Correlating Subjective User States with Objective Occurrences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current social networking platforms, particularly microblogs, primarily function as open diaries for commentaries and personal information sharing, lacking the ability to effectively correlate subjective user states with objective occurrences, which hinders the determination of causal relationships between user experiences and events.
Innovation Solution
A computationally implemented method and system that acquire and correlate subjective user state data with objective occurrence data, using sequential patterns to identify temporal relationships and causal connections between subjective mental, physical, and overall states with objective events, enabling the determination of correlations and potential recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If social networking platforms function as open diaries for personal information sharing, then users can freely express and share their experiences, but the platforms lack the ability to effectively correlate subjective user states with objective occurrences
Solution Approach 1:
The patent segments user-generated content into distinct components: subjective user states (feelings, thoughts, experiences) and objective occurrences (events, activities, contextual information). This segmentation allows the system to separately identify and correlate these two types of information, enabling causal relationship analysis while maintaining the open diary format for free expression.
Solution Approach 2:
The patent introduces computational processing as an intermediary layer between user content and analysis results. This intermediary processes raw user posts, extracts subjective states and objective occurrences, correlates them temporally and causally, and generates insights. This mediator enables the platform to maintain freedom of expression while systematically analyzing causal relationships.
2Measurement precision
If the platform collects and analyzes user state data and occurrence data, then causal relationships can be determined, but system complexity increases
Solution Approach 1:
The patent enables the system to automatically process and analyze user-generated content without requiring manual intervention. The computational system autonomously extracts subjective user states, identifies objective occurrences, determines temporal relationships, and correlates data to identify causal patterns. This self-service approach handles the complexity internally while presenting simple results to users.
Solution Approach 2:
The patent transforms unstructured user text into structured data by changing parameters such as extracting sentiment scores, identifying key entities, determining temporal sequences, and calculating correlation strengths. These parameter transformations convert raw narrative data into quantifiable metrics that can be systematically analyzed for causal relationships.
3Measurement precision
If sequential patterns are used to identify temporal relationships, then correlations between user experiences and events can be identified, but processing time increases
Solution Approach 1:
The patent performs preliminary processing of user content by pre-identifying and timestamping subjective user states and objective occurrences as they are posted. This preliminary action creates a structured temporal framework in advance, allowing for efficient subsequent correlation analysis without requiring intensive real-time processing of entire content histories.
Data Source
AI summary
A computationally implemented method includes, but is not limited to acquiring subjective user state data including data indicating incidence of at least a first subjective user state associated with a first user and data indicating incidence of at least a second subjective user state associated with a second user; acquiring objective occurrence data including data indicating incidence of at least a first objective occurrence and data indicating incidence of at least a second objective occurrence; and correlating the subjective user state data with the objective occurrence data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.