Event Abstraction for Software Interaction Analysis
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Solution Overview
Problem
Current software monitoring technologies fail to effectively analyze user interactions and infer relationships between data objects, limiting the ability to provide relevant information to users based on their current activities and tasks.
Innovation Solution
A system that monitors user interactions, generates evidence of relatedness between data objects through usage, location, and content analysis, and displays relevant data objects in a graphical user interface, allowing for context-based access and search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed sequences of user interactions are stored and analyzed, then user behavior analysis accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the essential elements from detailed interaction sequences by identifying and storing change events (specific types of user interactions) rather than all possible interactions. This selective extraction maintains analysis accuracy while reducing data volume and processing complexity.
Solution Approach 2:
Instead of analyzing all detailed interactions and then inferring behavior, the patent inverts the approach by first defining specific change events that directly indicate user behavior patterns, then storing only those events. This reversal simplifies the analysis process while maintaining behavioral insight accuracy.
2Loss of information
If comprehensive monitoring of all software interactions is implemented, then usage analysis completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The system extracts and stores only change events that are relevant to usage analysis, filtering out redundant interaction data. This selective storage maintains analytical completeness for behavior inference while significantly reducing processing time and computational resource requirements.
3Measurement precision
If detailed interaction data is stored for analysis, then inference accuracy is improved, but data storage requirements increase
Solution Approach 1:
The patent extracts and stores only the essential change events needed for accurate behavior inference, rather than storing complete interaction sequences. This selective data retention maintains inference accuracy while minimizing storage requirements by eliminating redundant information.
Solution Approach 2:
Instead of storing all interaction data and then filtering for analysis, the system inverts the approach by pre-identifying and storing only the change events that are necessary for inference. This reversal achieves both storage efficiency and inference accuracy simultaneously.
Data Source
AI summary
According to some embodiments, a system may be monitored to detect change events. A sequence associated with the detected change events may then be stored. The sequence may then be modified by deleting information associated with a detected change event. The sequence might also (or instead) be modified by adding information associated with a non-detected change event. Information associated with the normalized sequence may then be provided.