K-Nearest Neighbor Tool Process Data Analysis

The patent transitions from PCA-based statistical parameters to k-nearest neighbor distance-based parameters. The process index is calculated as the average distance to k-nearest reference samples, fundamentally changing the parameter representation from linear statistical transformations to non-linear distance-based metrics that can capture complex process variations and handle variable duration steps effectively

The patent replaces the mechanical PCA transformation system with a k-nearest neighbor classification system. Instead of using linear algebra-based PCA to reduce dimensions, the system uses distance calculation in the original feature space, substituting a mathematical transformation approach with a pattern recognition approach that naturally handles non-linearity

The patent extracts only the essential information needed for process assessment by calculating a single process index value that represents the overall similarity to reference processes. Instead of managing and analyzing all raw sensor data points, the system extracts the key discriminative feature (distance to k-nearest neighbors) that captures process quality, significantly reducing data management complexity

The patent segments the analysis process into distinct components: reference data collection, k-nearest neighbor identification, distance calculation, and process index computation. This segmentation allows the system to handle large volumes of sensor data by processing them through modular steps, where each step operates on manageable data subsets rather than the complete dataset simultaneously