Alert Group Summarization with LLMs for Alert Overload
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Solution Overview
Problem
Existing alert management systems face challenges with alert overload, leading to alert fatigue and potential service outages due to the need for manual review of large volumes of alerts, which can result in errors and inefficiencies.
Innovation Solution
A system utilizing a Bi-directional-Long-Short Term Memory-Conditional Random Field Named Entity Recognition (BILSTM-CRF-NER) model and a pre-trained Large Language Model (LLM) to generate comprehensive alert group summaries, incorporating domain knowledge graphs for context, reducing the need for manual review and improving alert processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of alerts is performed, then alert accuracy can be maintained, but alert processing time increases and alert fatigue occurs
Solution Approach 1:
The patent introduces an AI language model as an intermediary between alert generation and human review. The system automatically generates summary descriptions for alert groups using the language model, filtering and preprocessing alert information before presentation to operators. This intermediary layer reduces the cognitive load on human reviewers while maintaining accurate alert representation, thereby reducing processing time without sacrificing accuracy.
Solution Approach 2:
The system enables alerts to self-summarize through automated natural language generation. Instead of requiring human operators to manually analyze and summarize each alert group, the language model autonomously generates concise descriptions, root cause analyses, and impact assessments. This self-service capability allows the system to handle large volumes of alerts automatically, reducing both processing time and operator fatigue.
2Reliability
If comprehensive alert information is provided, then decision-making accuracy improves, but information overload increases
Solution Approach 1:
The patent extracts and highlights only the most critical information from alert groups using automated summarization. The language model identifies and presents key elements such as root causes, affected services, and recommended actions while omitting redundant details. This extraction approach provides decision-makers with essential information in a condensed format, maintaining accuracy while preventing information overload.
Solution Approach 2:
The system applies different levels of information detail to different aspects of alert presentation. Critical information such as root cause and impact receives prominent, detailed presentation, while less critical information is summarized or omitted. This localized quality approach ensures that decision-makers receive comprehensive information where needed while avoiding overload in areas where brevity suffices.
3Productivity
If alert groups are automatically generated, then processing efficiency increases, but complexity of alert management increases
Solution Approach 1:
The patent implements a universal alert management approach where the language model handles multiple functions: generating summaries, identifying root causes, assessing impact, and suggesting actions. This multi-functional system consolidates various alert management tasks into a single automated process, improving efficiency while managing complexity through a unified rather than fragmented approach.
Data Source
AI summary
Apparatuses, methods, systems, or computer-readable storage medium for generating alert group summaries in software management platforms. An alert group comprising a plurality of alert data objects may be identified. One or more alert features associated with the alert group may be extracted based on the plurality of alert data objects and using one or more feature extraction models. Action-related communication content for the alert group may be retrieved. An alert group summary for the alert group may be generated using one or more machine learning models and based on an input data set comprising the one or more alert features and the action-related communication content.