Pattern Oriented Software Troubleshooting via Symptom Matching
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Solution Overview
Problem
Current software support tools face challenges in accurately identifying and resolving application software problems, as users often struggle to pinpoint the root cause of issues, leading to inefficient troubleshooting and increased PMRs due to the complexity of problems and multiple possible solutions.
Innovation Solution
A method is introduced that loads pattern data sets into volatile memory, including symptom codes, problematic condition determination modules, and responsive actions, which generate runtime symptom codes, diagnose issues, and take corrective actions to address specific problematic conditions, thereby reducing runtime delays and improving troubleshooting efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software support tools provide multiple possible reasons and solutions for each problem, then the comprehensiveness of troubleshooting is improved, but the complexity of operation increases and troubleshooting efficiency decreases
Solution Approach 1:
The patent segments the troubleshooting process into distinct phases: symptom detection, pattern matching against stored problem profiles, root cause identification, and solution recommendation. This segmentation allows the system to present information in a structured, step-by-step manner rather than overwhelming users with all possible solutions simultaneously, thereby maintaining comprehensiveness while improving ease of operation.
Solution Approach 2:
The system performs preliminary actions by pre-storing problem patterns, symptoms, and solutions in a database before actual troubleshooting occurs. When a problem arises, the system quickly matches symptoms against pre-analyzed patterns rather than generating solutions from scratch, enabling fast and accurate troubleshooting without requiring users to navigate complex decision trees.
2Measurement precision
If users manually analyze troubleshooting information and determine root causes, then the accuracy of problem identification can be improved, but the time required for resolution increases
Solution Approach 1:
The system incorporates feedback mechanisms where troubleshooting outcomes are captured and used to refine and expand the problem pattern database. This feedback loop continuously improves the accuracy of pattern matching and root cause identification over time, while maintaining fast resolution times through automated analysis of symptom patterns rather than manual diagnostic processes.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computer-based system that uses algorithmic pattern matching and symptom correlation. This substitution maintains high accuracy in problem identification by systematically analyzing symptoms against stored patterns, while dramatically reducing the time required compared to manual troubleshooting procedures.
3Reliability
If extensive troubleshooting information and multiple solutions are provided to users, then the completeness of problem resolution is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal troubleshooting system that handles multiple types of software problems, symptoms, and solutions through a single integrated platform. The system uses a standardized problem pattern structure that can accommodate diverse issues while maintaining consistent processing logic, thereby providing complete problem resolution without proportionally increasing system complexity.
Solution Approach 2:
The system introduces an intermediary layer consisting of the problem pattern database and automated matching algorithms that mediate between the user and the complex troubleshooting information. This intermediary structures and filters information, presenting only relevant solutions based on symptom matching, thereby maintaining completeness of resolution while shielding users from underlying system complexity.
Data Source
AI summary
A process for determining a problematic condition while running software includes: loading a first pattern data set having a symptom code module, a problematic condition determination module, and a set of responsive action module(s), generating a runtime symptom code in response to a first problematic condition being caused by the running of the software on the computer, determining that the runtime symptom code matches a symptom code corresponding to the first pattern data set, determining that the first problematic condition caused the generation of the runtime symptom code, and taking a responsive action from a set of responsive action(s) that corresponds to the first problematic condition.