LLM-based techniques such as word embeddings, sentence embeddings, or graph embeddings to compute semantic similarity scores between metadata elements from different classifications and standards, taking into account their semantic context, relationships, and conceptual equivalences.

[0872] Mapping candidate generation: CTMAA generates mapping candidates between metadata elements based on their semantic similarity scores, ontology alignment, and domain-specific mapping rules, leveraging the reasoning capabilities of LLMs to identify the most probable and meaningful mappings.

[0873] Mapping validation and refinement: CTMAA uses the ability of LLMs to understand and apply complex validation rules to validate the generated mapping candidates against ISO 11179 and ISO 20022 semantic constraints, data quality rules, and industry-specific harmonization frameworks, and refine the mappings based on the validation results.

[0874] The time complexity of the ontology-based reasoning and semantic similarity process depends on the size and complexity of the ISO 11179 and ISO 20022 ontologies, the number and diversity of metadata elements, and the efficiency of the ontology alignment, semantic similarity computation, and mapping generation algorithms employed. The size of the ontologies determines the spatial complexity, semantic similarity matrix, and any intermediate data structures used during the mapping process.

[0875] Graph neural networks and transfer learning: CTMAA applies graph neural networks (GNNs) and transfer learning techniques to model complex relationships and dependencies between metadata elements across different classifications and standards and adapt the mapping and alignment models to new domains and contexts. The GNN-based mapping process involves the following steps:

[0876] Graph representation learning: CTMAA uses techniques such as knowledge graphs, property graphs, or hypergraphs to construct graph representations of ISO 11179 and ISO 20022 metadata elements, their relationships, and their alignment with industry-specific ontologies to capture the rich semantic structure of the metadata.

[0877] Graph neural network training: CTMAA trains GNN models, such as Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Relation Graph Convolutional Networks (RGCN), on constructed graph representations using supervised or unsupervised learning techniques to learn complex patterns and dependencies between metadata elements across different taxonomies and standards.

[0878] Transfer learning and domain adaptation: CTMAA applies transfer learning techniques, such as fine-tuning, domain adaptation, or meta-learning, to adapt trained GNN models to new taxonomies, jurisdictions, or financial messaging standards, leveraging the ability of LLMs to generalize and transfer knowledge between different domains and contexts.

[0879] Mapping inference and refinement: Considering learned patterns and dependencies between metadata elements, CTMAA uses trained and tuned GNN models to infer cross-taxonomy mappings and alignments. Mappings are then refined based on ontology-based reasoning and semantic similarity techniques.

[0880] The time complexity of the GNN-based mapping process depends on the size and complexity of the graph representation, the number and diversity of metadata elements and their relationships, and the efficiency of the employed GNN training, transfer learning, and reasoning algorithms. The size of the graph representation determines the spatial complexity, GNN model parameters, and any intermediate data structures used during the learning and inference processes.

[0881] Mapping artifacts generation and validation: CTMAA generates cross-references, mapping specifications, and conversion rules conforming to ISO 11179 and ISO 20022 to document cross-taxonomy mappings and alignments and validate them against semantic constraints and data quality rules. The artifacts generation and validation process involves the following steps:

[0882] Cross-reference generation: CTMAA uses the natural language generation capabilities of LLMs to create human-readable and machine-processable descriptions of mappings and their semantics, generating cross-references conforming to ISO 11179 between metadata elements from different taxonomies and standards.

[0883] Mapping specification generation: CTMAA uses the ability of LLMs to understand and generate complex data structures and specifications to generate mapping specifications conforming to ISO 20022, detailing relationships, equivalences, and derivations between metadata elements from different message types, business processes, and data dictionaries.

[0884] Conversion rule generation: CTMAA generates executable conversion rules, such as XSLT stylesheets, SQL scripts, or API specifications, to enable automatic translation and conversion of data between different taxonomies and standards. This leverages the ability of LLMs to generate code and data transformation pipelines.

[0885] Work product validation and consistency checking: CTMAA uses the LLM's ability to understand and apply complex validation rules to verify the generated cross-references, mapping specifications, and transformation rules against ISO 11179 and ISO 20022 semantic constraints, data quality rules, and industry-specific harmonization frameworks, and checks their consistency and completeness.

[0886] The time complexity of the work product generation and validation process depends on the number and complexity of cross-taxonomy mappings, the size and diversity of ISO 11179 and ISO 20022 standards and specifications, and the efficiency of the natural language generation, transformation rule generation, and validation algorithms employed. The size of the generated work product determines the space complexity, validation rule sets, and any intermediate data structures used during the generation and validation process.

[0887] Business layer

[0888] Process:

[0889] ISO 11179 / ISO 20022 metadata and schema acquisition: This process involves acquiring ISO 11179 / ISO 20022-compliant metadata and DataVault2.0 schemas from the DataVault2.0 Schema Abstraction Agent (DSAA) and preparing them for the cross-taxonomy mapping and alignment process.

[0890] Ontology alignment and semantic similarity computation: This process aligns ISO 11179 and ISO 20022 ontologies with industry-specific ontologies like FIBO and computes semantic similarity scores between metadata elements from different taxonomies and standards.

[0891] Graph-based mapping and transfer learning: This process involves constructing graph representations of metadata elements and their relationships, training GNN models to learn complex patterns and dependencies, and adapting the models to new domains and contexts using transfer learning techniques.

[0892] Mapping work product generation and validation: This process generates cross-references, mapping specifications, and transformation rules compliant with ISO 11179 and ISO 20022 and validates them against semantic constraints, data quality rules, and industry-specific harmonization frameworks.

[0893] Mapping metadata and schema delivery: This process involves delivering ISO 11179 / ISO 20022-aligned metadata, DataVault2.0 schemas, cross-taxonomy mappings, and semantic representations to the vector database agent and large language model agent for further processing and integration.

[0894] Functionality:

[0895] Automatic Cross-Category Mapping: CTMAA enables automatic mapping and translation of metadata elements across different taxonomies, jurisdictions, and financial messaging standards, reducing manual effort and increasing efficiency.

[0896] Semantic Reasoning and Similarity Analysis: CTMAA utilizes ontology-based reasoning and semantic similarity techniques to establish meaningful and context-aware mappings between metadata elements from different categories and standards.

[0897] Graph-Based Relationship Modeling: CTMAA applies GNN and transfer learning techniques to model complex relationships and dependencies between metadata elements across different categories and standards, and adapts the mapping model to new domains and contexts.

[0898] ISO-Compliant Artifacts Generation: CTMAA generates cross-references, mapping specifications, and conversion rules that comply with ISO 11179 and ISO 20022, providing transparent and traceable records of mappings and alignments.

[0899] Strict Validation and Quality Assurance: CTMAA validates the generated mappings and artifacts against semantic constraints, data quality rules, and industry-specific harmonization frameworks, ensuring accuracy, consistency, and reliability.

[0900] Application Layer

[0901] CTMAA comprises the following key components:

[0902] Metadata Schema Acquisition Manager: This component acquires ISO 11179 / ISO 20022-compliant metadata and DataVault 2.0 schemas from the DataVault 2.0 Schema Abstraction Agent (DSAA) and prepares them for cross-taxonomy mapping and alignment processes.

[0903] Ontology Alignment Engine: This component aligns ISO 11179 and ISO 20022 ontologies with industry-specific ontologies like FIBO and computes semantic similarity scores between metadata elements from different categories and standards.

[0904] Graph Mapping Learner: This component constructs graph representations of metadata elements and their relationships, trains GNN models to learn complex patterns and dependencies, and uses transfer learning techniques to adapt the model to new domains and contexts.

[0905] Mapping Artifact Generator: This component generates ISO 11179 and ISO 20022 compliant cross-references, mapping specifications, and conversion rules and validates them against semantic constraints, data quality rules, and industry-specific harmonization frameworks.

[0906] Mapped Metadata Delivery Manager: This component delivers ISO 11179 / ISO 20022 aligned metadata, DataVault2.0 schemas, cross-ontology mappings, and semantic representations to vector database agents and large language model agents for further processing and integration.

[0907] Services:

[0908] Metadata Schema Acquisition Service: Provides methods for acquiring ISO 11179 / ISO 20022 compliant metadata and DataVault2.0 schemas from DataVault2.0 Schema Abstraction Agents (DSAA) and preparing them for cross-ontology mapping and alignment processes.

[0909] Ontology Alignment Service: Provides services for aligning ISO 11179 and ISO 20022 ontologies with industry-specific ontologies (e.g., FIBO) and calculating semantic similarity scores between metadata elements from different taxonomies and standards.

[0910] Graph Mapping Learning Service: This service enables the construction of graph representations of metadata elements and their relationships, trains GNN models to learn complex patterns and dependencies, and adapts models to new domains and contexts using transfer learning techniques.

[0911] Mapping Artifact Generation Service: Facilitates the generation of ISO 11179 and ISO 20022 compliant cross-references, mapping specifications, conversion rules, and their validation against semantic constraints, data quality rules, and industry-specific harmonization frameworks.

[0912] Mapped Metadata Delivery Service: Provides services for delivering ISO 11179 / ISO 20022 aligned metadata, DataVault2.0 schemas, cross-ontology mappings, and semantic representations to vector database agents and large language model agents for further processing and integration.

[0913] Interfaces:

[0914] MetadataSchemaAcquisitionInterface: Defines methods and parameters for acquiring ISO 11179 / ISO 20022 compliant metadata and DataVault 2.0 schemas from DataVault 2.0 schema abstraction agents (DSAA) and preparing them for cross-ontology mapping and alignment processes.

[0915] OntologyAlignmentInterface: Specifies methods and input / output formats for aligning ISO 11179 and ISO 20022 ontologies with industry-specific ontologies like FIBO and computing semantic similarity scores between metadata elements from different taxonomies and standards.

[0916] GraphMappingLearningInterface: Describes methods and input / output formats for constructing graph representations of metadata elements and their relationships, training GNN models to learn complex patterns and dependencies, and adapting models to new domains and contexts using transfer learning techniques.

[0917] MappingArtifactGenerationInterface: This interface defines methods and parameters for generating ISO 11179 and ISO 20022 compliant cross-references, mapping specifications, and conversion rules, and validating them against semantic constraints, data quality rules, and industry-specific harmonization frameworks.

[0918] MappedMetadataDeliveryInterface: Specifies methods and input / output formats for delivering ISO 11179 / ISO 20022 aligned metadata, DataVault 2.0 schemas, cross-ontology mappings, and semantic representations to vector database agents and large language model agents for further processing and integration.

[0919] Vector Database Agent (VDA):

[0920] Vector Database Agent The Vector Database Agent (VDA) is a highly advanced component of MEGAN that leverages state-of-the-art vector database technology and semantic indexing techniques to store, retrieve, and analyze ISO 11179 / ISO 20022 aligned metadata, DataVault 2.0 schemas, cross-ontology mappings, and semantic representations. The VDA enables efficient and contextual discovery, exploration, and utilization of financial industry metadata and knowledge by leveraging the power of vector databases and semantic similarity search.

[0921] VDA employs advanced indexing and retrieval techniques, such as hierarchical navigable small-world graphs, approximate nearest neighbor search, and locality-sensitive hashing, to organize and index metadata elements, relationships, mappings, and semantic representations received from Cross Taxonomy Mapping and Alignment Agents (CTMAA). These techniques enable fast, accurate, and context-aware retrieval of relevant metadata based on ISO 11179 and ISO 20022 semantic similarity and correlation, supporting various financial industry use cases and regulatory compliance requirements.

[0922] In addition, VDA implements domain-specific optimizations tailored to financial industry needs, such as taxonomy navigation, faceted search, and semantic query expansion, to enhance the availability and effectiveness of metadata discovery and retrieval. These optimizations leverage the rich semantic information and contextual knowledge encoded in ISO 11179 / ISO 20022 metadata and schemas to provide intuitive and user-friendly query experiences.

[0923] VDA provides an extensible, high-performance, and API-driven query interface that allows users and applications to search, retrieve, and explore ISO 11179 / ISO 20022-compliant metadata, schemas, mappings, and related artifacts based on semantic similarity, contextual relevance, and user-defined criteria. The interface supports complex query scenarios, such as cross-taxonomy and cross-jurisdiction metadata discovery, impact analysis, and semantic traceability, enabling users to navigate and analyze metadata across different financial industry domains and regulatory contexts.

[0924] In addition, VDA provides advanced analysis and visualization capabilities, such as semantic clustering, topic modeling, and network analysis, to derive insights, patterns, and relationships from ISO 11179 / ISO 20022 metadata and schemas. These capabilities leverage the rich semantic information and latent structures encoded in vector representations to reveal hidden connections, trends, and anomalies within financial industry metadata, supporting data-driven decision-making and knowledge discovery.

[0925] VDA achieves high performance, scalability, and semantic richness in storing, retrieving, and analyzing ISO 11179 / ISO 20022-aligned metadata and schemas by leveraging vector database technology and semantic indexing techniques. This enables efficient and context-aware discovery, exploration, and utilization of financial industry metadata and knowledge, supporting various use cases and regulatory compliance requirements. VDA's advanced query, analysis, and visualization capabilities enable users and applications to gain valuable insights and make informed decisions based on the rich semantic information and contextual knowledge encoded in metadata.

[0926] Method

[0927] VDA employs advanced vector database technology, semantic indexing techniques, and domain-specific optimization techniques to ensure efficient storage, retrieval, and analysis of ISO 11179 / ISO 20022-aligned metadata and schemas.

[0928] Vector Database Storage and Indexing: VDA stores and indexes ISO 11179 / ISO 20022-compatible metadata, schemas, mappings, and semantic representations received from CTMAA in a high-performance, scalable, and distributed vector database. The storage and indexing process involves the following steps:

[0929] Metadata Ingestion: VDA ingests ISO 11179 / ISO 20022-aligned metadata, DataVault 2.0 schemas, cross-classification mappings, and semantic representations received from CTMAA, parses and converts them into a suitable format for vector database storage and indexing.

[0930] Vector Embedding Generation: VDA generates vector embeddings for metadata elements, relationships, mappings, and semantic representations using techniques such as word embeddings, graph embeddings, or semantic encoders, thereby capturing their semantic meaning and contextual information in a dense, continuous vector space.

[0931] Indexing and Partitioning: VDA indexes the generated vector embeddings using advanced techniques such as hierarchical navigable small-world graphs, approximate nearest neighbor search, or locality-sensitive hashing to enable fast and accurate similarity-based retrieval. It also partitions the vector database according to domain-specific standards (e.g., financial industry taxonomy, jurisdiction, or data management standards) to optimize storage and retrieval performance.

[0932] Metadata Persistence: VDA persists the indexed vector embeddings and their associated metadata, schemas, mappings, and semantic representations in a distributed and fault-tolerant manner, ensuring high availability, scalability, and data durability.

[0933] The time complexity of the vector database storage and indexing process depends on the volume and dimensionality of the metadata elements, the complexity of the vector embedding generation and indexing algorithms, and the efficiency of the database partitioning and persistence mechanisms. The space complexity is determined by the size of the vector embeddings, associated metadata, and any auxiliary data structures used for indexing and partitioning.

[0934] Semantic Similarity Search and Retrieval: VDA enables efficient and contextual retrieval of ISO 11179 / ISO 20022-compliant metadata, schemas, mappings, and related artifacts based on semantic similarity and relevance. The search and retrieval process involves the following steps:

[0935] Query parsing and expansion: VDA parses and analyzes user queries, extracting relevant keywords, entities, or semantic concepts. It then expands the query using techniques such as semantic query expansion, synonym resolution, or ontology-based reasoning to enhance the recall and relevance of search results.

[0936] Vector embedding retrieval: VDA uses an indexed vector database and efficient similarity search algorithms (such as cosine similarity, Euclidean distance, or max inner product search) to retrieve vector embeddings of metadata elements, relationships, mappings, and semantic representations that are semantically similar or relevant to the expanded user query.

[0937] Ranking and filtering: VDA ranks the retrieved vector embeddings based on semantic similarity scores, contextual relevance, and user-defined criteria using techniques such as TF-IDF weighting, BM25 scoring, or learned ranking models. It also applies domain-specific filters like category constraints, faceted navigation, or data management rules to refine search results.

[0938] Result aggregation and presentation: VDA aggregates the ranked and filtered search results, retrieving associated metadata, patterns, mappings, and semantic representations from the vector database. It then presents the results to the user or application in a structured and intuitive format, along with relevant contextual information and navigation cues.

[0939] The time complexity of the semantic similarity search and retrieval process depends on the size of the vector database, the dimensionality of the vector embeddings, the complexity of query expansion and ranking algorithms, and the efficiency of similarity search and result aggregation mechanisms. The space complexity is determined by the size of the retrieved vector embeddings, associated metadata, and any intermediate data structures used for ranking and filtering.

[0940] Domain-specific optimizations and analytics: VDA implements domain-specific optimizations and analytics to enhance the usability, performance, and insights derived from ISO 11179 / ISO 20022 metadata and patterns. The optimization and analytics process involves the following steps:

[0941] Taxonomy navigation and faceted search: VDA optimizes the search and retrieval process for financial industry taxonomies and hierarchies by implementing taxonomy navigation and faceted search functionality. It leverages the hierarchical relationships and semantic properties encoded in ISO 11179 / ISO 20022 metadata to enable users to browse and filter search results based on taxonomy categories, aspects, or properties.

[0942] Cross-Category and Cross-Jurisdictional Querying: VDA supports complex querying scenarios such as cross-category and cross-jurisdiction metadata discovery by leveraging cross-taxonomy mappings and semantic representations stored in the vector database. Semantic similarity and alignment techniques enable users to search and navigate metadata across different financial industry domains, standards, and jurisdictions.

[0943] Semantic Clustering and Topic Modeling: VDA applies semantic clustering and topic modeling techniques such as k-means clustering, hierarchical clustering, or latent Dirichlet allocation to group and classify ISO 11179 / ISO 20022 metadata and schema based on their semantic similarity and underlying topics. This facilitates user discovery and exploration of related metadata, identification of patterns and trends, and deep understanding of the semantic structure of financial industry knowledge.

[0944] Impact Analysis and Semantic Traceability: VDA enables impact analysis and semantic traceability by leveraging cross-taxonomy mappings, semantic representations, and contextual information stored in the vector database. It allows users to assess the potential impact of changes to metadata, schema, or rules on related artifacts and trace semantic ancestry and dependencies across different financial industry domains and standards.

[0945] The time complexity of the domain-specific optimization and analysis processes depends on the number and complexity of ISO 11179 / ISO 20022 metadata and schema, the efficiency of taxonomy navigation and faceted search algorithms, the complexity of semantic clustering and topic modeling techniques, and the performance of impact analysis and traceability mechanisms. The space complexity is determined by the size of the vector embeddings, associated metadata, and any intermediate data structures used for clustering, modeling, and analysis.

[0946] Business Layer

[0947] Process:

[0948] Metadata Ingestion and Vector Embedding Generation: This process involves ingesting ISO 11179 / ISO 20022-aligned metadata, DataVault2.0 schema, cross-taxonomy mappings, and semantic representations received from CTMAA and generating vector embeddings that capture their semantic meaning and contextual information.

[0949] Vector Database Storage and Indexing: This process focuses on storing and indexing the generated vector embeddings and their associated metadata, schema, mappings, and semantic representations in a high-performance, scalable, and distributed vector database using advanced techniques such as hierarchical navigable small-world graphs, approximate nearest neighbor search, or locality-sensitive hashing.

[0950] Semantic similarity search and retrieval: This process involves parsing and expanding user queries, retrieving semantically similar or related vector embeddings from the index vector database, ranking and filtering search results based on semantic similarity scores, contextual relevance, and user-defined criteria, and aggregating results and presenting them to users or applications.

[0951] Domain-specific optimizations and analytics: This process focuses on implementing domain-specific optimizations and analytics functions such as taxonomy navigation, faceted search, cross-taxonomy and cross-jurisdiction queries, semantic clustering, topic modeling, impact analysis, and semantic traceability to enhance the usability, performance, and insights derived from ISO 11179 / ISO 20022 metadata and schema.

[0952] API-driven query and result delivery: This process involves providing scalable, high-performance API-driven query interfaces for users and applications to search, retrieve, and explore ISO 11179 / ISO 20022-compatible metadata, schema, mappings, and related artifacts based on semantic similarity, contextual relevance, and user-defined criteria, and deliver results in structured and intuitive formats.

[0953] Features:

[0954] Efficient and scalable metadata storage: VDA enables efficient and scalable storage of ISO 11179 / ISO 20022-aligned metadata, DataVault 2.0 schema, cross-taxonomy mappings, and semantic representations in high-performance distributed vector databases, ensuring high availability, fault tolerance, and data durability.

[0955] Semantic similarity-based retrieval: VDA uses advanced indexing techniques and efficient similarity search algorithms to facilitate fast, accurate, and context-aware retrieval of related metadata, schema, mappings, and artifacts based on semantic similarity and relevance.

[0956] Domain-specific optimizations: VDA implements domain-specific optimizations tailored to financial industry needs, such as taxonomy navigation, faceted search, and semantic query expansion, to enhance the usability and effectiveness of metadata discovery and retrieval.

[0957] Advanced analytics and visualization: VDA provides advanced analytics and visualization capabilities such as semantic clustering, topic modeling, and network analysis to gain insights, patterns, and relationships from ISO 11179 / ISO 20022 metadata and schema, supporting data-driven decision-making and knowledge discovery.

[0958] Application layer

[0959] VDA includes the following key components:

[0960] MetadataIngesionProcessor: This component ingests and pre-processes ISO 11179 / ISO 20022-aligned metadata, DataVault 2.0 schemas, cross-taxonomy mappings, and semantic representations received from the CTMAA for vector embedding generation and database storage.

[0961] VectorEmbeddingGenerator: This component generates vector embeddings of metadata elements, relationships, mappings, and semantic representations using techniques such as word embeddings, graph embeddings, or semantic encoders, capturing their semantic meaning and contextual information.

[0962] VectorDatabaseIndexer: This component indexes the generated vector embeddings and their associated metadata, schemas, mappings, and semantic representations in a high-performance, scalable, and distributed vector database using advanced techniques such as hierarchical navigable small-world graphs, approximate nearest neighbor search, or locality-sensitive hashing.

[0963] SemanticSimilaritySearchEngine: This component enables fast, accurate, and context-aware retrieval of relevant metadata, schemas, mappings, and artifacts based on semantic similarity and relevance using efficient similarity search algorithms and ranking techniques.

[0964] DomainOptimizationAnalytics: This component implements domain-specific optimization and analytics functions such as taxonomy navigation, faceted search, cross-taxonomy querying, semantic clustering, topic modeling, impact analysis, and semantic traceability to enhance usability, performance, and insights derived from ISO 11179 / ISO 20022 metadata and schemas.

[0965] Services:

[0966] MetadataIngesionService: Provides methods for ingesting and pre-processing ISO 11179 / ISO 20022-aligned metadata, DataVault 2.0 schemas, cross-taxonomy mappings, and semantic representations received from the CTMAA.

[0967] VectorEmbeddingService: Provides services for generating vector embeddings of metadata elements, relationships, mappings, and semantic representations, capturing their semantic meaning and contextual information.

[0968] VectorDatabaseIndexingService: This service enables indexing and storing of vector embeddings and their associated metadata, schemas, mappings, and semantic representations in high-performance, scalable, and distributed vector databases.

[0969] SemanticSimilaritySearchService: This service facilitates fast, accurate, and context-aware retrieval of relevant metadata, schemas, mappings, and artifacts based on semantic similarity and relevance.

[0970] DomainOptimizationAnalyticsServices: Provides services for implementing domain-specific optimization and analytics functions such as taxonomy navigation, faceted search, cross-category querying, semantic clustering, topic modeling, impact analysis, and semantic traceability.

[0971] QueryingAPIService: Provides an extensible, high-performance API-driven interface for users and applications to search, retrieve, and explore metadata, schemas, mappings, and artifacts based on semantic similarity, contextual relevance, and user-defined criteria.

[0972] Interface:

[0973] MetadataIngesionInterface: This interface defines methods and parameters for ingesting and preprocessing ISO 11179 / ISO20022-aligned metadata, DataVault 2.0 schemas, cross-category mappings, and semantic representations received from CTMAA.

[0974] VectorEmbeddingInterface: This interface specifies methods and input / output formats for generating vector embeddings for metadata elements, relationships, mappings, and semantic representations.

[0975] VectorDatabaseIndexingInterface: Describes methods and parameters for indexing and storing vector embeddings in high-performance, scalable, and distributed vector databases, along with their associated metadata, schemas, mappings, and semantic representations.

[0976] SemanticSimilaritySearchInterface: Defines methods and input / output formats for fast, accurate, and context-aware retrieval of relevant metadata, schemas, mappings, and artifacts based on semantic similarity and relevance.

[0977] Domain Optimization Analytics Interface: Specifies methods and parameters for implementing domain-specific optimization and analytics capabilities, such as category navigation, faceted search, cross-category query, semantic clustering, topic modeling, impact analysis, and semantic traceability.

[0978] Querying API Interface: Describes methods and input / output formats for providing a scalable, high-performance, and API-driven querying interface for users and applications to search, retrieve, and explore metadata, schemas, mappings, and artifacts based on semantic similarity, contextual relevance, and user-defined criteria.

[0979] Data Lineage and Provenance Tracking Agent (DLPTA):

[0980] Data lineage and provenance tracking agent (DLPTA) is a key component of MEGAN that implements comprehensive data lineage and provenance tracking capabilities to capture and maintain complete audit trails of system metadata management, schema generation, and mapping processes. The DLPTA enables end-to-end traceability, reproducibility, and management of generated artifacts by leveraging advanced graph-based and temporal modeling techniques.

[0981] The DLPTA employs a combination of provenance graphs and temporal databases to represent and store complex relationships and dependencies between data elements, schemas, mappings, and processing activities. This allows the agent to capture and maintain detailed metadata about each step in the data lifecycle, including data sources, transformations, quality checks, validations, and usage, providing a rich and contextualized view of data provenance, evolution, and impact.

[0982] To ensure consistency and completeness of audit trails, the DLPTA integrates with other agents in the MEGAN architecture, automatically capturing and propagating lineage and provenance metadata as data flows through the system. This minimizes manual effort and reduces the risk of errors or omissions in lineage and provenance records.

[0983] The DLPTA provides intuitive querying and visualization interfaces that allow users to easily explore and analyze data lineage and provenance information. These interfaces enable users to trace the provenance and evolution of specific data elements, schemas, and mappings, understand their dependencies and impact, and gain insights into data quality, consistency, and compliance with governance policies and regulatory requirements.

[0984] In addition, DLPTA also supports sophisticated traceability and impact analysis scenarios, such as determining downstream consequences of metadata changes, troubleshooting data quality issues, and assessing compliance with data governance policies and regulatory requirements. By leveraging the rich lineage and provenance information captured by the agents, users can quickly identify and resolve issues, minimize the risk of data inconsistencies, and ensure the overall integrity and reliability of the generated artifacts.

[0985] To facilitate communication, collaboration, and knowledge sharing among stakeholders, the data dictionary and data catalog compile comprehensive lineage reports, data glossaries, and data catalogs that document the end-to-end flow and transformation of metadata. These artifacts provide a clear and concise view of the lineage and provenance of data, enabling users to quickly understand the context, purpose, and quality of data.

[0986] Finally, DLPTA continuously monitors and verifies the integrity and consistency of the captured lineage and provenance metadata, detecting and alerting any anomalies, gaps, or inconsistencies that may indicate data quality or compliance issues. This proactive monitoring ensures that the lineage and provenance information remains accurate, up-to-date, and trustworthy, supporting effective data governance and decision-making.

[0987] By implementing comprehensive data lineage and provenance tracking capabilities, DLPTA plays a crucial role in ensuring the traceability, reproducibility, and governance of MEGAN's metadata management, schema generation, and mapping processes. This enables organizations to maintain a complete and reliable audit trail of their data assets, comply with regulatory requirements, and make informed decisions based on a deep understanding of the provenance, evolution, and impact of data.

[0988] Method

[0989] DLPTA employs advanced graph-based and temporal modeling techniques, integrated methods, and monitoring and verification mechanisms to ensure comprehensive and reliable data lineage and provenance tracking.

[0990] Provenance graph modeling: DLPTA uses provenance graphs to represent and store the complex relationships and dependencies between data elements, schemas, mappings, and processing activities. The provenance graph modeling process involves the following steps:

[0991] Provenance data capture: DLPTA captures detailed metadata about each step in the data lifecycle, including data sources, transformations, quality checks, validations, and usage, by integrating with other agents in the MEGAN architecture and automatically extracting relevant source information.

[0992] Graph Construction: DLPTA constructs a Directed Acyclic Graph (DAG) representation of captured provenance data, where nodes represent data entities, processing activities, or agents, and edges represent their relationships or dependencies. The graph is enriched with temporal and contextual attributes to provide a comprehensive view of data provenance and lineage.

[0993] Graph Storage and Indexing: DLPTA stores the provenance graph in a graph database or specialized provenance storage, which supports efficient querying, traversal, and analysis of graph structures. The graph is indexed based on various attributes, such as data entity identifiers, timestamps, or source types, to enable fast and targeted retrieval of provenance information.

[0994] Graph Querying and Traversal: DLPTA provides graph querying and traversal capabilities, enabling users to quickly explore and analyze the provenance graph. This includes support for pattern matching, shortest path queries, reachability analysis, and subgraph extraction, allowing users to trace the lineage and impact of specific data elements, patterns, or mappings.

[0995] The time complexity of the provenance graph modeling process depends on the size and complexity of the data lifecycle, the number of captured entities and relationships, and the efficiency of the employed graph construction, storage, and querying algorithms. The space complexity is determined by the size of the provenance graph, the number of stored attributes and temporal dimensions, and any indexing structures used for efficient querying and traversal.

[0996] Temporal Provenance Modeling: DLPTA uses temporal database techniques to capture and represent the temporal aspects of data provenance and lineage. The temporal provenance modeling process involves the following steps:

[0997] Temporal Data Capture: DLPTA captures temporal metadata associated with each provenance event, such as start and end timestamps of processing activities, valid and transaction times of data entities, and temporal validity of relationships or dependencies.

[0998] Temporal Schema Design: DLPTA designs a temporal schema that extends the temporal dimensions of the provenance graph model, such as valid time, transaction time, or bitemporal attributes. The temporal schema allows for representing and querying historical and current states of data provenance and lineage.

[0999] Temporal Data Storage: DLPTA stores temporal provenance data in a database system that supports temporal querying and reasoning, such as a temporal relational database, temporal graph database, or specialized temporal provenance storage. Temporal data is organized and indexed based on temporal dimensions to enable efficient retrieval and analysis of provenance information across time.

[1000] Temporal Query and Analysis: DLPTA provides temporal query and analysis capabilities to enable users to explore and reason about the temporal aspects of data provenance and lineage. This includes support for temporal slice queries, temporal aggregation, temporal join, and temporal pattern matching, allowing users to understand the evolution and validity of data entities, patterns, and mappings over time.

[1001] The temporal complexity of the time provenance modeling process depends on the size and complexity of the time-traced data, the granularity and range of the temporal dimensions, and the efficiency of the temporal schema design, storage, and query techniques employed. The spatial complexity is determined by the size of the time-traced data, the number of stored temporal attributes and dimensions, and any index structures used for efficient temporal query and analysis.

[1002] Integration and Propagation: DLPTA integrates with other agents in the MEGAN architecture to automatically capture and propagate provenance and lineage metadata as data flows through the system. The integration and propagation process involves the following steps:

[1003] Provenance Metadata Extraction: DLPTA defines standard interfaces and protocols for extracting provenance metadata from other agents in the MEGAN architecture, such as data ingestion, schema generation, and mapping agents. This includes specifying the format, structure, and semantics of the provenance metadata to be captured.

[1004] Provenance Metadata Propagation: DLPTA establishes communication channels and data flow mechanisms to propagate captured provenance metadata between different agents and components of MEGAN. This ensures that the provenance and lineage information is consistently and continuously updated as data moves through the system.

[1005] Provenance Metadata Integration: DLPTA integrates the propagated provenance metadata into centralized provenance graphs and temporal provenance models, merging and reconciling any overlapping or conflicting information. This involves applying data integration techniques such as entity resolution, pattern matching, and data fusion to ensure the consistency and accuracy of the integrated provenance metadata.

[1006] Provenance Metadata Synchronization: DLPTA implements mechanisms for synchronizing provenance metadata across agents and components of MEGAN, ensuring that all parties have access to the latest and consistent view of data provenance and lineage. This may involve techniques such as distributed version control, conflict resolution, and real-time updates.

[1007] The temporal complexity of the integration and propagation process depends on the number of agents and components involved, the volume and frequency of provenance metadata updates, and the efficiency of the extraction, propagation, integration, and synchronization mechanisms employed. The spatial complexity is determined by the size of the provenance metadata exchanged and stored across different agents and components, as well as any intermediate data structures used for integration and synchronization.

[1008] Monitoring and Verification: DLPTA continuously monitors and verifies the integrity and consistency of captured provenance and traceability metadata, detecting and alerting any anomalies, gaps, or inconsistencies. The monitoring and verification process involves the following steps:

[1009] Provenance Data Quality Checks: DLPTA defines and executes a set of data quality checks and verification rules to assess the completeness, accuracy, consistency, and timeliness of captured provenance metadata. This includes checking for missing or invalid values, detecting inconsistencies or contradictions in provenance graphs or temporal provenance models, and verifying adherence to predefined data quality standards or constraints.

[1010] Anomaly Detection: DLPTA applies anomaly detection techniques, such as statistical analysis, pattern matching, or machine learning algorithms, to identify anomalies or suspicious patterns in provenance metadata that may indicate data quality issues, data drift, or potential compliance violations. This involves establishing baseline profiles and threshold values for standard provenance patterns and detecting deviations or outliers from these profiles.

[1011] Alerts and Notifications: DLPTA generates alerts and notifications when data quality issues, anomalies, or inconsistencies are detected in provenance metadata. Alerts are triggered based on predefined rules and threshold values. They are sent to relevant stakeholders, such as data administrators, data owners, or compliance officers, for further investigation and resolution.

[1012] Provenance Data Cleaning and Harmonization: Data cleaning and harmonization provides mechanisms to clean and harmonize provenance metadata to address any detected data quality issues or inconsistency problems. This includes applying data cleaning techniques, such as data standardization, duplicate removal, or data imputation, and harmonizing conflicting or inconsistent provenance information across different sources or agents.

[1013] The time complexity of the monitoring and verification process depends on the volume and complexity of provenance metadata, the number and complexity of data quality checks and verification rules, and the efficiency of employed anomaly detection and alert algorithms. The size of provenance metadata determines the space complexity, the number of maintained data quality metrics and thresholds, and any intermediate data structures used for anomaly detection and cleaning.

[1014] Business Layer

[1015] Process:

[1016] Provenance Metadata Capture and Extraction: This process involves capturing and extracting detailed metadata about each step in the data lifecycle, including data sources, transformations, quality checks, verifications, and usage, through integration with other agents in the MEGAN architecture.

[1017] Provenance graph construction and storage: This process focuses on constructing a directed acyclic graph (DAG) representation of captured provenance data and storing it in a graph database or specialized provenance store that supports efficient querying, traversal, and analysis.

[1018] Temporal provenance modeling and storage: This process involves designing temporal schemas that extend the provenance graph model with a temporal dimension and storing temporal provenance data in a database system that supports temporal querying and reasoning.

[1019] Provenance metadata integration and propagation: This process integrates provenance metadata captured from different agents and components of MEGAN, propagates the metadata across the system, and synchronizes it to ensure consistency and accuracy.

[1020] Provenance data quality monitoring and validation: This process involves continuously monitoring and validating the integrity and consistency of captured provenance and metadata, detecting anomalies, triggering alerts, and applying data cleansing and reconciliation techniques to address data quality issues.

[1021] Features:

[1022] End-to-end traceability and reproducibility: DLPTA enables end-to-end traceability and reproducibility of metadata management, schema generation, and mapping processes executed by MEGAN, providing a complete audit trail of data provenance, evolution, and impact.

[1023] Comprehensive provenance capture and storage: DLPTA uses advanced graph-based and temporal modeling techniques to capture and store detailed metadata about each step in the data lifecycle, ensuring a rich and contextualized view of data provenance and provenance.

[1024] Seamless integration and propagation: DLPTA integrates with other agents in the MEGAN architecture to automatically capture and propagate provenance and metadata as data flows through the system, minimizing manual effort and ensuring consistency and completeness of the audit trail.

[1025] Intuitive querying and visualization: DLPTA provides intuitive querying and visualization interfaces that allow users to quickly explore and analyze data provenance and metadata, enabling them to trace the provenance and evolution of specific data elements, schemas, and mappings.

[1026] Proactive data quality monitoring and alerts: DLPTA continuously monitors and validates the integrity and consistency of captured provenance and metadata, detects anomalies, triggers alerts, and applies data cleansing and reconciliation techniques to ensure the accuracy and reliability of provenance information.

[1027] Application layer

[1028] DLPTA includes the following key components:

[1029] ProvenanceMetadataExtractor: This component captures and extracts detailed metadata about each step in the data lifecycle by integrating with other agents in the MEGAN architecture and automatically extracting relevant provenance information.

[1030] ProvenanceGraphConstructor: This component constructs a directed acyclic graph (DAG) representation of the captured provenance data, enriching it with temporal and contextual attributes, and stores it in a graph database or dedicated provenance store.

[1031] TemporalLineageModeler: This component designs a temporal model that extends the provenance graph model with a temporal dimension and stores the temporal provenance data in a database system that supports temporal querying and reasoning.

[1032] ProvenanceMetadataIntegrator: This component integrates provenance metadata captured from different agents and components of MEGAN, propagates the metadata across systems, and synchronizes it to ensure consistency and accuracy.

[1033] ProvenanceDataQualityMonitor: This component continuously monitors and verifies the integrity and consistency of captured provenance and metadata, detects anomalies, triggers alerts, and applies data cleaning and reconciliation techniques to address data quality issues.

[1034] LineageQueryVisualizer: This component provides an intuitive query and visualization interface that allows users to quickly explore and analyze data lineage and provenance information, enabling them to trace the provenance and evolution of specific data elements, patterns, and mappings.

[1035] Services:

[1036] ProvenanceMetadataExtractionService: This service provides methods for capturing and extracting detailed metadata about each step in the data lifecycle by integrating with other agents in the MEGAN architecture.

[1037] ProvenanceGraphConstructionService: This service provides methods for constructing a directed acyclic graph (DAG) representation of captured provenance data, enriching it with temporal and contextual attributes, and storing it in a graph database or dedicated provenance store.

[1038] TemporalLineageModelingService: capable of designing temporal patterns that extend provenance graph models with a temporal dimension and store temporal provenance data in database systems that support temporal querying and reasoning.

[1039] ProvenanceMetadataIntegrationService: facilitates the integration of provenance metadata captured from different agents and components of MEGAN, propagates metadata across systems and synchronizes it to ensure consistency and accuracy.

[1040] ProvenanceDataQualityMonitoringService: provides services for continuously monitoring and verifying the integrity and consistency of captured provenance and metadata, detecting anomalies, triggering alerts, and applying data cleaning and reconciliation techniques to address data quality issues.

[1041] LineageQueryVisualizationService: provides intuitive query and visualization interfaces that allow users to quickly explore and analyze data lineage and provenance information, enabling them to trace the provenance and evolution of specific data elements, patterns, and mappings.

[1042] Interfaces:

[1043] ProvenanceMetadataExtractionInterface: this interface defines methods and parameters for capturing and extracting detailed metadata about each step in the data lifecycle by integrating with other agents in the MEGAN architecture.

[1044] ProvenanceGraphConstructionInterface: specifies methods and input / output formats for constructing a directed acyclic graph (DAG) representation of captured provenance data, enriching it with temporal and contextual attributes, and storing it in a graph database or dedicated provenance store.

[1045] TemporalLineageModelingInterface: describes methods and parameters for designing temporal patterns that extend provenance graph models with a temporal dimension and store temporal provenance data in database systems that support temporal querying and reasoning.

[1046] ProvenanceMetadataIntegrationInterface: defines methods and input / output formats for integrating provenance metadata captured from different agents and components of MEGAN, propagating metadata across systems, and synchronizing it to ensure consistency and accuracy.

[1047] ProvenanceDataQualityMonitoringInterface: Specifies methods and parameters for continuously monitoring and verifying the integrity and consistency of captured provenance and provenance metadata, detecting anomalies, triggering alerts, and applying data cleansing and harmonization techniques to address data quality issues.

[1048] SEPHYR

[1049] SEPHYR (Self-Evolving Pattern Harmonization for Unified Reporting) is an agent-based module that harmonizes different data patterns and characteristics into a unified and verifiable representation for reliable and efficient reporting across analysis and decision-making processes. At its core, SEPHYR employs a synergistic integration of three specialized agents collaborating through continuous optimization cycles supervised by a Petri net choreography model.

[1050] Main advantages of SEPHYR:

[1051] Digital native mathematical representation: This approach uses a unique and dynamic numbering system based on natural number sequences to represent data characteristics and relationships, ensuring non-overlapping and unified encoding.

[1052] Self-cataloging functionality: Supervised learning models are employed to enable self-discovery and cataloging of data columns, rows, or cells into standard object / characteristic models.

[1053] Proof of ownership consensus: Utilizing consensus methods to define primary parent-child relationships and categorize encodings ensures accurate data provenance and provenance.

[1054] Univariate and multivariate support: Mathematical algorithms are combined to handle single-variable (single characteristic) and multi-variable (characteristic combinations) data at different depths.

[1055] Cryptographic verifiability: This involves the use of SHA-256-based digital signatures to verify the authenticity and integrity of data records, ensuring immutability and trustworthiness.

[1056] SEPHYR agents:

[1057] SEPHYR agent architecture includes three key agents, each with specific roles and responsibilities, working together to ensure the harmonization of data patterns and characteristics:

[1058] SAFFRON (Self-Adaptive Feature Fusion and Representation Orchestration Network): SAFFRON is responsible for identifying, extracting, encoding, and cataloging data features into a standard object / feature model. It utilizes natural language processing, machine learning, mathematical algorithms, and consensus mechanisms to ensure accurate feature representation, data provenance, and traceability.

[1059] Key components of SAFFRON include:

[1060] FeatureIdentifier: Identifies and extracts relevant features from input data.

[1061] FeatureEncoder: Assigns unique numerical codes to features based on natural number sequences.

[1062] FeatureClassifier: Catalogs data into a common object / feature model using supervised learning models.

[1063] OwnershipConsensus: Establishes consensus on primary parent-child relationships and classification codes.

[1064] DataProcessor: Handles univariate and multivariate data processing.

[1065] FeatureStore: Centralized authority feature store for unified data models, encoding, and classification patterns.

[1066] GAMA (Governance-Aware Multilevel Access Management Architecture): GAMA converts user roles, requirements, and data access patterns into a unified mathematical representation for efficient role management, user access control, and security configuration. It utilizes natural language processing, machine learning, mathematical algorithms, consensus mechanisms, and encryption techniques.

[1067] Key components of GAMA include:

[1068] RoleIdentifier: Identifies and extracts relevant roles and security features.

[1069] RoleEncoder: Assigns unique numerical codes to roles and features.

[1070] AccessClassifier: Catalogs user access to roles and user associations to roles.

[1071] Management Consensus: Establish consensus on regulatory requirements, toxic combinations, and data shielding rules.

[1072] Access Control Engine: Central access control engine that unifies the security access model.

[1073] SENTINEL (Secure, Efficient, and Intelligent Access Control System): SENTINEL employs intelligent search algorithms to effectively manage and apply security access rules, enabling granular control of user access to data based on roles, hierarchies, and regulatory or organizational policies.

[1074] Key components of SENTINEL include:

[1075] UserProfileManager: Manages user profiles, roles, and positions.

[1076] SecurityRuleManager: Defines, maintains, and updates security rules.

[1077] AccessControlEngine: Implements defined security access rules during user access attempts.

[1078] RuleAdaptationMonitor: Monitors changes in requirements and policies to ensure adaptation and compliance.

[1079] SAFFRON, GAMA, and SENTINEL agents are orchestrated using Petri net models, which model their dependencies and interactions. This ensures coordinated and efficient execution of data coordination, access management, and security control tasks.

[1080] SAFFRON

[1081] SAFFRON (Self-Adapting Feature Fusion and Representation Orchestration Network) is an advanced agent that coordinates different data features into a unified and verifiable representation for efficient cataloging, searching, matching, and metadata management. At its core, SAFFRON employs a synergistic integration of specialized agents that collaborate through successive optimization cycles, supervised by a Petri net orchestration model.

[1082] SAFFRON agents (including FeatureIdentifier, FeatureEncoder, FeatureClassifier, OwnershipConsensus, DataProcessor, and FeatureStore) work collaboratively to identify, extract, encode, and catalog data features into a standard object / feature model. This process involves the use of natural language processing, machine learning, mathematical algorithms, and consensus mechanisms to ensure accurate feature representation, data provenance, and traceability.

[1083] SAFFRON's digital native mathematical representation, based on a unique dynamic numbering system, ensures non-overlapping and uniform encoding of data features and their relationships. Its self-cataloging functionality, supported by supervised learning models, enables automatic discovery and cataloging of data columns, rows, and cells, thereby reducing manual effort and enabling scalability.

[1084] Additionally, SAFFRON incorporates an ownership proof consensus method to define primary parent-child relationships and categorical encoding, ensuring accurate data management and regulatory compliance. It also supports single-variable and multi-variable data payloads, processing complex feature combinations of varying depths through general-purpose mathematical algorithms.

[1085] The trustworthiness of SAFFRON is its cryptographic verifiability through SHA-256 digital signatures, verifying the authenticity, integrity, and immutability of data records stored in the central authority's feature store. This robust feature repository is a unified repository for efficient data search, matching, and metadata management across consumer networks. SAFFRON, through its modular design and well-defined interfaces, integrates with enterprise data sources, identity systems, regulatory compliance platforms, business intelligence tools, and model serving environments, enabling flexible deployment in cloud, on-premises, or hybrid architectures.

[1086] Method

[1087] The self-built feature store employs advanced techniques to convert analytical and reporting feature columns and values into a unified mathematical representation, enabling efficient data cataloging, search, matching, and metadata management, even for large-scale datasets.

[1088] Digital native mathematical representation:

[1089] The self-built feature store uses a unique and dynamic numbering system based on natural number sequences to represent data features and their relationships (parent, child, combined permutations). This mathematical representation ensures non-overlapping and uniform encoding of data values, enabling efficient processing and analysis. The process involves the following steps:

[1090] Feature Recognition: A self-constructed feature repository uses natural language processing (NLP) and machine learning techniques to identify and extract relevant features from input data (such as columns, rows, or cells).

[1091] Feature Encoding: Each identified feature is assigned a unique numerical code based on its position in a natural number sequence, ensuring non-overlapping representation.

[1092] Relationship Modeling: Using mathematical operations and data structures, the self-constructed feature repository models relationships between features, such as parent-child hierarchies and combinatorial arrangements.

[1093] Mathematical Representation: Encoded features and their relationships are combined to create a unified mathematical representation of the data, enabling efficient storage, retrieval, and analysis.

[1094] The time complexity of the numerical native mathematical representation process depends on the number of features and their relationships, as well as the complexity of the feature recognition and encoding algorithms employed. The space complexity is determined by the size of the input data and the mathematical representation of the data structures.

[1095] Self-Cataloged Feature Dictionary:

[1096] The self-constructed feature repository employs a supervised learning classification model to enable the system to self-discover columns, rows, or cells of data and catalog them into a standard object / feature model. The self-cataloging process includes the following steps:

[1097] Training Data Preparation: The self-constructed feature repository prepares a training dataset by manually labeling a subset of input data with feature classification and annotation.

[1098] Model Training: The system trains a supervised learning classification model using the labeled training data, such as deep learning neural networks or ensemble methods.

[1099] Feature Classification: The trained model is used to classify and catalog the remaining input data into a shared object / feature model based on learned patterns and relationships.

[1100] Model Retraining: The self-constructed feature repository continuously monitors classification performance and retrains the model with additional labeled data to improve accuracy and adapt to changes in data distribution.

[1101] The time complexity of the self-cataloging process depends on the size of the input data, the complexity of the classification model, and the number of training iterations required. The size of the training data and the trained classification model determine the space complexity.

[1102] Ownership Consensus Proof:

[1103] The self-building feature store employs a consensus method that uses proof of ownership to define primary parent-child relationships and classification codes. The consensus process includes the following steps:

[1104] Ownership claim submission: Data owners or stakeholders submit claims of ownership over specific features or combinations of features, along with supporting evidence or documentation.

[1105] Claim verification: The self-building feature store verifies the submitted claims by validating the provided evidence and cross-checking against existing ownership records.

[1106] Consensus calculation: The system calculates a consensus score for each claim based on the strength of the evidence, the number of supporting claims, and the reputation or trust score of the claimant.

[1107] Consensus resolution: The self-building feature store resolves conflicting claims and establishes primary parent-child relationships and classification codes based on consensus scores and predefined resolution mechanisms.

[1108] The time complexity of the proof of ownership consensus process depends on the number of ownership claims, the complexity of the verification and consensus calculation algorithms, and the number of stakeholders involved. The space complexity is determined by the size of the ownership-required data and associated metadata. By employing these methods, the self-building feature store enables the creation of a unified data model, coding, and classification schema within a central authority feature store, leading to a common language across consumer networks and facilitating efficient data cataloging, searching, matching, and metadata management.

[1109] Business layer

[1110] Process:

[1111] Data ingestion and preprocessing: This process involves collecting and preprocessing data from various sources, ensuring data quality and consistency through validation and transformation techniques.

[1112] Feature identification and coding: This process focuses on identifying and extracting relevant features from input data, assigning unique numerical codes, and modeling their relationships using mathematical operations and data structures.

[1113] Feature classification and cataloging: This process employs supervised learning classification models to enable the system to self-discover columns, rows, or data cells and catalog them into a common object / feature model, continuously improving classification accuracy through model retraining.

[1114] Ownership consensus and coding: This process uses proof of ownership methods to establish consensus on primary parent-child relationships and classification codes, resolving conflicting claims and ensuring accurate data provenance and traceability.

[1115] Functionality:

[1116] Unified Data Representation: The self-building feature store enables the creation of a unified mathematical representation of data features and their relationships, facilitating efficient data processing, analysis, and storage.

[1117] Automatic Feature Discovery and Cataloging: The self-building feature store automates the feature discovery and cataloging process using supervised learning classification models. This reduces manual work and enables scalability for large-scale datasets.

[1118] Data Governance and Provenance: The ownership consensus proof method ensures accurate data provenance and traceability, enabling effective data governance and supporting regulatory compliance.

[1119] Efficient Data Search and Matching: The unified data model, encoding, and classification schema within the central authority feature store enable efficient data search, matching, and metadata management across consumer networks.

[1120] Application Layer

[1121] The self-building feature store includes the following key components:

[1122] Feature Identifier: This component uses NLP and machine learning techniques to identify and extract relevant features from input data.

[1123] Feature Encoder: This component assigns unique numerical codes to identified features based on their position in the natural number sequence and models their relationships using mathematical operations and data structures.

[1124] Feature Classifier: This component employs supervised learning classification models to catalog columns, rows, or data cells into a common object / feature model, continuously improving classification accuracy through model retraining.

[1125] Ownership Consensus: This component uses ownership proof methods to establish consensus on primary parent-child relationships and classification encoding, resolving conflicting claims and ensuring accurate data provenance and traceability.

[1126] Feature Store: This component is the central authority feature store that stores the unified data model, encoding, and classification schema, enabling efficient data search, matching, and metadata management across consumer networks.

[1127] Services:

[1128] Feature Identification Service: Provides methods for identifying and extracting relevant features from input data using NLP and machine learning techniques.

[1129] FeatureEncodingService: Provides services for assigning unique numerical codes to identified features and modeling their relationships using mathematical operations and data structures.

[1130] FeatureClassificationService: Enables cataloging of columns, rows, or cells of data into standard object / feature models using supervised learning classification models, including model training and retraining capabilities.

[1131] OwnershipConsensusService: Facilitates establishing consensus on primary parent-child relationships and classification codes using proof-of-ownership methods, resolving conflicting claims, and ensuring accurate data lineage and provenance.

[1132] FeatureStoreReservice: Provides services for storing, retrieving, and managing uniform data models, encoding, and classification patterns within a central authority feature store, enabling efficient data search, matching, and metadata management across consumption networks.

[1133] Interface:

[1134] FeatureIdentifierInterface defines methods and parameters for identifying and extracting relevant features from input data using NLP and machine learning techniques.

[1135] FeatureEncoderInterface: Specifies methods and parameters for assigning unique numerical codes to identified features and modeling their relationships using mathematical operations and data structures.

[1136] FeatureClassifierInterface: Describes methods and parameters for cataloging columns, rows, or cells of data into standard object / feature models using supervised learning classification models, including model training and retraining capabilities.

[1137] OwnershipConsensusInterface: Provides methods and parameters for establishing consensus on primary parent-child relationships and classification codes using proof-of-ownership methods, resolving conflicting claims, and ensuring accurate data lineage and provenance.

[1138] FeatureStoreInterface defines methods and parameters for storing, retrieving, and managing uniform data models, encoding, and classification patterns within a central authority feature store. This interface enables efficient data search, matching, and metadata management across consumption networks.

[1139] Figure 4is a functional block diagram 400 illustrating an example of a data management and access control system within a multi-agent artificial intelligence framework according to embodiments herein. The data management and access control system corresponds to embodiments of the SEPHYR agent-based framework.

[1140] SAFFRON (Self-Adaptive Feature Fusion and Representation Orchestration Network) 402:

[1141] SAFFRON 402 is a data ingestion interface and feature extraction module that identifies, extracts, encodes data features and catalogues them into standardized models, serving as the basic unit of data representation within the SEPHYR system.

[1142] SAFFRON 402 provides a unified data representation as input to both GAMA 404 and SENTINEL 406, ensuring that these subsequent systems receive consistent processing and standardized data.

[1143] SAFFRON 402 maintains an association with upstream data sources from which it collects and processes input data, ensuring a comprehensive and up-to-date data catalog.

[1144] SAFFRON 402 includes a security module that employs cryptographic hash functions to encrypt the extracted features, thereby generating secure data representations. A validation module in SAFFRON 402 validates this unified data representation against predefined standards, ensuring integrity and compliance.

[1145] GAMA (Governance Aware Multi-Level Access Management Architecture) 404:

[1146] GAMA 404 is an access management module that translates user roles, permissions, and policy information into integrated access models, serving as the system core for access management and policy integration.

[1147] GAMA 404 provides these encoded access models as input to SENTINEL 406, which uses them to enforce access controls.

[1148] GAMA 404 is also associated with the data representations of SAFFRON 402 to align access models with the latest data features and regulatory sources, ensuring compliance and relevance in their policy applications.

[1149] GAMA 404 can encode user roles and access permissions into unique numerical codes and includes a control engine that serves as the authority for enforcing access controls based on the unified security access models. GAMA 404 is also configured to automatically synchronize its access control rules with external compliance monitoring systems to remain compliant with regulatory changes.

[1150] SENTINEL (Secure, Efficient, and Intelligent Access Control System) 406:

[1151] SENTINEL 406 employs algorithms to effectively manage and apply security access rules across the SEPHYR system.

[1152] SENTINEL 406 is associated with both SAFFRON 402 and GAMA 404, receiving inputs including a unified data representation from SAFFRON and integrated access models from GAMA.

[1153] SENTINEL 406 enforces access rules on the data model provided by SAFFRON 402 and applies access control policies provided by GAMA 404, ensuring secure and effective control of data access.

[1154] As shown in the overview of the relationship: Figure 4

[1155] SAFFRON 402 acts as the primary provider of a unified encoded data representation, extracting, encoding, and cataloging features into a standard model. This foundational data is then provided to GAMA 404 and SENTINEL 406. GAMA 404 further processes this data based on the data representation from SAFFRON 402 and inputs from regulatory sources by translating roles, permissions, and policies into coherent access control models. These models are then provided to SENTINEL 406. SENTINEL 406 utilizes these models to manage and enforce detailed access control, applying complex rules on the data infrastructure formed by the representation of SAFFRON 402 and the policy framework of GAMA 404.

[1156] GAMA

[1157] GAMA (Governance Aware Multi-level Access Management Architecture) is a high-level intelligent agent that translates user roles, requirements, and data access patterns into a unified mathematical representation for efficient role management, user access control, and security configuration across large enterprise systems. At its core, GAMA employs a synergistic integration of specialized intelligent agents that collaborate through continuous optimization loops supervised by a Petri net orchestration model.

[1158] ​GAMA agents, including RoleIdentifier, RoleEncoder, AccessClassifier, RegulationConsensus, and accessControllengine, work collaboratively to identify, extract, encode, classify, and enforce user roles and access rights into a centralized access control model. This process involves the utilization of natural language processing, machine learning, mathematical algorithms, consensus mechanisms, and encryption techniques to ensure accurate role representation, regulatory compliance, and auditable user access management.

[1159] GAMA's digital native role representation is based on a unique dynamic numbering system that ensures non-overlapping and uniform encoding of user roles, security features, and complex relationships such as hierarchies and privilege inheritance. Its self-cataloging capability driven by supervised learning models like deep neural networks enables automatic discovery and classification of roles, user-role associations, and access privileges, reducing manual work and facilitating scalability.

[1160] Furthermore, GAMA introduces a regulatory consensus approach that defines toxic access combinations, data masking rules, and toxic permission restrictions by establishing evidence-based consensus in stakeholder declarations. This consensus integration mechanism ensures compliance with regulatory requirements and mitigates security risks.

[1161] The foundation of GAMA's trustworthiness is its use of cryptographic identities guaranteed by digital signatures, which verify user identities and enable non-repudiation and secure audit of access control activities. The central access control engine facilitates this robust auditing functionality as the authoritative repository of the unified encoded access control model, enabling efficient role management and user access management.

[1162] With its modular design and well-defined interfaces, GAMA integrates with enterprise user directories, identity providers, identity and access management (IAM) systems, cloud access agents, and API gateways. Its containerized microservices architecture enables flexible deployment across cloud, on-premises, or hybrid environments.

[1163] GAMA's architecture aligns with access governance frameworks such as policy-based access control (POLP) and privacy by design (PbD) principles, promoting ethical considerations such as privacy and least privilege access principles. GAMA's robust verification mechanisms, including regulatory frameworks, enterprise policies, and benchmarking against mock scenarios, as well as comprehensive auditing capabilities, facilitate internal adherence to security and governance standards.

[1164] Method

[1165] The dynamic security access system employs advanced techniques to convert user roles, requirements, and data access patterns into a unified mathematical representation, enabling efficient role management, user access control, and security configuration even for large-scale systems with billions or trillions of records.

[1166] Digital native mathematical representation of user roles:

[1167] The dynamic security access system uses a unique and dynamic numbering system based on natural number sequences to represent user roles, security features, and their relationships (parent, child, combined permutations), including sub-permission inheritance. This mathematical representation ensures non-overlapping and unified encoding of roles, users, and access permissions, enabling efficient processing and analysis. The process involves the following steps:

[1168] Role and feature identification: The system uses natural language processing (NLP) and machine learning techniques to identify and extract relevant roles and security features from input data, such as user management systems, organizational structures, and security policies.

[1169] Role and feature encoding: Each identified role and security feature is assigned a unique numerical code based on its position in the natural number sequence, ensuring non-overlapping representation.

[1170] Relationship modeling: The dynamic security access system models relationships between roles and features using mathematical operations and data structures, such as parent-child hierarchies, permission inheritance, and combined permutations.

[1171] Mathematical representation: The combined encoded roles, features, and their relationships are represented in a unified mathematical representation to create a security access model, enabling efficient storage, retrieval, and analysis.

[1172] The time complexity of the digital native mathematical representation process depends on the number of roles, features, and relationships, as well as the complexity of the identification and encoding algorithms employed. The space complexity is determined by the size of the input data and the mathematical representation of the data structures.

[1173] Self-cataloged role and access dictionary:

[1174] The dynamic security access system employs a supervised learning classification model to enable the system to self-discover and catalog roles, user access to roles, and user associations with roles. The self-cataloging process includes the following steps:

[1175] Training data preparation: The system prepares a training data set by manually labeling a subset of input data with role categories, access patterns, and user associations.

[1176] Model training: The dynamic security access system trains a supervised learning classification model using the labeled training data, such as deep learning neural networks or ensemble methods.

[1177] Role and Access Classification: The trained model classifies and catalogs the remaining input data based on the learned patterns and relationships, including roles, user access to roles, and user associations.

[1178] Model retraining: The system continuously monitors classification performance and retrains the model with additional labeled data to improve accuracy and adapt to changes in secure access models or user behavior patterns.

[1179] The time complexity of the self-cataloging process depends on the size of the input data, the complexity of the classification model, and the number of training iterations required. The size of the training data and the trained classification model determine the space complexity.

[1180] Evidence of management consensus, regulations, and toxic combinations:

[1181] The dynamic secure access system employs a consensus approach, using evidence of ownership to define all toxic combinations and regulatory requirements for data access, dynamic data masking permissions, and tokenization. The consensus process includes the following steps:

[1182] Required Declarations: Management, compliance officers, or stakeholders must submit a declaration of regulatory requirements, toxic combinations, or data blocking rules, along with supporting evidence or documentation.

[1183] Claim verification: The system verifies the submitted claim by verifying the provided evidence and cross-checking it against existing regulatory frameworks and security policies.

[1184] Consensus calculation: The dynamic secure access system calculates a consensus score for each statement based on the strength of the evidence, the number of supporting statements, and the reputation or trust score of the claimant.

[1185] Consensus resolution: The system resolves conflict statements and establishes regulatory requirements, toxic combinations, and data masking rules based on consensus scores and predefined resolution mechanisms.

[1186] The time complexity of proofs in the consensus process depends on the number of required declarations, the complexity of the verification and consensus computation algorithms, and the number of stakeholders involved. Space complexity is determined by the size of the required declaration data and the associated metadata. By employing these methods, dynamic secure access systems create unified secure access models, role coding, and user association patterns, thereby facilitating efficient role management, user access control, and security configuration across organizations.

[1187] Business layer

[1188] process:

[1189] User and role data ingestion: This process involves collecting and ingesting user, role, and security access data from various sources such as user management systems, organizational structures, and security policies.

[1190] Role and Feature Identification and Encoding: This process focuses on identifying and extracting relevant roles and security features from input data, assigning unique numerical codes, and modeling their relationships using mathematical operations and data structures.

[1191] Role and Access Classification and Cataloging: This process employs a supervised learning classification model to enable the system to self-discover and catalog roles, user access to roles, and user associations with roles, thereby continuously improving classification accuracy through model retraining.

[1192] Consensus Management and Security Configuration: This process uses a proof-of-ownership approach to establish consensus on regulatory requirements, toxic combinations, and data masking rules, resolve conflicting claims, and configure a secure access model accordingly.

[1193] Function:

[1194] Unified Secure Access Model: Dynamic secure access systems create a unified mathematical representation of roles, security characteristics, and their relationships, promoting efficient role management, user access control, and security configuration.

[1195] Automated Role and Access Discovery: This system automates role and access discovery by employing a supervised learning classification model, thereby reducing manual work and enabling scalability for large-scale systems.

[1196] Regulatory compliance and risk mitigation: The management consensus approach demonstrates how ensuring compliance with regulatory requirements, identifying toxic combinations, and implementing appropriate data masking rules can mitigate security risks and support compliance efforts.

[1197] Efficient access control and monitoring: A unified secure access model and mathematical representation enables efficient user access control, monitoring, and integration with network security systems to enhance protection and threat detection.

[1198] Application layer

[1199] The dynamic secure access system includes the following key components:

[1200] RoleIdentifier: This component uses NLP and machine learning techniques to identify and extract relevant roles and security features from the input data.

[1201] RoleEncoder: This component assigns a unique numeric code to the identified role and characteristic based on their position in a sequence of natural numbers. It uses mathematical operations and data structures to model the relationships between them.

[1202] AccessClassifier: This component uses a supervised learning classification model to classify user access to roles and user associations with roles, and continuously improves classification accuracy through model retraining.

[1203] ManagementConsensus: This component uses a proof-of-ownership approach to establish consensus on regulatory requirements, toxic combinations, and data masking rules, resolve conflicting claims, and configure secure access models accordingly.

[1204] AccessControlEngine: This component is the central access control engine, storing a unified secure access model, role codes, and user association patterns. It enables efficient role management, user access control, and security configuration.

[1205] Serve:

[1206] RoleIdentificationService: Provides methods for identifying and extracting relevant roles and security features from input data using NLP and machine learning techniques.

[1207] RoleEncodingService: This service provides a way to assign unique numeric codes to identified roles and characteristics and to model their relationships using mathematical operations and data structures.

[1208] AccessClassificationService enables the cataloging of user access to roles and user-role associations using supervised learning classification models, including model training and retraining capabilities.

[1209] ManagementConsensusService: Facilitates consensus on regulatory requirements, toxic combinations, and data masking rules using proof-of-ownership methods, resolves conflicting claims, and configures secure access models accordingly.

[1210] AccessControlService: Based on a unified secure access model, role coding, and user association pattern, it provides services for managing user access control, role assignment, and security configuration.

[1211] interface:

[1212] RoleIdentifierInterface: Defines the methods and parameters used to identify and extract relevant roles and security features from input data using NLP and machine learning techniques.

[1213] RoleEncoderInterface: Specifies the methods and parameters used to assign unique numeric codes to identified roles and traits and to model their relationships using mathematical operations and data structures.

[1214] AccessClassifierInterface: Describes the methods and parameters for cataloging user access to roles and user-role associations using a supervised learning classification model, including model training and retraining capabilities.

[1215] ManagementConsensusInterface: Provides methods and parameters for using proof-of-ownership approaches to establish consensus on regulatory requirements, toxic combinations, and data masking rules, resolve conflicting claims, and configure secure access models accordingly. [...

Claims

1. A multi-agent system for processing information, comprising: A data processing agent, the data agent being configured to ingest and standardize raw data input to produce standardized data; A standard integration agent is configured to apply reporting standards to the standardized data, thereby generating an integrated reporting standard. A performance alignment agent, configured to align performance metrics based on the standardized data and the integrated reporting standard; An information synthesis agent configured to process narrative information from the standardized data and the integrated reporting standard; as well as An orchestration framework configured to manage the operations of the data processing agent, the standards integration agent, the performance alignment agent, and the information synthesis agent to generate regulatory reports that comply with regulatory requirements, wherein the orchestration framework can be executed by a large language model.

2. The system according to claim 1, wherein, The data processing agent also includes a data cleaning module, which can remove errors and inconsistencies from the raw data input to improve the accuracy of the standardized data.

3. The system of claim 1 or 2 further includes an importance alignment agent configured to assess and align importance and boundaries based on the standardized data and the integrated reporting criteria.

4. The system according to any one of the preceding claims further includes a compliance alignment agent configured to align the assurance process based on the standardized data and the integrated reporting standard.

5. The system according to any one of the preceding claims, wherein, The performance alignment agent uses machine learning to dynamically adapt the performance metrics based on updates to the reporting criteria and corresponding real-time data.

6. The system according to any one of the preceding claims further includes a report summarizing agent configured to use a data integration platform to merge and align output data from the data processing agent, the standard integration agent, the performance alignment agent, and the information synthesis agent.

7. The system according to any one of the preceding claims, wherein, The orchestration framework also includes a scheduling module that adjusts the order and priority of tasks based on real-time assessments of data processing needs and agent capabilities.

8. The system according to any one of the preceding claims, wherein, The information synthesis agent also includes a context analysis module, which is configured to integrate contextual clues from the standardized data and the integrated reporting standard.

9. A system for managing metadata, comprising: An ingestion module, configured to preprocess data input in accordance with metadata standards; An extraction module is configured to use language processing techniques to extract and normalize metadata from preprocessed data input; An abstract module is configured to convert extracted and normalized metadata into a structured schema; A mapping module configured to use artificial intelligence technology based on a classification method to translate the transformed metadata; as well as A storage module configured to index translated metadata and enable searching of translated metadata.

10. The system according to claim 9, wherein, The mapping module is also configured to use ontology-based reasoning and semantic similarity metrics to establish mappings between metadata elements from different taxonomies.

11. The system according to claim 9 or 10, wherein, The mapping module is also configured to use neural networks and transfer learning to refine and enhance the conversion of metadata between taxonomies.

12. The system according to any one of claims 9 to 11, wherein, The storage module includes: The system includes a vector database, an indexing module, and a search engine module. The vector database is configured to store the translated metadata, the indexing module is configured to generate vector embeddings corresponding to the translated metadata, and the search engine module is configured to use the vector embeddings to perform searches based on contextual relevance and semantic similarity.

13. The system according to any one of claims 9 to 12, further comprising a data lineage and traceability module configured to capture audit trails of metadata management processes across the system to comply with regulatory requirements.

14. The system according to any one of claims 9 to 13, wherein, The ingestion module is also configured to interface directly with an external data source to automatically retrieve the data input.

15. A system for extracting and normalizing metadata, comprising: An input interface configured to receive preprocessed data input conforming to metadata standards; A processing module configured to apply natural language processing techniques to extract metadata from received preprocessed data input; A normalization engine, configured to normalize the extracted metadata according to a predetermined metadata standard; A refinement module is configured to enhance normalized metadata with additional information derived from external sources to generate refined metadata. as well as An output interface is configured to output the refined metadata for additional processing within the metadata management system.

16. A system for integrating data schemas into data representations, comprising: A data input interface configured to receive data from multiple sources; A feature extraction module is configured to extract features from the received data. A security module is configured to encrypt the extracted features to generate a secure data representation; A data integration module, configured to integrate encrypted features corresponding to the secure data representation into a unified data representation; as well as A verification module is configured to verify the unified data representation according to predefined standards or regulations.

17. The system according to claim 16, wherein, The security module uses a cryptographic hash function to verify the authenticity and integrity of the secure data representation.

18. The system according to claim 16 or 17, wherein, The feature extraction module is configured to use natural language processing and machine learning algorithms to extract features from the received data.

19. The system according to any one of claims 16 to 18, further comprising an access management module configured to convert at least one of user roles, user requests, or data access modes into a unified mathematical representation.

20. The system according to claim 19, wherein, The access management module is also configured to use natural language processing and machine learning to encode the user roles and access permissions into unique numerical codes.

21. The system according to claim 19 or 20, wherein, The access management module includes a control engine, which serves as an authorization authority for implementing access control based on a unified security access model.

22. The system according to any one of claims 19 to 21, wherein, The access management module is configured to automatically synchronize its access control rules with an external compliance monitoring system to maintain compliance with regulatory changes.

23. A method for processing information in a multi-agent system, comprising: Raw data input is obtained through a data processing agent; The data processing agent is used to normalize the raw data to generate standardized data. The standard integrated agent is used to apply the reporting standard to the standardized data to generate an integrated reporting standard; The performance alignment agent is used to align performance metrics based on the standardized data and the integrated reporting standard. The information synthesis agent is used to process narrative information from the standardized data and the integrated reporting standard; as well as The operations of the data processing agent, the standard integration agent, the performance alignment agent, and the information synthesis agent are orchestrated using an orchestration framework to generate regulatory reports that meet the regulatory requirements of the code, wherein the orchestration framework can be executed by a large language model.

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