SLI Forecasting for Contact Center Anomaly Detection

The patent replaces traditional mechanical monitoring systems with deep learning-based predictive systems. Specifically, it uses LSTM (Long Short-Term Memory) neural networks to analyze historical service level indicator data and predict future states, enabling anomaly detection through comparison of predicted versus actual values. This substitution transforms the system from reactive monitoring to proactive prediction, significantly improving detection accuracy while managing complexity through automated model training and inference pipelines.

The patent implements preliminary action by pre-training deep learning models on historical service level indicator data during off-peak periods. The models are trained to recognize patterns and predict future SLI metrics. Once trained, the models can rapidly generate predictions during operational periods, reducing real-time processing requirements. The system compares predicted values against actual measurements to detect anomalies, enabling fast response times while maintaining high forecast accuracy through the preliminary model training phase.

The patent applies universality by implementing a unified deep learning framework that processes multiple service level indicator metrics across numerous queues simultaneously. The LSTM-based model is designed to handle diverse input types (call volumes, wait times, abandonment rates) and queue configurations through a standardized architecture. This universal approach allows the system to scale to hundreds of queues while maintaining consistent anomaly detection performance, reducing the complexity that would otherwise arise from implementing separate monitoring systems for each queue type.

The patent implements partial action by applying real-time forecasting selectively rather than uniformly across all queues. The system identifies queues with higher anomaly risk or greater business impact and applies intensive deep learning-based forecasting to those specific queues. For lower-priority queues, the system uses lighter monitoring approaches or less frequent updates. This selective strategy maintains fast response times for critical queues while reducing overall computational energy consumption across the entire contact center ecosystem.