Telecommunication Alarm Management with Machine Learning Prioritization

The patent introduces neural networks as intermediary components that bridge deterministic rules and complex domain knowledge. The neural networks process alarm data and generate predictions about alarm behavior, which then inform the deterministic rule-based system. This intermediary layer captures complex patterns from historical data without requiring explicit programming of domain expertise, thereby maintaining automation while preserving knowledge representation.

The patent replaces traditional mechanical rule-based systems with intelligent systems using neural networks. Instead of manually programming deterministic rules to capture all domain knowledge, the system uses machine learning models that automatically learn patterns from historical alarm data, operator actions, and alarm correlations, substituting the mechanical rule-engine with an adaptive intelligent system.

The patent applies preliminary action by using neural networks to predict alarm behavior, duration, and resolution likelihood before alarms reach the NOC. The system pre-processes alarms by analyzing historical patterns and generating predictions about which alarms are likely to be transient or require immediate attention. This preliminary analysis filters and prioritizes alarms before they burden operators, reducing the volume of alarms requiring manual review while maintaining information completeness for critical issues.

The patent segments the alarm stream into different categories based on predicted behavior and characteristics. Neural networks classify alarms into groups such as transient alarms, critical alarms, and alarms requiring operator intervention. This segmentation allows the system to apply different handling strategies to different alarm types, presenting only the most relevant segmented alarm sets to operators rather than the complete unfiltered alarm stream.

The patent implements self-service capabilities where the alarm management system automatically performs updates and actions based on neural network predictions. The system autonomously adjusts alarm priorities, generates notifications, and triggers remediation actions without requiring constant manual operator intervention. This self-service approach maintains adaptability through learned patterns while significantly reducing the time required for alarm resolution by eliminating manual steps in the workflow.

The patent incorporates feedback loops where neural networks continuously learn from operator actions and alarm resolution outcomes. The system uses feedback from historical data about which alarms required manual intervention and how they were resolved to improve future predictions and automated responses. This feedback mechanism maintains flexibility by adapting to new patterns while increasing productivity through increasingly accurate automated decision-making.