Sleep Score Parameter Optimization via User Feedback Correlation
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Solution Overview
Problem
Current systems for computing sleep scores are unclear and fail to accurately reflect an individual's subjective analysis of their sleep, leading to a disconnect between the computed score and the user's perception of their sleep quality.
Innovation Solution
A method and system that optimize sleep score parameters by generating vectors based on correlation values, where each vector is associated with a user-indicated sleep score, allowing for the assignment of optimized parameter values that better align with the user's subjective sleep assessment, thereby improving the accuracy and clarity of the sleep score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional subtractive approach is used to compute sleep score, then calculation process is simple, but the sleep score does not accurately reflect user's subjective analysis
Solution Approach 1:
The system performs preliminary optimization of sleep-related parameters by generating multiple candidate vectors, evaluating their correlation with user-indicated scores, and selecting the best-performing parameters before actual sleep score computation. This pre-optimization step ensures high accuracy in reflecting user subjective analysis while keeping the actual scoring process efficient.
Solution Approach 2:
The system automatically optimizes its own parameters by using user feedback (subjective sleep ratings) to iteratively improve the correlation between computed scores and user perceptions. The optimization process is self-directed, requiring no external intervention, and continuously adapts to individual user preferences and sleep patterns.
2Measurement precision
If multiple parameter vectors are generated and optimized, then correlation with user-indicated score improves, but computational complexity increases
Solution Approach 1:
The system generates a large number of candidate parameter vectors (excessive action) to ensure thorough exploration of the parameter space, but only evaluates and retains the top-performing vectors based on correlation metrics. This approach achieves high correlation accuracy while managing computational load by focusing resources on the most promising candidates.
Solution Approach 2:
The parameter optimization is performed continuously in the background without interrupting the user experience. The system maintains an ongoing process of evaluating candidate vectors and updating optimal parameters, ensuring that the sleep scoring system continuously improves its accuracy over time without requiring explicit user initiation or causing delays.
3Reliability
If sleep score parameters are optimized based on user feedback, then user confidence increases, but system complexity increases
Solution Approach 1:
The system incorporates user feedback (subjective sleep ratings) into the parameter optimization process. User responses serve as ground truth for evaluating candidate parameter vectors, allowing the system to learn and adapt to individual preferences. This feedback loop continuously improves the reliability and user confidence in the scoring system.
Solution Approach 2:
The optimization algorithm systematically varies sleep-related parameters across multiple candidate vectors to explore different weighting schemes and computational approaches. By changing parameters methodically and evaluating their impact on correlation with user feedback, the system identifies the most reliable parameter configurations for accurate sleep scoring.
Data Source
AI summary
The present disclosure pertains to optimizing sleep score parameters. In one embodiment, a first set of vectors is obtained, where each vector includes parameter values of sleep-related parameters for calculating a sleep score. For each vector of the first set, a correlation value indicating a degree of a correlation between the vector and a user-indicated sleep score associated with a set of parameter values representative of a sleep metric of the user is determined. A second set of vectors is generated based on a first subset and a second subset of the first set of vectors satisfying a first and second criteria, respectively. A correlation value associated with each vector of the second set is determined. For each sleep-related parameter, a parameter value of a given vector of the second set is assigned based on the correlation values associated with the second set.