Sleep Pad Feedback for Real-Time Ultradian Rhythm Assistance

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Solution Overview

Problem

Conventional sleep technologies face challenges in inducing continuous user engagement and providing effective sound sleep assistance, as they primarily focus on monitoring physiological changes without effectively assisting users in achieving sound sleep, and often require costly bed driving devices.

Innovation Solution

A deep learning-based sleep assistance system that communicates with a sleep pad to acquire physiological index information, determines the user's sleep stage, and provides customized sound sources or lifestyle recommendations to optimize ultradian rhythm and improve sleep quality.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional sleep monitoring systems are used to track physiological changes, then sleep data can be collected, but the systems cannot effectively assist users in achieving sound sleep and fail to induce continuous user engagement

Engineering Contradiction:

Improvesleep stage detection accuracyVSAvoiduser engagement and service effectiveness

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system implements real-time feedback by continuously monitoring physiological signals (heart rate, breathing rate, body movement) and dynamically adjusting sound therapy parameters based on detected sleep stages. This closed-loop control enables the system to respond adaptively to user needs, improving both sleep assistance effectiveness and user engagement through personalized, responsive intervention.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system employs automated sleep stage classification using machine learning algorithms that analyze physiological signals without requiring manual input or interpretation. The system self-adjusts therapy parameters and provides automated sleep quality reports, eliminating the need for user expertise in sleep physiology while maintaining high measurement precision and continuous engagement.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If bed driving devices with motors are provided to prevent snoring and provide sleep environment control, then sleep assistance function is improved, but device complexity and cost increase significantly

Engineering Contradiction:

Improvesleep environment control capabilityVSAvoiddevice structure and resource requirements

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent extracts the core sleep assistance function from complex mechanical bed systems and implements it through a simplified wearable device combined with a mobile application. By removing unnecessary mechanical components (motors, complex actuators) and retaining only the essential physiological monitoring and sound therapy functions, the system achieves comparable sleep assistance effectiveness with dramatically reduced device complexity and cost.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system replaces mechanical bed driving devices with electronic and acoustic solutions. Instead of using motors to adjust bed positions or apply pressure, the system uses wearable sensors to monitor physiological signals and delivers therapy through sound waves generated by a mobile device or simple speaker, eliminating complex mechanical subsystems while maintaining adaptability to user needs.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If conventional sleep technology merely monitors physiological changes, then data collection is achieved, but the effect of serviced content is poor and users cannot be assisted in sleeping sound

Engineering Contradiction:

Improvesleep data collection volumeVSAvoidsleep assistance effectiveness

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The system performs preliminary classification of sleep stages using machine learning models trained on physiological signal patterns before delivering therapy. By pre-processing and analyzing physiological data to identify specific sleep stages (light sleep, deep sleep, REM), the system can proactively select and apply appropriate sound therapy interventions, transforming raw data collection into reliable, actionable sleep assistance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically changes therapy parameters (sound frequency, intensity, type) based on detected sleep stage parameters. By continuously monitoring physiological signals and adjusting acoustic intervention parameters in real-time according to the user's sleep state, the system transforms static data collection into adaptive, reliable sleep assistance that responds to physiological changes.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20230233794A1Deep learning-based sleep assistance system through optimization of ultradian rhythm

Publication Date: 2023.07.27 ROH SEUNG WAN

AI summary

Disclosed herein are a sound sleep assistance apparatus, a sound sleep assistance method, and a sound sleep assistance system. According to an embodiment, there is provided a sound sleep assistance apparatus for assisting the sound sleep of a user by communicating with a sleep pad, the sound sleep assistance apparatus including: a communication interface configured to communicate with the sleep pad that acquires the physiological index information of the user while the user lies down; and a controller configured to determine the sleep stage of the user based on the physiological index information, and to provide a sound source corresponding to the determined sleep stage.