Machine Learning Incident Prediction System
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Solution Overview
Problem
Existing incident management systems are reactive and fail to anticipate incidents, leading to inefficient resource utilization, increased energy consumption, and environmental impact.
Innovation Solution
Implementing a method that uses machine learning models to predict incidents and services likely to trigger incidents by identifying temporal associations between historically occurring incidents and services, allowing for proactive notification and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a reactive incident management system is used, then the system structure is simple, but incident anticipation capability is poor and resource utilization is inefficient
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict incidents before they occur. The system analyzes historical incident data, identifies temporal patterns and associations, and generates predictions about future incidents. This allows the system to take proactive measures rather than reacting after incidents happen, thereby improving incident anticipation capability while managing system complexity through automated predictive analytics.
2Productivity
If proactive incident prediction is implemented, then resource utilization is optimized, but computational energy consumption increases
Solution Approach 1:
The patent applies partial action by focusing computational resources on predicting only those incidents that are most likely to occur based on historical patterns. The machine learning model identifies temporal associations and prioritizes predictions for incidents with higher probability, rather than attempting to predict all possible incidents. This selective approach optimizes resource utilization while managing computational energy consumption by avoiding unnecessary predictions.
3Measurement precision
If machine learning models are trained on historical incident data, then prediction accuracy improves, but data processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical incident data before actual prediction is needed. The system performs data processing, feature extraction, and model training in advance, storing the trained models for rapid deployment. When prediction is required, the pre-trained models can quickly analyze new data without requiring extensive real-time processing, thus improving prediction accuracy while minimizing data processing time during critical operations.
Data Source
AI summary
In an aspect, a current state that includes incidents occurring in a lookback window is identified. Predicted incidents likely to occur in a prediction window based on the current state are identified. The predicted incidents are identified using a machine learning model that is trained to identify temporal associations between historically occurring incidents, a length of the lookback window, and a length of the prediction window. A notification is transmitted with respect to at least one of the predicted incidents. In another aspect, a current state that includes services that triggered incidents in a lookback window is identified. Predicted services likely to trigger incidents in a prediction window based on the current state are identified using a machine learning model that is trained to identify temporal associations between historically incident triggering services, a length of the lookback window, and a length of the prediction window.