AI-Powered Issue Detection and Resolution Across Diverse Devices
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Solution Overview
Problem
Existing systems lack efficient and automated methods for identifying and resolving issues across a diverse network of computing devices with varying hardware and software configurations, requiring manual intervention and limited scalability.
Innovation Solution
A cloud-based issue resolution service utilizing generative artificial intelligence to analyze device metrics, generate solutions dynamically, and deploy them through agents integrated with monitoring applications, enabling automated issue detection and resolution across large numbers of devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual intervention is used to identify and resolve issues across computing devices, then issue resolution can be performed with simple systems, but the productivity and response time deteriorate due to limited scalability
Solution Approach 1:
The system implements self-service through automated issue detection and resolution mechanisms. Monitoring agents continuously collect device metrics and transmit them to the server, which automatically analyzes the data, identifies issues, and generates resolution recommendations without requiring manual intervention for each issue.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. The monitoring agents, data transmission protocols, and AI-driven analysis systems substitute human operators, enabling scalable automated issue detection and resolution across large numbers of devices.
2Productivity
If automated issue detection is implemented across diverse device configurations, then productivity improves through scaling, but the difficulty of detecting and measuring issues worsens due to hardware and software variations
Solution Approach 1:
The monitoring agent is designed as a universal component that can operate across diverse device configurations. It collects multiple types of device metrics (CPU usage, memory usage, storage capacity, network traffic) and transmits them to a centralized server that applies unified analysis algorithms regardless of the specific hardware or software environment.
Solution Approach 2:
The system handles device diversity by dynamically adjusting analysis parameters based on received device metrics. The server adapts its issue detection thresholds and evaluation criteria according to the specific characteristics of each device type, enabling consistent issue detection across varying hardware and software configurations.
3Adaptability or versatility
If generative AI is used to generate solutions dynamically, then adaptability to diverse device environments improves, but the extent of automation increases system complexity
Solution Approach 1:
The system performs preliminary actions by pre-training the generative AI model on extensive device data and issue patterns before deployment. The model is prepared in advance to generate context-appropriate solutions, reducing the complexity of real-time decision-making while maintaining high adaptability to diverse device environments.
Solution Approach 2:
The generative AI model serves as an intermediary between raw device metrics and resolution recommendations. It translates complex multi-parameter device data into actionable solutions, bridging the gap between automated data collection and practical issue resolution while managing the complexity of the automation process.
Data Source
AI summary
A specification of a condition to trigger detection of a specific issue of an information technology component is received. Computer usage data is collected via one or more computer agents on one or more clients. A portion of the computer usage data that satisfies the condition of the specific issue is identified. A prompt based on the portion of the computer usage data is determined. Using a generative machine learning model, a solution to the specific issue based on the prompt is generated.