INTELLIGENT CONTROL MECHANISM FOR DEVICE MANAGEMENT USING AI

Abstract

ABSTRACT Intelligent Control Mechanism for Device Management Using AI The present disclosure introduces intelligent control mechanism for device management using AI 100. It is designed to optimize device operation across various environments through AI-driven components. The system features a data acquisition layer 102 for collecting data and data processing layer 104 that analyzes this data using machine learning algorithms. The decision-making layer 106 generates context-aware configurations, while control layer 108 implements optimized device settings. The other key components of invention are control layer 108, user interface layer 110, predictive maintenance module 112, adaptive security module 114, multi-device coordination module 116, energy optimization algorithms 118, edge computing integration 120, feedback loop mechanism 122, interoperability protocols 124, multi-modal interaction interfaces 126, customizable ai models 128, energy harvesting capabilities 130, resource scheduling module 132, compliance and reporting tools 134, scenario-based configuration management 136, behavioural pattern recognition module 138 and visualization tools 140. Reference Fig 1

Specification

Description:Intelligent Control Mechanism for Device Management Using AI
TECHNICAL FIELD
[0001] The present innovation relates to an AI-based intelligent control mechanism for optimizing the management, performance, and configuration of interconnected devices across various domains.

BACKGROUND

[0002] In recent years, the rapid expansion of connected devices and Internet of Things (IoT) ecosystems has revolutionized various fields, including home automation, industrial operations, healthcare, and smart cities. However, this growth has introduced significant challenges in managing the vast and varied functionalities of these devices. Traditional device management options, which often rely on manual configuration and static controls, can lead to operational inefficiencies, increased costs, and user dissatisfaction. These conventional solutions typically lack the capability for real-time monitoring, dynamic optimization, and predictive capabilities, resulting in suboptimal device performance and elevated energy consumption. Users can sometimes opt for fragmented third-party applications or basic automation settings, but these options often fail to scale effectively or adapt to complex interactions, leaving gaps in energy efficiency, security, and user experience.
[0003] The invention of an intelligent control mechanism powered by AI addresses these limitations by offering a system capable of real-time decision-making, context-aware adaptation, and predictive maintenance. This intelligent mechanism continuously learns from data, allowing it to autonomously optimize device configurations, enhance security, and reduce energy usage without requiring extensive manual intervention. Unlike existing solutions, this invention integrates multiple device management functions into a unified, scalable platform that can operate seamlessly across diverse environments, including smart homes and industrial setups.
[0004] What sets this invention apart is its ability to dynamically adapt to changing conditions, leveraging machine learning to predict and preemptively address potential device failures, security risks, and operational inefficiencies. The system's novelty lies in its multi-layered approach, which includes adaptive learning algorithms, a distributed control architecture, and context-sensitive decision-making capabilities. These features enable the mechanism to improve energy efficiency, lower operational costs, and provide a user-centric experience. By overcoming the drawbacks of traditional management systems, this invention delivers a comprehensive and sustainable solution to modern device management challenges.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to provide an intelligent control mechanism that optimizes device performance through real-time decision-making and data-driven insights.
[0006] Another object of the invention is to reduce energy consumption by dynamically adjusting device settings based on usage patterns and environmental conditions.
[0007] Another object of the invention is to enhance user experience by offering a personalized, adaptive interface that learns user preferences and behavior.
[0008] Another object of the invention is to improve system security by incorporating adaptive protocols that detect and respond to potential threats in real time.
[0009] Another object of the invention is to increase operational efficiency by automating device management tasks, reducing the need for manual intervention.
[00010] Another object of the invention is to facilitate predictive maintenance by analyzing data trends and identifying potential device issues before they escalate.
[00011] Another object of the invention is to enable seamless integration across various device ecosystems, supporting interoperability and cross-platform functionality.
[00012] Another object of the invention is to promote scalability by utilizing a distributed control architecture capable of managing large numbers of connected devices.
[00013] Another object of the invention is to provide a user-friendly interface that presents data insights and recommendations in an intuitive format, enabling informed decision-making.
[00014] Another object of the invention is to support sustainable practices by minimizing waste and reducing environmental impact through optimized resource allocation


SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention intelligent control mechanism for device management using AI is presented. It is an AI-driven control mechanism designed for efficient, scalable device management across various environments like smart homes and industrial settings. It optimizes device performance, energy use, and security by analyzing real-time data and adapting settings dynamically. The system automates configuration, predictive maintenance, and multi-device coordination, reducing operational costs and improving user experience. Additionally, it seamlessly integrates with existing device ecosystems, supporting sustainability through responsible resource management.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for intelligent control mechanism for device management using AI.

[00021] FIG 2 is working methodology of intelligent control mechanism for device management using AI.

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 intelligent control mechanism for device management using AI 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, intelligent control mechanism for device management using AI 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data acquisition layer 102, data processing layer 104, decision-making layer 106, control layer 108, user interface layer 110, predictive maintenance module 112, adaptive security module 114, multi-device coordination module 116, energy optimization algorithms 118, edge computing integration 120, feedback loop mechanism 122, interoperability protocols 124, multi-modal interaction interfaces 126, customizable ai models 128, energy harvesting capabilities 130, resource scheduling module 132, compliance and reporting tools 134, scenario-based configuration management 136, behavioral pattern recognition module 138 and visualization tools 140.

[00029] Referring to Fig. 1, the present disclosure provides details of intelligent control mechanism for device management using AI 100. It is designed to optimize the operation and performance of interconnected devices across various environments using AI. It enables real-time data processing, dynamic decision-making, and context-aware adjustments, enhancing efficiency, security, and user experience. In one embodiment, the intelligent control mechanism 100 may include key components such as data acquisition layer 102, data processing layer 104, and decision-making layer 106, which facilitate data collection, analysis, and optimization. The system also incorporates control layer 108 and user interface layer 110 to enact adjustments and provide user insights. Additional components like predictive maintenance module 112 and adaptive security module 114 enable proactive maintenance and continuous threat monitoring. The system's multi-device coordination module 116 and energy optimization algorithms 118 further improve network performance and reduce energy consumption.

[00030] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with data acquisition layer 102, which gathers real-time data from interconnected devices, capturing parameters such as energy consumption, device status, and environmental conditions. This layer serves as the primary input source for data processing layer 104, enabling the system to analyze and respond to changing conditions. The data acquisition layer 102 works closely with control layer 108 to ensure accurate data is available for device adjustments, enhancing operational efficiency.
[00031] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with data processing layer 104, which uses machine learning algorithms to analyze incoming data for patterns, trends, and anomalies. This layer transforms raw data from data acquisition layer 102 into actionable insights, helping decision-making layer 106 to make optimal configurations. By detecting anomalies, the data processing layer 104 supports adaptive security module 114 in identifying and addressing potential security threats in real time.
[00032] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with decision-making layer 106, which applies AI algorithms to generate context-aware, real-time decisions for device management. Using insights from data processing layer 104, this layer optimizes device configurations, scheduling, and interactions to enhance performance. The decisions from decision-making layer 106 are enacted by control layer 108 and are displayed to users through user interface layer 110 for seamless management.
[00033] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with control layer 108, which executes commands generated by decision-making layer 106 to optimize device performance. This layer directly adjusts device settings, initiates actions, and reconfigures interactions, ensuring continuous optimization. It collaborates with predictive maintenance module 112 to schedule timely maintenance and with energy optimization algorithms 118 to reduce energy consumption effectively.
[00034] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with user interface layer 110, an intuitive interface that enables users to monitor and manage device settings, access insights, and receive alerts. It presents real-time data from data processing layer 104 and actionable recommendations from decision-making layer 106, promoting informed user decisions. This layer also integrates with adaptive security module 114 to notify users of any potential security risks, enhancing user trust and system usability.
[00035] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with predictive maintenance module 112, which uses historical data patterns to anticipate potential device failures and schedule proactive maintenance. This module receives processed data from data processing layer 104 to identify indicators of malfunction, helping to prevent unexpected downtimes. Predictive maintenance module 112 works in conjunction with control layer 108 to initiate timely interventions, reducing maintenance costs and enhancing device longevity.
[00036] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with adaptive security module 114, which continuously monitors device activity to detect and respond to security threats in real-time. Leveraging anomaly insights from data processing layer 104, it implements countermeasures automatically, such as restricting access or adjusting security protocols. This module enhances system resilience and works with user interface layer 110 to inform users of any detected threats, maintaining a secure and user-aware environment.
[00037] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with multi-device coordination module 116, which synchronizes interactions among multiple devices to optimize network performance and reduce operational conflicts. It coordinates with decision-making layer 106 to manage device priorities and dependencies, ensuring efficient resource usage across devices. This module enables seamless communication among diverse devices and enhances the overall functionality of complex IoT ecosystems.
[00038] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with energy optimization algorithms 118, which assess device energy usage and adjust operations to minimize consumption without compromising performance. These algorithms analyze data from data acquisition layer 102 and collaborate with control layer 108 to adjust settings that reduce energy waste. The algorithms also support sustainable practices, contributing to reduced operational costs and environmental impact.
[00039] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with edge computing integration 120, which processes data closer to the device source, minimizing latency and enabling faster responses to changing conditions. This integration enhances real-time decision-making within decision-making layer 106 and reduces dependency on cloud processing, improving both speed and reliability. Edge computing integration 120 is critical for applications requiring immediate adjustments, such as in industrial automation.
[00040] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with feedback loop mechanism 122, which evaluates the effectiveness of decisions and adjustments to refine future actions continuously. It interacts with decision-making layer 106 to assess outcomes, enabling adaptive learning and ongoing optimization. Feedback loop mechanism 122 ensures the system evolves in response to new data, improving efficiency over time and enhancing user satisfaction.
[00041] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with interoperability protocols 124, which allow seamless communication among devices from different manufacturers or ecosystems. These protocols support data acquisition layer 102 and multi-device coordination module 116 by standardizing communication, ensuring cohesive operation across heterogeneous devices. Interoperability protocols 124 contribute to an integrated user experience, simplifying device management.
[00042] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with multi-modal interaction interfaces 126, which enable user interaction through various channels such as voice commands, mobile applications, and web dashboards. These interfaces are linked to user interface layer 110 to enhance accessibility and user engagement. By offering flexible interaction modes, multi-modal interaction interfaces 126 make the system more adaptable to diverse user preferences and scenarios.
[00043] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with customizable AI models 128, allowing users to adjust the AI algorithms to meet their specific operational needs and preferences. These models adapt based on user input and historical data, aligning with data processing layer 104 to ensure tailored decision-making. Customizable AI models 128 offer a personalized experience, making the system versatile for various applications.
[00044] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with energy harvesting capabilities 130, which enable devices to capture and use ambient energy sources, such as solar or thermal energy, for self-sustaining operations. These capabilities work in tandem with energy optimization algorithms 118 to further reduce reliance on traditional power sources, enhancing sustainability and operational efficiency.
[00045] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with resource scheduling module 132, which optimizes the timing of device operations based on usage patterns, energy costs, and user preferences. This module collaborates with decision-making layer 106 to schedule activities that reduce peak energy consumption and minimize costs. Resource scheduling module 132 helps in maintaining efficiency while adapting to dynamic operational demands.
[00046] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with compliance and reporting tools 134, which monitor adherence to regulatory and operational standards, generating reports as needed. These tools leverage data from data acquisition layer 102 and provide users with compliance insights through user interface layer 110. Compliance and reporting tools 134 support organizations in meeting regulatory requirements, especially in industries with strict standards.
[00047] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with scenario-based configuration management 136, enabling predefined settings for different operational modes, such as "Home," "Away," or "Night." This feature works with decision-making layer 106 to apply context-specific configurations, optimizing device performance for each scenario. Scenario-based configuration management 136 enhances user convenience by automating adjustments based on situational requirements.
[00048] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with behavioral pattern recognition module 138, which analyzes user behavior over time to detect patterns and proactively adjust device settings. This module is linked to data processing layer 104 and decision-making layer 106 to ensure decisions are aligned with user habits. Behavioral pattern recognition module 138 enhances comfort and user satisfaction by tailoring device interactions.
[00049] Referring to Fig. 1, intelligent control mechanism for device management 100 is provided with visualization tools 140, which present data insights and performance metrics in a user-friendly format. These tools are connected to user interface layer 110, making it easy for users to monitor device efficiency, energy usage, and overall system performance. Visualization tools 140 facilitate informed decision-making by translating complex data into intuitive visualizations.

[00050] Referring to Fig 2, there is illustrated method 200 for intelligent control mechanism for device management using AI 100. The method comprises:

At step 202, method 200 includes data acquisition layer 102 collecting real-time data from connected devices, capturing parameters such as energy consumption, operational status, and environmental conditions;
At step 204, method 200 includes data processing layer 104 analyzing the data from data acquisition layer 102 using machine learning algorithms to identify patterns and detect anomalies, preparing the data for decision-making;
At step 206, method 200 includes decision-making layer 106 receiving insights from data processing layer 104 and generating real-time, context-aware decisions regarding optimal device configurations and operations;
At step 208, method 200 includes control layer 108 receiving commands from decision-making layer 106 to adjust settings or initiate actions on connected devices, implementing optimized configurations;
At step 210, method 200 includes feedback loop mechanism 122 evaluating the effectiveness of adjustments made by control layer 108 by assessing outcomes against expected performance, continuously refining future decision-making strategies;
At step 212, method 200 includes multi-device coordination module 116 orchestrating interactions among devices adjusted by control layer 108 to ensure seamless network operations and prevent device conflicts;
At step 214, method 200 includes energy optimization algorithms 118 analyzing data from data acquisition layer 102 and decisions from decision-making layer 106 to minimize energy consumption without compromising device performance;
At step 216, method 200 includes edge computing integration 120 processing data locally from data acquisition layer 102, reducing latency and enabling faster responses for time-sensitive adjustments;
At step 218, method 200 includes adaptive security module 114 monitoring activity from data acquisition layer 102 and identifying anomalies flagged by data processing layer 104 to detect potential threats, automatically adjusting security settings if a risk is identified;
At step 220, method 200 includes predictive maintenance module 112 using historical data patterns from data processing layer 104 to predict potential device failures and scheduling maintenance actions before issues arise, which is communicated to control layer 108 for implementation;
At step 222, method 200 includes user interface layer 110 providing users with real-time insights from data processing layer 104, recommendations from decision-making layer 106, and maintenance alerts from predictive maintenance module 112, allowing users to make informed adjustments;
At step 224, method 200 includes interoperability protocols 124 enabling seamless communication between multi-vendor devices coordinated by multi-device coordination module 116, ensuring cohesive operation across the network;
At step 226, method 200 includes multi-modal interaction interfaces 126 integrating with user interface layer 110 to enable user control and monitoring through multiple channels like voice commands, mobile apps, and web dashboards;
At step 228, method 200 includes customizable AI models 128 adapting to user-specific requirements and feeding updated preferences to decision-making layer 106 to personalize device management;
At step 230, method 200 includes resource scheduling module 132 synchronizing with decision-making layer 106 to optimize the timing of device operations based on usage patterns and energy costs, ensuring efficient resource allocation;
At step 232, method 200 includes compliance and reporting tools 134 monitoring data from data acquisition layer 102 to ensure regulatory standards are met, generating reports accessible to users through user interface layer 110;
At step 234, method 200 includes scenario-based configuration management 136 applying pre-set configurations from decision-making layer 106 for various scenarios, such as "Home" or "Away," dynamically adjusting device operations as per user-defined preferences;
At step 236, method 200 includes behavioral pattern recognition module 138 analyzing long-term user behavior from data acquisition layer 102, allowing decision-making layer 106 to proactively align device settings with user habits for a personalized experience;
At step 238, method 200 includes visualization tools 140 integrating with user interface layer 110 to present users with intuitive data visualizations of device performance, energy savings, and efficiency metrics, enhancing user understanding and decision-making.


[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 intelligent control mechanism for device management using AI 100 comprising of
data acquisition layer 102 to collect real-time data from connected devices;
data processing layer 104 to analyze collected data and identify patterns and anomalies;
decision-making layer 106 to generate context-aware decisions based on analyzed data;
control layer 108 to implement settings and commands for optimal device performance;
user interface layer 110 to display insights, recommendations, and alerts to users;
predictive maintenance module 112 to anticipate device failures and schedule maintenance;
adaptive security module 114 to monitor and respond to potential security threats;
multi-device coordination module 116 to manage interactions and optimize network performance;
energy optimization algorithms 118 to minimize energy consumption across devices;
edge computing integration 120 to process data locally and reduce latency;
feedback loop mechanism 122 to refine decision-making strategies based on outcomes;
interoperability protocols 124 to enable communication among multi-vendor devices;
multi-modal interaction interfaces 126 to support user control through various channels;
customizable AI models 128 to tailor device management to user preferences;
energy harvesting capabilities 130 to utilize ambient energy for sustainable operation;
resource scheduling module 132 to optimize device operation timing and resource allocation;
compliance and reporting tools 134 to monitor regulatory adherence and generate reports;
scenario-based configuration management 136 to apply user-defined settings for different scenarios;
behavioral pattern recognition module 138 to adjust settings based on user habits; and
visualization tools 140 to present performance and efficiency metrics visually to users.

2. The intelligent control mechanism for device management 100 as claimed in claim 1, wherein data acquisition layer 102 is configured to gather real-time data from connected devices, including parameters such as energy consumption, operational status, and environmental conditions, providing a continuous data stream for optimized device management.

3. The intelligent control mechanism for device management 100 as claimed in claim 1, wherein data processing layer 104 is configured to analyze the data from data acquisition layer 102 using machine learning algorithms to identify patterns, trends, and anomalies, facilitating data-driven insights for device performance improvement.

4. The intelligent control mechanism for device management 100 as claimed in claim 1, wherein decision-making layer 106 is configured to receive insights from data processing layer 104 and generate real-time, context-aware decisions for optimizing device configurations, operations, and scheduling based on user preferences and environmental factors.

5. The intelligent control mechanism for device management 100 as claimed in claim 1, wherein control layer 108 is configured to implement commands from decision-making layer 106 by adjusting device settings, initiating commands, and managing device interactions to maximize efficiency and performance across the network.

6. The intelligent control mechanism for device management 100 as claimed in claim 1, wherein adaptive security module 114 is configured to monitor device activity for potential threats by analyzing data from data acquisition layer 102 and flagging anomalies identified by data processing layer 104, enabling real-time security protocol adjustments and threat mitigation.

7. The intelligent control mechanism for device management 100 as claimed in claim 1, wherein predictive maintenance module 112 is configured to use historical data patterns from data processing layer 104 to predict potential device failures and schedule proactive maintenance, reducing downtime and extending device lifespan.

8. The intelligent control mechanism for device management 100 as claimed in claim 1, wherein energy optimization algorithms 118 are configured to assess energy consumption patterns across connected devices and execute adjustments that minimize energy usage while maintaining operational performance, contributing to sustainable energy practices.

9. The intelligent control mechanism for device management 100 as claimed in claim 1, wherein user interface layer 110 is configured to provide real-time insights, notifications, and recommendations to users based on data processed by data processing layer 104 and decisions from decision-making layer 106, enabling informed user interactions and adjustments for enhanced control and satisfaction

10. The intelligent control mechanism for device management using AI 100 as claimed in claim 1, wherein method comprises of
data acquisition layer 102 collecting real-time data from connected devices, capturing parameters such as energy consumption, operational status, and environmental conditions;
data processing layer 104 analyzing the data from data acquisition layer 102 using machine learning algorithms to identify patterns and detect anomalies, preparing the data for decision-making;
decision-making layer 106 receiving insights from data processing layer 104 and generating real-time, context-aware decisions regarding optimal device configurations and operations;
control layer 108 receiving commands from decision-making layer 106 to adjust settings or initiate actions on connected devices, implementing optimized configurations;
feedback loop mechanism 122 evaluating the effectiveness of adjustments made by control layer 108 by assessing outcomes against expected performance, continuously refining future decision-making strategies;
multi-device coordination module 116 orchestrating interactions among devices adjusted by control layer 108 to ensure seamless network operations and prevent device conflicts;
energy optimization algorithms 118 analyzing data from data acquisition layer 102 and decisions from decision-making layer 106 to minimize energy consumption without compromising device performance;
edge computing integration 120 processing data locally from data acquisition layer 102, reducing latency and enabling faster responses for time-sensitive adjustments;
adaptive security module 114 monitoring activity from data acquisition layer 102 and identifying anomalies flagged by data processing layer 104 to detect potential threats, automatically adjusting security settings if a risk is identified;
predictive maintenance module 112 using historical data patterns from data processing layer 104 to predict potential device failures and scheduling maintenance actions before issues arise, which is communicated to control layer 108 for implementation;
user interface layer 110 providing users with real-time insights from data processing layer 104, recommendations from decision-making layer 106, and maintenance alerts from predictive maintenance module 112, allowing users to make informed adjustments;
interoperability protocols 124 enabling seamless communication between multi-vendor devices coordinated by multi-device coordination module 116, ensuring cohesive operation across the network;
multi-modal interaction interfaces 126 integrating with user interface layer 110 to enable user control and monitoring through multiple channels like voice commands, mobile apps, and web dashboards;
customizable AI models 128 adapting to user-specific requirements and feeding updated preferences to decision-making layer 106 to personalize device management;
resource scheduling module 132 synchronizing with decision-making layer 106 to optimize the timing of device operations based on usage patterns and energy costs, ensuring efficient resource allocation;
compliance and reporting tools 134 monitoring data from data acquisition layer 102 to ensure regulatory standards are met, generating reports accessible to users through user interface layer 110;
scenario-based configuration management 136 applying pre-set configurations from decision-making layer 106 for various scenarios, such as "Home" or "Away," dynamically adjusting device operations as per user-defined preferences;
behavioral pattern recognition module 138 analyzing long-term user behavior from data acquisition layer 102, allowing decision-making layer 106 to proactively align device settings with user habits for a personalized experience;
visualization tools 140 integrating with user interface layer 110 to present users with intuitive data visualizations of device performance, energy savings, and efficiency metrics, enhancing user understanding and decision-making.

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