Automated Runbook Script Generation for IT Issue Resolution
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Solution Overview
Problem
Current methods for resolving hardware and software issues in IT infrastructure require significant human effort and time, with automation systems needing frequent updates and human intervention to match issue descriptions with runbook scripts.
Innovation Solution
A system utilizing a Convolution Neural Network-based neural language model and an artificial intelligence-based recommendation engine to categorize tickets, recommend runbook scripts, and generate new scripts using natural language processing and text extraction algorithms, enabling automated issue resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Subject Matter Experts manually resolve issues by executing commands based on their knowledge, then issue resolution accuracy is maintained, but time consumption and human effort increase significantly
Solution Approach 1:
The system enables self-service automation by having the issue resolution system automatically execute runbook scripts without requiring continuous human intervention. The system autonomously categorizes tickets, identifies appropriate runbooks, executes scripts, and updates repositories, allowing the system to serve itself in resolving issues while maintaining accuracy through automated decision-making processes.
Solution Approach 2:
The patent replaces the manual mechanical process of SMEs executing commands with an automated computational system. The system uses ticket description analysis, runbook matching algorithms, and automated script execution to substitute human manual operations, thereby reducing time consumption while maintaining resolution accuracy through systematic automated processes.
2Productivity
If automation systems use runbook scripts stored in repositories to resolve issues, then time consumption is reduced, but human intervention is required for frequent updates and maintenance
Solution Approach 1:
The system implements feedback mechanisms where execution results and issue resolution outcomes are automatically fed back into the system. This feedback enables the system to learn from past resolutions, automatically update runbook repositories with new scripts, and refine matching algorithms, thereby reducing the need for manual updates while maintaining high productivity in issue resolution.
Solution Approach 2:
The system performs self-updates by automatically analyzing execution results, generating new runbook scripts when needed, and storing them in the repository without requiring human intervention. This self-service capability allows the system to maintain its runbook library autonomously, reducing the operational burden of frequent updates while sustaining high resolution speed.
3Measurement precision
If automation systems regularly update runbook scripts in the repository to match issue descriptions, then matching accuracy is improved, but system complexity and maintenance burden increase
Solution Approach 1:
The system automatically updates the runbook repository by analyzing execution results and generating new scripts as needed. This self-service update mechanism maintains high matching accuracy between issue descriptions and runbook scripts without requiring complex manual maintenance processes, thereby improving precision while managing system complexity through automation.
Solution Approach 2:
The patent replaces manual runbook update processes with automated computational procedures. The system uses algorithmic analysis of ticket descriptions and execution outcomes to automatically generate and store updated runbook scripts, substituting complex manual maintenance operations with simpler automated processes that maintain high matching accuracy.
4Adaptability or versatility
If human engineers resolve issues manually, then adaptability to new and complex issues is maintained, but productivity and efficiency decrease
Solution Approach 1:
The system adapts to new and complex issues through self-learning from execution results and feedback. When encountering new issue types, the system automatically analyzes patterns, generates appropriate runbook scripts, and updates the repository, thereby maintaining adaptability to evolving issues while operating autonomously to preserve high resolution efficiency without requiring continuous human engagement.
Data Source
AI summary
The present disclosure relates to system(s) and method(s) for assisting a user to resolve a hardware issue and a software issue. The system identifies, a target cluster, associated with a new ticket received from the user, from the set of clusters. Further, the system recommends one or more runbook scripts, from a runbook repository, associated with the new ticket. The system further identifies a new runbook script, corresponding to the new ticket, from a set of external repositories. Further, the system executes at least one of the one or more runbook scripts or the new runbook script, associated with the new ticket. The system further generates a document based on the execution of the one or more runbook scripts or the new runbook script, thereby assisting the user to resolve a target issue.