Browser-Based Workflow Compliance with Privacy-Preserving Event Pointers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current browser-based application monitoring solutions face challenges in providing comprehensive workflow analysis while maintaining data privacy, especially in regulated industries, as they either compromise privacy or lack the necessary context for efficient monitoring and analysis.
Innovation Solution
A system and method for privacy-preserving workflow analysis that captures event data excluding sensitive content, generates event pointers with temporal and interaction type data, and reconstructs workflows for analysis, supporting synthetic data substitution and multiple privacy contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive screen recording is used for workflow monitoring, then complete visibility into business processes is achieved, but privacy risks and storage requirements increase significantly
Solution Approach 1:
The patent extracts only the essential workflow metadata from complete screen recordings. Event pointers capture temporal data, interaction type data, and application context while deliberately excluding sensitive content such as personal information, financial data, and confidential business information. This extraction approach maintains workflow visibility while eliminating privacy risks associated with comprehensive recording.
Solution Approach 2:
The patent segments workflow monitoring data into distinct components: event pointers (containing temporal and interaction type information), application context data, and excluded sensitive content. This segmentation allows the system to retain only the necessary elements for workflow analysis while discarding privacy-sensitive portions, thereby resolving the contradiction between monitoring completeness and privacy protection.
2Measurement precision
If comprehensive screen recording is used for workflow monitoring, then complete visibility into business processes is achieved, but storage requirements increase significantly
Solution Approach 1:
The system extracts only essential workflow metadata into compact event pointer structures, storing temporal data, interaction type identifiers, and application context. By excluding redundant visual information and sensitive content, the storage footprint is reduced from gigabytes of screen recordings to kilobytes of structured event data, dramatically lowering storage requirements while maintaining analytical completeness.
Solution Approach 2:
Instead of storing actual screen recordings, the patent creates simplified copies in the form of event pointers that reference key workflow characteristics. These pointer copies contain only the metadata necessary for replay and analysis, such as event timestamps, interaction types, and application context, rather than duplicating the full visual content, thereby minimizing storage requirements.
3Volume of stationary object
If log-based approaches are used for workflow monitoring, then storage efficiency is improved, but context for meaningful analysis is lost
Solution Approach 1:
The patent transforms traditional log data into enriched event pointers by changing the data parameters from simple text logs to structured objects containing temporal data, interaction type data, and application context. This parameter transformation maintains storage efficiency while recovering the contextual information necessary for meaningful workflow analysis, effectively resolving the contradiction between storage efficiency and information completeness.
4Object-affected harmful factors
If event data excluding sensitive content is captured, then privacy is protected, but detailed analysis capability is reduced
Solution Approach 1:
The patent applies preliminary action by pre-defining exclusion rules and patterns that identify sensitive content types (personal information, financial data, health information) before data capture. Event pointers are constructed to inherently exclude these predefined sensitive categories while systematically capturing all non-sensitive workflow metadata. This preliminary filtering ensures privacy protection without sacrificing analysis detail in the remaining non-sensitive data.
Data Source
AI summary
A system and method for privacy-preserving workflow analysis that monitors user interactions with an application interface while maintaining data privacy. The system generates event data that excludes sensitive content while preserving workflow sequence information, enabling reconstruction and analysis of user workflows without exposing protected information. Event data is stored and used to reconstruct workflow sequences, which can be presented through a user interface for analysis, training, and compliance monitoring. The system supports multiple privacy contexts and can substitute synthetic data during workflow reconstruction, allowing organizations to analyze business processes without compromising sensitive information. The system enables comprehensive workflow monitoring and analysis while addressing privacy requirements in regulated environments, supporting use cases including quality assurance, training, and process optimization.