ML-Based Past Incident Insight Interface for Software Monitoring
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Solution Overview
Problem
Existing incident management systems struggle to efficiently identify and provide insights on past similar incidents in complex, dynamic, and evolving enterprise software platforms with interdependent service/microservice topographies, leading to resource-intensive incident resolution and undesirable downtime.
Innovation Solution
A system utilizing machine learning models to categorize and rank past incident data objects based on similarity and relevance, generating a past incident insight interface component for incident alert management, which includes summary and detailed information, and refining models through user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional incident management systems are used to identify past similar incidents, then the system can provide basic incident tracking, but the identification process is resource-intensive and time-consuming
Solution Approach 1:
The system pre-processes and stores incident data objects with extracted features and embeddings before they are needed for comparison. Past incident data objects are预先 categorized and indexed using machine learning models, so when a current incident occurs, the system can quickly retrieve and compare against pre-processed historical data without performing heavy computation in real-time.
Solution Approach 2:
The patent replaces traditional manual or rule-based incident matching mechanisms with machine learning-based semantic similarity analysis. Instead of using simple keyword matching or manual review, the system uses trained ML models to compute semantic embeddings and determine similarity between incident descriptions, automatically identifying relevant past incidents with higher accuracy and speed.
2Reliability
If comprehensive incident analysis is performed to ensure accurate identification of similar incidents, then the reliability of incident matching improves, but the computational resources and time required increase significantly
Solution Approach 1:
The incident analysis process is divided into distinct stages: data extraction, feature processing, embedding generation, similarity computation, and ranking. Each stage processes only the necessary portion of data at the appropriate time. For example, extraction logic identifies only relevant features from incident descriptions, and the ML model generates embeddings only for candidate incidents that pass initial filtering thresholds.
Solution Approach 2:
The system performs partial analysis by first applying lightweight extraction logic to all incident data objects to identify candidate matches, then applies the more computationally intensive ML-based similarity analysis only to a subset of promising candidates. This two-tier approach ensures high reliability for top matches while avoiding unnecessary computational expenditure on clearly unrelated incidents.
3Loss of information
If detailed information from multiple past incidents is provided to incident managers, then the comprehensiveness of incident insights improves, but the complexity of the interface and information overload increase
Solution Approach 1:
The interface presents different levels of information detail to different users or in different contexts based on their needs. The system analyzes the current incident and the user's role to determine which past incident details are most relevant, then selectively displays only those specific pieces of information rather than presenting all available data uniformly to all users.
Solution Approach 2:
The interface dynamically adapts its content and structure based on the specific incident being analyzed and the user's interactions. As users explore past incident suggestions, the system dynamically updates the display to show progressively more detailed information about the most relevant matches, allowing the interface complexity to scale with user need rather than presenting static comprehensive information upfront.
Data Source
AI summary
Apparatus, methods, and computer program products for outputting a past incident insight interface component in a software monitoring data management system are provided. An apparatus may detect a past incident insight interface component request; identify a past incident candidate data object set based on a current incident data object; determine a primary ranking of the past incident candidate data object set or a subset thereof; determine one or more subsequent rankings of the primarily ranked past incident candidate data objects of the past incident candidate data object set or the subset thereof; generate a past incident insight interface component comprising a listing of a past incident candidate data object suggestion set, the past incident candidate data object suggestion set listing one or more of the subsequently ranked past incident candidate data objects; and output the past incident insight interface component for rendering to an incident alert management user interface.