Automated Remediation Confidence Control Through Network Path Observation

The patent implements feedback mechanisms where service health measurements and path change observations are collected after automated remediation actions are executed. This feedback is used to update confidence scores associated with automated actions, enabling the system to learn from past successes and failures. The feedback loop ensures that automated actions are continuously improved based on actual service impact, resolving the contradiction between fast automated response and reliable remediation.

The patent performs service health measurements and path change observations before and after automated remediation actions to predict and prevent harmful chain reactions. By monitoring service impact in advance and observing path changes, the system can identify potential negative effects before they manifest as service degradation. This cushioning approach allows extensive automation while protecting against harmful side effects through proactive monitoring and analysis.

The patent implements self-service learning mechanisms where the automated remediation system automatically collects service health data, analyzes path changes, updates confidence scores, and improves its own decision-making without external human intervention. The system serves itself by learning from executed actions and using this knowledge to prevent repeat failures, achieving adaptability while maintaining relatively simple system architecture through automated self-improvement processes.