Temporal Footprint Task Identification System
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Solution Overview
Problem
Conventional task management systems fail to utilize temporal information for describing and detecting routine tasks, relying on window switching analysis without considering duration between events.
Innovation Solution
A system and method for generating temporal footprints by recording user events, identifying significant patterns, and determining temporal distances to create a temporal footprint for task identification, which includes a pattern value based on occurrences and temporal distance, and comparing new patterns to a database of footprints for task assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional task management systems use window switching analysis to detect tasks, then task detection capability is provided, but temporal information between events is not utilized
Solution Approach 1:
The system pre-computes and stores temporal footprints containing sequential patterns of events with their temporal distances for various task types in a database. This preliminary preparation allows the system to efficiently compare new event sequences against pre-analyzed patterns without performing complex temporal analysis in real-time, thus capturing temporal information while avoiding excessive computational complexity during operation.
Solution Approach 2:
The system creates simplified representations (temporal footprints) of task patterns that capture essential temporal characteristics without storing complete raw event data. These footprint copies enable efficient comparison and task detection while reducing the computational burden of analyzing full event sequences with their temporal relationships.
2Measurement precision
If temporal footprints with multiple patterns are generated and compared, then task identification accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments complex task patterns into smaller sequential event patterns, each with defined temporal distances. By breaking down tasks into manageable pattern segments and storing them as individual footprints in a database, the system can compare new events against multiple segmented patterns efficiently, improving identification accuracy while maintaining computational tractability through modular comparison.
3Adaptability or versatility
If temporal distances between events are calculated and stored, then pattern recognition capability is enhanced, but data storage requirements increase
Solution Approach 1:
The system extracts only the essential temporal characteristics (temporal distances between sequential events) from complete event data and stores these extracted features as temporal footprints. By taking out and storing only the critical temporal pattern information rather than complete raw event sequences, the system enhances pattern recognition capability while minimizing data storage requirements.
Data Source
AI summary
A computer-implemented system and method for identifying tasks using temporal footprints is provided. A database of temporal footprints is maintained. Each temporal footprint is representative of a different task and includes one or more significant patterns of two or more sequential events. Events performed by one or more users are tracked. At least one pattern including sequential occurrences of two or more of the tracked events is identified. The identified pattern is compared to each of the significant patterns of the temporal footprints. A footprint score for the identified pattern is determined with respect to each temporal footprint. The task associated with the temporal footprint having the highest footprint score is assigned to the identified pattern.