SYSTEM AND METHOD FOR ASSESSING THE WIRELESS COMMUNICATION CAPABILITIES OF ARTIFICIAL INTELLIGENCE SYSTEMS

Abstract

ABSTRACT SYSTEM AND METHOD FOR ASSESSING THE WIRELESS COMMUNICATION CAPABILITIES OF ARTIFICIAL INTELLIGENCE SYSTEMS The present disclosure describes a system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 across diverse network environments. The system comprises data collection module 102 to gather real-time metrics, performance metrics module 104 to process and evaluate KPIs, and comparative analysis module 106 to benchmark performance against industry standards. The other components are Predictive modeling module 108 , anomaly detection module 110 , optimization recommendation module 112, user interface unit 114, reporting and visualization module 116, customizable assessment module 118, scalability module 120, multi-protocol compatibility module 122, simulated network conditions module 124, environment calibration module 126, feedback loop module 128, adaptive learning module 130, historical performance database module 132, privacy and security assessment module 134, api integration module 136, cloud storage and analytics module 138, geolocation integration module 140, cross-domain benchmarking module 142, interactive visualization unit 144. Reference Fig 1

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 assessing the wireless communication capabilities of artificial intelligence 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 assessing the wireless communication capabilities of artificial intelligence systems 100 is disclosed in accordance with one embodiment of the present invention. It comprises of data collection module 102, performance metrics module 104, comparative analysis module 106, predictive modeling module 108, anomaly detection module 110, optimization recommendation module 112, user interface unit 114, reporting and visualization module 116, customizable assessment module 118, scalability module 120, multi-protocol compatibility module 122, simulated network conditions module 124, environment calibration module 126, feedback loop module 128, adaptive learning module 130, historical performance database module 132, privacy and security assessment module 134, api integration module 136, cloud storage and analytics module 138, geolocation integration module 140, cross-domain benchmarking module 142 and interactive visualization unit 144

[00029] Referring to Fig. 1, the present disclosure provides details of wireless communication capability assessment system for AI systems 100. This framework is designed to evaluate and optimize wireless communication performance in various network environments, enhancing reliability and adaptability in AI applications. In one embodiment, the wireless communication capability assessment system 100 may include components such as data collection module 102, performance metrics module 104, and comparative analysis module 106, which gather and benchmark communication data. The system incorporates predictive modeling module 108 and anomaly detection module 110 for proactive performance management. Additional components, including optimization recommendation module 112 and environment calibration module 126, ensure dynamic adjustments based on real-time conditions. It also features multi-protocol compatibility module 122 and simulated network conditions module 124 to assess communication across diverse scenarios and network protocols, making the invention adaptable to various industries and applications.

[00030] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with data collection module 102, which gathers real-time data from the AI system's communication interfaces, including signal strength, network congestion, and interference levels. This module plays a foundational role by feeding essential data into other components, such as performance metrics module 104 and comparative analysis module 106. By providing accurate and timely data, data collection module 102 enables comprehensive evaluations, supporting proactive adjustments by predictive modeling module 108 and anomaly detection module 110.

[00031] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with performance metrics module 104, which evaluates critical KPIs like latency, throughput, packet loss, and error rates. This module serves as the analytical backbone, translating raw data from data collection module 102 into actionable insights. These insights are then processed by comparative analysis module 106 to benchmark performance and by optimization recommendation module 112 to identify areas for improvement, enabling the system to deliver reliable communication assessments.

[00032] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with comparative analysis module 106, which benchmarks the AI system's communication performance against predefined industry standards and thresholds. This module compares data from performance metrics module 104 and highlights areas needing enhancement. Comparative analysis module 106 works closely with optimization recommendation module 112 to provide actionable feedback and fine-tunes the system's communication capabilities, ensuring optimal performance across applications.

[00033] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with predictive modeling module 108, which uses historical and real-time data trends to forecast future communication performance. By analyzing inputs from data collection module 102 and performance metrics module 104, this module anticipates potential issues and suggests adaptive adjustments. Predictive modeling module 108 interacts with optimization recommendation module 112 to enable the AI system to proactively handle changing network conditions, enhancing reliability and adaptability.
[00034] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with anomaly detection module 110, which identifies irregular patterns in communication performance that may signal issues. Working alongside predictive modeling module 108, this module alerts users to potential disruptions. Anomaly detection module 110 relies on data from data collection module 102 and supports feedback loop module 128 in refining the system's responses, ensuring continuous and dependable performance for AI systems across different network conditions.

[00035] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with optimization recommendation module 112, which offers actionable suggestions for improving communication protocols and parameters based on performance assessments. This module leverages data insights from performance metrics module 104 and comparative analysis module 106 to optimize the AI system's communication capabilities in real-time. Optimization recommendation module 112 works closely with predictive modeling module 108 and environment calibration module 126 to ensure the system maintains high performance under varying conditions.

[00036] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with user interface unit 114, a centralized dashboard that displays real-time metrics, trends, alerts, and recommendations. This unit allows users to interact with the data collected by data collection module 102 and processed by performance metrics module 104. The user interface unit 114 also integrates reporting and visualization module 116 to provide visual insights and detailed reports, facilitating easier decision-making and system monitoring.

[00037] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with reporting and visualization module 116, which generates detailed reports and graphical representations of performance data. This module compiles information from comparative analysis module 106 and user interface unit 114, providing users with performance trends, benchmark comparisons, and actionable insights. Reporting and visualization module 116 enhances user understanding by presenting complex data in a comprehensible format, supporting operational and strategic decisions.

[00038] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with customizable assessment module 118, which allows users to set specific evaluation parameters tailored to particular applications or network environments. This module enables flexibility by allowing customization of the KPIs evaluated by performance metrics module 104 and the conditions tested in simulated network conditions module 124. Customizable assessment module 118 ensures the system meets the specific needs of diverse applications and industries.

[00039] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with scalability module 120, which supports concurrent assessments of multiple AI systems across various environments. This module enables the system to operate efficiently on a large scale, processing data from data collection module 102 for each connected system. Scalability module 120 enhances the assessment framework's versatility, making it suitable for organizations managing extensive AI deployments.

[00040] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with multi-protocol compatibility module 122, which ensures the system can assess communication performance across various wireless protocols, including Wi-Fi, Bluetooth, and cellular networks. This module integrates with data collection module 102 to analyze protocol-specific data and works with simulated network conditions module 124 to test adaptability across different communication standards, enhancing the system's applicability across diverse use cases.

[00041] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with simulated network conditions module 124, which creates virtual scenarios of network congestion, interference, and signal degradation to evaluate AI system responses. This module allows for stress-testing the communication capabilities and works in tandem with comparative analysis module 106 to assess performance under challenging conditions. Simulated network conditions module 124 helps identify potential weaknesses, ensuring AI systems are resilient in real-world environments.

[00042] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with environment calibration module 126, which adjusts performance metrics based on specific geographic and environmental factors, such as urban or rural settings. This module collaborates with performance metrics module 104 to ensure context-relevant assessments and enhances predictive modeling module 108 by accounting for environmental variability, resulting in more accurate and reliable performance evaluations.

[00043] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with feedback loop module 128, which uses past assessment results to continuously refine the system's configurations and responses. This module integrates insights from predictive modeling module 108 and anomaly detection module 110 to create an adaptive feedback loop that improves the system over time, allowing AI systems to maintain high performance as network conditions evolve.

[00044] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with adaptive learning module 130, which enhances the assessment algorithms by learning from previous data and improving predictive accuracy. This module interacts closely with feedback loop module 128 and anomaly detection module 110 to adapt assessment strategies based on historical insights, resulting in a more robust system capable of handling a wide range of network conditions.

[00045] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with historical performance database module 132, which stores past assessment results, enabling long-term performance analysis and trend tracking. This module works in conjunction with reporting and visualization module 116 to offer users insights into system improvements over time. Historical performance database module 132 supports future optimizations by serving as a comprehensive repository of assessment data.

[00046] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with privacy and security assessment module 134, which evaluates the security of wireless communication channels to identify vulnerabilities. This module integrates with data collection module 102 to monitor communication security metrics and provides recommendations for data protection. Privacy and security assessment module 134 ensures that the AI system's communication remains secure and compliant with privacy standards.

[00047] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with API integration module 136, which allows third-party developers to incorporate the assessment framework into external applications or platforms. This module enables data exchange with other systems and works with reporting and visualization module 116 to extend functionality, enhancing the versatility of the wireless communication assessment system.

[00048] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with cloud storage and analytics module 138, which supports scalable data storage and advanced analytical processing. This module integrates with historical performance database module 132 for data retention and with reporting and visualization module 116 for enhanced performance analysis, enabling high-capacity data management and computational efficiency.

[00049] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with geolocation integration module 140, which assesses how geographic factors, such as terrain and urban density, impact wireless communication performance. This module collaborates with environment calibration module 126 and data collection module 102 to provide location-specific insights, refining assessments based on geographic context.

[00050] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with cross-domain benchmarking module 142, which compares communication performance across different application domains, such as healthcare, smart cities, and transportation. This module works with comparative analysis module 106 to provide industry-specific insights, supporting organizations in identifying best practices and optimizing AI system performance according to domain-specific requirements.

[00051] Referring to Fig. 1, wireless communication capability assessment system for AI systems 100 is provided with interactive visualization unit 144, which offers advanced data visualizations, including graphs and interactive charts. This unit connects with user interface unit 114 and reporting and visualization module 116 to present performance data in an intuitive format, making it easier for users to understand and act on assessment results.

[00052] Referring to Fig 2, there is illustrated method 200 for wireless communication capability assessment system for AI systems 100 . The method comprises:
At step 202, method 200 includes initializing the system to configure the AI system's communication parameters, such as the network type (e.g., Wi-Fi, cellular) and specific performance metrics to be assessed by customizable assessment module 118;
At step 204, method 200 includes data collection module 102 gathering real-time data on communication parameters, including signal strength, network congestion, bandwidth, and environmental conditions from the AI system's communication interfaces;
At step 206, method 200 includes performance metrics module 104 processing the collected data to assess key performance indicators (KPIs) like latency, throughput, packet loss, and error rates, establishing a baseline performance assessment;
At step 208, method 200 includes simulated network conditions module 124 emulating various network scenarios, such as high congestion or interference, to observe the AI system's adaptability under challenging conditions;
At step 210, method 200 includes environment calibration module 126 adjusting the performance metrics based on the AI system's geographic location, such as urban or rural settings, to ensure context-sensitive assessments;
At step 212, method 200 includes comparative analysis module 106 benchmarking the performance metrics against predefined industry standards and thresholds to identify areas of strength and weakness in the AI system's communication performance;
At step 214, method 200 includes predictive modeling module 108 analyzing the historical and real-time data trends to anticipate future communication challenges, generating forecasts for potential performance fluctuations;
At step 216, method 200 includes anomaly detection module 110 scanning for unusual patterns in the communication data that may indicate issues, allowing operators to proactively address any anomalies;
At step 218, method 200 includes optimization recommendation module 112 providing specific suggestions to enhance communication protocols based on the assessment, ensuring the AI system maintains high performance across network conditions;
At step 220, method 200 includes feedback loop module 128 incorporating insights from the assessment into future configurations, creating a continuous improvement cycle for maintaining optimal performance as network conditions evolve.
[00053] The wireless communication capability assessment system 100 offers significant benefits that enhance the performance and resilience of AI systems. Enhanced reliability is achieved through systematic assessment and optimization, ensuring dependable communication in critical applications across various industries. Increased efficiency is driven by predictive modeling and optimization techniques, which improve data transmission rates and minimize latency and error rates, supporting smoother operation. The system's adaptability allows it to adjust dynamically to varying network conditions, enabling AI systems to maintain optimal performance in diverse environments. With proactive management features like anomaly detection and real-time monitoring, the system anticipates and addresses potential communication issues before they escalate, minimizing the risk of failures and supporting seamless operational continuity. These benefits collectively make the system a robust solution for enhancing AI-driven processes in complex and dynamic communication settings.

[00054] The wireless communication capability assessment system 100 is highly versatile and applicable across a wide range of industries in different embodiments. In one of the embodiments, in smart cities the system ensures efficient data communication between interconnected devices and urban infrastructure, enhancing resource management and urban planning.

[00055] In another embodiment for autonomous vehicles, it supports reliable communication between vehicles and surrounding infrastructure, improving safety and real-time navigation capabilities under varying network conditions.

[00056] In yet another embodiment in healthcare industry, the system benefits from seamless communication between medical devices and health information systems, enabling improved patient monitoring and faster response times.

[00057] In yet another embodiment in industrial automation, the system facilitates robust communication between IoT devices within manufacturing environments, optimizing processes and reducing downtime.

[00058] Additionally, in environmental monitoring and climate action, the system enables accurate data collection and analysis for climate-related challenges, enhancing response strategies for disaster management. These diverse applications highlight the system's adaptability, enabling industries to maximize AI functionality while maintaining reliable, efficient, and secure wireless communication.

[00059] 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.

[00060] 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.

[00061] 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 assessing the wireless communication capabilities of artificial intelligence systems 100 comprising of
data collection module 102 to gather real-time data on communication parameters from the AI system;
performance metrics module 104 to evaluate key performance indicators such as latency and throughput;
comparative analysis module 106 to benchmark performance metrics against industry standards;
predictive modeling module 108 to forecast future communication performance based on historical data;
anomaly detection module 110 to identify unusual patterns in communication data;
optimization recommendation module 112 to provide suggestions for enhancing communication protocols;
user interface unit 114 to display real-time metrics, alerts, and recommendations;
reporting and visualization module 116 to generate detailed performance reports and visualizations;
customizable assessment module 118 to tailor evaluation parameters for specific applications;
scalability module 120 to support concurrent assessments across multiple AI systems;
multi-protocol compatibility module 122 to assess performance across diverse communication protocols; simulated network conditions module 124 to emulate challenging network scenarios for adaptability testing;
environment calibration module 126 to adjust assessments based on geographic location;
feedback loop module 128 to incorporate insights for continuous improvement;
adaptive learning module 130 to refine assessment algorithms based on past data;
historical performance database module 132 to store past assessment data for long-term analysis;
privacy and security assessment module 134 to evaluate communication security and recommend protections;
api integration module 136 to enable integration with external applications;
cloud storage and analytics module 138 to provide scalable data storage and advanced analytics;
geolocation integration module 140 to assess communication performance impacted by geographic factors;
cross-domain benchmarking module 142 to compare performance across different application domains; and
interactive visualization unit 144 to provide advanced visual representations of assessment results.

2. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein data collection module 102 is configured to capture real-time communication metrics, including signal strength, network congestion, and environmental interference, providing foundational data for comprehensive performance evaluation in AI systems.

3. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein performance metrics module 104 is configured to process and evaluate key indicators such as latency, throughput, and error rates, establishing a detailed baseline of communication performance critical for real-time assessments.

4. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein comparative analysis module 106 is configured to benchmark performance data against predefined standards, identifying strengths and areas for optimization to ensure reliable communication in varied network environments.

5. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein predictive modeling module 108 is configured to analyze historical and real-time data trends, enabling forecasts of future performance and allowing proactive adjustments to the AI system's communication protocols.

6. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein anomaly detection module 110 is configured to monitor for irregular patterns in communication, providing alerts for potential issues, and ensuring immediate intervention in the event of performance deviations.

7. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein optimization recommendation module 112 is configured to generate actionable suggestions based on real-time assessment data, enhancing protocol adaptability and maintaining optimal communication performance.

8. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein simulated network conditions module 124 is configured to create virtual network scenarios such as congestion and interference, evaluating the AI system's resilience under challenging conditions to ensure robustness.

9. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein feedback loop module 128 is configured to incorporate continuous assessment insights, dynamically refining system configurations and supporting adaptive performance improvements as network conditions evolve.

10. The system and method for assessing the wireless communication capabilities of artificial intelligence systems 100 as claimed in claim 1, wherein method comprises of
customizable assessment module 118 initializing the system to configure the AI system's communication parameters, such as the network type (e.g., Wi-Fi, cellular) and specific performance metrics to be assessed;
data collection module 102 gathering real-time data on communication parameters, including signal strength, network congestion, bandwidth, and environmental conditions from the AI system's communication interfaces;
performance metrics module 104 processing the collected data to assess key performance indicators (KPIs) like latency, throughput, packet loss, and error rates, establishing a baseline performance assessment;
simulated network conditions module 124 emulating various network scenarios, such as high congestion or interference, to observe the AI system's adaptability under challenging conditions;
environment calibration module 126 adjusting the performance metrics based on the AI system's geographic location, such as urban or rural settings, to ensure context-sensitive assessments;
comparative analysis module 106 benchmarking the performance metrics against predefined industry standards and thresholds to identify areas of strength and weakness in the AI system's communication performance;
predictive modelling module 108 analysing the historical and real-time data trends to anticipate future communication challenges, generating forecasts for potential performance fluctuations;
anomaly detection module 110 scanning for unusual patterns in the communication data that may indicate issues, allowing operators to proactively address any anomalies;
optimization recommendation module 112 providing specific suggestions to enhance communication protocols based on the assessment, ensuring the AI system maintains high performance across network conditions;
feedback loop module 128 incorporating insights from the assessment into future configurations, creating a continuous improvement cycle for maintaining optimal performance as network conditions evolve.

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