Biological Degradation Function Generation via Machine Learning Segmentation
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Solution Overview
Problem
Current systems face challenges in accurately sampling and tracking age-related biological degradations, particularly in identifying how degradations occur, measuring their amounts, and predicting degradation trajectories due to confounding variables.
Innovation Solution
A system and method using a computing device to generate body degradation functions by mapping biological extraction data to current physiological integrity, determining instantaneous rates of change, and creating statistical associations to predict future degradation, enabling the generation of a body degradation packet for user access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive sampling of physiological parameters is performed to track age-related biological degradations, then measurement precision and reliability improve, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent segments the comprehensive tracking of biological degradations into multiple specialized machine learning models, each focused on specific physiological parameters (e.g., cardiovascular health, metabolic health, neurological health). This segmentation allows the system to maintain high measurement precision for each parameter while managing device complexity through modular architecture, where each model can be independently trained and optimized.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that process raw physiological data and transform it into meaningful degradation metrics. These models act as mediators between the complex sensing infrastructure and the user-facing application, handling the computational complexity of analyzing multiple physiological parameters while providing simplified, actionable insights about biological aging.
2Reliability
If multiple physiological parameters are tracked to capture cross-body degradations, then reliability of degradation tracking improves, but difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements universal machine learning models that can process multiple types of physiological data across different body systems. These models are designed to handle diverse input parameters (heart rate, blood pressure, glucose levels, neural signals) using common analytical frameworks, thereby improving the reliability of cross-body degradation tracking while reducing the difficulty of measurement through standardized processing protocols.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning models continuously learn from accumulated physiological data, refining their predictions of biological degradation over time. This feedback loop improves reliability by adapting to individual user patterns and reducing measurement difficulties through iterative optimization of detection algorithms based on real-world performance data.
3Measurement precision
If machine learning models are trained on comprehensive biological data to predict degradation trajectories, then predictive accuracy improves, but loss of information increases due to data processing requirements
Solution Approach 1:
The patent applies local quality by training specialized machine learning models for specific physiological domains (cardiovascular, metabolic, neurological, etc.), where each model is optimized to preserve and analyze the unique characteristics of its particular data type. This domain-specific approach improves predictive accuracy for each physiological system while minimizing information loss by maintaining appropriate data representations tailored to each biological parameter type rather than forcing uniform processing.
Data Source
AI summary
System and method for identifying enumerating cross-body degradations, the system comprising a computing device designed and configured to receive a biological extraction pertaining to a user, generate a first body degradation function, wherein generating the first body degradation function further includes mapping at least a biological extraction datum of the user to a current level of physiological integrity, determining an instantaneous rate of change of the current level of physiological integrity, and generating, as a function of the current level of physiological integrity and the instantaneous rate of change of the current level of physiological integrity, a first body degradation function, determine a second body degradation function, wherein the second body degradation function describes a rate of biological degradation that is statistically associated with the first body degradation function, and generate a body degradation packet as a function of the first body degradation function and the second body degradation function.