Automated Wi-Fi Authentication Failure Remediation

The patent replaces manual mechanical analysis of syslog data with automated machine learning models and natural language processing systems. The system automatically collects, parses, and analyzes authentication failure logs from multiple network devices, using trained ML models to identify root causes without human intervention, thereby maintaining high precision while dramatically reducing analysis time.

The patent creates a virtual representation of the authentication failure problem by copying and analyzing syslog data patterns through machine learning models. Instead of manually examining actual logs, the system uses trained models that have learned from historical data to replicate and identify root cause patterns, enabling rapid automated diagnosis with high accuracy.

The patent implements preliminary action by pre-training machine learning models on historical authentication failure data before actual incidents occur. The system pre-establishes correlation patterns and root cause relationships through offline training, so that when authentication failures occur, the pre-trained models can immediately apply learned patterns for rapid and reliable detection without requiring comprehensive real-time analysis.

The patent replaces the manual correlation discovery process with automated machine learning systems that continuously analyze and learn relationships between syslog events. The ML models automatically discover and update correlation patterns between different network device logs and authentication failures, maintaining high detection reliability while enabling real-time automated remediation.

The patent implements self-service by enabling the network system to automatically remediate authentication failures without human intervention. The machine learning system not only identifies root causes but also automatically generates and applies remediation actions, such as configuration changes or service restarts, allowing the system to fix its own problems rapidly while maintaining simplicity through automated decision-making.

The patent implements feedback mechanisms where the system continuously monitors authentication failures, analyzes root causes using ML models, applies remediation actions, and then verifies whether the remediation was successful. This closed-loop feedback process ensures that automated repairs are both simple and effective, with the system learning from outcomes to improve future remediation decisions.