ML Body Measurement for Compatible Substance
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Solution Overview
Problem
Accurate assessment of compatibility between substances is challenging due to the vast number of factors involved, leading to potential misidentification and prolonged illness.
Innovation Solution
A system utilizing a camera and machine learning models to capture images, determine body measurements, and identify compatible substances based on biomarker data, including tissue samples and bodily fluid analyses, to provide informed recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to assess substance compatibility, then the process is simpler, but the accuracy and reliability of the assessment deteriorates due to the vast number of factors that cannot be adequately considered
Solution Approach 1:
The system segments the complex compatibility assessment task into distinct functional modules: image capture module, machine learning model processing module, biomarker analysis module, and recommendation generation module. Each module handles specific aspects of the assessment, making the overall complex system manageable and maintainable while achieving high accuracy through specialized processing in each segment.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between raw input data (images and biomarker data) and compatibility assessment results. This intermediary layer processes and transforms the data, enabling accurate assessment of multiple factors simultaneously without requiring direct complex interactions between all assessment parameters.
2Productivity
If manual analysis of compatibility factors is performed, then the system is easier to operate, but the time required for assessment increases and productivity decreases
Solution Approach 1:
The system implements self-service through automated machine learning models that independently process images and biomarker data without human intervention. The models automatically extract features, perform analysis, and generate compatibility assessments, enabling the system to serve itself in the assessment process and dramatically increasing productivity while maintaining high automation levels.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated machine learning-based electronic processing. Instead of human experts manually evaluating compatibility factors, the system uses trained ML models to process data electronically, substituting mechanical human cognition with automated computational processes that are faster and more consistent.
3Reliability
If comprehensive biomarker analysis is performed to ensure accurate compatibility determination, then the reliability improves, but the complexity of data processing and analysis increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on extensive datasets before actual compatibility assessments. The models are prepared in advance with learned patterns and relationships, so that during actual use, they can reliably process biomarker data without requiring complex real-time analysis, thus maintaining high reliability while reducing operational complexity.
Data Source
AI summary
Described herein are systems and methods for determining a compatible substance. In some embodiments, a system may include a camera; a user interface; and a computing device configured to, using the camera, capture a first image; generate a first body measurement by training a body measurement machine learning model on a training dataset including a plurality of example images correlated to a plurality of example body measurements; and generating the first body measurement as a function of the first image using the trained body measurement machine learning model; determine a first compatible substance as a function of the first body measurement; and using the user interface, display the first compatible substance.