Machine Learning Menopause State Prediction from Sensor Data
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Solution Overview
Problem
Women experiencing menopause face challenges in identifying their menopause state and related anomalies due to the variability of symptoms and changes, which can impact health and well-being, and existing methods lack accurate, user-specific prediction capabilities.
Innovation Solution
A system that generates a longitudinal dataset of features from tracked physical measurements using sensor circuitry, applies machine learning models to identify patterns indicative of menopause states and anomalies, and communicates predictive information to users, including the use of multiple machine learning models to predict current menopause states and detect deviations from baseline patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional menopause tracking methods are used, then women can monitor their menstrual cycles, but they cannot accurately identify menopause states or predict menopause-related anomalies due to symptom variability
Solution Approach 1:
The patent transforms the menopause tracking approach by changing from monitoring single parameters (menstrual cycles) to analyzing multiple physiological parameters simultaneously (heart rate, temperature, sleep patterns, activity levels). This multi-parameter approach enables accurate menopause state identification by detecting patterns across different physiological domains, resolving the contradiction between accuracy and complexity through comprehensive yet integrated monitoring.
Solution Approach 2:
The patent replaces traditional mechanical/manual tracking methods with machine learning-based predictive modeling. The system uses trained models that automatically analyze sensor data patterns to predict menopause states and anomalies, substituting complex manual analysis with automated computational approaches that improve accuracy while managing system complexity through algorithmic processing.
2Reliability
If multiple sensor measurements are collected to improve prediction accuracy, then user-specific menopause state prediction improves, but data processing complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct components: sensor data collection, feature extraction, pattern recognition, and prediction generation. By dividing the processing pipeline into manageable segments with specialized functions, the system handles multiple sensor measurements efficiently, improving prediction reliability while controlling processing complexity through modular architecture.
Solution Approach 2:
The patent introduces machine learning models as intermediary components between raw sensor data and menopause state predictions. These models act as mediators that automatically process and interpret multi-modal sensor measurements, transforming complex raw data into meaningful predictions without requiring direct complex processing logic in the main system, thus improving reliability while managing complexity.
3Measurement precision
If longitudinal datasets are analyzed to identify user-specific patterns, then personalized menopause state prediction is achieved, but computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing sensor data during collection and storing it in organized longitudinal datasets. The system prepares and structures data in advance, creating ready-to-analyze repositories of user-specific measurements. This preliminary organization reduces the computational burden during actual analysis phases, enabling precise user-specific predictions while managing energy consumption through advance data preparation.
Data Source
AI summary
Embodiments are directed to a non-transitory computer-readable storage medium comprising instructions that when executed cause processor circuitry to generate a longitudinal dataset of a set of features from tracked physical measurements of a user received from sensor circuitry, apply at least one machine learning model to the longitudinal dataset of the set of features to identify a pattern within the set of features indicative of a probability of the user being in a state of menopause, predict a current state of menopause for the user based on the identified pattern, and communicate a data message indicative of the current state of menopause for the user. In some embodiments, the set of features are compressed to pseudo-features and input to a first ML model using a second ML model.