CLOUD BASED CONFIGURATION AND MANAGEMENT SYSTEM FOR ARTIFICIAL INTELLIGENCE DEVICE

Abstract

ABSTRACT CLOUD BASED CONFIGURATION AND MANAGEMENT SYSTEM FOR ARTIFICIAL INTELLIGENCE DEVICE The present disclosure introduces a cloud-based configuration and management system for artificial intelligence devices 100 designed to enable remote configuration, monitoring, and optimization of AI devices. Key components include cloud server 102 for centralized data storage and processing, user interface 104 for remote access and control, and AI device agent 106 for real-time device communication. The data analytics engine 108 provides insights and predictions, while the multi-layered security framework 112 ensures secure data transmission. The unified management dashboard 114 aggregates device performance data, and the predictive maintenance module 116 forecasts potential issues to reduce downtime. Additional components include version control for configuration settings 118, context-aware resource allocation mechanism 120 for dynamic resource management, and real-time collaboration tools 122 to support teamwork. Customizable alerting mechanisms 124 notify users of critical events, energy consumption analytics 126 optimize power usage, multi-tenancy support 128, offline device management 130, customizable workflow automation engine 132, and geo-location services integration 134 for location-based device management. Reference Fig 1

Specification

Description:CLOUD BASED CONFIGURATION AND MANAGEMENT SYSTEM FOR ARTIFICIAL INTELLIGENCE DEVICE
TECHNICAL FIELD
[0001] The present innovation relates to a cloud-based configuration and management system for efficient deployment, monitoring, and control of artificial intelligence (AI) devices.

BACKGROUND

[0002] The rapid proliferation of artificial intelligence (AI) devices across industries-spanning healthcare, manufacturing, transportation, and smart cities-has increased the need for effective device management and configuration. Traditionally, users relied on on-site, localized systems to install, monitor, and update these devices. However, this approach presents significant drawbacks, such as high operational costs, maintenance downtime, and the risk of human error in configuration processes. Additionally, the lack of remote access capabilities limits scalability, making it challenging to integrate AI devices seamlessly into broader digital ecosystems. Existing solutions, such as general-purpose cloud platforms for IoT or other computing devices, offer some remote management options. Still, these lack the sophistication needed for AI devices, which require real-time data processing, adaptive learning, and performance optimization. Without a system tailored to AI-specific needs, users struggle with inefficient device management, poor scalability, and limited optimization tools.

[0003] This invention addresses these challenges by providing a dedicated cloud-based configuration and management system designed specifically for AI devices. Unlike conventional platforms, this system enables remote configuration, real-time monitoring, and adaptive performance management across multiple devices through a centralized cloud platform. Key features include a user-friendly web interface, an AI device agent for seamless communication, a data analytics engine for performance insights, and API integration for interoperability with other enterprise systems. The invention overcomes existing limitations by streamlining device configuration and optimizing operational parameters using machine learning to predict and address potential failures. Its novel features, such as multi-layered security, automated updates, and predictive maintenance, reduce costs and improve device performance. Ultimately, this invention provides a comprehensive, scalable solution, enabling organizations to fully leverage AI technology while ensuring enhanced efficiency, lower maintenance costs, and seamless integration across industries.

OBJECTS OF THE INVENTION

[0004] The primary object of the invention is to streamline the management of AI devices by enabling remote configuration and monitoring through a centralized cloud platform.

[0005] Another object of the invention is to enhance operational efficiency by providing automated updates and configuration management, reducing the need for on-site personnel.


[0006] Another object of the invention is to improve device performance and reduce downtime by offering predictive maintenance capabilities based on real-time data analysis.

[0007] Another object of the invention is to enable scalability across industries by allowing multiple AI devices to be managed and configured simultaneously, without the need for significant infrastructure changes.
[0008] Another object of the invention is to promote security and data privacy through multi-layered protection, including encryption, multi-factor authentication, and role-based access control.

[0009] Another object of the invention is to facilitate seamless integration with third-party applications and services via API, enhancing interoperability within enterprise systems.

[00010] Another object of the invention is to support data-driven decision-making by providing customizable analytics reports on device performance, resource utilization, and operational efficiency.

[00011] Another object of the invention is to reduce energy consumption and contribute to sustainability efforts by offering insights into power usage patterns and optimization opportunities.

[00012] Another object of the invention is to improve user experience by providing a customizable user interface and alert system, allowing users to receive tailored notifications and manage devices efficiently.

SUMMARY OF THE INVENTION

[00013] In accordance with the different aspects of the present invention, cloud based configuration and management system for artificial intelligence device is presented. It enables remote configuration, monitoring, and optimization for AI devices across industries. With features like predictive maintenance, multi-layered security, and seamless API integration, the system provides a centralized platform for efficient device management. It enhances operational efficiency by automating updates, facilitating real-time analytics, and supporting energy optimization. The system also offers customizable reporting and alerts, improving user experience and enabling data-driven decisions. This invention addresses current challenges in AI device management, promoting scalability, security, and sustainability.

[00014] 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.

[00015] 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
[00016] 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.

[00017] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00018] FIG. 1 is component wise drawing for cloud based configuration and management system for artificial intelligence device.

[00019] FIG 2 is working methodology of cloud based configuration and management system for artificial intelligence device.

DETAILED DESCRIPTION

[00020] 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.

[00021] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of cloud based configuration and management system for artificial intelligence device and programs 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.

[00022] 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.

[00023] 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.

[00024] 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.

[00025] 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.

[00026] Referring to Fig. 1, cloud based configuration and management system for artificial intelligence device 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of cloud server 102, user interface (UI) 104, AI device agent 106, data analytics engine 108, API integration layer 110, multi-layered security framework 112, unified management dashboard 114, predictive maintenance module 116, version control for configuration settings 118, context-aware resource allocation mechanism 120, real-time collaboration tools 122, customizable alerting mechanisms 124, energy consumption analytics 126, multi-tenancy support 128, offline device management 130, customizable workflow automation engine 132, geo-location services integration 134.

[00027] Referring to Fig. 1, the present disclosure provides details of cloud based configuration and management system for artificial intelligence device 100. This system is designed to enable remote configuration, real-time monitoring, and adaptive performance management of AI devices, improving operational efficiency and scalability. In one embodiment, the cloud-based configuration and management system for artificial intelligence devices 100 may be provided with the following key components: cloud server 102, user interface 104, AI device agent 106, and data analytics engine 108, which together facilitate device configuration, monitoring, and data analysis. The system incorporates multi-layered security framework 112 and unified management dashboard 114 for enhanced security and comprehensive oversight. Additional components such as context-aware resource allocation mechanism 120 and energy consumption analytics 126 enable optimized resource management and power efficiency, while customizable workflow automation engine 132 streamlines operational tasks.

[00028] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with cloud server 102, which serves as the central platform for data storage, processing, and execution of configuration management tasks. The cloud server 102 hosts the entire management interface and ensures secure data exchange with AI device agent 106, enabling real-time communication and updates. Additionally, cloud server 102 processes and stores analytics data from data analytics engine 108, facilitating predictive maintenance and performance optimization. Through secure APIs, it integrates seamlessly with third-party applications and services managed by API integration layer 110.

[00029] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with user interface 104, offering a web-based interface accessible from desktops, tablets, and smartphones. This user interface 104 allows users to configure devices remotely, monitor performance, and receive real-time alerts. By displaying analytics insights generated by data analytics engine 108, user interface 104 empowers users with valuable device performance data. It works closely with the multi-layered security framework 112 to ensure only authorized personnel can access device configurations, enhancing system security.

[00030] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with AI device agent 106, a lightweight module installed on each AI device to facilitate real-time communication with cloud server 102. The AI device agent 106 transmits data on device status, operational metrics, and configuration changes to the server. This data feeds into data analytics engine 108 for performance insights and predictive maintenance. The AI device agent 106 also applies configuration updates from user interface 104, keeping devices up-to-date and optimized without manual intervention.

[00031] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with data analytics engine 108, which processes device data to provide actionable insights and recommendations. The data analytics engine 108 employs machine learning to detect performance patterns and predict maintenance needs, aiding in efficient device management. It communicates insights to user interface 104 for display and collaborates with context-aware resource allocation mechanism 120 to optimize device resources. This component ensures devices run at peak performance, reducing downtime and enhancing system reliability.

[00032] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with API integration layer 110, which supports seamless connectivity with third-party applications and services. This layer enables interoperability between the cloud platform and existing enterprise systems, allowing for data exchange and expanded functionality. The API integration layer 110 works with cloud server 102 to ensure secure, efficient data transfer, supporting customizable workflow automation engine 132 and enabling organizations to leverage AI device management within a broader digital ecosystem.

[00033] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with multi-layered security framework 112, which implements end-to-end encryption, multi-factor authentication, and role-based access control. This framework ensures that all data exchanged between cloud server 102 and AI device agent 106 is protected against unauthorized access. It works closely with user interface 104 to control access permissions, restricting sensitive device configurations to authorized personnel only. This security layer enhances data integrity and protects the overall system from potential vulnerabilities.

[00034] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with unified management dashboard 114, which aggregates data from all AI devices and presents a comprehensive overview of device status, performance metrics, and operational insights. The unified management dashboard 114 enables users to manage multiple devices from a single interface, simplifying complex monitoring and decision-making processes. It integrates data from data analytics engine 108 and displays key insights on user interface 104 for easy access, improving the operational visibility of the entire AI device ecosystem.

[00035] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with predictive maintenance module 116, which uses machine learning algorithms to analyze device performance data and forecast potential issues. This module works with data analytics engine 108 to identify patterns associated with device malfunctions, allowing for proactive maintenance scheduling. By providing predictive insights on user interface 104, the predictive maintenance module 116 helps reduce downtime and operational disruptions, thereby enhancing the reliability and efficiency of AI device operations.

[00036] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with version control for configuration settings 118, which tracks and manages changes to device configurations over time. This feature allows users to revert to previous settings and maintain an audit trail of modifications, which is essential for compliance and troubleshooting. Version control for configuration settings 118 interacts with cloud server 102 to store configuration histories securely and ensures that user interface 104 displays accurate and current configuration information.

[00037] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with context-aware resource allocation mechanism 120, which dynamically adjusts the distribution of cloud resources based on the specific requirements of AI devices at any given time. This mechanism leverages insights from data analytics engine 108 to allocate resources efficiently, optimizing device performance under varying conditions. It collaborates with cloud server 102 to ensure that resources are scaled to meet demand, reducing the risk of over-provisioning or resource waste.

[00038] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with real-time collaboration tools 122, which enable team members to communicate, share insights, and work collaboratively on device management tasks. These tools include live chat, document sharing, and collaborative editing capabilities within user interface 104. Real-time collaboration tools 122 enhance teamwork and streamline operational processes by ensuring that all stakeholders have access to the same up-to-date information, enabling coordinated decision-making.


[00039] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with customizable alerting mechanisms 124, which allow users to set specific performance thresholds and operational triggers for alert notifications. Alerts can be configured to notify users via multiple channels, including email, SMS, or in-app messages through user interface 104. Customizable alerting mechanisms 124 ensure timely awareness of critical issues, enhancing responsiveness and minimizing potential disruptions to device operations.

[00040] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with energy consumption analytics 126, which monitors the power usage of AI devices and provides insights into energy efficiency. This component works with data analytics engine 108 to analyze usage patterns and identify opportunities for optimizing power consumption, contributing to sustainability efforts. Energy consumption analytics 126 displays this information on user interface 104 to help organizations make data-driven decisions about energy management.

[00041] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with multi-tenancy support 128, allowing multiple organizations to securely manage their AI devices within a shared infrastructure. This component isolates each tenant's data, ensuring privacy and security while optimizing resource utilization. Multi-tenancy support 128 integrates with cloud server 102 to partition data and resources appropriately, providing each organization with an independent, secure environment.

[00042] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with offline device management 130, which allows for local configuration and management of AI devices when an internet connection is unavailable. Once connectivity is restored, offline device management 130 synchronizes all changes with cloud server 102 to ensure continuous, updated operations. This component provides operational continuity and resilience, particularly in remote or connectivity-challenged environments.

[00043] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with customizable workflow automation engine 132, which allows users to define and automate specific operational processes related to device management. Users can set up triggers, conditions, and actions through user interface 104 to streamline repetitive tasks and improve productivity. The customizable workflow automation engine 132 works in tandem with API integration layer 110 to connect with third-party applications and expand automation capabilities across platforms.

[00044] Referring to Fig. 1, the cloud-based configuration and management system for artificial intelligence devices 100 is provided with geo-location services integration 134, which provides context-aware management of AI devices based on their physical locations. This feature is particularly useful for applications in fleet management, asset tracking, and smart city infrastructure. Geo-location services integration 134 interacts with context-aware resource allocation mechanism 120 to adjust operational parameters and optimize performance based on location-based data, enhancing location-specific decision-making and management.

[00045] Referring to Fig 2, there is illustrated method 200 for cloud-based configuration and management system for artificial intelligence devices 100. The method comprises:

At step 202, method 200 includes user accessing the cloud-based configuration and management system for artificial intelligence devices 100 through user interface 104;
At step 204, method 200 includes cloud server 102 receiving data inputs from AI device agent 106 installed on each AI device;
At step 206, method 200 includes data analytics engine 108 processing real-time data received from AI device agent 106 to generate performance insights and recommendations;
At step 208, method 200 includes predictive maintenance module 116 analyzing performance data to forecast potential issues and notify users of maintenance needs through user interface 104;
At step 210, method 200 includes multi-layered security framework 112 encrypting and securing data transmission between AI device agent 106 and cloud server 102;
At step 212, method 200 includes context-aware resource allocation mechanism 120 dynamically adjusting cloud resources for optimal AI device performance based on real-time data;
At step 214, method 200 includes API integration layer 110 facilitating data exchange and interoperability with third-party applications for seamless integration into enterprise systems;
At step 216, method 200 includes customizable workflow automation engine 132 automating operational tasks based on user-defined triggers and conditions;
At step 218, method 200 includes geo-location services integration 134 adjusting device management settings based on the physical location of each AI device;
At step 220, method 200 includes user interface 104 providing users with customizable alerts and notifications regarding device performance, energy consumption, and security events;
At step 222, method 200 includes energy consumption analytics 126 monitoring power usage patterns of AI devices and suggesting optimization strategies to reduce energy consumption;
At step 224, method 200 includes multi-tenancy support 128 isolating data for different organizations, allowing secure, independent management of AI devices within the same infrastructure;
At step 226, method 200 includes offline device management 130 enabling configuration changes for AI devices in offline mode and synchronizing updates with cloud server 102 once connectivity is restored;
At step 228, method 200 includes unified management dashboard 114 consolidating device performance data and configuration information into a single, centralized view for user convenience.

[00046] 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.

[00047] 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.

[00048] 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 cloud-based configuration and management system for artificial intelligence devices 100 comprising of
cloud server 102 to provide the central platform for data storage and processing; user interface 104 to enable remote access and monitoring for users;
AI device agent 106 to facilitate real-time communication with cloud server; data analytics engine 108 to generate performance insights and optimization recommendations; API integration layer 110 to support seamless data exchange with third-party applications;
multi-layered security framework 112 to ensure data protection and secure access; unified management dashboard 114 to offer a comprehensive overview of device status and metrics;
predictive maintenance module 116 to forecast potential issues and schedule proactive maintenance;
version control for configuration settings 118 to track changes and allow reversion as needed;
context-aware resource allocation mechanism 120 to adjust cloud resources dynamically for optimal performance;
real-time collaboration tools 122 to enable team communication and task sharing; customizable alerting mechanisms 124 to notify users of performance or security events;
energy consumption analytics 126 to monitor power usage and suggest optimizations;
multi-tenancy support 128 to allow secure, isolated management for multiple organizations;
offline device management 130 to enable configuration changes when offline, syncing later;
customizable workflow automation engine 132 to automate repetitive tasks based on user-defined triggers;
geo-location services integration 134 to provide location-based adjustments for device management.

2. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed, wherein cloud server 102 is configured to provide a central platform for secure data storage, processing, and execution of configuration management tasks, supporting communication between AI device agent 106 and enabling centralized control of all connected AI devices.

3. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed in claim 1, wherein user interface 104 is configured to enable remote access, monitoring, and control, allowing users to configure devices, view real-time performance data, and receive alerts for efficient device management.

4. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed in claim 1, wherein AI device agent 106 is configured to facilitate real-time communication between each AI device and cloud server 102, transmitting device status and operational data, and applying configuration updates received from user interface 104.

5. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed in claim 1, wherein data analytics engine 108 is configured to process real-time data received from AI device agent 106, generating insights for performance optimization, predictive maintenance, and resource allocation adjustments.

6. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed in claim 1, wherein multi-layered security framework 112 is configured to ensure secure data transmission and access control through end-to-end encryption, multi-factor authentication, and role-based permissions, protecting the integrity and confidentiality of data exchanged between AI device agent 106 and cloud server 102.

7. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed in claim 1, wherein predictive maintenance module 116 is configured to analyze device performance data, identify potential issues, and notify users of maintenance requirements, thereby reducing downtime and operational disruptions.

8. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed in claim 1, wherein customizable workflow automation engine 132 is configured to allow users to automate operational tasks based on predefined triggers and conditions, optimizing device management processes and reducing manual intervention.

9. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed in claim 1, wherein geo-location services integration 134 is configured to adjust device management settings and resource allocation based on the physical location of each AI device, enabling context-aware optimizations for applications in fleet management and smart city infrastructure.

10. The cloud-based configuration and management system for artificial intelligence devices 100 as claimed in claim 1, wherein method comprises of
cloud-based configuration and management system for artificial intelligence devices 100 accessed by the user through user interface 104;
cloud server 102 receiving data inputs from AI device agent 106 installed on each AI device;
data analytics engine 108 processing real-time data received from AI device agent 106 to generate performance insights and recommendations;
predictive maintenance module 116 analyzing performance data to forecast potential issues and notify users of maintenance needs through user interface 104;
multi-layered security framework 112 encrypting and securing data transmission between AI device agent 106 and cloud server 102;
context-aware resource allocation mechanism 120 dynamically adjusting cloud resources for optimal AI device performance based on real-time data;
API integration layer 110 facilitating data exchange and interoperability with third-party applications for seamless integration into enterprise systems;
customizable workflow automation engine 132 automating operational tasks based on user-defined triggers and conditions;
geo-location services integration 134 adjusting device management settings based on the physical location of each AI device;
user interface 104 providing users with customizable alerts and notifications regarding device performance, energy consumption, and security events;
energy consumption analytics 126 monitoring power usage patterns of AI devices and suggesting optimization strategies to reduce energy consumption;
multi-tenancy support 128 isolating data for different organizations, allowing secure, independent management of AI devices within the same infrastructure;
offline device management 130 enabling configuration changes for AI devices in offline mode and synchronizing updates with cloud server 102 once connectivity is restored;
unified management dashboard 114 consolidating device performance data and configuration information into a single, centralized view for user convenience.

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