Neural Network Data Loss Prediction for Storage Systems

The patent applies preliminary action by training neural network models on historical data to predict future data unavailability and data loss events before they occur. The system parses historical data storage information, formats it for neural network models, and trains these models to identify patterns that precede critical events. This enables proactive alerting and preventive measures to be taken before failures occur, reducing the need for excessive redundancy while maintaining reliability.

The patent replaces the mechanical approach of physical redundancy (multiple backup storage systems) with an information-based approach using neural networks and machine learning. Instead of relying on duplicate hardware systems, the invention uses computational models to predict failures and alert operators, substituting complex physical backup infrastructure with intelligent software-based prediction systems.

The patent implements feedback by continuously parsing data storage information from customer machines, training neural network models on this data, and using the trained models to generate predictions about future events. The system creates a closed-loop feedback mechanism where historical data informs model training, which then generates predictions that can alert operators to potential issues. This feedback loop provides deep insights into machine behavior patterns while maintaining data protection.

The patent introduces neural network models as intermediaries between raw data storage information and actionable insights. These models parse and analyze historical data, identifying patterns and relationships that would be difficult to detect through conventional methods. The neural networks serve as a mediator that transforms raw operational data into predictive intelligence, providing both data protection and valuable machine behavior insights simultaneously.

The patent applies preliminary action by pre-parsing data storage information into formats suitable for neural network training before the actual prediction task. The system parses historical data, formats it appropriately, and trains models in advance so that when predictions are needed, the models are already prepared and can provide rapid predictions without requiring intensive real-time processing.

The patent uses partial action by focusing the neural network training on specific patterns and events that are most predictive of data unavailability and data loss. Rather than analyzing all possible data aspects equally, the system concentrates computational resources on identifying the most relevant patterns, achieving high prediction accuracy with reduced processing requirements compared to comprehensive analysis of all data.