Dynamic Sleep Management Framework Correlating Demographics
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Solution Overview
Problem
Conventional sleep tracking devices fail to capture data on life choices impacting sleep quality and do not provide context for individual sleep quality measures.
Innovation Solution
A computerized framework that collects sleep data from users, correlates it with demographic data, and provides personalized insights and recommendations by analyzing user data in relation to aggregated data from similar demographics, using sensors and machine learning models to identify factors influencing sleep quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional sleep tracking devices are used, then sleep data can be collected, but the data lacks context and correlation with demographic information
Solution Approach 1:
The patent combines sleep tracking functionality with demographic data collection and analysis by integrating multiple data sources (wearable devices, survey responses, environmental sensors) into a unified system that correlates sleep metrics with user demographics to provide contextualized insights
Solution Approach 2:
The system introduces an intermediary processing layer that aggregates sleep data from wearable devices, combines it with demographic information from user profiles, and generates contextualized sleep metrics that relate individual sleep patterns to population averages
2Measurement precision
If comprehensive sleep data collection is implemented, then sleep quality measurement improves, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct modules: raw sleep data collection from wearables, demographic data aggregation, correlation analysis between sleep metrics and demographics, and generation of contextualized sleep quality measures, allowing complex processing to be managed through modular components
Solution Approach 2:
The system transforms raw sleep parameters (heart rate, movement, temperature) into derived sleep quality metrics (sleep stages, sleep efficiency, deep sleep percentage) through standardized processing algorithms that enable precise measurement while managing complexity through parameter transformation
3Adaptability or versatility
If individualized sleep analysis is provided, then sleep optimization recommendations improve, but system complexity increases
Solution Approach 1:
The patent applies local quality by tailoring sleep analysis and recommendations to individual users based on their specific demographic characteristics and sleep patterns, while using population-level data to provide contextualized insights that are relevant to each user's unique situation
Solution Approach 2:
The system implements feedback loops where individual sleep data is continuously compared against demographic benchmarks, and recommendations are generated based on deviations from population norms, with results fed back to refine future analysis and provide adaptive personalization
Data Source
AI summary
Disclosed are systems and methods that provide a novel framework for personalized sleep management for a user. The framework can provide dynamically determined sleep data for a user, determined from data collected from the sensor(s) of device(s) in a location of a user, that is correlated with sleep data determined in a same or similar manner for other users (e.g., users who share a demographic with the user). The framework provides a comprehensive sleep optimization system that provides personalized insights and recommendations to aid and/or effectuate users achieving a restful and rejuvenating sleep.