Machine Learning Wake Window Predictions From Infant Sleep Data
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Solution Overview
Problem
Existing methods for helping infants sleep are inadequate as they lack personalization based on individual sleep patterns and circadian rhythms, relying on rule-based systems that require frequent updates and fail to account for actual sleep/wake windows, leading to reduced accuracy and increased stress for parents and caregivers.
Innovation Solution
A machine learning-based system that uses data from a large user base to predict personalized wake windows by analyzing recent sleep patterns, incorporating expert-determined values, and ensuring predictions stay within acceptable ranges, even with incomplete data, using LightGBM and Google Cloud Platform tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based systems with expert-determined wake window values are used, then the system is simple to implement and maintain, but the measurement precision and personalization accuracy deteriorate because they cannot account for individual infant sleep patterns and circadian rhythms
Solution Approach 1:
The patent replaces the mechanical rule-based system with a machine learning model that processes infant sleep data to generate personalized wake window predictions. The model uses historical sleep data, circadian rhythm patterns, and individual infant characteristics to dynamically predict optimal wake windows, substituting static expert rules with adaptive computational intelligence that continuously learns from data patterns.
Solution Approach 2:
The system transforms fixed expert-determined wake window values into dynamic, personalized predictions by changing the parameters from static constants to variable outputs based on individual infant data. The machine learning model adjusts wake window parameters based on each infant's unique sleep patterns, circadian rhythms, and developmental stage, allowing continuous optimization rather than relying on predetermined values.
2Device complexity
If rule-based systems are used to provide sleep recommendations, then the device complexity is low, but the adaptability to individual sleep patterns and changes in sleep behavior deteriorates
Solution Approach 1:
The patent implements dynamics by transitioning from static rule-based recommendations to dynamic machine learning predictions that adapt to changing infant sleep patterns. The system continuously processes new sleep data, updates predictions based on evolving circadian rhythms and behavioral patterns, and adjusts wake window recommendations in real-time, enabling the system to respond to developmental changes and individual variations in sleep behavior.
Solution Approach 2:
The machine learning model enables self-service by automatically learning from infant sleep data without requiring manual updates or expert intervention. The system autonomously identifies patterns, adjusts predictions based on new data, and personalizes recommendations for each infant independently, eliminating the need for frequent rule updates and patches that characterize traditional rule-based systems.
3Ease of operation
If rule-based systems with fixed wake window values are used, then the ease of operation is high, but the reliability of predictions deteriorates because the system is brittle to changes in sleep patterns and requires frequent updates
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning model continuously receives new sleep data from infants and uses this feedback to refine and update predictions. The system processes ongoing sleep pattern data, compares actual sleep outcomes with predicted wake windows, and adjusts future predictions accordingly, creating a closed-loop system that improves reliability through continuous learning rather than requiring manual rule updates.
Solution Approach 2:
The system maintains ease of operation while improving reliability through self-service capabilities where the machine learning model automatically adapts to changing sleep patterns without user intervention. The model autonomously handles data processing, pattern recognition, and prediction adjustment, maintaining simple user interaction while internally managing the complexity of adapting to individual infant needs and changing sleep behaviors.
Data Source
AI summary
According to certain aspects of the present disclosure, systems and methods are disclosed for tracking and predicting the optimal wake windows of individual infants at scale and thus, the optimal sleep times of a user on a screen customized to each child prioritizing the use of highly personalized values based on machine learning to augment existing expert opinions of wake windows by age that will be automatically adaptive to the changes in child sleep and to generate predictions personalized for each child.