ML Alert Routing for Fast Response Team Prediction
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Solution Overview
Problem
Existing methods for generating and transmitting response team predictions for incident alerts in complex platforms are inefficient and prone to errors, leading to delayed resolution of issues and increased costs due to manual routing and outdated response team data.
Innovation Solution
A machine learning-based system that extracts alerts from monitoring tools, applies a responder prediction model to determine appropriate response teams, and transmits predictions efficiently, using trained models to reduce errors and manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual routing methods are used to determine response teams for incident alerts, then system complexity remains low, but response time increases and accuracy decreases
Solution Approach 1:
The patent replaces manual routing mechanisms with an automated machine learning-based prediction system. The ML model processes alert attributes and historical data to automatically determine appropriate response teams, eliminating the need for manual intervention and significantly reducing response time while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously route alerts to response teams without human intervention. The model continuously learns from historical data and automatically makes routing decisions, freeing up human operators to focus on more complex incident resolution tasks.
2Measurement precision
If machine learning models are implemented for automatic response team prediction, then response time and accuracy improve, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the routing system into distinct modular components: data collection module, ML model training module, prediction module, and feedback module. Each component performs a specific function and can be independently developed, tested, and maintained. This modular architecture manages system complexity while enabling high routing accuracy through sophisticated ML algorithms.
Solution Approach 2:
The system performs preliminary actions by pre-training ML models on historical alert and response team data before deployment. The models are trained offline using extensive historical datasets, and the learned patterns are then applied in real-time for rapid and accurate routing decisions, reducing online computational complexity.
3Reliability
If continuous model training with extensive historical data is performed, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements periodic action by training ML models at scheduled intervals using accumulated historical data, rather than continuously processing all data in real-time. The system collects data continuously, performs batch training periodically, and updates the models at defined intervals. This approach maintains high prediction reliability while avoiding the computational overhead of continuous real-time training.
Solution Approach 2:
The system applies partial action by using a representative subset of historical data for each training cycle rather than processing the entire historical dataset continuously. The ML model is trained on carefully selected training sets that capture the essential patterns, achieving high reliability with reduced processing time and computational resources.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured accurately and programmatically train a responder prediction machine learning model for generating response team predictions based on the systematic collection of one or more responder prediction training corpuses comprising one or more alert related datasets in a responder prediction server system. For example, the responder prediction server system may extract one or more alert attributes for each of the one or more alert related datasets for training one or more responder prediction machine learning models and/or one or more prioritization machine learning models. The responder prediction machine learning model and prioritization machine learning models may process one or more alerts, in real-time, to generate one or more response team prediction objects for rendering in a response team suggestion interface.