Ovulation Prediction Algorithm Using Cycle Data Relationships
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Solution Overview
Problem
Existing methods for predicting the day of ovulation are unreliable, particularly for women with insufficient menstrual cycle data, as they often assume a constant period between ovulation and menstruation, which varies among individuals, and are limited in providing immediate predictions.
Innovation Solution
A computer-based process that calculates a predicted ovulation day by applying menstrual cycle data to a relationship between menstrual days and ovulation days using average tendencies from a large population, incorporating a relational equation or table data to provide accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the Knaus-Ogino method assumes a constant period of 14 days between ovulation and menstruation, then the prediction method is simple to use, but the prediction reliability is poor because the period varies among individuals
Solution Approach 1:
The invention changes the fixed parameter of 14 days to a variable parameter that adapts to each woman's actual menstrual cycle characteristics. By calculating the period between ovulation and menstruation based on individual historical data rather than assuming a constant value, the system maintains simplicity while improving prediction reliability through parameter customization.
Solution Approach 2:
The system incorporates feedback by using actual menstrual cycle data from multiple periods to refine and update the prediction model. The calculated period between ovulation and menstruation is continuously adjusted based on observed variations in each woman's cycle, allowing the prediction to become progressively more accurate while maintaining the simplicity of the calculation method.
2Loss of time
If the coverline calculation method uses basal body temperature to predict ovulation, then immediate prediction is possible, but the prediction can only be made after ovulation has occurred
Solution Approach 1:
The invention performs preliminary action by calculating and storing the period between ovulation and menstruation in advance, based on historical data from previous cycles. This pre-calculated information enables the system to predict future ovulation dates before they occur, rather than relying on temperature changes that only indicate ovulation after it has happened.
Solution Approach 2:
The system transitions from a static approach (fixed 14-day assumption) to a dynamic approach where the period between ovulation and menstruation is continuously updated based on individual variations observed in each menstrual cycle. This dynamic adaptation allows for more accurate timing predictions that reflect each woman's unique physiological patterns.
3Reliability
If prediction methods rely on sufficient historical menstrual cycle data, then prediction accuracy improves, but the method cannot provide reliable predictions for women with insufficient data
Solution Approach 1:
The invention creates a universal prediction method that functions effectively regardless of the amount of historical data available. By using a standardized calculation approach based on the relationship between ovulation and menstruation periods, the system can provide reliable predictions for women with limited data while maintaining accuracy for those with extensive historical records.
Data Source
AI summary
An object is to provide programs for reliable predictions of the day of ovulation. A process is executed in a program for predicting a day of ovulation on a computer, the process including calculating a predicted ovulation day data corresponding to a particular menstrual cycle by applying the particular menstrual cycle to a relationship between a period between a menstrual day and a day of ovulation and an average length of a menstrual cycle, the relationship being estimated based on data of a plurality of persons which have been previously obtained.