Granular Event Clustering for Predictive User Action Modeling

The patent extracts the categorization step from the data processing pipeline, removing the predefined business category grouping that causes information loss. Instead, it directly processes granular user event data through clustering algorithms that automatically discover patterns without requiring predefined categories, thus preserving predictive information while maintaining data organization benefits.

Instead of grouping data into predefined categories first and then analyzing it, the patent inverts the approach by directly clustering granular events to discover categories or patterns automatically. This inversion eliminates the information loss that occurs when forcing granular data into predefined buckets, while still achieving organized data structures through algorithmic clustering.

The patent implements dynamic clustering that automatically adapts to new event types without requiring updates to predefined category structures. The clustering algorithms continuously learn from new granular event data, dynamically adjusting category definitions and relationships. This dynamic approach maintains simple processing procedures while providing high adaptability to emerging user behaviors and event types.

The clustering system serves multiple functions simultaneously: it organizes data, discovers patterns, identifies user segments, and adapts to new event types all within a single unified framework. This multi-functionality eliminates the need for separate predefined category systems, making the platform universally applicable to various event types while maintaining processing simplicity through algorithmic automation.

The patent applies preliminary clustering actions to organize granular events into meaningful groups before feeding them to predictive models. This preliminary organization reduces the complexity of subsequent processing by transforming high-dimensional granular data into a more manageable representation that preserves predictive accuracy. The clustering step acts as a preprocessing transformation that simplifies the data structure while maintaining information integrity.

The system changes the parameter representation of granular events through clustering, transforming individual event records into aggregated cluster representations. This parameter transformation reduces the dimensionality and complexity of the data while preserving the essential predictive signals. By changing how data is represented rather than processing every granular detail, the system achieves high prediction accuracy with reduced computational complexity.