Automated Support System for Data Processing Issue Resolution
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Solution Overview
Problem
Data processing systems face issues such as unexpected software crashes, failed backups, unpredictable hardware behavior, and the need for proactive and automated solution application, which current systems address inadequately due to reliance on human intervention and lack of immediate notification to users.
Innovation Solution
An automated support system that utilizes a knowledge base with computer-executable instructions to diagnose issues and apply solutions proactively, leveraging API calls, configuration file management, and logging systems, reducing reliance on human expertise and enabling immediate notification and solution application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting by technical specialists is used, then accurate problem diagnosis can be achieved, but substantial delays occur and human intervention is required
Solution Approach 1:
The system enables automated self-diagnosis and self-resolution of technical issues. The automated support system executes computer-executable instructions from the knowledge base to detect problems, notify users, and apply solutions without requiring manual intervention by technical specialists, thus eliminating delays while maintaining diagnostic accuracy through systematic automated processes
Solution Approach 2:
The system proactively applies known solutions before issues manifest or escalate. By continuously monitoring system state and comparing it against known problem patterns in the knowledge base, the system can preemptively apply fixes or notify users of potential issues, preventing problems from developing into full-blown failures that would require manual intervention
2Productivity
If automated solution application is implemented, then response time is reduced, but system complexity increases
Solution Approach 1:
The system introduces an automated support system as an intermediary layer between the data processing system and the knowledge base. This intermediary executes computer-executable instructions to bridge the gap between system monitoring and solution application, automating the troubleshooting process while managing complexity through structured intermediate processing steps
Solution Approach 2:
The system implements continuous feedback loops where system state is monitored, compared against known problems, and automatically adjusted through solution application. The feedback mechanism ensures that automated actions are based on actual system conditions and that solutions are applied only when appropriate, managing complexity through controlled iterative processes
3Reliability
If comprehensive system monitoring is performed, then proactive issue detection is enabled, but computational resources are consumed
Solution Approach 1:
The system extracts and monitors only the critical system state parameters and logs that are relevant to known problems in the knowledge base. Rather than analyzing all system data, it selectively extracts pertinent information for comparison against known issue patterns, reducing computational overhead while maintaining effective problem detection capability
Data Source
AI summary
A knowledge base record can include executable language. Executing of the instructions of the record can include at least one of: a) searching for one or more terms in a log file associated with a data processing system; b) performing an API call; or c) getting or setting a field in a configuration file or database. The instructions can be executed to determine whether an issue is present with the data processing system, or to take corrective action.