Pregnancy Delivery Prediction Using Body Parameter Pattern Recognition
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Solution Overview
Problem
Current methods for predicting delivery time in pregnant women are imprecise and do not account for the actual development of pregnancy, often requiring medical support and being influenced by various factors, making reliable prediction challenging without medical expertise.
Innovation Solution
A computer-implemented method that detects multiple body parameters of a pregnant woman over time, uses pattern recognition to calculate a predicted delivery time or period, and displays this information visually, acoustically, or haptically, incorporating correlations between body parameters such as hormone levels and vital signs, utilizing artificial neural networks for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the Naegele rule is used to predict delivery date based on last menstrual period, then the prediction can be calculated simply, but the prediction accuracy is low and does not account for actual pregnancy development
Solution Approach 1:
The patent transitions from using fixed menstrual cycle parameters (Naegele rule) to dynamically monitoring multiple body parameters (temperature, heart rate, hormone levels) that actually change during pregnancy. This allows the system to adapt predictions to individual pregnancy progression rather than relying on average cycle patterns.
Solution Approach 2:
The system continuously monitors body parameters and uses this feedback to update predictions. By comparing actual monitored parameters against expected patterns, the system can adjust delivery time predictions in real-time based on actual pregnancy development rather than relying on static calculation rules.
2Reliability
If body temperature is monitored continuously to detect delivery signs, then prediction reliability may improve, but the complexity of detection and evaluation increases and is overlaid by other factors
Solution Approach 1:
The patent combines multiple body parameter measurements (temperature, heart rate, hormone levels, activity) into a single integrated prediction system. Rather than evaluating each parameter separately, the system merges them into a comprehensive pattern recognition approach that reduces ambiguity from individual factor interference.
Solution Approach 2:
The system uses a multi-functional monitoring approach where a single sensor suite can detect multiple physiological parameters simultaneously. This universal monitoring system can track various delivery indicators without requiring separate specialized devices for each parameter, reducing overall system complexity.
3Measurement precision
If self-learning algorithms are used to adapt to individual users based on monthly cycle repetition, then prediction accuracy improves, but this approach cannot be applied to pregnancy prediction as pregnancy development is not cyclical
Solution Approach 1:
The system performs preliminary pattern analysis during the pregnancy period to establish individual baseline patterns. By monitoring body parameters throughout pregnancy and identifying unique patterns specific to each individual, the system creates personalized prediction models before delivery occurs, enabling accurate predictions without requiring cyclical data.
Data Source
AI summary
A computer-implemented method for predicting the delivery time of a pregnant woman, including detecting at least one body parameter of the pregnant woman at multiple points in time, a calculation unit carrying out a pattern recognition with regard to the development of the at least one body parameter over time, and calculating a predicted delivery time or delivery period based on the pattern recognition. An electronic system for predicting the delivery time of a pregnant woman is also disclosed.