IT Alert Rule Generation Using LLM Context and Pattern Analysis
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Solution Overview
Problem
Existing IT operations systems face scalability challenges in processing large volumes of IT alert data, leading to inefficiencies in real-time anomaly detection, computational overhead, and manual rule configuration, which hampers timely issue resolution.
Innovation Solution
An AIOps module automatically generates alert processing rules using a large language model (LLM) to filter, group, and enrich IT alerts, reducing noise by 90% and enabling proactive issue resolution through machine learning-based pattern recognition and context analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual rule configuration is used for IT alert processing, then processing accuracy can be maintained, but processing efficiency and scalability deteriorate due to manual effort requirements
Solution Approach 1:
The system enables self-service by automatically generating alert processing rules through machine learning analysis of historical alert data, eliminating the need for manual rule configuration by operators while maintaining high processing accuracy
Solution Approach 2:
The patent replaces the mechanical manual configuration process with an automated machine learning system that analyzes patterns in historical alert data and generates processing rules automatically, significantly improving productivity while reducing manual effort
2Productivity
If traditional alert processing methods are used, then system complexity remains manageable, but scalability deteriorates when processing large volumes of IT alert data
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it analyzes historical alert data, identifies patterns, generates processing rules, and adapts to new alert types, enabling the system to scale efficiently without proportionally increasing complexity
Solution Approach 2:
The system changes the fundamental parameter of rule generation from static manual creation to dynamic machine learning-based automatic generation, allowing the system to handle increasing volumes of alert data while maintaining manageable complexity through adaptive pattern recognition
3Loss of time
If real-time anomaly detection is implemented, then issue resolution timeliness improves, but computational overhead increases
Solution Approach 1:
The system performs preliminary action by pre-processing and analyzing historical alert data offline to train machine learning models and generate processing rules in advance, reducing the computational overhead required for real-time anomaly detection while maintaining fast issue resolution
Solution Approach 2:
The patent applies partial action by focusing computational resources on detecting only the most critical anomalies and patterns identified by the machine learning model, rather than analyzing every alert in real-time, thus reducing computational overhead while maintaining timely detection of significant issues
Data Source
AI summary
In the present application, improved techniques for automatically generating alert processing rules are disclosed. One aspect of the disclosure includes a method for automatically generating alert processing rules. In some embodiments, the method includes receiving information technology (IT) alert data comprising a plurality of IT alerts. Context information relevant to IT alert processing is identified from a subset of the IT alert data. One or more patterns indicative of IT alert processing in response to the subset of the IT alert data are extracted from the subset of the IT alert data. An IT alert processing rule is determined based on the one or more patterns and the context information. The IT alert processing rule is enabled in a production environment.