Body Temperature Moving Average Ovulation Prediction
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Solution Overview
Problem
Existing methods for estimating ovulation dates and predicting menstrual dates based on body temperature fluctuations are inaccurate due to temperature fluctuations before and after the transition from the low-temperature phase to the high-temperature phase, leading to estimation errors.
Innovation Solution
An information processing device determines short and long term lengths for moving averages of body temperatures to identify the timing of the transition from the low-temperature phase to the high-temperature phase, allowing for accurate prediction of ovulation and menstrual dates by smoothing temperature fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If body temperature reaches a high body temperature during the low-temperature phase, then the high-temperature phase start is determined early, but estimation error occurs
Solution Approach 1:
The patent introduces moving average values as an intermediary to smooth out temporary temperature fluctuations. Instead of directly using raw body temperature readings to determine phase transitions, the system calculates moving averages over multiple days to filter out noise and false signals, thereby accurately identifying the true transition point from low- to high-temperature phase
Solution Approach 2:
The patent dynamically adjusts the detection criteria by comparing current moving average temperature against historical moving average temperatures. The system adapts to individual menstrual cycle patterns by learning from past data, making the detection threshold dynamic rather than fixed, which improves both responsiveness and accuracy
2Measurement precision
If it is determined that the high-temperature phase starts on the basis that the body temperature reaches the high temperature for a predetermined number of consecutive days, then false early detection is prevented, but it is impossible to estimate or predict menstrual dates after transition
Solution Approach 1:
The patent performs preliminary analysis by calculating moving average temperatures and comparing them against historical patterns before making the final determination of phase transition. This preliminary processing of data allows the system to accurately identify the transition point and subsequently perform reliable ovulation date estimation and menstrual date prediction
Solution Approach 2:
The system uses feedback from historical menstrual cycle data to continuously refine its predictions. By comparing actual cycle lengths and temperature patterns with predicted values, the system adjusts its models to improve future estimations of ovulation dates and menstrual dates, enabling continuous productivity after transition detection
3Speed
If moving average with short term length is used to detect transition timing, then responsiveness to temperature change is improved, but sensitivity to fluctuations increases
Solution Approach 1:
The patent employs dynamic adjustment of the moving average term length based on the characteristics of the current menstrual cycle. The system can switch between short-term and long-term moving averages depending on the phase and stability of temperature data, optimizing both responsiveness and reliability adaptively
Solution Approach 2:
Before using short-term moving average for detection, the system performs preliminary validation by comparing results with long-term moving average trends and historical patterns. This preliminary check ensures that detected transitions are genuine and not artifacts of temporary fluctuations, maintaining reliability while using responsive short-term averages
Data Source
AI summary
An information processing device determines a short term length and a long term length so that a timing(s) at which a short-term moving average of a plurality of days of body temperatures measured during past menstrual cycles falls below a long-term moving average of the body temperatures coincides with a menstrual date. The information processing device identifies the timing at which a short-term moving average calculated with the determined short term length exceeds a long-term moving average calculated with the determined long term length, of body temperatures measured during a target menstrual cycle. Based on the identified timing, the information processing device predicts the next menstrual date or estimates the arrival of an ovulation date.