Application Co-Pilot for Context-Aware Exception Remediation
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Solution Overview
Problem
Existing exception handling methods in information processing systems are largely manual and time-consuming, often failing to address issues before application crashes, and current automated methods are ineffective due to lack of application context awareness, leading to memory leaks and system overload.
Innovation Solution
Deploy a parallel application assistant (application co-pilot) that maintains application contexts and uses AI/ML models to monitor and address exceptions proactively, generating context-based code corrections with SME collaboration and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual reactive exception handling methods are used, then exceptions can be identified and fixed, but the process is time-consuming and takes two to three months
Solution Approach 1:
The patent deploys a parallel application assistant that proactively monitors and identifies exceptions before they cause application crashes. This preliminary detection and analysis eliminates the need for lengthy manual reactive processes, reducing exception resolution time from months to minutes while maintaining high reliability through continuous monitoring and AI-based analysis.
Solution Approach 2:
The application assistant autonomously performs exception identification, analysis, and remedy generation without requiring manual intervention. The system self-services by continuously monitoring application state, detecting exceptions, and generating code corrections automatically, eliminating the time-consuming manual triaging and analysis processes.
2Loss of time
If automated self-healing methods are used, then response time is improved, but accuracy is poor due to lack of application context awareness
Solution Approach 1:
The application assistant acts as an intermediary between the application and the exception handling process. It maintains application context by shadowing the application's execution state and using this contextual information to accurately analyze exceptions. This intermediary role enables both fast automated response and high accuracy by bridging the gap between quick automated detection and context-aware analysis.
Solution Approach 2:
The application assistant creates a shadow copy of the application's execution context, maintaining a parallel representation of application state without interfering with the actual application运行. This copied context enables accurate exception analysis while the assistant operates independently, providing both speed and precision.
3Reliability
If a parallel application assistant is deployed to monitor and handle exceptions proactively, then exception handling accuracy and response time are improved, but system complexity increases
Solution Approach 1:
The application assistant is implemented as a simplified shadow copy of the application with a reduced code set that maintains only the necessary execution context for monitoring. This copying approach enables proactive exception handling without requiring a full duplicate of the application's complexity, reducing the overhead burden on the system.
Solution Approach 2:
The application assistant extracts only the essential monitoring and exception handling functionality from the full application, separating these functions into a dedicated assistant component. This extraction reduces system complexity by isolating the monitoring logic from the main application while maintaining the ability to provide accurate, context-aware exception handling.
4Quantity of substance
If the application assistant comprises a reduced code set of the first application, then resource overhead is reduced, but context maintenance capability may be limited
Solution Approach 1:
The application assistant extracts and maintains only the specific application context information necessary for exception monitoring and analysis, rather than duplicating the entire application state. This selective extraction reduces the code volume and resource overhead while preserving the essential context needed for accurate exception handling.
Data Source
AI summary
Application program management techniques in an information processing system environment are disclosed. For example, a method comprises assessing an issue with an execution of a first application, by a second application, wherein the second application operates in association with the first application and comprises a reduced code set of the first application, and wherein the issue is assessed by the second application at least in part based on an artificial intelligence model trained on a context of the first application. The method further comprises generating, by the second application, a remedy for the issue based on the assessing.