Generative Sleep Audio Feedback Loop for Personalized Sleep Assistance
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Solution Overview
Problem
Existing sleep assistance technologies struggle to provide personalized and effective audio content for inducing or maintaining sleep, as users often lack insight into why certain audio tracks are effective and how to adapt them to individual needs and environments, and AI models face challenges in coordination and efficiency.
Innovation Solution
A system comprising an earbud, a sleep assistance server, and a generative content server, utilizing AI models to generate customized audio by integrating physiological data, narrative prompts, and environmental factors, and evaluating effectiveness through sleep metrics to refine audio generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI models are used to generate personalized sleep audio content, then sleep assistance effectiveness is improved, but device complexity and model coordination challenges increase
Solution Approach 1:
The patent divides the complex AI system into specialized models with distinct functions: a first AI model generates sleep audio content while a second AI model evaluates its effectiveness. This segmentation allows each model to be optimized for its specific task, improving overall reliability while managing complexity through clear functional separation.
Solution Approach 2:
The patent implements a feedback loop where the second AI model evaluates the effectiveness of audio generated by the first model, and this evaluation information is used to refine future generation. This feedback mechanism improves sleep assistance effectiveness by continuously optimizing content based on measured outcomes, while the automated nature of the feedback process manages complexity through algorithmic rather than manual coordination.
2Reliability
If multiple AI models are coordinated to generate and evaluate sleep content, then content effectiveness is improved, but computational efficiency decreases
Solution Approach 1:
The patent performs preliminary actions by training both AI models offline before deployment. The first model is trained to generate effective sleep audio, and the second model is trained to evaluate such audio. This preliminary training reduces computational burden during actual sleep sessions, improving efficiency while maintaining content effectiveness through pre-optimized models.
Solution Approach 2:
The patent applies partial action by using the second AI model to evaluate only critical aspects of the generated audio content rather than进行全面 analysis. This selective evaluation maintains content effectiveness by focusing on key effectiveness indicators while reducing computational overhead through targeted rather than exhaustive assessment.
3Reliability
If sleep audio content is customized for individual users, then sleep quality is improved, but difficulty in identifying effective characteristics increases
Solution Approach 1:
The patent uses the second AI model to provide automated feedback on which characteristics of customized audio content are effective for each user. By analyzing evaluation results from multiple sessions, the system identifies patterns in effective characteristics, making them detectable and measurable even as customization increases complexity. This feedback loop improves sleep quality while solving the identification difficulty through data-driven insights.
4Adaptability or versatility
If AI models are retrained with effectiveness data, then adaptability to individual needs is improved, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action in model retraining by updating only the portions of AI models that are most affected by new effectiveness data, rather than complete retraining. This selective updating maintains adaptability to individual needs by incorporating new learning while significantly reducing training time and computational resources compared to full model retraining.
Solution Approach 2:
The patent optimizes training efficiency by changing parameters such as learning rate, batch size, or training epochs based on the amount and quality of new effectiveness data. This allows the system to adapt to individual user needs effectively while managing training time through intelligent parameter adjustment rather than fixed training protocols.
Data Source
AI summary
Disclosed are a system, a device, and/or a method of sleep assistance through generation and evaluation of generative sleep content and/or sleep improvement interventions including training, adjusting, mediating, and/or integrating outputs of one or more AI models. In one embodiment, a system includes an earbud generating an audio generation request through a voice interface, which is parsed by a sleep assistance server to extract a narrative prompt. A generative content server inputs the narrative prompt into an artificial neural network to generate a text data, and then text-to-speech model to generate a narrative audio. A content integration routine may overlay the narrative audio data with additional music and/or physiological guidance data. A generative audio data is returned to the earbud to assist in achieving sleep. The system may evaluate physiological data to determine effectiveness of the sleep assistance audio, and may augment, fine-tune, and/or retrain one or more AI models.