Automated Technical Issue Diagnosis System
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Solution Overview
Problem
Technical support agents face challenges in efficiently diagnosing and resolving technical issues due to the complexity and size of computer log and trace files, which require highly skilled engineers and consume significant time, often resulting in unpredictable outcomes.
Innovation Solution
A system and method utilizing machine learning to diagnose and resolve technical issues by collecting data, extracting features, determining diagnoses, and suggesting actions to support agents, incorporating feedback loops and data from various sources such as sensors, computer logs, and trace files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of computer log and trace files is performed by technical support agents, then diagnostic accuracy can be achieved, but time consumption increases significantly
Solution Approach 1:
An automated analysis system acts as an intermediary between the complex log files and technical support agents. The system processes large volumes of log and trace files, extracts relevant features, and presents structured diagnostic information to agents, thereby reducing their time burden while maintaining diagnostic accuracy.
Solution Approach 2:
The manual mechanical process of reviewing log files by human agents is replaced with an automated computational system that uses machine learning models and algorithms to analyze logs, extract features, and generate diagnoses automatically, significantly reducing time consumption.
2Reliability
If highly skilled engineers perform log analysis, then diagnostic quality improves, but resource requirements and costs increase
Solution Approach 1:
The system enables self-service diagnostic capabilities by automatically analyzing log files and generating diagnoses without requiring highly skilled engineers to manually review each case. The automated system serves itself to perform the analysis function, reducing dependency on scarce expert resources.
Solution Approach 2:
The expertise of highly skilled engineers is captured and replicated through machine learning models trained on historical diagnostic data. The system copies the diagnostic reasoning patterns of experts and applies them automatically to new cases, making expert-level diagnostic quality accessible without requiring expert human resources for each case.
3Measurement precision
If comprehensive log file analysis is performed, then diagnostic completeness improves, but system complexity increases
Solution Approach 1:
The analysis system is segmented into distinct functional modules: data collection components that gather log files, feature extraction modules that identify relevant patterns, machine learning models that perform classification, and output generation components that present diagnoses. This segmentation manages system complexity while enabling comprehensive analysis through coordinated specialized components.
Data Source
AI summary
The exemplary embodiments disclose a system and method, a computer program product, and a computer system for diagnosing technical issues. The exemplary embodiments may include collecting data relating to one or more technical issues, extracting one or more features from the collected data, determining one or more diagnoses based on the extracted one or more features and one or more models, and suggesting to a support agent one or more actions based on the one or more determined diagnoses.