Hypothesis-Based Data Solicitation for Subjective-Objective Correlation
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Solution Overview
Problem
Current social networking platforms, such as microblogs, primarily function as open diaries for reporting feelings, thoughts, and daily life events but lack the ability to effectively correlate subjective user states with objective occurrences, limiting their use beyond commentary and diary-keeping.
Innovation Solution
A computationally implemented method and system that solicits and acquires objective occurrence data based on hypotheses linking subjective user states with objective occurrences, allowing for the correlation of subjective user state data with objective occurrence data to determine causal relationships and present results to users or third parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If social networking platforms function as open diaries for reporting feelings and thoughts, then users can freely express subjective user states, but the platforms lack the ability to effectively correlate subjective user states with objective occurrences
Solution Approach 1:
The system segments data into two distinct types: subjective user state data (feelings, thoughts, emotions) and objective occurrence data (external events, activities, measurable occurrences). This segmentation allows the platform to maintain the simplicity of user expression while introducing structured correlation capabilities through separate data collection channels and processing mechanisms.
Solution Approach 2:
The patent introduces hypothesis-based data solicitation as an intermediary mechanism that bridges subjective user states and objective occurrences. The system generates hypotheses linking the two data types, solicits relevant objective data based on these hypotheses, and performs correlation analysis without requiring users to manually structure their expressions.
2Loss of information
If the platform solicits and acquires objective occurrence data based on hypotheses, then causal relationships can be determined, but data collection complexity increases
Solution Approach 1:
The system performs preliminary action by generating hypotheses that predict potential causal relationships between subjective user states and objective occurrences. These hypotheses are created based on existing data patterns and are used to guide targeted data solicitation, ensuring that the platform collects only the specific objective occurrence data needed to validate or refute each hypothesis, thereby minimizing unnecessary data collection complexity.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting data solicitation strategies based on hypothesis priorities and existing data completeness. The system modifies collection parameters such as solicitation intensity, data types requested, and correlation depth according to the specific hypothesis being tested, allowing flexible adaptation without requiring a uniformly complex data collection infrastructure.
Data Source
AI summary
A computationally implemented method includes, but is not limited to: soliciting, based at least in part on a hypothesis that links one or more objective occurrences with one or more subjective user states and in response at least in part to an incidence of at least one subjective user state associated with a user, at least a portion of objective occurrence data including data indicating incidence of at least one objective occurrence; and acquiring the objective occurrence data including the data indicating incidence of at least one objective occurrence. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.