Behavior Pattern Identification Using Multidimensional Density Scoring

The patent transforms behavior state information into a multidimensional space where each dimension represents a specific parameter (date, time, place, duration). This dimensional transformation enables more accurate behavior pattern identification by analyzing density distributions across multiple dimensions simultaneously, resolving the contradiction between measurement precision and device complexity.

The patent introduces density as a new parameter to characterize behavior patterns in multidimensional space. By calculating density values at different points and comparing them against thresholds, the system can accurately differentiate between ordinary and extraordinary behaviors without requiring overly complex device architectures.

The patent segments the multidimensional space into discrete regions and calculates density independently for each region. This segmentation approach allows the system to process behavior data in manageable chunks rather than computing across the entire multidimensional space at once, reducing computational power requirements while maintaining high identification accuracy.

The patent implements density thresholding to identify only significant behavior patterns. By setting thresholds and focusing computational resources on regions exceeding these thresholds, the system achieves accurate behavior differentiation without performing exhaustive computations across all possible patterns, thus optimizing power consumption.