Loop-Aware Footprint Matrix for Transparent Path Prediction
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Solution Overview
Problem
Existing solutions in employee-focused, user-focused, or customer-focused industries fail to provide accurate predictions of an individual's next interactions or events, relying on aggregated analysis and providing information only after the fact, which hinders individual retention and planned intervention.
Innovation Solution
A computer-implemented method using a loop-aware footprint matrix (LaFM) data structure to store and predict data process traces, incorporating a process discovery engine that generates a process tree data structure, populates a matrix with operator functions, and recursively generates path predictions based on observed events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If aggregated analysis is used to analyze individual behavior, then computational complexity is reduced, but prediction accuracy for individual events deteriorates
Solution Approach 1:
The patent segments the analysis by creating separate process traces and footprints for each individual (employee, user, patient, or customer). Instead of aggregating all behavior data together, the system divides the data into individual segments that can be analyzed separately, enabling accurate individual-level predictions while maintaining manageable computational complexity through structured processing.
Solution Approach 2:
The patent introduces a new dimensional approach by creating process trees and footprints that capture the temporal and contextual dimensions of individual behavior. This adds depth to the analysis by considering the sequence and context of events, moving beyond simple aggregation to multi-dimensional individual-level analysis.
2Loss of time
If aggregated analysis is used, then processing time is reduced, but information timeliness deteriorates as it only provides information after the fact
Solution Approach 1:
The patent implements preliminary action by using the process footprint matrix to predict future events before they occur. The system analyzes historical process traces to identify patterns and predict upcoming events, enabling organizations to take proactive measures rather than reacting to past events. This allows for timely intervention while maintaining efficient processing through structured data organization.
3Ease of manufacture
If process traces are stored in traditional data structures, then implementation simplicity is maintained, but prediction accuracy for complex processes with loops deteriorates
Solution Approach 1:
The patent applies the nested doll principle by organizing process data into hierarchical structures: individual events are nested within process traces, which are nested within process models, which are nested within the footprint matrix. This nested structure efficiently handles complex processes with loops and iterations while maintaining clear organization and relatively simple implementation through systematic data structuring.
Data Source
AI summary
The present disclosure generally relates to the field of data structures and in particular, a loop-aware footprint matrix data structure adapted for data process traversal. The proposed approach is directed to a computer-based analytic system and corresponding method that uses a specific data structure and processing thereof, in some embodiments, adapted to computationally estimate predictions of next events by first generating a data structure based on business process models obtained using process mining techniques, and then using the improved data structure for generating predictions, which can then be encapsulated in the form of computer instructions or machine instruction sets, having a specific sequence for execution.