Automated Feature Analysis for Anomaly Detection

The patent transforms qualitative data characteristics (importance, reliability) into quantitative parameters through normalization processes. Feature vectors are converted to standardized ranges (0-1) using min-max normalization, allowing automated comparison across diverse data types. This parameter transformation enables the system to automatically determine data reliability and importance without human intervention, resolving the contradiction between automation extent and system capability.

The patent introduces feature vectors as intermediary representations between raw data and analysis results. These vectors serve as a universal language that translates diverse data types (visual images, x-ray images, geological data, financial data) into a common format. The intermediary feature vectors enable automated comparative analysis across different applications and data sources, eliminating the need for human judgment while maintaining analysis quality.

The patent creates a universal data representation framework using feature vectors that can handle multiple data types and applications simultaneously. The same normalization and clustering algorithms are applied across diverse domains including geology, medicine, and finance. This universal approach allows the system to adapt to different applications without increasing complexity, as the core processing mechanism remains consistent across all use cases.

The patent standardizes diverse data parameters through normalization to common ranges (0-1). By converting features from different data sources (visual images, x-ray images, sensor data) into standardized parameter formats, the system eliminates the complexity of handling heterogeneous data types. The parameter transformation process creates a unified framework that simplifies cross-application analysis while maintaining the ability to handle diverse data sources.

The patent applies preliminary normalization to feature vectors before clustering and anomaly detection. By pre-processing data to standardize ranges and distributions, the system prepares data for more efficient processing in subsequent steps. This preliminary action reduces the computational burden during clustering and anomaly detection, as the data is already in an optimized format that requires less intensive processing to achieve high precision results.

The patent divides the data processing into distinct segments: feature extraction, normalization, clustering, and anomaly detection. Each segment handles a specific aspect of the analysis independently. This segmentation allows for optimized processing in each stage, where normalization prepares data efficiently, clustering groups similar features quickly, and anomaly detection operates on pre-organized data. The segmented approach reduces overall computational time compared to unoptimized monolithic processing while maintaining high measurement precision.