AI-Powered IT Assistance Engine for Scalable Issue Resolution
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Solution Overview
Problem
Current IT support methods heavily rely on manual skills and human resources, leading to limitations in response time, scalability, and capacity to analyze large volumes of system data, making it inefficient for resolving IT issues in a timely and scalable manner.
Innovation Solution
A system utilizing AI-powered Machine Learning models to automatically identify, determine, and generate resolution profiles for IT issues on client devices, leveraging cloud-based resources to collect and analyze system data, and execute solutions without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual IT support methods are used, then human expertise can be applied to resolve issues, but response time is slow and scalability is limited
Solution Approach 1:
The system implements self-service through automated agents that independently diagnose and resolve IT issues without human intervention. The local agent on the client device and remote agent work together to automatically apply fixes, update configurations, and resolve problems, eliminating the need for manual IT support and dramatically reducing response time.
Solution Approach 2:
The patent replaces manual mechanical IT support processes with an automated digital system. Machine learning models analyze system data and generate resolution profiles automatically, substituting human analysts with algorithms that can process data at machine speed, thereby improving productivity while reducing response time.
2Productivity
If more human IT resources are allocated, then capacity to handle issues increases, but scalability remains limited and costs increase
Solution Approach 1:
The automated IT support system is designed to be universal and multi-functional, capable of handling diverse IT issues across multiple client devices simultaneously. The same agent architecture and machine learning models can resolve various types of problems (software issues, hardware failures, network problems) across different devices, providing unlimited scalability without additional human resources.
Solution Approach 2:
The system uses virtual copies of IT support capabilities through software agents deployed on client devices. Instead of replicating human IT staff, the system creates digital copies of diagnostic and repair functions that can be instantiated infinitely across multiple devices, enabling scalable support capacity without linear increases in human resources.
3Measurement precision
If manual analysis of system data is performed, then detailed issue diagnosis is achieved, but the volume of data that can be analyzed is limited
Solution Approach 1:
The system replaces manual data analysis with machine learning-based automated analysis. The machine learning models can process large volumes of system data (logs, performance metrics, configuration files) at machine speed while maintaining or improving diagnostic accuracy compared to human analysts, thereby handling greater data volumes without sacrificing precision.
Solution Approach 2:
The system transforms the analysis process by changing parameters from human cognitive processing to computational processing. Machine learning models can analyze numerous data parameters simultaneously (CPU usage, memory allocation, network traffic, error logs) with high precision, whereas human analysts can only focus on limited parameters at once, enabling comprehensive analysis of large data volumes.
4Productivity
If automated systems are implemented, then response time and scalability improve, but complexity of the system increases
Solution Approach 1:
The automated IT support system is segmented into distinct modular components: a local agent on the client device, a remote agent, machine learning models for analysis, and a resolution profile generator. This segmentation allows each component to perform specific functions independently, reducing overall system complexity while maintaining high automation capabilities and fast response times.
Data Source
AI summary
A system for automatically resolving information technology (IT) issues, comprising a plurality of client devices each executing a respective local IT assistance agent, and one or more remote servers communicatively coupled to the plurality of client devices via one or more network and adapted to execute an IT assistance engine. The IT assistance engine is adapted to receive, from the local IT assistance agent executed by one or more client devices, an assistance request for resolving one or more IT issue relating to the client device, collecting system data relating to the IT issue(s), using Machine Learning model(s) applied to analyze the system data to automatically determine one or more resolution profiles comprising a set of actions implementing one or more solutions for resolving the IT issue(s), and transmitting the resolution profile(s) to one or more agents adapted to execute automatically the set of actions to resolve the IT issue(s).