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MANAGEMENT TECHNIQUES FOR CROSS-DOMAIN AI AND MACHINE LEARNING PIPELINES
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Abstract
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ORDINARY APPLICATION
Published
Filed on 11 November 2024
Abstract
ABSTRACT Management Techniques for Cross-Domain AI and Machine Learning Pipelines The present disclosure introduces management techniques for cross-domain AI and ML pipelines 100, optimizing model deployment, adaptation, and compliance across various domains. The system comprise of centralized model repository 102 to store and manage models, and metadata management system 104 to catalog detailed model information. A standardized API framework 106 facilitates seamless cross-domain communication, while interoperability assessment tools 126 ensure compatibility with diverse systems. Secure data exchange protocol 130 and role-based access control system 132 enhance secure, privacy-compliant data transfers. The automated model deployment system 108 and dynamic resource allocation mechanism 110 enable efficient model deployment. Additional components are automated monitoring and maintenance tools 116, compliance tracking system 118, transfer learning algorithms 122, adaptive learning feedback loop 142, knowledge sharing platform 124, visualization dashboard 128, real-time collaborative training module 138, domain-specific model enhancement tools 146, and automated ethics review mechanism 148. Reference Fig 1
Patent Information
Application ID | 202441086973 |
Invention Field | COMPUTER SCIENCE |
Date of Application | 11/11/2024 |
Publication Number | 46/2024 |
Inventors
Name | Address | Country | Nationality |
---|---|---|---|
Madigela Ruchitha | Anurag University, Venkatapur (V), Ghatkesar (M), Medchal Malkajgiri DT. Hyderabad, Telangana, India | India | India |
Applicants
Name | Address | Country | Nationality |
---|---|---|---|
Anurag University | Venkatapur (V), Ghatkesar (M), Medchal Malkajgiri DT. Hyderabad, Telangana, India | India | India |
Specification
Description:
DETAILED DESCRIPTION
[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.
[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of multi-management techniques for cross domain AI and machine learning is pipelines and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.
[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.
[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.
[00028] Referring to Fig. 1, multi-management techniques for cross domain AI and machine learning pipelines 100 is disclosed in accordance with one embodiment of the present invention. It comprises of centralized model repository 102, metadata management system 104, standardized API framework 106, automated model deployment system 108, dynamic resource allocation mechanism 110, collaborative workspaces for cross-domain teams 112, stakeholder engagement tools 114, automated monitoring and maintenance tools 116, compliance tracking system 118, ethical guidelines repository 120, transfer learning algorithms 122, knowledge sharing platform 124, interoperability assessment tools 126, visualization dashboard 128, secure data exchange protocol 130, role-based access control (RBAC) system 132, simulation and testing environment 134, predictive maintenance system 136, real-time collaborative training module 138, AI-driven recommendation engine 140, adaptive learning feedback loop 142, cross-domain impact analysis tool 144, domain-specific model enhancement tools 146 and automated ethics review mechanism 148.
[00029] Referring to Fig. 1, the present disclosure provides details of multi-management techniques for cross domain AI and machine learning pipelines 100. This invention enables seamless integration of AI models across various domains, with core components such as centralized model repository 102, metadata management system 104, and standardized API framework 106 for efficient model storage and communication. The automated model deployment system 108 and dynamic resource allocation mechanism 110 facilitate scalable model deployment and resource optimization. Additional components include compliance tracking system 118 and ethical guidelines repository 120, ensuring regulatory adherence and responsible AI practices. For continuous improvement, transfer learning algorithms 122 and knowledge sharing platform 124 support collaborative learning, while predictive maintenance system 136 and real-time collaborative training module 138 maintain model performance. The framework offers a secure data exchange protocol 130 and role-based access control system 132 for secure, role-specific access and privacy in cross-domain data sharing.
[00030] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with centralized model repository 102, which serves as the primary storage and access point for all AI and ML models across domains. This repository enables model version control and centralized tracking, allowing users to store and retrieve models with detailed metadata provided by metadata management system 104. The centralized model repository 102 ensures that models are accessible across domains, facilitating transfer learning and efficient cross-domain deployment.
[00031] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with metadata management system 104, which catalogs each model's purpose, training data, and performance metrics to support model selection and deployment. This system is crucial for identifying suitable models for new applications and enables seamless integration with centralized model repository 102. By enhancing model discoverability, metadata management system 104 supports interoperability and efficient model reuse across various domains.
[00032] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with standardized API framework 106, which enables diverse models to communicate and share data across domains. The standardized API framework 106 ensures compatibility and easy integration, allowing models from centralized model repository 102 to be deployed in different workflows without compatibility issues. This component works closely with automated model deployment system 108 to facilitate smooth model transitions across environments.
[00033] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with automated model deployment system 108, which streamlines the process of deploying models across various domains, reducing manual intervention. This system works in coordination with dynamic resource allocation mechanism 110, enabling efficient scaling based on model demand. By automating deployment, automated model deployment system 108 enhances responsiveness and scalability within cross-domain applications.
[00034] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with dynamic resource allocation mechanism 110, which optimally adjusts computational resources for deployed models based on real-time performance demands. It operates in sync with automated model deployment system 108 to ensure resources are scaled dynamically, promoting cost-effectiveness and operational efficiency. This component is essential for handling variable workloads across diverse AI and ML applications, supporting scalability.
[00035] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with collaborative workspaces for cross-domain teams 112, which enable real-time collaboration between teams from different domains. These virtual spaces allow users to experiment with models, share insights, and make collective decisions. Collaborative workspaces 112 integrate with stakeholder engagement tools 114, ensuring continuous feedback and fostering a culture of innovation through shared perspectives across domains.
[00036] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with stakeholder engagement tools 114, which facilitate feedback collection on model performance and implementation across different domains. These tools enable stakeholders to provide input, helping to refine and improve model effectiveness continuously. Working alongside collaborative workspaces for cross-domain teams 112, stakeholder engagement tools 114 ensure diverse insights are incorporated into the model lifecycle, enhancing cross-domain applicability.
[00037] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with automated monitoring and maintenance tools 116, which continuously assess model performance and detect anomalies. These tools help maintain model reliability by triggering maintenance or corrective actions as needed. Integrated with the compliance tracking system 118, automated monitoring and maintenance tools 116 ensure that deployed models meet regulatory and ethical standards, thereby supporting reliable and accountable AI solutions.
[00038] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 are provided with compliance tracking system 118, which monitors models for adherence to regulatory and ethical guidelines. This system tracks compliance in real-time, ensuring models operate within legal and ethical boundaries. The compliance tracking system 118 works closely with ethical guidelines repository 120 to align models with industry standards, fostering transparency and trust in cross-domain AI applications.
[00039] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with ethical guidelines repository 120, which houses a dynamic collection of ethical standards and best practices for AI and ML. This repository is regularly updated to reflect current regulations, guiding organizations in responsible AI deployment. Integrated with the compliance tracking system 118, the ethical guidelines repository 120 promotes ethical AI practices, helping teams implement responsible and compliant AI solutions.
[00040] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with transfer learning algorithms 122, which identify and adapt models for new applications across different domains. These algorithms evaluate model relevance based on performance metrics and contextual factors, supporting cross-domain adaptability. In combination with knowledge sharing platform 124, transfer learning algorithms 122 optimize model transfer processes, reducing time and resources needed for model reapplication.
[00041] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with knowledge sharing platform 124, which facilitates documentation and exchange of insights gathered from model performance and applications. This platform acts as a repository of shared learning, allowing teams to benefit from collective knowledge. Working in tandem with transfer learning algorithms 122, the knowledge sharing platform 124 enhances cross-domain learning and continuous model improvement.
[00042] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with interoperability assessment tools 126, which evaluate model compatibility before deployment across domains. These tools identify potential integration challenges, ensuring smooth operation in varied environments. By working closely with the standardized API framework 106, interoperability assessment tools 126 help achieve seamless integration of models, promoting collaboration across different applications.
[00043] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with visualization dashboard 128, an intuitive interface for displaying key performance indicators (KPIs), model interactions, and cross-domain relationships. This dashboard provides stakeholders with clear insights into model performance and interactions, improving transparency. Visualization dashboard 128 integrates with automated monitoring and maintenance tools 116 to provide real-time updates, facilitating informed decision-making.
[00044] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with secure data exchange protocol 130, which ensures secure and compliant data sharing between models across domains. This protocol includes encryption, anonymization, and access control mechanisms, protecting sensitive information. Working alongside role-based access control (RBAC) system 132, the secure data exchange protocol 130 upholds data privacy, ensuring secure interactions in cross-domain AI applications.
[00045] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with role-based access control (RBAC) system 132, which governs permissions for accessing and modifying models and data. This system restricts access based on user roles, enhancing security and governance. In coordination with the secure data exchange protocol 130, the RBAC system 132 helps maintain data integrity and confidentiality, supporting safe cross-domain collaboration.
[00046] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with simulation and testing environment 134, where users can assess model performance in hypothetical scenarios. This controlled environment allows stakeholders to evaluate model robustness and reliability before deployment. Simulation and testing environment 134 integrates with predictive maintenance system 136 to proactively identify any potential issues, ensuring readiness for real-world applications.
[00047] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with predictive maintenance system 136, which analyzes historical performance data to anticipate maintenance needs and minimize downtime. By scheduling maintenance before issues arise, this system ensures operational continuity. Predictive maintenance system 136 works in alignment with automated monitoring and maintenance tools 116 to uphold model reliability and performance over time.
[00048] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with real-time collaborative training module 138, enabling distributed teams to jointly train AI and ML models. This module facilitates simultaneous input from multiple users, enhancing model performance with diverse datasets and expertise. In combination with collaborative workspaces for cross-domain teams 112, real-time collaborative training module 138 supports efficient, cooperative model development.
[00049] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with AI-driven recommendation engine 140, which suggests models based on application requirements, performance history, and domain needs. This engine optimizes model selection, saving time and improving deployment accuracy. It interacts closely with metadata management system 104 to provide recommendations tailored to specific use cases, enhancing model applicability across domains.
[00050] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with adaptive learning feedback loop 142, which collects real-time performance data to adaptively retrain models, ensuring relevance and accuracy in changing environments. This feedback loop is essential for maintaining model efficacy over time and works closely with automated monitoring and maintenance tools 116 to continuously refine model performance based on new data.
[00051] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with cross-domain impact analysis tool 144, which assesses how a model's deployment in one domain may affect outcomes in another. This tool enables informed decision-making by evaluating cross-domain implications. Working alongside collaborative workspaces for cross-domain teams 112, cross-domain impact analysis tool 144 supports strategic deployment planning across industries.
[00052] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with domain-specific model enhancement tools 146, designed to tailor models with algorithms and feature extraction methods unique to each domain. These tools ensure models are optimized for their intended applications, promoting domain-specific adaptability. Domain-specific model enhancement tools 146 work closely with transfer learning algorithms 122 to support efficient cross-domain model adaptation.
[00053] Referring to Fig. 1, management techniques for cross-domain AI and ML pipelines 100 is provided with automated ethics review mechanism 148, which conducts pre-deployment ethics evaluations to ensure models align with organizational and industry ethical standards. This mechanism analyzes potential ethical implications, promoting responsible AI practices. It integrates with compliance tracking system 118 and ethical guidelines repository 120 to uphold ethical standards across cross-domain AI applications.
[00054] Referring to Fig 2, there is illustrated method 200 for management techniques for cross-domain AI and ML pipelines 100. The method comprises:
At step 202, method 200 includes a user accessing centralized model repository 102 to select an AI/ML model for cross-domain deployment;
At step 204, method 200 includes the user consulting metadata management system 104 to review model details, ensuring compatibility with the target domain;
At step 206, method 200 includes standardized API framework 106 establishing communication protocols for seamless model integration across systems;
At step 208, method 200 includes interoperability assessment tools 126 verifying model compatibility with domain-specific systems and data;
At step 210, method 200 includes secure data exchange protocol 130 enabling privacy-compliant data transfers during deployment;
At step 212, method 200 includes automated model deployment system 108 deploying the model within the target domain's workflow;
At step 214, method 200 includes dynamic resource allocation mechanism 110 adjusting computational resources to optimize model performance;
At step 216, method 200 includes simulation and testing environment 134 running initial tests to ensure model reliability before full deployment;
At step 218, method 200 includes predictive maintenance system 136 scheduling proactive maintenance to minimize downtime;
At step 220, method 200 includes automated monitoring and maintenance tools 116 tracking model performance and detecting anomalies;
At step 222, method 200 includes compliance tracking system 118 ensuring model adherence to regulatory and ethical standards;
At step 224, method 200 includes ethical guidelines repository 120 providing up-to-date ethical standards for responsible AI;
At step 226, method 200 includes collaborative workspaces 112 enabling cross-domain team collaboration to refine and adapt the model;
At step 228, method 200 includes stakeholder engagement tools 114 gathering performance feedback for continuous improvement;
At step 230, method 200 includes transfer learning algorithms 122 adapting the model for enhanced domain performance;
At step 232, method 200 includes domain-specific model enhancement tools 146 customizing the model with domain-specific algorithms;
At step 234, method 200 includes adaptive learning feedback loop 142 retraining the model to maintain accuracy with new data;
At step 236, method 200 includes cross-domain impact analysis tool 144 assessing model effects across different domains;
At step 238, method 200 includes AI-driven recommendation engine 140 suggesting optimized models for future applications;
At step 240, method 200 includes knowledge sharing platform 124 documenting deployment insights for future cross-domain use;
At step 242, method 200 includes visualization dashboard 128 displaying real-time performance metrics and cross-domain relationships;
At step 244, method 200 includes role-based access control (RBAC) system 132 managing secure, role-based model access;
At step 246, method 200 includes real-time collaborative training module 138 supporting joint model training with diverse datasets;
At step 248, method 200 includes automated ethics review mechanism 148 conducting final ethical evaluations to ensure compliance.
[00055] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.
[00056] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.
[00057] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A management techniques for cross-domain AI and ML pipelines 100 comprising of
centralized model repository 102 to store and manage AI/ML models across domains for easy access and deployment;
metadata management system 104 to catalog model details like performance metrics and applicability;
standardized API framework 106 to establish seamless communication between models across domains;
automated model deployment system 108 to streamline deployment of models into target domain workflows;
dynamic resource allocation mechanism 110 to optimize computational resources based on model demand;
collaborative workspaces 112 to enable cross-domain team collaboration on model refinement;
stakeholder engagement tools 114 to gather feedback on model performance for continuous improvement;
automated monitoring and maintenance tools 116 to continuously track model performance and identify anomalies;
compliance tracking system 118 to ensure models adhere to regulatory and ethical standards;
ethical guidelines repository 120 to provide up-to-date standards and best practices for responsible AI;
transfer learning algorithms 122 to adapt models for enhanced performance in new domains;
knowledge sharing platform 124 to document insights from deployments for future cross-domain use;
interoperability assessment tools 126 to verify compatibility of models with various domain-specific systems;
visualization dashboard 128 to display real-time performance metrics and model interactions;
secure data exchange protocol 130 to enable encrypted and privacy-compliant data transfers during deployment;
role-based access control system 132 to manage secure, role-based access to models and data;
simulation and testing environment 134 to test model reliability under controlled conditions before deployment;
predictive maintenance system 136 to anticipate and schedule maintenance for minimizing downtime;
real-time collaborative training module 138 to enable distributed teams to jointly train models;
AI-driven recommendation engine 140 to suggest suitable models for specific domain requirements;
adaptive learning feedback loop 142 to retrain models based on real-time data for maintained accuracy;
cross-domain impact analysis tool 144 to assess potential effects of models across different domains;
domain-specific model enhancement tools 146 to customize models with domain-specific algorithms and features; and
automated ethics review mechanism 148 to conduct final ethical evaluations before deployment.
2. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein centralized model repository 102 is configured to serve as a unified storage and access point for diverse AI/ML models across domains, supporting version control and seamless model retrieval to enable efficient cross-domain deployment and model adaptation.
3. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein standardized API framework 106 is configured to facilitate model interoperability across diverse systems by providing standardized communication protocols, thus enabling flexible integration and consistent data exchange across domains.
4. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein dynamic resource allocation mechanism 110 is configured to automatically adjust computational resources in response to real-time model demand, optimizing performance and resource efficiency for scalable model operation.
5. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein automated monitoring and maintenance tools 116 are configured to track model performance continuously, detect anomalies, and trigger proactive maintenance actions, ensuring sustained model reliability and minimal downtime in cross-domain applications.
6. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein compliance tracking system 118 is configured to provide real-time monitoring of regulatory and ethical adherence, automatically alerting users to compliance issues and ensuring models meet legal and ethical standards across domains.
7. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein transfer learning algorithms 122 are configured to analyze and adapt existing models for new domain-specific applications, facilitating efficient reusability and enhancing model relevance with reduced retraining efforts.
8. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein adaptive learning feedback loop 142 is configured to dynamically retrain models based on real-time data, enabling continuous improvement and adaptation to new data patterns across varied applications.
9. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein automated ethics review mechanism 148 is configured to evaluate potential ethical implications of models pre-deployment, supporting responsible AI practices by ensuring models align with ethical guidelines and industry standards across domains.
10. The management techniques for cross-domain AI and ML pipelines 100 as claimed in claim 1, wherein method comprises of
centralized model repository 102 allowing a user to select an AI/ML model for cross-domain deployment;
metadata management system 104 providing model details to ensure compatibility with the target domain;
standardized API framework 106 establishing communication protocols for seamless model integration across systems;
interoperability assessment tools 126 verifying model compatibility with domain-specific systems and data;
secure data exchange protocol 130 enabling privacy-compliant data transfers during deployment;
automated model deployment system 108 deploying the model within the target domain's workflow;
dynamic resource allocation mechanism 110 adjusting computational resources to optimize model performance;
simulation and testing environment 134 running initial tests to ensure model reliability before full deployment;
predictive maintenance system 136 scheduling proactive maintenance to minimize downtime;
automated monitoring and maintenance tools 116 tracking model performance and detecting anomalies;
compliance tracking system 118 ensuring model adherence to regulatory and ethical standards;
ethical guidelines repository 120 providing up-to-date ethical standards for responsible AI;
collaborative workspaces 112 enabling cross-domain team collaboration to refine and adapt the model;
stakeholder engagement tools 114 gathering performance feedback for continuous improvement;
transfer learning algorithms 122 adapting the model for enhanced domain performance;
domain-specific model enhancement tools 146 customizing the model with domain-specific algorithms;
adaptive learning feedback loop 142 retraining the model to maintain accuracy with new data;
cross-domain impact analysis tool 144 assessing model effects across different domains;
AI-driven recommendation engine 140 suggesting optimized models for future applications;
knowledge sharing platform 124 documenting deployment insights for future cross-domain use;
visualization dashboard 128 displaying real-time performance metrics and cross-domain relationships;
role-based access control (RBAC) system 132 managing secure, role-based model access;
real-time collaborative training module 138 supporting joint model training with diverse datasets;
automated ethics review mechanism 148 conducting final ethical evaluations to ensure compliance.
Documents
Name | Date |
---|---|
202441086973-COMPLETE SPECIFICATION [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-DECLARATION OF INVENTORSHIP (FORM 5) [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-DRAWINGS [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-EDUCATIONAL INSTITUTION(S) [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-EVIDENCE FOR REGISTRATION UNDER SSI [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-EVIDENCE FOR REGISTRATION UNDER SSI(FORM-28) [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-FIGURE OF ABSTRACT [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-FORM 1 [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-FORM FOR SMALL ENTITY(FORM-28) [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-FORM-9 [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-POWER OF AUTHORITY [11-11-2024(online)].pdf | 11/11/2024 |
202441086973-REQUEST FOR EARLY PUBLICATION(FORM-9) [11-11-2024(online)].pdf | 11/11/2024 |
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