Dynamic Heuristic Packages for Network Assurance

The patent applies dynamics by transitioning from static heuristic packages to dynamic, automatically generated monitoring rules. The system continuously learns from network telemetry data and adapts monitoring parameters in real-time, allowing the heuristic package to evolve with network changes without manual intervention. This resolves the contradiction by making the system both adaptable to changes and automated in its updates.

The patent implements self-service through automated machine learning models that generate and update heuristic packages autonomously. The system uses unsupervised learning to identify patterns in network data and automatically creates monitoring rules without requiring expert intervention. This eliminates manual revision requirements while maintaining adaptability to network changes through continuous self-learning.

The patent applies universality by creating a standardized automated heuristic generation framework that can be deployed across diverse network types. The machine learning models learn universal patterns from multi-vendor network data and generate applicable monitoring rules for different network configurations. This enables the system to generalize across networks while maintaining appropriate local adaptations through automated parameter tuning.

The patent uses parameter changes by allowing the system to automatically adjust monitoring thresholds, metrics, and rules based on learned network characteristics. The machine learning models dynamically modify heuristic parameters to match specific network conditions while maintaining a consistent underlying framework. This resolves the contradiction by enabling generalizability through standardized methods while accommodating network-specific variations through automated parameter adaptation.

The patent implements feedback through continuous monitoring of network telemetry data and automated evaluation of heuristic package effectiveness. The system uses unsupervised learning to detect when current monitoring rules become ineffective and automatically generates updated rules. This closed-loop feedback mechanism maintains monitoring accuracy while eliminating the time delay associated with manual revision cycles.

The patent applies preliminary action by proactively generating updated heuristic packages before performance degradation occurs. The machine learning models continuously analyze network patterns and prepare updated monitoring rules in advance, deploying them automatically when changes are detected. This prevents accuracy loss before it happens and eliminates reactive manual intervention delays.