Correlating Subjective User States with Objective Context Data
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Solution Overview
Problem
Current social networking platforms, such as microblogs, primarily limit personal data usage to maintaining diaries and commentaries, failing to effectively correlate subjective user states with objective context data to determine causal relationships between user experiences and occurrences.
Innovation Solution
A computationally implemented method and system that acquires and correlates subjective user state data with objective context data, analyzing time sequential patterns and relationships between reported subjective states and objective occurrences to identify correlations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If subjective user state data and objective context data are collected and correlated to determine causal relationships, then insight into user behaviors and well-being is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments data into distinct categories: subjective user state data (mood, emotions, physical condition) and objective context data (environmental factors, activities, social interactions). This segmentation allows for organized collection and targeted correlation analysis, managing complexity through structured data classification while preserving comprehensive user experience information.
Solution Approach 2:
The system introduces computational algorithms and correlation analysis mechanisms as intermediaries between raw data collection and user experience insights. These intermediaries process, correlate, and analyze the relationship between subjective states and objective contexts, transforming complex multi-source data into meaningful causal relationships without requiring direct complex system architecture.
2Measurement precision
If time sequential patterns are analyzed to correlate user states with objective occurrences, then causal relationship determination is improved, but computational processing time increases
Solution Approach 1:
The system performs preliminary organization and timestamping of both subjective user state data and objective context data as they are collected. By pre-structuring the data with temporal markers and categorizing information in advance, the system reduces the computational burden during correlation analysis, enabling efficient time-sequential pattern recognition without sacrificing causal relationship accuracy.
Data Source
AI summary
A computationally implemented method includes, but is not limited to: acquiring subjective user state data including at least a first subjective user state and a second subjective user state; acquiring objective context data including at least a first context data indicative of a first objective occurrence associated with a user and a second context data indicative of a second objective occurrence associated with the user; and correlating the subjective user state data with the objective context data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.