Hypothesis-Based Data Solicitation for Causal Analysis
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Solution Overview
Problem
Current social networking platforms, such as microblogs, primarily focus on recording objective occurrences and subjective user states separately, lacking a method to effectively correlate and analyze these data sets to determine causal relationships between them.
Innovation Solution
A computationally implemented method and system that solicits and acquires subjective user state data based on hypotheses linking objective occurrences with subjective user states, correlating the data to identify temporal and causal relationships between the two.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If social networking platforms record objective occurrences and subjective user states separately, then data collection is simple and straightforward, but the ability to determine causal relationships between occurrences and states is lost
Solution Approach 1:
The patent combines separate collections of objective occurrences and subjective user states into a unified data structure that preserves temporal relationships. The system collects both types of data together and maintains their temporal sequencing, enabling subsequent analysis of causal relationships while keeping the collection process relatively simple.
Solution Approach 2:
The patent adds a temporal dimension to the data collection by recording timestamps for both objective occurrences and subjective user states. This temporal ordering creates a new dimension that enables causal analysis without complicating the basic collection mechanism, allowing reconstruction of event-state relationships over time.
2Loss of information
If the system solicits subjective user state data based on hypotheses linking objective occurrences with user states, then causal relationship determination is enabled, but data collection complexity increases
Solution Approach 1:
The system performs preliminary hypothesis generation based on collected data patterns, then uses these hypotheses to guide subsequent data collection. By pre-identifying potential causal relationships through hypothesis generation, the system focuses collection efforts on relevant data points, reducing overall complexity while maintaining causal analysis capability.
Solution Approach 2:
The patent implements feedback loops where collected data is analyzed to generate hypotheses about causal relationships, which then inform further data collection. This feedback mechanism allows the system to adaptively focus on relevant occurrences and states, making the complex collection process more efficient and targeted.
3Loss of information
If the system correlates subjective user state data with objective occurrence data to identify temporal and causal relationships, then insights into user experiences are provided, but processing complexity increases
Solution Approach 1:
The patent segments the correlation process into distinct stages: temporal relationship identification, hypothesis generation, and causal relationship determination. By breaking down the complex correlation task into manageable segments, the system can process data more efficiently while still providing comprehensive user experience insights.
Solution Approach 2:
The system dynamically adjusts its correlation analysis based on data patterns and hypothesis strength. Rather than applying uniform complex processing to all data, the system adapts its analysis depth and methods based on preliminary findings, reducing unnecessary processing complexity while maintaining insight quality.
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 objective occurrence, subjective user state data including data indicating incidence of at least one subjective user state associated with a user; and acquiring the subjective user state data including the data indicating incidence of at least one subjective user state associated with the user. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.