User Action Estimation Using Time-Linked Multi-Source Data
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Solution Overview
Problem
Conventional techniques are inadequate in accurately estimating a user's actions.
Innovation Solution
An action acquisition apparatus that utilizes position information, activity data, vital data, and emotion information, along with past action information, to estimate user actions through an action estimation unit that employs learning information from a learning management unit to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques using only energy consumption data are used to determine user actions, then the system complexity is low, but the measurement precision of action estimation is insufficient
Solution Approach 1:
The patent combines multiple data sources including position information from GPS receivers, activity data from accelerometers, vital data from sensors, and emotion information from recognition units into a unified action estimation system. This merging of multiple information sources resolves the contradiction by improving measurement precision through comprehensive data integration while managing system complexity through coordinated processing of diverse inputs.
Solution Approach 2:
The action estimation unit serves as a universal processing component that handles multiple types of input data (position, activity, vital signs, emotion) and generates comprehensive action determinations. This multi-functional approach improves action estimation accuracy by considering multiple factors simultaneously while maintaining a centralized processing architecture that manages overall system complexity.
2Measurement precision
If multiple data sources including position, activity, and vital data are integrated to estimate user actions, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The action estimation unit functions as an intermediary component that receives and processes multiple types of data from different sources (position information, activity data, vital data, emotion information) and produces unified action determinations. This intermediary approach resolves the contradiction by providing a centralized processing layer that improves measurement precision through comprehensive data analysis while managing integration complexity through a single coordination point.
Solution Approach 2:
The system segments data collection and processing into specialized components: position acquisition units for location data, activity acquisition units for motion data, vital acquisition units for physiological data, and emotion recognition units. Each segment handles specific data types independently, improving overall measurement precision through specialized processing while reducing integration complexity by dividing the system into manageable functional modules.
Data Source
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AI summary
Conventional techniques have not been able to estimate a user's action, using, for example, position information associated with time. A user's action can be estimated using, for example, position information associated with a time, by an action acquisition apparatus 3 including: a time acquisition unit 331 configured to acquire a time; a position acquisition unit 332 configured to acquire position information associated with the time; an action estimation unit 335 configured to, using two or more pieces of action source information including the position information associated with the time, acquire action information that specifies an action of a user during a time slot specified by the times contained in the two or more pieces of action source information; and an action output unit 341 configured to output the action information corresponding to the time slot.