Correlating Subjective User States with Objective Occurrences
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Solution Overview
Problem
Current social networking platforms, such as microblogs, primarily focus on objective occurrences and lack the ability to effectively correlate subjective user states with objective occurrences, limiting the understanding of causal relationships between the two.
Innovation Solution
A computationally implemented method and system that acquires both subjective user state data and objective occurrence data, correlates them based on sequential patterns, and presents the results, enabling the determination of causal relationships between subjective user states and objective occurrences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social networking platforms focus on objective occurrences, then data collection is simplified, but the ability to understand causal relationships between user states and events is limited
Solution Approach 1:
The patent combines subjective user state data and objective occurrence data into a unified data structure with temporal sequencing. This merging allows the system to capture both types of information together, enabling correlation analysis while maintaining manageable complexity through structured integration rather than separate systems
Solution Approach 2:
The patent introduces an intermediary computational framework that processes and correlates subjective and objective data. This intermediary layer analyzes temporal patterns and sequences between user states and events, extracting causal relationships without requiring direct modification of the underlying data collection systems
2Loss of information
If the system correlates subjective user state data with objective occurrence data, then insights into causal relationships are provided, but data processing complexity increases
Solution Approach 1:
The patent segments the correlation process into distinct computational steps: data acquisition, temporal sequencing, pattern recognition, and result generation. This segmentation breaks down the complex correlation task into manageable modules, reducing overall processing complexity while maintaining comprehensive analysis capabilities
Solution Approach 2:
The patent implements partial correlation analysis by focusing on specific temporal patterns and sequences rather than attempting to analyze all possible relationships. This selective approach provides meaningful insights into causal relationships while avoiding the computational burden of exhaustive analysis
Data Source
AI summary
A computationally implemented method includes, but is not limited to: acquiring subjective user state data including data indicating at least one subjective user state associated with a user; acquiring objective occurrence data including data indicating at least one objective occurrence associated with the user; correlating the subjective user state data with the objective occurrence data based, at least in part, on a determination of at least one sequential pattern associated with the at least one subjective user state and the at least one objective occurrence; and presenting one or more results of the correlating. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.