Automated Error Resolution System Using Cognitive Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current information technology operations teams manually monitor applications for errors, leading to a time-consuming and inefficient process of identifying root causes and deploying solutions, with a need for a system that can automatically identify and resolve computing errors.
Innovation Solution
A system and method that automatically monitor log files, identify errors, find solutions from various sources, and implement them using cognitive learning to build a knowledge base for future retrieval, integrating DevOps for improved collaboration and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual monitoring and error resolution processes are used, then human teams can identify and fix errors, but the process becomes very time consuming and inefficient
Solution Approach 1:
The system enables self-service automation where the error resolution system automatically monitors log files, identifies errors, searches for solutions in knowledge bases, and implements fixes without requiring manual human intervention for each error event. The system serves itself by maintaining and querying its own knowledge base of solutions.
Solution Approach 2:
The patent replaces the mechanical human-operated process of manual monitoring and error resolution with an automated computer-based system that uses algorithms to scan log files, pattern matching to identify errors, and automated procedures to search and implement solutions, substituting human mechanical actions with computational processes.
2Extent of automation
If automated error identification and resolution is implemented, then efficiency is improved, but the system requires cognitive learning and knowledge base building
Solution Approach 1:
The system performs preliminary action by pre-building a knowledge base of error solutions before actual error resolution is needed. Manual solutions and external solutions are collected, stored, and organized in advance, so that when errors occur, the system can quickly search and apply proven solutions without needing to learn in real-time.
Solution Approach 2:
The knowledge base acts as an intermediary between the automated error identification system and the solution implementation process. It stores structured error patterns and corresponding solutions, serving as a mediator that translates identified errors into actionable fixes, reducing the complexity of direct cognitive processing.
3Reliability
If the system searches multiple sources for solutions, then solution accuracy is improved, but the search process becomes more complex
Solution Approach 1:
The system merges multiple solution sources including internal knowledge bases, external knowledge bases, and manual solutions into a unified search process. By combining these sources and using pattern matching algorithms, the system evaluates solutions from all sources simultaneously to identify the most accurate fix, achieving high reliability through integrated multi-source search.
Data Source
AI summary
The proposed system and method provide streamlined procedures for automatically identifying and resolving computing errors that improve efficiency and accuracy by providing a way to automatically monitor log files, automatically find and identify errors in the log files, automatically find solutions the identified errors from a variety of sources, and automatically implement the found solutions. The system and method further provide streamlined procedures that use cognitive learning (e.g., machine learning) to learn new solutions that are manually defined and implemented by a user to resolve the automatically found and identified errors, and then automatically finding and implementing the new solutions in subsequent cycles.