Temporal Reasoning Graph for User Activity Confidence Tracking
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Solution Overview
Problem
Existing systems struggle to effectively integrate and analyze scattered contextual data across various applications and devices, lacking a comprehensive understanding of user activities and social interactions, which hampers personalized recommendations and predictive analysis.
Innovation Solution
A contextual understanding system that generates life events by processing collected data over time, utilizing a graph-based data structure to represent activities and social interactions, and employs activity detectors within sliding windows to identify patterns, enhancing data analysis and utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If scattered contextual data from various applications and devices is collected, then the quantity of contextual information increases, but the ability to effectively integrate and analyze the data deteriorates
Solution Approach 1:
The patent merges scattered contextual data from multiple applications and devices into a unified graph-based data structure. This structure integrates diverse data sources (sensor data, application data, device data) into a cohesive framework that represents user activities and social interactions, enabling effective analysis despite the increased quantity of data.
Solution Approach 2:
The graph-based data structure serves as a universal framework that can handle multiple types of contextual information simultaneously. It provides a multi-functional platform for representing various data sources, performing temporal reasoning, and supporting different analysis tasks, thereby managing the complexity of integrating diverse data types.
2Loss of information
If a comprehensive mapping of user life context is created, then the understanding of user activities improves, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and organizing contextual data into the graph-based data structure in real-time or near-real-time. This pre-processing and structuring of data enables faster retrieval and analysis when comprehensive user context is needed, reducing the processing time at the moment of query while maintaining comprehensiveness.
Solution Approach 2:
The comprehensive user context is segmented into manageable components within the graph-based data structure, organized by entities, activities, and relationships. This segmentation allows the system to retrieve and process only relevant portions of the comprehensive context rather than analyzing the entire data set, thereby reducing processing time while maintaining understanding completeness.
3Measurement precision
If temporal reasoning capabilities are enhanced, then predictive analysis accuracy improves, but the system complexity increases
Solution Approach 1:
The graph-based data structure acts as an intermediary that simplifies temporal reasoning operations. It provides a structured framework with entities, properties, and relationships that naturally represent temporal concepts, enabling predictive analysis without requiring complex external processing systems. The structure itself facilitates temporal relationships through its organized representation of user context over time.
Data Source
AI summary
The subject technology provides for temporal reasoning. A system can receive contextual information from a plurality of data sources on an electronic device. The system can identify a predetermined pattern that is indicative of a particular activity in the contextual information within a time interval. The system can determine a confidence score for the particular activity based at least in part on one or more confidence values of a corresponding activity signal associated with the time interval. The system can update a graph-based data structure by adding a representation of the particular activity as a node to the graph-based data structure when a confidence score of the particular activity exceeds a confidence threshold. The system also can provide, for display on the electronic device, a user activity interface that provides access to an indexed collection of events organized by activity type by querying the graph-based data structure.