AI Error Mapping for Faster Application Troubleshooting
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Solution Overview
Problem
Conventional application error troubleshooting and resolution techniques are time-consuming and require significant manual effort, often involving duplicative work due to the inability to identify existing solutions for known issues, and are not scalable with growing error message signatures.
Innovation Solution
An AI-based error resolution module that automates log analysis and signature creation using machine learning to match error mappings with historic troubleshooting cases, enabling automated identification and execution of solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting processes are used with multiple support teams, then comprehensive error analysis can be performed, but the time and resources required to resolve issues increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically analyzing error messages against a knowledge base of known issues before human support teams intervene. The error resolution module pre-processes and categorizes errors, preparing potential solutions in advance, which eliminates duplicative manual analysis steps and reduces overall troubleshooting time while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The patent introduces an error resolution module as an intermediary between the error message and human support teams. This intermediary automatically matches errors with known issues using machine learning models, filters out routine problems, and only escalates complex cases to human analysts, thereby reducing the time burden on support teams while preserving thorough error analysis for cases that require it.
2Reliability
If multiple levels of support teams process service tickets, then complex issues can be resolved, but manual effort and resource consumption increase
Solution Approach 1:
The system implements self-service by enabling automatic error resolution through the error resolution module, which autonomously matches errors with known solutions from the knowledge base and executes remediation actions without requiring manual intervention from support teams. This reduces manual effort for routine issues while maintaining reliable resolution capabilities through automated decision-making and escalation protocols for complex cases.
Solution Approach 2:
The patent replaces the mechanical system of manual ticket processing across multiple support levels with an automated error resolution system using machine learning models and knowledge base matching. This substitution eliminates duplicative manual analysis across support tiers while maintaining comprehensive issue resolution capability through intelligent automated diagnosis and remediation, significantly reducing manual effort requirements.
3Ease of manufacture
If conventional troubleshooting methods are used, then existing solutions can be applied, but the ability to identify known issues is limited leading to duplicative work
Solution Approach 1:
The system implements feedback mechanisms where the error resolution module continuously learns from resolved cases and updates the knowledge base, improving its ability to identify known issues over time. The machine learning models are trained on historical error data and continuously refined based on resolution outcomes, enabling the system to progressively improve its recognition of known issues and reduce duplicative work while maintaining solution applicability.
Solution Approach 2:
The patent uses copying by creating a digital representation of known issues and solutions in the knowledge base, which the error resolution module queries to identify matching errors. Instead of manually comparing each error against known solutions, the system copies and stores structured representations of historical issues, enabling rapid automated matching and identification of known problems, thereby improving troubleshooting efficiency without sacrificing solution accuracy.
4Measurement precision
If manual log analysis is performed to create error signatures, then accurate error identification is possible, but the process is not scalable with growing error message signatures
Solution Approach 1:
The system applies parameter changes by transforming error messages into standardized feature vectors that capture essential characteristics while reducing complexity. The machine learning models process error messages by extracting key parameters and converting them into a standardized representation format, enabling accurate error identification that scales with growing error signatures. This parameter transformation allows the system to maintain high identification accuracy while handling increasingly diverse and numerous error types through automated feature extraction and pattern recognition.
Data Source
AI summary
Techniques are provided for artificial intelligence (AI) based application error detection and resolution. Extensive amounts of time and resources are consumed by service providers when attempting to resolve application errors experienced by customers. Unfortunately, a service provider may spend tedious amounts of manual effort to evaluate and solve an error that is already known or already solved. The techniques provided herein reduce the amount of time and resources involved in detecting and resolving errors associated with applications. In particular, an error mapping is generated for a current troubleshooting case to resolve for an application. The error mapping is compared to error mappings of previously resolved troubleshooting cases. If a match is found, then a troubleshooting action associated with a previously resolved troubleshooting case is suggested or executed. Otherwise, a service ticket is created for solving the current troubleshooting cases.