Unsupervised ML for Log Issue Clustering and Ranking

The patent segments the monolithic log analysis system into multiple specialized components: a log processing module that parses and structures raw logs, a pattern extraction module that identifies relevant patterns, a machine learning module that trains classification models, and a recommendation module that provides issue identification. This segmentation reduces system complexity by making each component manageable and independently optimizable while maintaining automated issue identification capability.

The patent extracts only the most relevant features and patterns from millions of log lines using the pattern extraction module, rather than processing entire log files. It extracts key error patterns, stack traces, and diagnostic information that are most indicative of issues, discarding redundant data. This extraction approach maintains automation while reducing the computational burden and system complexity.

The patent performs preliminary actions by pre-processing logs during system initialization: parsing log files into structured formats, extracting relevant patterns and features, and training machine learning models before actual issue detection is needed. This preliminary preparation stores processed information in optimized data structures, enabling rapid query-time analysis without re-processing entire log files, thus maintaining detection accuracy while reducing analysis time.

The patent applies partial action by analyzing only the most relevant portions of log files during operational issue detection. Instead of re-processing entire log files, it queries pre-processed extracted patterns and features that are most likely to indicate current issues. This selective partial analysis maintains reliable issue detection while significantly reducing the time required for operational troubleshooting.

The patent extracts a curated subset of the most informative features and patterns from large log datasets before training machine learning models. The pattern extraction module identifies and extracts key error signatures, stack trace patterns, and diagnostic features that are most predictive of issues. By training models on this extracted feature set rather than raw log lines, the system achieves high pattern recognition accuracy with reduced training complexity and computational requirements.

The patent segments the training data into distinct categories and feature types: error patterns, warning patterns, information patterns, and contextual features. Each segment is processed and trained separately using appropriate machine learning techniques. This segmented approach allows for more efficient training compared to treating all log data uniformly, reducing overall training complexity while maintaining or improving pattern recognition accuracy through specialized processing of each data segment.

The patent performs computationally intensive log processing operations in advance: parsing logs into structured formats, extracting relevant patterns, and computing feature representations during system initialization or data ingestion phases. This preliminary computation stores processed information in optimized formats, enabling rapid low-compute queries during operational issue identification. This approach maintains high identification accuracy while reducing real-time computational resource requirements.

The patent extracts and stores only the most computationally valuable features and patterns from comprehensive log analysis, rather than retaining or re-processing all raw log data. The extraction module identifies and retains key error patterns, diagnostic features, and relevant metadata that drive accurate issue identification, discarding redundant information. This selective extraction maintains identification accuracy while significantly reducing the computational resources needed for operational analysis.