ADME-Based Supplement Selection System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumers face challenges in accurately selecting supplements due to a lack of understanding about how these substances are metabolized and distributed in the body, which can lead to potentially harmful consequences.
Innovation Solution
A system and method that utilize a computing device to capture user data, generate inquiries about supplements, and apply ADME (Absorption, Distribution, Metabolism, Excretion) models through machine-learning algorithms to identify compatible supplements based on individual biological factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If consumers select supplements without understanding ADME factors, then supplement selection is simple and quick, but the accuracy and safety of supplement decisions deteriorates
Solution Approach 1:
The patent introduces an intermediary system (computing device with ADME analysis capabilities) that bridges the gap between consumers and supplement information. This intermediary automatically analyzes ADME factors, biological extractions, and supplement compatibility, providing accurate recommendations without requiring consumers to understand complex metabolic processes themselves.
Solution Approach 2:
The system enables self-service by allowing consumers to input their biological data and receive automated ADME-based supplement recommendations. The computing device performs the complex analysis independently, eliminating the need for consumers to manually evaluate metabolic compatibility while still achieving accurate supplement selection.
2Measurement precision
If comprehensive biological data is collected for ADME analysis, then supplement compatibility accuracy is improved, but data privacy and security risks increase
Solution Approach 1:
The patent extracts only the necessary biological data elements required for ADME analysis from comprehensive biological profiles. By selectively collecting and processing only relevant parameters (absorption, distribution, metabolism, excretion factors), the system maintains analysis accuracy while minimizing data exposure and associated security risks.
3Reliability
If ADME modeling is applied to each supplement decision, then supplement safety and effectiveness are improved, but computational resources and time consumption increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing biological extraction data and ADME parameters in accessible formats. When a supplement decision is needed, the system quickly retrieves and compares relevant pre-processed information, significantly reducing analysis time while maintaining comprehensive ADME evaluation for safety and effectiveness.
Data Source
AI summary
A system for identifying a longevity element to optimize supplement decisions is disclosed. The system includes a computing device configured to capture an identifier of a first longevity element using a data capturing device. The computing device is configured to receive a longevity inquiry from a remote device generating a longevity inquiry from the identifier, the longevity query identifying the first longevity element. The system retrieves a biological extraction pertaining to a user and identifies a longevity element associated with a user. The system selects an ADME model utilizing a biological extraction. The system generates a machine-learning algorithm utilizing the selected ADME model to input a longevity element associated with a user as an input and output an ADME factor. The system identifies a tolerant longevity element utilizing an ADME factor. A method for identifying a longevity element to optimize supplement decisions is also disclosed.