Event Error Correction via Contextual Bandit Feature Vectors
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Solution Overview
Problem
Current computing systems lack effective methods for generating corrective action recommendations for event errors, relying on manual intervention and lacking automated predictive capabilities based on historical data and user feedback.
Innovation Solution
A computer-implemented method utilizing machine learning and artificial intelligence, transforming textual descriptions of event errors into feature vectors, analyzing them with a contextual bandit model optimized by particle swarm optimization to provide actionable recommendations for error correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention is used for error correction, then system reliability is maintained through human expertise, but productivity decreases due to time-consuming manual processes
Solution Approach 1:
The system enables self-service by automatically generating corrective action recommendations without requiring manual human intervention. The machine learning model processes event logs and error data to autonomously identify patterns and suggest corrections, allowing the system to serve itself in diagnosing and resolving errors while maintaining high accuracy through learned patterns from historical data
Solution Approach 2:
The patent replaces the mechanical human intervention process with an automated computational system. Instead of manual analysis of error logs and generation of correction recommendations, the system uses machine learning algorithms to process event data, identify error patterns, and generate corrective actions automatically, substituting human cognitive processes with computational mechanisms
2Productivity
If automated predictive capabilities are implemented using machine learning, then productivity increases through faster error correction, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system achieves multi-functionality by using a single machine learning model that handles multiple tasks: processing event logs, identifying error patterns, generating corrective recommendations, and learning from feedback. This universal approach consolidates what could be multiple separate systems into one integrated solution, improving productivity while managing complexity through functional consolidation
Solution Approach 2:
The patent introduces an intermediary layer between raw event data and corrective actions in the form of feature vectors. The machine learning model transforms complex event logs into structured feature representations, which serve as an intermediary that simplifies the processing pipeline and enables efficient pattern recognition without requiring direct complex analysis of raw data
3Measurement precision
If historical data and user feedback are leveraged for predictive analysis, then measurement precision of error patterns improves, but loss of time increases due to data collection and processing requirements
Solution Approach 1:
The system performs preliminary action by continuously collecting and processing historical event data in the background to train and update the machine learning model. This ongoing preliminary processing ensures that when actual errors occur, the model is already trained on relevant patterns and can generate recommendations quickly without needing to process raw historical data at the moment of error detection
Solution Approach 2:
The patent implements continuous learning where the system constantly processes new event logs and user feedback to update its models. This continuous action maintains measurement precision by keeping the model current with evolving error patterns while optimizing processing efficiency through incremental learning rather than periodic batch processing
Data Source
AI summary
An approach to recommending corrective action to computing system event errors. The approach may include generating a textual description of an event error. The approach may include transforming the textual description into feature vectors with a domain-specific word embedding module. The approach may also include generating a recommendation to correct the event error based on an analysis of the feature vectors. Additionally, the recommendation may be presented for verification.