Correlating User and Sensor Data for Hypothesis Development
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Solution Overview
Problem
Current systems fail to effectively correlate and utilize subjective user states with objective occurrences, limiting the ability to develop meaningful hypotheses or insights from user-reported data and sensor-generated data in social networking and monitoring environments.
Innovation Solution
A computationally implemented method and system that acquire both user-reported events and sensor-reported events to develop hypotheses based on these data, enabling the identification of relationships between subjective user states and objective occurrences, and potentially executing actions or advisories related to these hypotheses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user-reported data and sensor-generated data are collected separately without correlation, then data acquisition is simple, but the ability to develop meaningful hypotheses is limited
Solution Approach 1:
The patent combines user-reported data and sensor-generated data into a unified correlation system that processes both data types together. The system merges previously separate data streams, enabling the development of hypotheses by correlating subjective user states with objective sensor measurements, thereby preventing loss of correlational information without requiring entirely separate systems.
2Adaptability or versatility
If subjective user states are correlated with objective occurrences, then hypothesis development capability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing function into distinct modules: one for acquiring user-reported data indicating subjective user states, another for acquiring sensor-generated data indicating objective occurrences, and a third for correlating these data types to develop hypotheses. This segmentation allows the system to achieve enhanced hypothesis development capability while managing processing complexity through modular architecture.
3Productivity
If multiple data sources are integrated to develop hypotheses, then data utilization effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal data correlation system that can handle multiple data sources (user-reported data and sensor-generated data) through a single integrated platform. This multi-functional system performs both data acquisition and hypothesis development functions, improving data utilization effectiveness while avoiding the need for separate specialized systems for each data type.
Data Source
AI summary
A computationally implemented method includes, but is not limited to: acquiring a first data indicating at least one reported event as originally reported by a user and a second data indicating at least a second reported event as originally reported by one or more sensing devices; and developing a hypothesis based, at least in part, on the first data and the second data. In addition to the foregoing, other method aspects are described in the claims, drawings, and text forming a part of the present disclosure.