image
image
user-login
Patent search/

SYSTEM AND METHOD FOR EVALUATION OF HOMEOSTATIC CAPACITIES IN AI SYSTEMS

search

Patent Search in India

  • tick

    Extensive patent search conducted by a registered patent agent

  • tick

    Patent search done by experts in under 48hrs

₹999

₹399

Talk to expert

SYSTEM AND METHOD FOR EVALUATION OF HOMEOSTATIC CAPACITIES IN AI SYSTEMS

ORDINARY APPLICATION

Published

date

Filed on 11 November 2024

Abstract

ABSTRACT System and Method for Evaluation of Homeostatic Capacities in AI Systems The present disclosure introduces a system and method for evaluation of homeostatic capacities in AI systems 100, enhancing stability across dynamic environments. It comprises a dynamic feedback loop mechanism 102 for continuous performance monitoring and adaptive learning algorithms 104 to adjust decision-making based on historical data. The comprehensive evaluation metrics suite 106 provides standardized metrics, while the environmental dynamics simulation tool 108 models complex scenarios to test system resilience. Homeostatic control layers 110 regulate operational parameters, ensuring stable performance, and resource optimization algorithms 112 allocate resources efficiently based on real-time data. Real-time anomaly detection system 114 identifies deviations and triggers corrective actions, maintaining consistent functionality. The system also incorporates predictive maintenance alerts 128 to analyze long-term data and forecast potential issues. Additional components include user-centric adjustment mechanism 116, performance benchmarking dashboard 120, and multi-agent homeostatic coordination 130 for synchronized adaptability in multi-agent environments. Reference Fig 1

Patent Information

Application ID202441086976
Invention FieldCOMPUTER SCIENCE
Date of Application11/11/2024
Publication Number46/2024

Inventors

NameAddressCountryNationality
Kaki Uday ReddyAnurag University, Venkatapur (V), Ghatkesar (M), Medchal Malkajgiri DT. Hyderabad, Telangana, IndiaIndiaIndia

Applicants

NameAddressCountryNationality
Anurag UniversityVenkatapur (V), Ghatkesar (M), Medchal Malkajgiri DT. Hyderabad, Telangana, IndiaIndiaIndia

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 system and method for evaluation of homeostatic capacities in AI systems 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, system and method for evaluation of homeostatic capacities in AI systems 100 is disclosed in accordance with one embodiment of the present invention. It comprises of dynamic feedback loop mechanism 102, adaptive learning algorithms 104, comprehensive evaluation metrics suite 106, environmental dynamics simulation tool 108, homeostatic control layers 110, resource optimization algorithms 112, real-time anomaly detection system 114, user-centric adjustment mechanism 116, multi-scale homeostasis evaluation framework 118, performance benchmarking dashboard 120, scenario-based training framework 122, ethical compliance framework 124, hierarchical homeostatic control 126, predictive maintenance alerts 128, multi-agent homeostatic coordination 130, self-optimizing algorithmic adjustments 132, intervention trigger mechanism 134, energy efficiency optimization algorithms 136, longitudinal performance tracking system 138.

[00029] Referring to Fig. 1, the present disclosure provides details of system and method for evaluation of homeostatic capacities in AI systems 100. This framework enhances stability and adaptability by implementing real-time feedback loops and adaptive learning algorithms. It enables AI systems to maintain optimal performance in dynamic conditions and supports efficient resource management. In one embodiment, the system may be provided with key components such as dynamic feedback loop mechanism 102, adaptive learning algorithms 104, and comprehensive evaluation metrics suite 106 to ensure resilience. Additional components, including environmental dynamics simulation tool 108 and homeostatic control layers 110, facilitate adaptability, while user-centric adjustment mechanism 116 and performance benchmarking dashboard 120 support user engagement and system optimization. The system incorporates ethical compliance framework 124 and predictive maintenance alerts 128 to enhance reliability and long-term stability.

[00030] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with dynamic feedback loop mechanism 102, which continuously monitors system performance and detects deviations from optimal operation. This component enables the AI to make real-time adjustments, ensuring stability across changing conditions. It closely interacts with adaptive learning algorithms 104 to modify decision-making strategies based on feedback, supporting a smooth adaptation process. The dynamic feedback loop mechanism 102 also coordinates with the comprehensive evaluation metrics suite 106 to benchmark adjustments and maintain a balanced performance output.

[00031] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with adaptive learning algorithms 104, which refine the AI's behavior based on historical data and feedback. These algorithms allow the AI system to proactively adjust to new conditions by analyzing patterns and adapting strategies. Working in sync with dynamic feedback loop mechanism 102, the adaptive learning algorithms 104 ensure that the AI learns from real-time data to improve resilience. They also interact with homeostatic control layers 110 to implement necessary changes in the AI's operational parameters, enhancing stability.

[00032] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with comprehensive evaluation metrics suite 106, which consists of standardized metrics such as stability index, adaptability score, and efficiency ratio. This suite provides quantitative assessments, enabling objective evaluation of the AI's homeostatic capacities. By working closely with dynamic feedback loop mechanism 102 and resource optimization algorithms 112, the evaluation metrics suite 106 helps monitor performance and resource allocation efficiency, contributing to a well-regulated AI system.

[00033] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with environmental dynamics simulation tool 108, which models complex and dynamic conditions to test the AI system's adaptability. This tool allows for rigorous scenario-based testing, ensuring the AI is prepared for real-world operational challenges. It works in conjunction with adaptive learning algorithms 104 to identify potential areas of improvement and integrates with the performance benchmarking dashboard 120 to visualize how the AI performs under simulated conditions.

[00034] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with homeostatic control layers 110, which act as regulatory mechanisms within the AI system, adjusting operational parameters to maintain stability. These control layers serve as an oversight for real-time system performance, interfacing with dynamic feedback loop mechanism 102 to initiate changes when performance deviates from set benchmarks. Homeostatic control layers 110 also work with ethical compliance framework 124 to ensure that any adjustments align with ethical standards and operational goals, enhancing the system's reliability and trustworthiness.

[00035] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with resource optimization algorithms 112, which dynamically allocate system resources based on performance needs and operational demands. These algorithms are essential for efficient energy and resource usage, particularly during peak processing periods. They interact with comprehensive evaluation metrics suite 106 to gauge efficiency ratios, ensuring optimal resource deployment. Resource optimization algorithms 112 also work closely with adaptive learning algorithms 104 to adjust resource allocation as the AI system adapts, promoting sustainability and cost-effectiveness.

[00036] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with real-time anomaly detection system 114, which identifies any deviations from expected behaviors, allowing the AI to respond quickly to potential disturbances. This system is crucial for maintaining stability under unexpected conditions, such as data anomalies or sudden shifts in user behavior. It interfaces with dynamic feedback loop mechanism 102 to alert the system of anomalies and engages homeostatic control layers 110 to restore balance. Real-time anomaly detection system 114 enhances the system's resilience and ensures consistent performance.

[00037] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with user-centric adjustment mechanism 116, which enables the AI system to adapt its operations based on user preferences and interactions. This component improves the user experience by aligning system outputs with user needs and expectations. It works alongside adaptive learning algorithms 104 to incorporate user feedback into decision-making processes and collaborates with performance benchmarking dashboard 120 to monitor user satisfaction and make adjustments accordingly, fostering engagement and trust.
[00038] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with multi-scale homeostasis evaluation framework 118, which assesses the homeostatic capabilities of the AI system at multiple levels, from individual components to the entire system. This framework provides a comprehensive overview of stability and adaptability, ensuring the AI maintains equilibrium across different operational scales. The framework interacts with evaluation metrics suite 106 to gather data on each level's performance, allowing the system to fine-tune its functions based on multi-layered insights, promoting robust and scalable stability.

[00039] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with performance benchmarking dashboard 120, which visualizes real-time performance metrics related to the AI system's homeostatic capabilities. This dashboard provides operators with insights into system stability, adaptability, and efficiency, supporting informed decision-making. It integrates data from comprehensive evaluation metrics suite 106 and feedback from user-centric adjustment mechanism 116, offering a centralized platform to monitor and optimize performance. The dashboard also works with predictive maintenance alerts 128 to notify users of potential issues.

[00040] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with scenario-based training framework 122, which prepares the AI system for a range of dynamic environments by simulating diverse conditions. This framework strengthens the AI's adaptability by allowing it to learn from varied scenarios before real-world deployment. It works closely with adaptive learning algorithms 104 to refine strategies and with environmental dynamics simulation tool 108 to ensure realistic testing conditions. Scenario-based training framework 122 enables the AI to develop robust responses to complex challenges.

[00041] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with ethical compliance framework 124, which ensures that the AI system's self-regulation and decision-making processes align with ethical standards. This framework governs the actions taken by the homeostatic control layers 110, supporting transparency and accountability in the system's adaptive behavior. It integrates with user-centric adjustment mechanism 116 to align ethical considerations with user expectations and collaborates with intervention trigger mechanism 134 to define appropriate responses to ethical challenges.

[00042] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with hierarchical homeostatic control 126, a structured control system with multiple levels that manage specific parameters and overarching goals. Higher levels of this hierarchy oversee general system stability, while lower levels focus on fine-tuning operational parameters. This hierarchical control interacts with resource optimization algorithms 112 to distribute resources according to needs at different levels. It also coordinates with multi-scale homeostasis evaluation framework 118 to assess stability across layers, improving overall system robustness.

[00043] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with predictive maintenance alerts 128, which monitor system health by using real-time data and historical trends to predict potential component failures. This proactive approach allows the AI system to address issues before they escalate, ensuring continuous stability. Predictive maintenance alerts 128 work with performance benchmarking dashboard 120 to notify users of possible interventions and collaborate with resource optimization algorithms 112 to allocate resources effectively for preventive actions, reducing downtime.

[00044] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with multi-agent homeostatic coordination 130, which facilitates coordination among multiple AI agents operating within the same environment. This component ensures agents work harmoniously, sharing data and adjusting behaviors to maintain collective stability. It interacts with dynamic feedback loop mechanism 102 to synchronize responses to environmental changes and aligns with scenario-based training framework 122 to enhance collaborative adaptability, promoting efficiency in multi-agent systems.

[00045] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with self-optimizing algorithmic adjustments 132, which autonomously modify their parameters based on real-time data to improve performance. This component enables the AI system to evolve continuously, enhancing resilience without manual intervention. It interacts with comprehensive evaluation metrics suite 106 to track optimization outcomes and with adaptive learning algorithms 104 to incorporate findings from previous adjustments, ensuring ongoing performance refinement.

[00046] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with intervention trigger mechanism 134, which defines specific thresholds and conditions that prompt the AI system to take corrective actions to restore stability. This mechanism works closely with real-time anomaly detection system 114 to monitor for any deviations requiring intervention. It also collaborates with hierarchical homeostatic control 126 to prioritize responses across levels, ensuring timely adjustments and maintaining system integrity.

[00047] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with energy efficiency optimization algorithms 136, which manage energy consumption by adjusting resource allocation based on real-time operational demands. These algorithms reduce overall energy usage while sustaining optimal performance. They work in conjunction with resource optimization algorithms 112 to balance energy and computational resources efficiently and interact with comprehensive evaluation metrics suite 106 to monitor energy efficiency outcomes, promoting sustainability.
[00048] Referring to Fig. 1, system and method for evaluation of homeostatic capacities in AI systems 100 is provided with longitudinal performance tracking system 138, which monitors and records the AI system's performance over extended periods. This tracking enables the identification of trends and long-term homeostatic capabilities, supporting continuous improvement. It interfaces with performance benchmarking dashboard 120 for real-time visualization and collaborates with scenario-based training framework 122 to adapt training based on observed patterns, ensuring ongoing system resilience and stability.


[00049] Referring to Fig 2, there is illustrated method 200 for system and method for evaluation of homeostatic capacities in AI systems 100. The method comprises:
At step 202, method 200 includes the dynamic feedback loop mechanism 102 continuously monitoring the AI system's performance and identifying any deviations from optimal operation;
At step 204, method 200 includes the dynamic feedback loop mechanism 102 sending real-time data to adaptive learning algorithms 104 for analysis and adjustments based on historical patterns and feedback;
At step 206, method 200 includes adaptive learning algorithms 104 modifying decision-making strategies as needed and relaying updated parameters to the homeostatic control layers 110 to ensure stability;
At step 208, method 200 includes the comprehensive evaluation metrics suite 106 assessing the system's homeostatic performance, including stability and adaptability metrics, and providing feedback to the dynamic feedback loop mechanism 102;
At step 210, method 200 includes the environmental dynamics simulation tool 108 generating varied scenarios to test the AI system's resilience, which allows adaptive learning algorithms 104 to further refine decision-making processes based on simulated responses;
At step 212, method 200 includes resource optimization algorithms 112 allocating resources dynamically in response to performance data from the evaluation metrics suite 106, thereby optimizing energy and processing efficiency;
At step 214, method 200 includes the real-time anomaly detection system 114 identifying any unexpected changes or deviations in system behavior and triggering alerts for immediate adjustments through the homeostatic control layers 110;
At step 216, method 200 includes the user-centric adjustment mechanism 116 making additional adjustments based on user feedback and preferences, allowing the system to align outputs with user expectations;
At step 218, method 200 includes the performance benchmarking dashboard 120 displaying real-time metrics and insights for operators, assisting in monitoring stability, efficiency, and user satisfaction;
At step 220, method 200 includes predictive maintenance alerts 128 analyzing long-term data to forecast potential issues and alerting the system to initiate preventive measures for continued stability and performance;
At step 222, method 200 includes hierarchical homeostatic control 126 coordinating these adjustments across multiple levels within the system, with higher levels managing broad goals and lower levels focusing on specific parameters;
At step 224, method 200 includes energy efficiency optimization algorithms 136 fine-tuning energy consumption based on current processing needs and feedback from resource optimization algorithms 112 to promote sustainability;
At step 226, method 200 includes the longitudinal performance tracking system 138 recording and analyzing the AI system's performance over time to identify trends and inform further improvements in homeostatic capacity;
At step 228, method 200 includes multi-agent homeostatic coordination 130 synchronizing responses among multiple AI agents in shared environments, allowing for collaborative stability adjustments based on shared data;
At step 230, method 200 includes the self-optimizing algorithmic adjustments 132 autonomously fine-tuning parameters based on collected data, thereby continuously enhancing the system's resilience and adaptability.
[00050] The invention for evaluating homeostatic capacities in AI systems has wide-ranging applications across several industries. In healthcare, AI systems with homeostatic regulation can adapt to fluctuations in patient data, enhancing personalized treatment, diagnostics, and monitoring. Autonomous vehicles benefit from homeostatic capacities as they navigate changing road conditions and traffic patterns, improving both safety and efficiency. In smart manufacturing, adaptive AI systems optimize production workflows by adjusting to supply chain variables, equipment performance, and workforce availability, reducing waste and boosting productivity. Environmental monitoring also sees significant advantages, with AI systems that respond dynamically to shifts in ecological data, aiding in real-time conservation and climate monitoring. Additionally, in finance, homeostatic AI systems enable adaptive risk management, responding to market volatility and optimizing investment strategies. By integrating homeostasis, these AI systems become more resilient, efficient, and capable of delivering reliable performance across diverse and demanding environments.
[00051] 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.

[00052] 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.

[00053] 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 system and method for evaluation of homeostatic capacities in AI systems 100 comprising of
dynamic feedback loop mechanism 102 to continuously monitor and adjust system performance in real-time;
adaptive learning algorithms 104 to refine decision-making based on historical data and feedback;
comprehensive evaluation metrics suite 106 to assess stability, adaptability, and resource efficiency;
environmental dynamics simulation tool 108 to model complex conditions and test resilience;
homeostatic control layers 110 to regulate operational parameters for stability;
resource optimization algorithms 112 to dynamically allocate resources based on performance needs;
real-time anomaly detection system 114 to identify deviations and trigger corrective actions;
user-centric adjustment mechanism 116 to adapt system behavior based on user feedback;
multi-scale homeostasis evaluation framework 118 to evaluate stability across various levels of the system;
performance benchmarking dashboard 120 to display real-time metrics and insights;
scenario-based training framework 122 to simulate diverse conditions for enhanced adaptability;
ethical compliance framework 124 to govern system self-regulation with ethical standards;
hierarchical homeostatic control 126 to manage stability across multiple operational levels;
predictive maintenance alerts 128 to forecast potential issues and initiate preventive measures;
multi-agent homeostatic coordination 130 to synchronize responses among multiple AI agents;
self-optimizing algorithmic adjustments 132 to autonomously refine system parameters;
intervention trigger mechanism 134 to define thresholds for automatic stability restoration;
energy efficiency optimization algorithms 136 to manage energy consumption based on operational demands; and
longitudinal performance tracking system 138 to analyze performance over time for ongoing improvements.
2. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein the dynamic feedback loop mechanism 102 is configured to continuously monitor system performance metrics and make real-time adjustments to maintain stability, enabling enhanced adaptability and responsiveness under fluctuating conditions.

3. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein adaptive learning algorithms 104 are configured to modify decision-making processes based on historical and real-time data, allowing the system to proactively adjust its behavior in response to evolving environmental inputs.

4. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein comprehensive evaluation metrics suite 106 is configured to assess homeostatic performance using standardized metrics including stability, adaptability, and resource efficiency, facilitating precise benchmarking and improvement tracking across different AI configurations.

5. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein environmental dynamics simulation tool 108 is configured to model diverse, complex environmental scenarios, enabling the AI system to test resilience and refine adaptive responses before deployment in real-world settings.

6. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein homeostatic control layers 110 are configured to regulate operational parameters by dynamically adjusting processes based on feedback from the evaluation metrics suite 106, supporting stability across varying operational demands.

7. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein resource optimization algorithms 112 are configured to allocate computational and energy resources efficiently in response to real-time performance data, optimizing system efficiency while minimizing resource wastage.

8. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein real-time anomaly detection system 114 is configured to identify deviations from expected behavior and trigger corrective actions, enabling the system to rapidly restore stability and prevent performance degradation.

9. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein predictive maintenance alerts 128 are configured to analyze long-term performance data, forecast potential issues, and initiate preventive measures, ensuring consistent operation and reducing downtime through proactive maintenance.

10. The system and method for evaluation of homeostatic capacities in AI systems 100 as claimed in claim 1, wherein method comprises of
dynamic feedback loop mechanism 102 continuously monitoring the AI system's performance and identifying any deviations from optimal operation;
dynamic feedback loop mechanism 102 sending real-time data to adaptive learning algorithms 104 for analysis and adjustments based on historical patterns and feedback;
adaptive learning algorithms 104 modifying decision-making strategies as needed and relaying updated parameters to the homeostatic control layers 110 to ensure stability;
comprehensive evaluation metrics suite 106 assessing the system's homeostatic performance, including stability and adaptability metrics, and providing feedback to the dynamic feedback loop mechanism 102;
environmental dynamics simulation tool 108 generating varied scenarios to test the AI system's resilience, which allows adaptive learning algorithms 104 to further refine decision-making processes based on simulated responses;
resource optimization algorithms 112 allocating resources dynamically in response to performance data from the evaluation metrics suite 106, thereby optimizing energy and processing efficiency;
real-time anomaly detection system 114 identifying any unexpected changes or deviations in system behavior and triggering alerts for immediate adjustments through the homeostatic control layers 110;
user-centric adjustment mechanism 116 making additional adjustments based on user feedback and preferences, allowing the system to align outputs with user expectations;
performance benchmarking dashboard 120 displaying real-time metrics and insights for operators, assisting in monitoring stability, efficiency, and user satisfaction;
predictive maintenance alerts 128 analyzing long-term data to forecast potential issues and alerting the system to initiate preventive measures for continued stability and performance;
hierarchical homeostatic control 126 coordinating these adjustments across multiple levels within the system, with higher levels managing broad goals and lower levels focusing on specific parameters;
energy efficiency optimization algorithms 136 fine-tuning energy consumption based on current processing needs and feedback from resource optimization algorithms 112 to promote sustainability;
longitudinal performance tracking system 138 recording and analyzing the AI system's performance over time to identify trends and inform further improvements in homeostatic capacity;
multi-agent homeostatic coordination 130 synchronizing responses among multiple ai agents in shared environments, allowing for collaborative stability adjustments based on shared data;
self-optimizing algorithmic adjustments 132 autonomously fine-tuning parameters based on collected data, thereby continuously enhancing the system's resilience and adaptability.

Documents

NameDate
202441086976-COMPLETE SPECIFICATION [11-11-2024(online)].pdf11/11/2024
202441086976-DECLARATION OF INVENTORSHIP (FORM 5) [11-11-2024(online)].pdf11/11/2024
202441086976-DRAWINGS [11-11-2024(online)].pdf11/11/2024
202441086976-EDUCATIONAL INSTITUTION(S) [11-11-2024(online)].pdf11/11/2024
202441086976-EVIDENCE FOR REGISTRATION UNDER SSI [11-11-2024(online)].pdf11/11/2024
202441086976-EVIDENCE FOR REGISTRATION UNDER SSI(FORM-28) [11-11-2024(online)].pdf11/11/2024
202441086976-FIGURE OF ABSTRACT [11-11-2024(online)].pdf11/11/2024
202441086976-FORM 1 [11-11-2024(online)].pdf11/11/2024
202441086976-FORM FOR SMALL ENTITY(FORM-28) [11-11-2024(online)].pdf11/11/2024
202441086976-FORM-9 [11-11-2024(online)].pdf11/11/2024
202441086976-POWER OF AUTHORITY [11-11-2024(online)].pdf11/11/2024
202441086976-REQUEST FOR EARLY PUBLICATION(FORM-9) [11-11-2024(online)].pdf11/11/2024

footer-service

By continuing past this page, you agree to our Terms of Service,Cookie PolicyPrivacy Policy  and  Refund Policy  © - Uber9 Business Process Services Private Limited. All rights reserved.

Uber9 Business Process Services Private Limited, CIN - U74900TN2014PTC098414, GSTIN - 33AABCU7650C1ZM, Registered Office Address - F-97, Newry Shreya Apartments Anna Nagar East, Chennai, Tamil Nadu 600102, India.

Please note that we are a facilitating platform enabling access to reliable professionals. We are not a law firm and do not provide legal services ourselves. The information on this website is for the purpose of knowledge only and should not be relied upon as legal advice or opinion.