Multi-tenant distributed computing system and computer-implemented method for adaptive quality management in artificial intelligence (AI) processing

Abstract

The present invention provides a computer system and a computer-implemented method for adaptive quality management in artificial intelligence (AI) processing. The system comprises a memory unit storing machine-readable instructions and a processor configured to execute the instructions to store, in a persistent correction memory, records of prior corrections to AI outputs; retrieve a correction for a current input based on similarity between the input and stored records; generate, by an audit-trail module, a tamper-evident record of the correction and its application; schedule, by a predictive resource manager, AI workloads in dependence on service-level constraints, carbon-intensity data, and quality metrics; and update, by a feedback controller, retrieval and scheduling parameters based on effectiveness information. The processor thereby enables the persistent correction memory, audit-trail module, predictive resource manager, and feedback controller to operate as a closed-loop adaptive system that reduces recurrence of AI errors, ensures verifiable auditability, and optimizes computational efficiency.

Description

MULTI-TENANT DISTRIBUTED COMPUTING SYSTEM AND COMPUTER- IMPLEMENTED METHOD FOR ADAPTIVE QUALITY MANAGEMENT IN ARTIFICIAL INTELLIGENCE (Al) PROCESSINGFIELD OF THE INVENTION

[0001] The present invention generally relates to computer systems and computer-implemented methods, and more particularly to a multi-tenant distributed computing system and a computer- implemented method for adaptive quality management in artificial intelligence (Al) processing utilizing persistent correction memory, blockchain-based audit trails, and predictive resource management. The invention is particularly applicable to regulated and sustainability-sensitive environments such as healthcare, financial services, enterprise governance, and multi-tenant distributed computing networks.BACKGROUND OF THE INVENTION

[0002] Artificial intelligence (Al) systems deployed across modern industries frequently encounter recurring challenges associated with maintaining consistent output quality, regulatory accountability, and equitable resource utilization. These challenges are particularly pronounced in environments where system decisions directly influence regulated operations, such as clinical diagnostics, financial compliance, and enterprise process automation. In such contexts, repeated manifestation of similar computational or inferential errors across sessions not only undermines trust in Al systems but also introduces measurable operational risk. Additionally, Al systems, particularly large language models (LLMs) and generative models, typically lack persistent correction memory across sessions, traceable quality assurance mechanisms, and adaptive control feedback, resulting in transient improvements that are lost over time.

[0003] Conventional systems typically employ transient feedback or isolated correction mechanisms that address individual instances of error without persisting corrective data across sessions. These approaches often depend on manual curation or session-bounded retraining, resulting in limited capacity for adaptive learning from prior corrective events. As a consequence, prior solutions fail to maintain institutional memory of past improvements and cannot systematically enforce quality consistency or traceability over extended operational lifecycles.

[0004] Existing Al quality management practices also rely heavily on mutable or internally stored logs that do not provide immutable verification of model adjustments or corrective actions. The absence of tamper-evident, verifiable audit records restricts the ability of enterprises todemonstrate regulatory compliance or to satisfy third-party verification standards. Moreover, most existing resource schedulers treat Al workloads as computationally isolated tasks, lacking correlation between quality metrics, service-level obligations, and environmental performance factors such as energy consumption or carbon intensity. The resulting inefficiencies manifest as higher operational costs, inconsistent performance between tenants, and elevated environmental impact.

[0005] These limitations are further compounded by the lack of integrated coordination between quality control, adaptive scheduling, and federated learning frameworks. Prior art systems — such as isolated feedback loops (e.g., US 11,003,456), blockchain-based audit mechanisms without carbon optimization (e.g., WO 2023 / 045678), or retrieval-augmented generation systems lacking hybrid persistence (e.g., US 10,956,789) — address individual aspects but fail to provide a unified, closed-loop architecture capable of adaptive correction, verifiable auditing, and sustainable workload optimization.

[0006] Therefore, there exists a need in the art for a multi-tenant distributed computing system and computer-implemented method for adaptive quality management in artificial intelligence (Al) processing utilizing persistent correction memory, blockchain-based audit trails, and predictive resource management, configured to deliver continuous quality improvement, verifiable compliance, and efficient resource scheduling across regulated and multi-tenant computing environments.SUMMARY OF THE INVENTION

[0007] According to one aspect of the invention, there is provided a computing system for adaptive quality management in artificial intelligence (Al) processing. The system comprises a memory unit that stores machine-readable instructions and a processor operably coupled with the memory unit and configured to execute the instructions to store, in a persistent correction memory, records of prior corrections to Al outputs. The processor retrieves, from the persistent correction memory, a correction for a current input based on similarity between the input and the stored records. The processor generates, by an audit trail module, a tamper-evident record of the retrieved correction and its application, and schedules, by a predictive resource manager, Al processing workloads in dependence on service-level agreement constraints, carbon-intensity information of available computing resources, and quality metrics derived from the persistent correction memory and the audit trail module. The processor updates, by a feedback controller, retrieval parameters of the persistent correction memory and scheduling parameters of the predictive resource manager basedon effectiveness information from the audit trail. The processor, executing the instructions, causes the persistent correction memory, audit trail module, predictive resource manager, and feedback controller to operate as a closed-loop adaptive system configured to reduce recurrence of Al errors, generate verifiable audit records, and optimize efficiency of Al workload execution.

[0008] In accordance with an embodiment of the present invention, the system further comprises a Federated Learning and Ethics Integration (FLEI) module configured to coordinate secure aggregation of correction model updates across distributed nodes, wherein the aggregation employs a federated-averaging protocol or equivalent mechanism to combine model deltas based on node participation weights, optionally incorporating differential-privacy noise to preserve data confidentiality, and wherein the aggregated updates are used to refine a global persistent correction memory embedding index, thereby enabling privacy-preserving, multi-organizational adaptive learning.

[0009] It is advantageous that the computing system thus operates through an adaptive, multimodule cognitive quality pipeline comprising, in sequential order, an Evidence-Conditioned Content Assembly (E-CCA) module for complexity and context evaluation, an Adaptive Rate- Limiting and Escalation Engine (ARLEE) for controlled task admission, an Adaptive Quality and Audit Controls (AQAC) module for multi-metric output verification, a Predictive Resource Manager (PRM) for carbon-aware workload scheduling, a Federated Learning and Ethics Integration (FLEI) module for privacy-preserving cross-node coordination, and a Persistent Correction Memory (PgSelfCorrection) for durable reuse of validated corrections. These modules collectively implement a quality-thresholded, feedback-driven, and persistence-enabled correction loop that continuously improves Al reliability across sessions. Within this framework, PgSelfCorrection serves as the semantic memory layer that stores and retrieves correction embeddings, while the PRM dynamically orchestrates workload execution in response to quality and sustainability signals. This integration establishes the inventive step of the present system — an adaptive, self-correcting Al control loop that unifies persistent learning, auditability, and predictive resource optimization into a single verifiable framework.

[0010] In accordance with an embodiment of the present invention, the persistent correction memory stores corrections as embedding vectors in an approximate nearest-neighbor index. Similarity is computed using a cosine similarity score further weighted by recency and effectiveness of prior corrections.

[0011] In accordance with another embodiment of the present invention, the audit trail module is configured to generate a Merkle tree of correction records and anchor a Merkle root to an external verification service. The external verification service comprises, but is not limited to, a public blockchain or a timestamp authority compliant with RFC-3161. The audit trail module further provides selective disclosure of audit proofs to external verifiers or regulators.

[0012] In accordance with an embodiment of the present invention, the predictive resource manager obtains carbon-intensity forecasts from an external carbon-intensity service and defers non-latency-critical workloads based on the forecasts while maintaining service-level agreement constraints. The predictive resource manager applies a multi-objective optimization function balancing latency thresholds, throughput requirements, and carbon-intensity minimization.

[0013] In accordance with an embodiment of the present invention, the feedback controller updates retrieval weighting parameters of the persistent correction memory in proportion to measured effectiveness scores of applied corrections.

[0014] In accordance with an embodiment of the present invention, the persistent correction memory, audit trail module, and predictive resource manager are deployed in a distributed cloudedge environment supporting federated learning with differential privacy and secure aggregation.

[0015] In accordance with an embodiment of the present invention, the system further comprises an Evidence-Conditioned Content Assembly configured to compute, for each incoming Al request, a complexity score based on one or more of, but not limited to, semantic depth, computational requirement, domain specificity, and expected processing time.

[0016] In accordance with an embodiment of the present invention, the system further comprises an Adaptive Rate-Limit and Escalation (ARLE) Engine configured to selectively throttle or accept Al requests based on the computed complexity score and current system workload utilization.

[0017] In accordance with an embodiment of the present invention, the processor executes an Adaptive Quality & Audit Controls (AQAC) module configured to assign quantitative quality metrics to Al outputs across multiple dimensions including, but not limited to, accuracy, coherence, completeness, and fairness. The AQAC module triggers retrieval of prior corrections from the persistent correction memory when the quality metrics fall below predefined thresholds.

[0018] In accordance with an embodiment of the present invention, the processor re-evaluates corrected outputs iteratively until the quality metrics exceed a predetermined acceptance threshold and logs each iteration in the audit trail module.

[0019] According to another aspect of the invention, there is provided a computer-implemented method for adaptive quality management in artificial intelligence (Al) processing. The method comprises storing, in a persistent correction memory, records of prior corrections to Al outputs, retrieving, from the persistent correction memory, a correction for a current input based on similarity between the input and the stored records, generating, by an audit trail module, a tamper- evident record of the retrieved correction and its application, scheduling, by a predictive resource manager, Al processing workloads in dependence on service-level agreement constraints, carbon- intensity information of available computing resources, and quality metrics derived from the persistent correction memory and the audit trail, and updating, by a feedback controller, retrieval parameters of the persistent correction memory and scheduling parameters of the predictive resource manager based on effectiveness information from the audit trail.

[0020] In accordance with an embodiment of the present invention, the method further comprises storing the corrections as embedding vectors in an approximate nearest-neighbor index and computing similarity using a cosine similarity score weighted by recency and effectiveness of prior corrections.

[0021] In accordance with an embodiment of the present invention, the method further comprises generating a Merkle tree of correction records and anchoring a Merkle root to an external verification service comprising, but not limited to, a public blockchain or a timestamp authority compliant with RFC-3161, and selectively disclosing audit proofs to external verifiers or regulators.

[0022] In accordance with an embodiment of the present invention, the method further comprises obtaining carbon-intensity forecasts from an external carbon-intensity service, deferring nonlatency-critical workloads based on the forecasts while maintaining service-level agreement constraints, and applying a multi -objective optimization function balancing latency thresholds, throughput requirements, and carbon-intensity minimization.

[0023] In accordance with an embodiment of the present invention, the method further comprises updating retrieval weighting parameters of the persistent correction memory in proportion to measured effectiveness scores of applied corrections.

[0024] In accordance with an embodiment of the present invention, the method further comprises deploying the persistent correction memory, audit trail module, and predictive resource manager in a distributed cloud-edge environment supporting federated learning with differential privacy and secure aggregation.

[0025] In accordance with an embodiment of the present invention, the method further comprises computing, by an Evidence-Conditioned Content Assembly, a complexity score for each incoming Al request based on one or more of, but not limited to, semantic depth, computational requirement, domain specificity, and expected processing time.

[0026] In accordance with an embodiment of the present invention, the method further comprises throttling or accepting, by an Adaptive Rate-Limit and Escalation (ARLE) Engine, the Al requests based on the computed complexity score and current system workload utilization.

[0027] In accordance with an embodiment of the present invention, the method further comprises assigning, by an Adaptive Quality & Audit Controls module, quantitative quality metrics to Al outputs across multiple dimensions including, but not limited to, accuracy, coherence, completeness, and fairness, and triggering retrieval of prior corrections from the persistent correction memory when the quality metrics fall below predefined thresholds.

[0028] In accordance with an embodiment of the present invention, the method further comprises iteratively re-evaluating corrected outputs until the quality metrics exceed a predetermined acceptance threshold and logging each iteration in the audit trail module.BRIEF DESCRIPTION OF DRAWINGS

[0029] So that the manner in which the above recited features of the present invention can be understood in detail, a more particular to the description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, the invention may admit to other equally effective embodiments.

[0030] These and other features, benefits and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:FIG. 1 illustrates an example environment of computing devices for cognitive Al processing, to which various embodiments of the present invention may be implemented;FIG. 2 illustrates a computer-implemented method for adaptive quality management in artificial intelligence processing, in accordance with an embodiment of the present invention;FIG. 3 A illustrates a functional flow diagram depicting interaction among major system components, in accordance with an embodiment of the present invention;FIG. 3B illustrates a data and control flow representation demonstrating the exchange of information between the various modules of the system during closed-loop correction, verification, and scheduling operations, in accordance with an embodiment of the present invention;FIG. 3C illustrates a temporal optimization diagram representing dynamic coordination between resource allocation, correction propagation, and feedback evaluation, in accordance with an embodiment of the present invention;FIG. 4A illustrates an exemplary implementation of the adaptive quality management system in a healthcare diagnostics environment, in accordance with an embodiment of the present invention;FIG. 4B illustrates another exemplary implementation of the adaptive quality management system applied to a multi-tenant enterprise platform, in accordance with an embodiment of the present invention;FIG. 5 illustrates a structural representation of the PgSelfCorrection module depicting a hybrid vector-relational schema for persistent correction storage and retrieval, in accordance with an embodiment of the present invention;FIG. 6 illustrates a schematic diagram of the Audit-Trail module showing cryptographically verifiable event batching and Merkle-root anchoring for immutable audit records, in accordance with an embodiment of the present invention;FIG. 7 illustrates a temporal scheduling diagram of the Predictive Resource Manager (PRM) showing workload deferral and carbon-intensity optimization across time intervals, in accordance with an embodiment of the present invention;FIG. 8A-8B illustrates a communication-sequence diagram of the Federated Learning and Ethics Integration (FLEI) module coordinating secure aggregation and dissemination of global correction models, in accordance with an embodiment of the present invention; andFIG. 9 illustrates a process diagram of the Adaptive Quality and Audit Controls (AQAC) module depicting iterative scoring, retrieval, and feedback exchange with the PgSelfCorrection memory, in accordance with an embodiment of the present invention.DETAILED DESCRIPTION OF THE DRAWINGS

[0031] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claims. As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like is included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.

[0032] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element or group of elements with transitional phrases “consisting of’, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.

[0033] The invention provides an integrated system for adaptive quality management in artificial intelligence processing. The system combines persistent correction memory with semantic vector embeddings, cognitive complexity analysis, adaptive rate limiting, blockchain-secured audit trails, and predictive resource management to ensure improved Al output quality, regulatory compliance, and efficient resource utilization. The persistent correction memory is configured to store, index,and retrieve corrections based on high-dimensional embeddings generated by transformer-based language models, utilizing an approximate nearest neighbor search with composite scoring factors such as cosine similarity, recency, and historical effectiveness. The system further comprises an Evidence-Conditioned Content Assembly (E-CCA) and an Adaptive Rate-Limit & Escalation (ARLE) engine that evaluate each incoming request for linguistic, contextual, and ethical complexity while managing per-tenant resource credits to enforce fairness and performance thresholds.

[0034] The system additionally incorporates a blockchain-based audit chain that generates cryptographic digests for each correction event, organizes events into Merkle trees, and anchors Merkle roots to external verification services for tamper-evident record keeping. The Predictive Resource Management module is configured to obtain carbon-intensity data and forecast workload scheduling based on service-level agreements, thereby deferring non-latency-critical tasks during periods of low carbon intensity. A Federated Learning and Ethics Integration module facilitates privacy-preserving correction sharing and bias detection, enabling secure multi-organizational collaboration with differential privacy and secure aggregation. The assembled components operate in a closed-loop architecture whereby feedback from quality assessments and audit trails continually refines the retrieval weighting and scheduling parameters, accomplishing selfreinforcing Al quality management while optimizing energy consumption and ensuring regulatory compliance.

[0035] The invention will now be described in detail with reference to the accompanying drawings:

[0036] Figure 1 illustrates an exemplary environment 100 comprising a computing system 102 that incorporates multiple interrelated modules for adaptive quality management in artificial intelligence processing. As illustrated in FIG. 1, the core of the system 100 is a computing system 102 that functions as the central processing unit for adaptive quality management in artificial intelligence (Al) processing. The computing system 102 comprises a processor 106 and a memory unit 106. The processor 106 is configured to execute machine-readable instructions stored within the memory unit 106 to implement various functional modules of the system, including, but not limited to, a persistent correction memory, an audit trail module, a predictive resource manager, a feedback controller, and auxiliary subsystems that collectively form a closed-loop adaptive architecture for quality management in Al operations. The processor 106 may be one of, but not limited to, a general -purpose processor, a graphics processing unit (GPU), a digital signalprocessor (DSP), a field-programmable gate array (FPGA), or an application-specific integrated circuit (ASIC).

[0037] The memory unit 106 of the computing system 102 is configured to store the machine- readable instructions that, when executed by the processor 106, enable the system to perform adaptive correction, verification, and scheduling operations as described in detail herein. The memory unit 106 may store, but is not limited to, executable code, configuration data, and models associated with cognitive quality assessment, correction propagation, and predictive workload optimization. In one embodiment, the memory unit 106 stores embedded Al models and operational parameters for managing correction reuse, maintaining audit trails, and optimizing resource allocation based on service-level and environmental constraints. The memory unit 106 can be selected from a group comprising, but not limited to, Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Flash memory, or dynamic random-access memory (DRAM). The machine-readable instructions may be provided via a non-transitory computer-readable medium such as, but not limited to, CD- ROMs, DVD-ROMs, Flash drives, or cloud-based deployment.

[0038] In one embodiment, the computing system 102 further comprises a communication module (not shown) configured to facilitate secure data exchange among the internal modules of the system and with external entities, including but not limited to, external verification services, carbon-intensity data providers, and user-facing applications. The communication module establishes connections over a communication network 130 and supports wired or wireless communication interfaces. The wireless communication may utilize protocols such as Wi-Fi, Bluetooth, Near Field Communication (NFC), or 5G / 6G networks to ensure seamless and realtime connectivity between distributed components of the adaptive quality management framework. The communication module enables reliable transfer of corrective data, audit logs, and scheduling metadata between the computing system 102 and associated edge or cloud nodes while maintaining compliance with security and encryption standards applicable to regulated environments.

[0039] The communication network 130 may comprise a short-range communication network, a long-range communication network, or a combination thereof. The communication interface may include, but is not limited to, serial or parallel communication interfaces or other data transport mechanisms capable of ensuring low-latency and high-integrity data transmission. The communication network 130 may operate through one or more infrastructures, such as the Internet,intranets, virtual private networks (VPNs), or cloud orchestration frameworks, enabling distributed deployment of modules while maintaining synchronized operation.

[0040] Additionally, the system 100 includes a data repository that serves as the centralized or distributed storage unit for the system. The data repository may be implemented as local storage, such as a solid-state drive (SSD), Flash memory, or extended memory, or as a cloud-hosted database accessible through authenticated APIs. The data repository is configured to store information associated with correction records, quality metrics, audit trail entries, and resource utilization statistics generated during Al operations. The repository maintains data as append-only, time-stamped entries to preserve integrity and traceability, ensuring that all quality improvement cycles are verifiable and reproducible.

[0041] In one embodiment, the data repository further stores Al training data, embedding indices, and optimization profiles utilized by the persistent correction memory and predictive resource manager. The repository may additionally store blockchain anchors and Merkle proofs associated with correction events, ensuring that each correction and audit event recorded by the system can be externally validated. The data repository operates under secure access control, enabling the computing system 102 to query, retrieve, and append data based on authenticated session keys, compliance metadata, and regulatory protocols applicable to the deployment environment.

[0042] Continuing from Fig. 1, the processor 106 executes a persistent correction memory (also referred to herein as the PgSelfCorrection module) 108 to maintain and index semantic correction records using transformer-generated vector embeddings. This high-dimensional vector database utilizes advanced vector-indexing methods such as IVFFlat or HNSW for approximate nearest neighbor searches, enabling rapid and accurate retrieval of past corrections. In addition to storing raw embeddings, the memory data structure includes metadata fields such as timestamps, correction effectiveness metrics, and provenance information that support statistical learning and cross-session validation. The cryptographically verifiable operations of the Audit-Trail module (110) are further depicted in FIG. 6, showing the generation of event hashes, Merkle-tree batching, and anchoring of Merkle roots to an external verification service.

[0043] In one configuration, the PgSelfCorrection layer employs a hybrid vector-relational retrieval approach that combines semantic-similarity scoring with metadata-based filtering, enhancing contextual precision while maintaining scalability across stored corrections. This module is optimized for both write-intensive correction logging and read-intensive queryoperations, ensuring resilience and scalability even in high-throughput Al processing environments.

[0044] The audit trail module 110 operates as a cryptographically secured, append-only ledger designed to record every correction event for compliance purposes. It employs secure storage mechanisms and cryptographic hardware to generate and verify SHA-256 digests as well as to perform Merkle tree-based batch processing. In practice, each correction event received, typically originating from the AQAC module 122 via the feedback controller 114, is compiled into batches and incorporated into a Merkle tree structure. The resulting Merkle root is then periodically anchored to an external verification service, such as a public blockchain or RFC-3161-compliant timestamp authority. The cryptographically verifiable operations of the Audit-Trail module (110) are further depicted in FIG. 6, showing the generation of event hashes, Merkle-tree batching, and anchoring of Merkle roots to an external verification service. This design ensures that all events are tamper-evident and that selective disclosure is available for audits or regulatory inspections.

[0045] The Predictive Resource Manager (PRM) 112 is assembled for real-time workload scheduling and optimization based on dynamic external energy signals, such as carbon intensity data, as well as internal quality metrics. This module utilizes a combination of real-time API connectors and sophisticated optimization engines to retrieve both historical and forecasted carbon intensity information from an external carbon intensity service 128. With these inputs, the predictive resource manager adjusts the scheduling of Al processing tasks, deferring non-latency- critical workloads to low-energy periods while ensuring that service-level agreement commitments are maintained. In addition, it applies multi-objective optimization functions that consider latency thresholds, throughput requirements, quality and carbon-intensity parameters to determine an optimal scheduling outcomes across competing workloads. As an example, Fig. 7 graphically illustrates the temporal scheduling logic of the Predictive Resource Manager (112), where non- critical workloads are deferred to low-carbon intervals to optimize energy efficiency while preserving SLA performance. Such configuration improves computational throughput per unit energy consumed and maintains compliance with carbon-intensity standards.

[0046] The feedback controller 114 orchestrates telemetry and inter-module communication, thereby facilitating closed-loop operations. It functions as the central intelligence of the system 100, aggregating performance, quality, and resource utilization data from various modules such as the AQAC module 122, ARLE engine 116, and E-CCA 118. Based on this telemetry, it dynamically updates retrieval parameters in the persistent correction memory 108 and adjusts scheduling parameters in the predictive resource manager 112. Accordingly, the feedbackcontroller 114 closes the operational loop between quality assurance and workload management, ensuring autonomous system adaptation without human intervention. The feedback controller may further update confidence metrics associated with stored corrections based on user or system feedback, enabling dynamic refinement of retrieval weights and long-term learning stability. This continuous feedback loop helps refine both the semantic retrieval mechanisms and resource allocation policies, ensuring the system 100 consistently improves Al output quality.

[0047] The Adaptive Rate-Limit & Escalation (ARLE) engine 116 implements adaptive rate limiting tailored to the complexity of incoming requests and current system load. Positioned adjacent to the E-CCA 118 within the control loop, the ARLE engine utilizes complexity scores produced by the E-CCA module to determine per-tenant resource distribution and enforce dynamic rate limits. Following rate-limiting evaluation, the ARLEE engine may transmit a routing decision to the predictive resource manager (PRM), optionally tagging requests with a deferral flag to enable carbon-aware scheduling when latency thresholds permit. It adjusts throughput in real time based on system telemetry received from the feedback controller 114, thus ensuring that resource allocation remains fair and efficient among multiple users in a multi-tenant environment.

[0048] The Evidence-Conditioned Content Assembly (E-CCA) 118 evaluates the multidimensional complexity of incoming Al requests. In operation, the E-CCA module first verifies prerequisite evidence or contextual data associated with an incoming request before initiating content assembly, thereby ensuring that downstream modules operate on validated and context- qualified inputs. It computes a comprehensive complexity score by considering factors such as semantic depth, computational burden, domain specificity, and expected processing time. These scores are crucial for both the ARLE engine 116 and the predictive resource manager 112, as they inform decisions regarding rate limiting and the appropriate scheduling window for processing each request. By providing detailed complexity assessments, the E-CCA module ensures that the system can dynamically adjust to varying input characteristics and maintain high-quality output.

[0049] The Adaptive Quality & Audit Controls (AQAC) module 122 is situated within the quality control sequence and is responsible for assessing Al-generated outputs against predefined quality thresholds. It employs quantitative metrics across multiple dimensions, including accuracy, coherence, completeness, and fairness, to determine if the produced content meets the necessary standards. When quality metrics fall below the required thresholds, the AQAC module triggers the retrieval of prior corrections from the persistent correction memory 108. It then applies these corrections, iteratively re-evaluating the outputs until the quality metrics exceed the predetermined acceptance threshold. Each iteration is logged and forwarded via the feedback controller 114 tothe audit trail module 110 for compliance and traceability. In certain embodiments, the AQAC module operates in a bidirectional interaction with the PgSelfCorrection memory, retrieving a topranked set of prior corrections for re-scoring and storing resulting feedback and confidence updates for continuous learning.

[0050] The Al interface engine 120 provides the ingress path for external requests and draft submissions. It is designed to operate in accordance with standardized communication protocols to ensure seamless integration with client applications. This module not only handles the request routing and session management but also orchestrates the interaction between external inputs and internal processing modules, including handing off requests to the E-CCA for complexity evaluation and then to the ARLE engine for rate control. It ensures that all external interactions are secure and that data flow integrity is maintained throughout the processing pipeline.

[0051] In one embodiment, the system 100 further comprises a Federated Learning and Ethics Integration (FLEI) module 132 configured to enable decentralized learning, bias detection, and compliance alignment across distributed nodes. The FLEI module 132 coordinates model updates between the persistent correction memory 108, the AQAC module 122, and remote participant systems through privacy-preserving aggregation protocols. It facilitates the sharing of learned correction weights or quality-adjustment parameters without transmitting sensitive data, thereby maintaining confidentiality and compliance with data protection regulations. In certain embodiments, the FLEI module 132 includes fairness evaluation subroutines and ethical compliance validators that assess model adaptations for bias mitigation, ensuring that global corrections remain consistent with institutional, regional, and regulatory ethical standards. The FLEI module 132 thereby extends the adaptive quality framework into multi-organizational environments, enabling federated improvement of Al quality while preserving data sovereignty and governance integrity.

[0052] In one embodiment, the FLEI module 132 further comprises a federation coordinator service responsible for orchestrating learning cycles among participating nodes. The federation coordinator initiates training rounds, receives locally derived model updates, aggregates them into a unified correction model, and disseminates the aggregated results to all nodes. Each participating node executes local training on its instance of the persistent correction memory 108 using domainspecific correction data and quality metrics produced by the AQAC module 122. The locally computed updates, represented as model-delta vectors, are transmitted to the coordinator through secure communication channels employing encryption or differential-privacy mechanisms.

[0053] The aggregation process performed by the FLEI module 132 may utilize a weighted- averaging or equivalent algorithm, wherein each node’s contribution is proportionate to its dataset volume or correction relevance. Alternative aggregation techniques, such as median-based or robust estimators, may be applied to enhance fault tolerance against anomalous or biased updates. To further strengthen privacy assurance, the FLEI module 132 may employ secure-aggregation protocols, such as homomorphic encryption or multi-party secret sharing, allowing the federation coordinator to compute only the aggregated outcome without access to individual participant updates.

[0054] Upon completion of each learning round, the aggregated global correction model is distributed to all participating nodes. Each node then updates its local persistent correction memory 108 to re-index embeddings and adjust correction-weighting parameters in accordance with the newly aggregated model. This process enables cross-organizational learning without requiring the exchange of raw data, thereby maintaining regulatory compliance and ethical isolation between institutions.

[0055] In certain embodiments, the FLEI module 132 also interacts with the predictive resource manager 112 to optimize scheduling of federated learning rounds based on system load, energy availability, and carbon-intensity indicators obtained from the carbon-intensity service 128. This integration ensures that federated coordination is performed during energy-efficient periods while respecting tenant-level resource policies. In distributed deployments, the FLEI module may further coordinate multiple PRM instances across participating nodes, ensuring that resource optimization policies remain aligned under federated governance while preserving privacy and compliance constraints. Through this combined orchestration of privacy, ethics, and sustainability, the FLEI module 132 reinforces the adaptive quality -management framework of the system 100, promoting continuous global improvement of Al reliability and fairness across multi-tenant environments. The federated-coordination workflow of the FLEI module (132) is exemplified in FIG. 8A-8B, depicting secure aggregation of locally trained correction models and dissemination of global updates through privacy-preserving protocols.

[0056] In one embodiment, the computing system 102 implements a composite adaptive framework comprising six interrelated modules namely, PgSelfCorrection for vector-relational hybrid correction persistence, E-CCA for evidence-conditioned request analysis, ARLE for adaptive rate control and escalation, AQAC for multi-metric quality scoring and correction management, PRM for predictive resource optimization, and FLEI (Federated Learning & Ethics Integration) for privacy-preserving model synchronization and ethical compliance acrossdistributed environments. These modules interact through well-defined interfaces governed by the feedback controller 114 and operate as a unified closed-loop system 102 that enables continuous quality improvement, regulatory verifiability, and energy-aware workload scheduling across multi-tenant distributed infrastructures.

[0057] Returning to FIG. 1, the environment further comprises one or more external entities that are communicatively coupled with the computing system 102 via a network 130. The network 130 provides a secure, authenticated communication channel that facilitates bidirectional data exchange between the internal modules of the computing system 102 and the external entities. The network 130 may include, but is not limited to, the Internet, intranets, virtual private networks (VPNs), or cloud-based communication frameworks utilizing encrypted protocols to ensure data confidentiality and integrity during transmission.

[0058] In one embodiment, an external verification service 126 is connected to the audit trail module 110 through the network 130. The external verification service 126 serves as an anchoring endpoint for cryptographic proofs generated by the audit trail module 110. The verification service 126 may comprise, but is not limited to, a public blockchain, a permissioned blockchain, or a timestamp authority compliant with RFC-3161. The audit trail module 110 transmits Merkle tree roots or batch hash digests to the external verification service 126, which anchors these proofs to create immutable, tamper-evident records verifiable by independent third parties. This configuration ensures regulatory traceability and compliance in environments that require external auditability of Al correction events.

[0059] In another embodiment, a carbon intensity service 128 is connected to the predictive resource manager 112 through the network 130. The carbon intensity service 128 provides realtime and forecasted carbon-intensity data derived from regional or global energy grid sources. The predictive resource manager 112 retrieves these data feeds through authenticated application programming interfaces (APIs) and utilizes the data to dynamically adjust workload scheduling, deferring non-latency-critical Al processing tasks to periods of lower carbon intensity. This integration enables the computing system 102 to achieve improved energy efficiency and supports environmentally sustainable operation while maintaining service-level commitments.

[0060] In one exemplary implementation, the computing system 102 is deployed using a clean layered architecture comprising Application, Domain, Infrastructure, and API layers within a .NET 9 runtime environment. Persistent correction data and relational metadata are maintained in a PostgreSQL database utilizing the pgvector extension, providing a hybrid vector-relationalpersistence layer. The inter-module communication schemas, such as ascq corrections and ascq feedback, facilitate normalized exchange of correction data, quality metrics, and workload optimization parameters between the ASCQ modules. These architectural and persistence choices demonstrate a concrete reduction to practice of the claimed framework while remaining exemplary and non-limiting to the invention’s broader scope.

[0061] The network 130 also enables interconnection between the computing system 102 and other client or administrative interfaces, allowing secure transmission of configuration data, correction logs, and audit results. Communication between internal and external components may occur over wired or wireless mediums utilizing protocols such as Transmission Control Protocol / Intemet Protocol (TCP / IP), Secure Sockets Layer (SSL), or Transport Layer Security (TLS). In this manner, the network 130 provides the communication backbone for the adaptive quality management system, ensuring that each module and external entity remains synchronized, verifiable, and operationally secure.

[0062] In one embodiment of the invention, the computing system 102 may be assembled on a cloud-hosted environment with distributed storage across several memory units 104, wherein the persistent correction memory 108 utilizes a sharded vector database architecture to achieve efficient, scalable retrieval and correction reuse. Additional embodiments may incorporate hardware acceleration in the processor 106, such as GPU-based inference for transformer encoder models and parallelized hashing operations in the audit trail module 110 to support high- throughput event processing. The predictive resource manager 112 may be provided with machine learning models for carbon intensity prediction, adapted for integration with real-time signals from the carbon intensity service 128. In another embodiment, the ARLE engine 116 and E-CCA 118 operate in tandem with per-tenant credit pooling and complexity thresholds that dynamically adjust according to system telemetry and historical load patterns, preventing resource monopolization and reinforcing fairness metrics.

[0063] Additionally, the audit trail module 110 may be extended to support batching and anchoring of Merkle tree roots to external verification services 126, facilitating compliance for regulated industries requiring independently verifiable audit chains. This may involve the use of RFC-3161-compliant timestamping or anchoring records to public blockchain networks, with each correction event cryptographically linked to the associated persistent correction memory entry. The AQAC module 122, leveraging semantic similarity scores and effectiveness metrics stored in the persistent correction memory 108, may automatically reapply validated corrections to new drafts flagged below quality thresholds. In yet another embodiment, the feedback controller 114provides closed-loop telemetry to all modules, enabling dynamic adjustment of correction retrieval weights, scheduling criteria, and rate limits based on real-time quality and efficiency outcomes. Each module is designed to communicate over the network 130 using secure protocols, and the integration with external services 126, 128 helps maintain verifiable compliance and energy efficiency within the Al processing environment.

[0064] The described configurations cover multiple implementation variations, including edge- deployed nodes for federated learning and ethics integration, not shown in Figure 1 for simplification, where additional modules may handle privacy-preserving aggregation and bias detection, supporting scalable multi-organizational collaboration. The system 100 comprises, but is not limited to, components illustrated in Figure 1, permitting alternative assemblies and operational enhancements such as failover clusters, dynamic scalability, modular expansion, and integration with further audit or resource optimization services within the scope of the present invention. Such alternative implementations are to be understood as functionally equivalent variants encompassed within the scope of the present invention.

[0065] It should be appreciated that the described modules may be implemented as software executed by one or more processors, as dedicated hardware circuits, or as hybrid configurations combining both.Method of Operation:

[0066] Referring now to FIG. 2, there is illustrated a computer-implemented method for adaptive quality management in artificial-intelligence (Al) processing. The method is executed by the computing system previously described with reference to FIG. 1, and includes sequential operations identified as steps 202 through 210. The method 200 operates upon Al-generated outputs that originate from one or more artificial-intelligence models accessed through an Al interface engine. These outputs are evaluated by an Adaptive Quality & Audit Controls (AQAC) module which detects deficiencies across quantitative quality metrics such as accuracy, coherence, completeness, and fairness, and generates corresponding correction data. The correction data may be derived automatically by the AQAC module or, in certain embodiments, may further incorporate verified edits received from human reviewers or domain-specific validators. The corrections thus produced serve as the primary input to the adaptive quality-management process described herein. The method 200 would be better understood by referring to FIGS. 3A-3C simultaneously. These steps collectively enable persistent reuse of validated corrections, verifiableaudit logging, predictive workload scheduling, and feedback-driven optimization of Al performance and sustainability metrics.

[0067] The method begins at step 202, where the processor is configured to store, in a persistent correction memory, records of prior corrections to Al outputs. In one embodiment, the automated quality assessment and correction (AQAC) module transmits a corrected output together with its associated performance metrics to the persistent correction memory. Each correction is represented as a high-dimensional vector embedding generated by a transformer-based encoder and indexed within an approximate-nearest-neighbor structure such as IVFFlat or HNSW. Alongside the embeddings, the processor stores metadata comprising, but not limited to, a timestamp, a provenance tag, a quality-improvement score, and a model identifier. This establishes a persistent, queryable record of validated corrections across Al sessions. This operation is better understood with reference to FIG. 3 A, which depicts the flow of corrected outputs 302 from the AQAC module 310 to the persistent correction memory 108, where they are transformed into vector embeddings 304 and indexed with metadata 306 for future retrieval. Also, the iterative evaluation and selfcorrection process is further represented in FIG. 9, showing the bidirectional feedback between the AQAC module (122) and the PgSelfCorrection memory (108) until quality thresholds are satisfied.

[0068] Then, at step 204, the processor 106 is configured to retrieve, from the persistent correction memory, a stored correction that most closely corresponds to a current input. The processor 106 generates a query embedding for the input and performs similarity computation against stored vectors using a cosine-similarity metric. The results are weighted by recency and historical effectiveness to produce a ranked list of candidate corrections. The top-ranked correction is selected and returned to the AQAC module for evaluation. In the embodiment shown in FIG. 3 A, this process occurs through a bi-directional interaction between the AQAC module 310 and the persistent correction memory 108, mediated by a feedback controller 114 that refines retrieval weights over time. In an enhanced configuration, the weighting coefficients are dynamically adapted according to measured success ratios derived from previous correction applications, thereby improving retrieval precision as the system matures.

[0069] After that the retrieved correction is applied to a newly generated Al output, and the corrected result is evaluated across multiple quantitative metrics such as accuracy, coherence, completeness, and fairness. When any metric falls below a predetermined threshold, the AQAC module triggers another retrieval cycle and reapplies corrections iteratively until the metrics exceed an acceptance value. As illustrated in FIG. 3 A, a closed feedback path 312 between theAQAC module 310 and the persistent correction memory 108 supports these iterative refinements. The feedback controller 114 governs convergence criteria to prevent excessive iterations or latency violations. Each correction event, along with its pre- and post-evaluation scores, is transmitted to the audit-trail module 110 for permanent recording.

[0070] At step 206, the processor is configured to generate, by means of the audit-trail module, a tamper-evident record of each applied correction. This process will be better understood with reference to FIG. 3B, which illustrates the construction of cryptographically verifiable event batches. The audit-trail module receives individual correction records 320, computes a secure digest (e.g., SHA-256) for each, aggregates them into a Merkle-tree structure 322, and derives a Merkle root 324 representing the integrity of the batch. The Merkle root is anchored to an external verification service 126, such as a blockchain network or RFC-3161-compliant timestamp authority, and the returned anchoring metadata is stored locally alongside the batch. Each event retains a corresponding Merkle path 326, thereby enabling selective disclosure of proof data during external verification. In accordance with an embodiment of the present invention, this selective- disclosure mechanism allows compliance audits to confirm data integrity without revealing the full correction history.

[0071] Then, at step 208, the processor is configured to performs predictive scheduling of AI- processing workloads in dependence on service-level-agreement constraints, carbon-intensity data of available resources, and quality metrics derived from the persistent correction memory and the audit-trail module. This operation is better understood with reference to FIG. 3C, which illustrates the temporal optimization mechanism of the predictive resource manager 112. The manager continuously acquires carbon-intensity forecasts from an external carbon-intensity service 128 and classifies time intervals as low- or high-intensity. The forecasts are obtained through authenticated API connections at periodic intervals and are normalized into a structured dataset that maintains entries for regional energy attributes, observed carbon intensity, and timestamps. Non-latency- critical tasks, such as model retraining or vector-index rebuilding, are deferred to low-intensity periods, while latency-sensitive inference workloads are scheduled immediately to preserve SLA performance.

[0072] In one embodiment, the predictive resource manager employs a configurable multiobjective optimization function, expressed as Score = wrlatency + W2 throughput + ws quality + W4-(-carbon_intensity), where the weighting factors are tunable per tenant to balance performance and sustainability preferences. Tenant-specific routing policies and thresholds may be maintained within the system, allowing entities to opt-in for carbon-aware scheduling through feature flagsand preference profiles. A multi -objective optimization function balances latency, throughput, and carbon-efficiency criteria while aligning with each tenant’s operational priorities.

[0073] Next at step 210, the feedback controller 114 operates as the supervisory element that adjusts retrieval and scheduling parameters based on telemetry. For retrieval, it modifies similarity-weighting coefficients within the persistent correction memory in proportion to measured effectiveness of prior corrections. For scheduling, it refines queue priorities and deferral thresholds in view of observed latency margins and forecast variations. As depicted in FIG. 3C, telemetry inputs 334 flow back from both the audit-trail module and the AQAC module, closing the adaptive loop. In some embodiments, the feedback controller operates under proportional- adaptive control rules to ensure stability, and the participating modules are deployed in a federated cloud-edge environment employing differential privacy and secure aggregation to preserve data confidentiality.

[0074] In certain embodiments, after completion of the feedback-controlled correction cycle, the system invokes a Federated Learning and Ethics Integration (FLEI) module 132 for decentralized aggregation of learned correction parameters. The FLEI module 132 coordinates integration of local correction models generated through the AQAC-PgSelfCorrection loop with corresponding models maintained by distributed nodes. This coordination is executed through secure-aggregation protocols, such as homomorphic encryption or differential-privacy mechanisms, ensuring that only aggregated correction weights are exchanged. Each completion of a correction cycle triggers the generation of an audit event recorded by the audit-trail module 110 and, in parallel, a scheduling update signal transmitted to the predictive resource manager for evaluating energy- and carbon- intensity conditions. This ensures that both compliance verification and sustainability optimization occur synchronously at every iterative update.

[0075] Returning now to FIG. 3A, the figure further illustrates several auxiliary functions integrated into the method sequence. Prior to the storing operation 202, in some embodiments, the Evidence-Conditioned Content Assembly (E-CCA) 118 evaluates each incoming request received via the Al interface engine 120 and computes a complexity score based on semantic depth, computational requirement, domain specificity, and expected processing time. The computed score is relayed to an Adaptive Rate-Limiting (ARLE) engine 116, which dynamically throttles or admits incoming requests according to the complexity score and current workload utilization. These operations ensure equitable resource distribution in multi-tenant environments and prevent overload of downstream modules.

[0076] The lower portion of FIG. 3 A shows the continuous interaction between the predictive resource manager 112, the feedback controller 114, and the audit-trail module 110. Together, these modules maintain telemetry flow and state synchronization, thereby forming a self-adjusting ecosystem that progressively enhances Al output quality, operational transparency, and energy efficiency.

[0077] In further embodiments, the AQAC module performs iterative quality reassessment, recording each iteration as a discrete audit event. The audit-trail module batches these events into Merkle trees as illustrated in FIG. 3B, thereby establishing a verifiable chronological chain of improvement. The feedback controller aggregates success metrics from these logs and propagates refined parameters to both the persistent correction memory and the predictive resource manager.

[0078] In further embodiments, the adaptive system maintains structured operational datasets that record correction and feedback transactions across modules. A correction dataset may store, for each processed request, the input identifier, generated output, corrected output, associated confidence score, and corresponding embedding vector. A feedback dataset may maintain evaluator identifiers, outcome scores, and timestamps linked to respective correction entries. These datasets form the basis for persistent learning within the PgSelfCorrection memory and for federated synchronization within the FLEI module. Through these structured interactions, the AQAC quality metrics directly inform future correction retrievals, enabling autonomous model refinement without manual intervention.

[0079] Thus, as depicted collectively in FIGS. 2 through 3A-3C, the method operates as a closed- loop adaptive process: inputs are evaluated, corrections are persistently recorded and reused, audit evidence is cryptographically secured, resources are predictively optimized, and the entire system self-calibrates through feedback. This cooperative framework enables sustained quality enhancement, verifiable compliance, and environmentally efficient operation across Al-driven infrastructures.Exemplary Implementations and Use Cases

[0080] The present invention is adaptable for deployment across multiple operational domains where reliability, auditability, and sustainability of artificial-intelligence (Al) systems are critical. While the preceding description has referred to general Al-processing environments, the disclosed system and method can be implemented in, but are not limited to, healthcare, financial services, autonomous operations, and sustainable computing infrastructures. The following embodiments illustrate exemplary use cases that demonstrate practical implementation of the invention.

[0081] In one exemplary implementation directed to healthcare diagnostics and clinical decision support, the computing system integrates with hospital information systems to process diagnostic suggestions generated by Al models trained on medical imagery or patient records. The Adaptive Quality & Audit Controls (AQAC) module evaluates the Al-generated recommendations against validated clinical guidelines and historical physician-approved corrections. The persistent correction memory stores prior correction data — such as adjustments to dosage suggestions, anomaly detection thresholds, or textual report phrasing — together with contextual metadata identifying patient demographics or imaging modality. During subsequent sessions, when similar diagnostic scenarios arise, the system retrieves and reuses previously verified corrections, thereby reducing error recurrence and ensuring that Al recommendations remain consistent with current clinical protocols. The audit-trail module records every applied correction and anchors the event proofs to an external verification service for regulatory traceability under frameworks such as HIPAA and FDA AI / ML device guidelines. The predictive resource manager, when deployed in a hospital network, schedules model retraining and image-processing workloads during off-peak hours or when the facility’s renewable energy availability is high, thereby improving energy efficiency in medical Al operations.

[0082] In another implementation corresponding to financial auditing and compliance automation, the system operates within an enterprise data-govemance framework that processes Al-generated financial risk assessments, transaction reviews, or fraud detection alerts. The AQAC module evaluates each Al output for compliance with internal policy rules and external regulations such as anti-money-laundering (AML) directives or Basel III standards. When a discrepancy or falsepositive classification is identified by human compliance officers, the correction data is embedded into the persistent correction memory for reuse. Over time, the system builds a verifiable correction corpus that reflects regulatory interpretations and contextual outcomes. Each correction event is immutably recorded by the audit-trail module and anchored to a blockchain-based verification service that allows independent auditors or regulators to confirm the authenticity of historical assessments without accessing sensitive transactional details. The predictive resource manager, operating in conjunction with the feedback controller, dynamically allocates compute resources based on workload urgency, regulatory deadlines, and data locality, ensuring operational compliance with carbon-accounting frameworks such as the Greenhouse Gas Protocol.

[0083] In yet another embodiment related to sustainable Al and edge-cloud computing, the system governs distributed Al workloads across data centers and edge nodes. Here, the predictive resource manager communicates with external carbon-intensity services to continuously monitor grid carbon signals across geographic regions. Al training and inference tasks are scheduled adaptivelyto minimize emissions while maintaining real-time response guarantees for latency-sensitive applications. The feedback controller aggregates telemetry from edge nodes, including energy consumption, inference latency, and quality-performance metrics, and continuously refines retrieval and scheduling parameters. This configuration enables federated-learning environments to achieve measurable sustainability improvements without compromising quality assurance or compliance transparency. The audit-trail module provides verifiable proof of emission-aware scheduling decisions, thereby supporting environmental reporting obligations for data-center operators.

[0084] In a further exemplary scenario, the invention can be applied to multi-tenant Al platforms offering generative or analytical services to multiple clients. Each tenant’s outputs are processed independently through the AQAC and feedback-control subsystems, ensuring data segregation while sharing global correction learning via federated aggregation under differential privacy. The persistent correction memory thus evolves into a shared adaptive repository capable of improving Al response quality across tenants without revealing proprietary content. The audit-trail system, configured for selective disclosure, allows platform administrators to demonstrate algorithmic fairness, trace corrective interventions, and verify operational integrity before regulators or enterprise clients.

[0085] In certain embodiments, the invention may be implemented through a domain-specific reference configuration such as the Adaptive Self-Correction and Quality (ASCQ) Healthcare Kit, illustrated in FIG. 4A. The figure depicts a system-level integration wherein diagnostic-processing requests are received through the Al interface engine 120 and sequentially processed by the Evidence-Conditioned Content Assembly (E-CCA) 118, Adaptive Rate-Limit & Escalation Engine (ARLEE) 116, and Adaptive Quality & Audit Controls (AQAC) 122 modules. The AQAC module interacts bi-directionally with the PgSelfCorrection index maintained in the persistent correction memory 108 to retrieve validated correction vectors, while the Predictive Resource Manager 112 optimizes execution timing based on carbon-intensity forecasts received from the external carbon-intensity service 128. The Audit-Trail Module 110 records decision events and anchors verification proofs through the external verification service 126. This arrangement exemplifies a healthcare deployment in which diagnostic recommendations are continuously refined and verified under the adaptive quality-management framework, ensuring traceable corrections, controlled inference throughput, and carbon-aware scheduling across the hospital network.

[0086] FIG. 4B further illustrates an exemplary end-to-end method flow corresponding to the ASCQ Healthcare Kit implementation. Upon receiving a diagnostic input, the E-CCA 118 computes a cognitive-complexity score which is used by the ARLEE 116 to determine admission or deferral of the request. The AQAC 122 evaluates the resulting output for quantitative quality metrics and applies iterative self-corrections via PgSelfCorrection until an acceptance threshold is reached. If the output remains below the acceptance threshold after predefined iterations, the PRM 112 assesses resource conditions and defers non-critical retraining or recalibration tasks to low- carbon-intensity intervals, thereby achieving emission-aware optimization. Each acceptance or deferral decision is logged by the audit-trail module 110, creating a verifiable chronological chain of adaptive actions. These figures collectively demonstrate an applied embodiment of the generic adaptive-quality control process within a healthcare diagnostic environment while remaining consistent with the architecture described in FIGS. 1-3C.

[0087] The foregoing examples illustrate only a subset of possible implementations. It will be understood that the described system and method are not limited to these domains and may be applied to any environment in which artificial-intelligence processing requires adaptive quality control, verifiable auditability, and energy-aware resource optimization. The specific configurations, parameter values, and deployment topologies described herein are exemplary and may be varied without departing from the scope of the invention as defined in the appended claims.

[0088] The present invention offers a number of technical advantages, some of which are discussed below:

[0089] The disclosed computing system and computer-implemented method provide a unified framework for adaptive quality management in artificial-intelligence (Al) processing that achieves measurable improvements in reliability, compliance traceability, and operational sustainability. The integration of a persistent correction memory, an audit-trail module, a predictive resource manager, and a feedback controller within a closed-loop adaptive architecture yields several distinct technical advantages over conventional Al management systems.

[0090] A first technical advantage arises from the persistent correction memory, which provides a durable and queryable representation of prior corrections encoded as vector embeddings. Unlike transient cache-based or per-session learning systems, this architecture enables the reuse of validated corrective actions across independent Al sessions. The persistent correction memory therefore serves as a knowledge substrate that evolves continuously through verified feedback,effectively transforming historical quality outcomes into predictive inputs for subsequent processing.

[0091] A second advantage is provided by the audit-trail module, which ensures cryptographically verifiable traceability of every correction event through Merkle tree batching and periodic anchoring to an external verification service. By generating tamper-evident proofs of correction lineage, the system allows regulators, auditors, or other external verifiers to confirm the authenticity and completeness of quality-control actions without accessing confidential Al data. This verifiability, achieved through cryptographic integrity mechanisms, addresses long-standing compliance challenges associated with opaque Al behavior and model governance.

[0092] A further advantage is achieved through the predictive resource manager, which dynamically allocates and schedules Al workloads in dependence on service-level constraints, real-time carbon-intensity forecasts, and quality metrics retrieved from the persistent correction memory. This multi -objective optimization capability enables Al infrastructure to self-adjust for both performance and environmental efficiency, thereby aligning technical operation with sustainability and compliance objectives. The continuous feedback mechanism provided by the feedback controller ensures that optimization parameters are adaptively refined as new telemetry data becomes available, resulting in measurable gains in system throughput, latency control, and carbon-efficiency without manual tuning.

[0093] In one aspect, the combination of these modules achieves an emergent capability that would not result from a mere aggregation of their individual functions. A conventional system implementing error correction, audit logging, or workload scheduling in isolation would lack a persistent semantic linkage among quality data, verification records, and resource management parameters. In contrast, the disclosed configuration enables cross-domain propagation of correction effectiveness metrics — originating from the quality-assessment layer, influencing scheduling and retrieval behavior, and ultimately feeding back into audit validation. This cyclical data coupling between persistence, auditability, and resource adaptation produces an intelligent self-regulating ecosystem. Such interplay is neither taught nor suggested by prior art systems that treat model correction, logging, and resource optimization as disjoint administrative functions. The inventive step lies not in any one component but in the orchestrated architecture that transforms isolated operations into an adaptive and verifiable control loop governing Al quality, efficiency, and compliance.1

[0094] From an industrial standpoint, the invention is readily applicable to regulated and high- reliability domains including, but not limited to, healthcare diagnostics, financial risk management, and sustainable cloud computing. The disclosed architecture can be implemented using standard computing hardware and deployed within cloud, edge, or hybrid environments without modification of the underlying Al models. This compatibility ensures wide industrial adoption potential while maintaining compliance with emerging standards for trustworthy and responsible Al systems. Furthermore, by providing measurable transparency through verifiable audit trails and quantifiable correction metrics, the system facilitates certification and regulatory validation of Al-based services in industries that demand accountability and explainability.

[0095] Therefore, it can be seen that the invention introduces a technically distinctive and industrially applicable framework that unifies correction persistence, cryptographic verification, and predictive resource optimization into a single adaptive mechanism. This integration results in operational reliability, regulatory trustworthiness, and environmental sustainability that cannot be achieved through conventional or modularly combined Al management techniques.

[0096] In particular, the invention further achieves an emergent multi-domain capability through the integration of the persistent correction memory (PgSelfCorrection), blockchain-secured audit trails, Federated Learning & Ethics Integration (FLEI), and carbon-intensity optimization via the Predictive Resource Manager (PRM). This coordinated configuration enables cross-disciplinary alignment of quality assurance, regulatory compliance, and sustainability objectives, providing a distinctive operational synergy not taught or suggested in prior art systems.

[0097] Terminology and Module Reference Summary:For clarity and consistency, the following modules are referenced throughout this specification as functional components of the adaptive quality -management architecture. Each module designation represents a logical subsystem that may be implemented through software, firmware, or distributed microservices within the computing environment:

[0098] In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, for example, Java, C, or assembly. One or more software instructions in the modules may be embedded in firmware, such as an EPROM. It will be appreciated that modules may comprised connected logic units, such as gates and flip-flops, and may comprise programmable units, such as programmable gate arrays or processors. The modules described herein may be implemented as either software and / or hardware modules and may be stored in any type of computer-readable medium or other computer storage device.

[0099] Further, while one or more operations have been described as being performed by or otherwise related to certain modules, devices or entities, the operations may be performed by or otherwise related to any module, device or entity. As such, any function or operation that has been described as being performed by a module could alternatively be performed by a different server, by the cloud computing platform, or a combination thereof. It is implied that the techniques of the present disclosure might be implemented using a variety of technologies. For example, the methods described herein may be implemented by a series of computer executable instructions residing on a suitable computer readable medium. Suitable computer readable media may include volatile (e.g., RAM) and / or non-volatile (e.g., ROM, disk) memory, carrier waves and transmission media. Exemplary carrier waves may take the form of electrical, electromagnetic or optical signals conveying digital data streams along a local network or a publicly accessible network such as the Internet.

[0100] Further, the operations need not be performed in the disclosed order, although in some examples, an order may be preferred. Also, not all functions need to be performed to achieve the desired advantages of the disclosed system and method, and therefore not all functions are required.

[0101] The terms and descriptions used herein are set forth by way of illustration only and are not meant as limitations. Examples and limitations disclosed herein are intended to be not limiting in any manner, and modifications may be made without departing from the spirit of the present disclosure. Those skilled in the art will recognize that many variations are possible within the spirit and scope of the disclosure, and their equivalents, in which all terms are to be understood in their broadest possible sense unless otherwise indicated.

[0102] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the embodiments shown in the accompanying drawings, but is to be accorded the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and the appended claims.

Claims

CLAIMS1. A computing system for adaptive quality management in artificial intelligence (Al) processing, the system comprising: a memory unit storing machine-readable instructions; and a processor operably coupled with the memory unit and configured to execute the instructions to: store, in a persistent correction memory, records of prior corrections to Al outputs; retrieve, from the persistent correction memory, a correction for a current input based on similarity between the input and the stored records; generate, by an audit trail module, a tamper-evident record of the retrieved correction and its application; schedule, by a predictive resource manager, Al processing workloads in dependence on service-level agreement constraints, carbon-intensity information of available computing resources, and quality metrics derived from the persistent correction memory and the audit trail module; and update, by a feedback controller, retrieval parameters of the persistent correction memory and scheduling parameters of the predictive resource manager based on effectiveness information from the audit trail, wherein the processor, executing the instructions, is configured to cause the persistent correction memory, audit trail module, predictive resource manager, and feedback controller to operate as a closed-loop adaptive system, reduce recurrence of Al errors, generate verifiable audit records, and optimize efficiency of Al workload execution.

2. The system of claim 1, further comprising a Federated Learning and Ethics Integration (FLEI) module configured to coordinate secure aggregation of correction model updates across distributed nodes, wherein the aggregation employs a federated-averaging protocol or equivalent mechanism to combine model deltas based on node participation weights, optionally incorporating differentialprivacy noise to preserve data confidentiality, and wherein the aggregated updates are used to refine a global persistent correction memory embedding index, thereby enabling privacypreserving, multi-organizational adaptive learning.

3. The system of claim 1, wherein the persistent correction memory stores corrections as embedding vectors in an approximate nearest-neighbor index;wherein similarity is computed using a cosine similarity score and further weighted by recency and effectiveness of prior corrections.

4. The system of claim 1, wherein the audit trail module is configured to generate a Merkle tree of correction records and anchor a Merkle root to an external verification service; wherein the external verification service comprises a public blockchain or a timestamp authority compliant with RFC-3161; and wherein the audit trail module is further configured to provide selective disclosure of audit proofs to external verifiers or regulators.

5. The system of claim 1, wherein the predictive resource manager is configured to: obtain carbon-intensity forecasts from an external carbon-intensity service and defer nonlatency-critical workloads based on the forecasts while maintaining service-level agreement constraints; apply a multi-objective optimization function balancing latency thresholds, throughout requirements, and carbon-intensity minimization.

6. The system of claim 1, wherein the feedback controller updates retrieval weighting parameters of the persistent correction memory in proportion to measured effectiveness scores of applied corrections.

7. The system of claim 1, wherein the persistent correction memory, audit trail module, and predictive resource manager are deployed in a distributed cloud-edge environment supporting federated learning with differential privacy and secure aggregation.

8. The system of claim 1, further comprising an Evidence-Conditioned Content Assembly configured to compute, for each incoming Al request, a complexity score based on one or more of semantic depth, computational requirement, domain specificity, and expected processing time.

9. The system of claim 7, further comprising an Adaptive Rate-Limit and Escalation (ARLE) Engine configured to selectively throttle or accept Al requests based on the computed complexity score and current system workload utilization.

10. The system of claim 1, wherein the processor executes an Adaptive Quality & Audit Controls (AQAC) module configured to assign quantitative quality metrics to Al outputs across multiple dimensions including accuracy, coherence, completeness, and fairness, and to trigger retrieval ofprior corrections from the persistent correction memory when the quality metrics fall below predefined thresholds.

11. The system of claim 10, wherein the processor is further configured to iteratively re-evaluate corrected outputs until the quality metrics exceed a predetermined acceptance threshold, and to65 log each iteration in the audit trail module.

12. A computer-implemented method for adaptive quality management in artificial intelligence (Al) processing, the method comprising: storing, in a persistent correction memory, records of prior corrections to Al outputs; retrieving, from the persistent correction memory, a correction for a current input based on 70 similarity between the input and the stored records; generating, by an audit trail module, a tamper-evident record of the retrieved correction and its application; scheduling, by a predictive resource manager, Al processing workloads in dependence on service-level agreement constraints, carbon-intensity information of available computing 75 resources, and quality metrics derived from the persistent correction memory and the audit trail module; and updating, by a feedback controller, retrieval parameters of the persistent correction memory and scheduling parameters of the predictive resource manager based on effectiveness information from the audit trail.8013. The method of claim 12, further comprising storing the corrections as embedding vectors in an approximate nearest-neighbor index and computing similarity using a cosine similarity score weighted by recency and effectiveness of prior corrections.

14. The method of claim 12, further comprising generating a Merkle tree of correction records and anchoring a Merkle root to an external verification service comprising a public blockchain or a85 timestamp authority compliant with RFC-3161, and selectively disclosing audit proofs to external verifiers or regulators.

15. The method of claim 12, further comprising obtaining carbon-intensity forecasts from an external carbon-intensity service, deferring non-latency-critical workloads based on the forecasts while maintaining service-level agreement constraints, and applying a multi -objective optimization90 function balancing latency thresholds, throughput requirements, and carbon-intensity minimization.

16. The method of claim 12, further comprising updating retrieval weighting parameters of the persistent correction memory in proportion to measured effectiveness scores of applied corrections.9517. The method of claim 12, wherein the persistent correction memory, audit trail module, and predictive resource manager are deployed in a distributed cloud-edge environment supporting federated learning with differential privacy and secure aggregation.

18. The method of claim 12, further comprising computing, by an Evidence-Conditioned Content Assembly (E-CCA), a complexity score for each incoming Al request based on one or more of00 semantic depth, computational requirement, domain specificity, and expected processing time.

19. The method of claim 18, further comprising throttling or accepting, by an Adaptive Rate-Limit and Escalation (ARLE) Engine , the Al requests based on the computed complexity score and current system workload utilization.

20. The method of claim 12, further comprising assigning, by an Adaptive Quality & Audit Controls05 (AQAC) module, quantitative quality metrics to Al outputs across multiple dimensions including accuracy, coherence, completeness, and fairness, and triggering retrieval of prior corrections from the persistent correction memory when the quality metrics fall below predefined thresholds.The method of claim 20, further comprising iteratively re-evaluating corrected outputs until the quality metrics exceed a predetermined acceptance threshold and logging each iteration in the audit10 trail module.

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