Wearable Sensor Automatic Scheduling via Behavioral Pattern Inference
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Solution Overview
Problem
Current scheduling technologies only accommodate typical schedules explicitly set by users and do not utilize behavioral patterns to infer atypical schedules, limiting their ability to predict user activities in real-time.
Innovation Solution
A schedule inference-based automatic scheduling device and method that infers atypical schedules by analyzing user behavioral patterns, including frequently visited places, to determine a user's expected schedule in real-time, combining this with a typical schedule using a weighted reward system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If scheduling is done only based on typical schedules explicitly entered by the user, then the system is simple to operate, but it cannot predict atypical schedules or infer user behavioral patterns
Solution Approach 1:
The system performs preliminary analysis of user behavioral patterns by collecting and processing location data, movement patterns, and activity information in advance. This allows the system to pre-infer atypical schedules and behavioral characteristics before they are needed for prediction, enabling more accurate real-time schedule prediction without adding complexity to the user interface
Solution Approach 2:
The system introduces an intermediary layer that processes raw behavioral data and transforms it into inferred schedule information. This intermediary processing layer analyzes patterns in location data, movement characteristics, and activity sequences to generate atypical schedule predictions, separating the complexity of pattern recognition from both the data collection and the final prediction output
2Reliability
If the system collects and analyzes user behavioral patterns to infer atypical schedules, then schedule prediction accuracy improves, but the amount of data processing and system complexity increases
Solution Approach 1:
The system extracts only the essential and most relevant features from raw behavioral data for schedule inference. Instead of processing all collected data equally, it identifies and extracts key patterns such as frequent location visits, regular movement routes, and characteristic activity sequences. This extraction process reduces data processing complexity while maintaining prediction reliability by focusing on the most informative features
Solution Approach 2:
The system transforms raw behavioral data into standardized parameters that are optimized for pattern recognition and schedule prediction. By converting diverse behavioral information into consistent parameter formats with appropriate weightings and thresholds, the system simplifies the analysis process while improving the reliability of atypical schedule inference
Data Source
AI summary
The present invention relates to a wearable sensor-based automatic scheduling device and method. The wearable sensor-based automatic scheduling device includes: a typical schedule setting part that sets a typical schedule received from the user and specifying a specific event; an atypical schedule inferring part that infers an atypical schedule by analyzing the user's current location and the user's behavioral patterns occurring over a specific period of time in the past; and a schedule determining part that determines the schedule from this point on based on the typical schedule and the atypical schedule. Accordingly, the present invention may provide a technology that can predict the user's expected schedule in real time by taking into account both a typical schedule recorded in a scheduler and an atypical schedule mapped out based on daily behavioral patterns.