Edible Score Calculation Using Machine Learning
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Solution Overview
Problem
Users face challenges in determining the nutritional impact of food choices due to overwhelming conflicting information, lacking personalized and effective methods to assess how specific foods affect their health.
Innovation Solution
A system and method that utilize a computing device to calculate an edible score by integrating a user's performance profile and nourishment information through a machine-learning process, training with edible training data to output a score reflecting the nutritional impact, and display this score within a user interface, along with a cost-to-score ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users rely on traditional nutritional information sources, then they receive general food data, but they cannot obtain personalized and effective assessment of how specific foods affect their individual health
Solution Approach 1:
The system performs preliminary actions by collecting user-specific data (performance profile, health metrics, dietary preferences) before making food recommendations. The machine learning model is pre-trained with extensive nutritional data and health outcomes, enabling it to immediately provide personalized assessments when a user queries about a specific food item, rather than requiring real-time data collection during the interaction.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user responses to food recommendations, tracking actual health outcomes, and using this information to refine future personalized assessments. The model learns from user feedback loops, adjusting its predictions about how specific foods will affect individual users based on observed patterns in their health data and preferences.
2Loss of information
If comprehensive nourishment information is provided to users, then they can make informed decisions, but they become overloaded with conflicting information
Solution Approach 1:
The system extracts only the most relevant and actionable nutritional information from comprehensive data sources, filtering out conflicting or less significant details. The machine learning model identifies and extracts key nutritional parameters that matter most to the user's specific health goals and performance profile, presenting a curated subset of information rather than overwhelming users with all available data.
Solution Approach 2:
The system dynamically changes the parameters of information presentation based on user preferences, health goals, and the specific food being analyzed. Rather than providing static comprehensive nutritional data, the model adapts which nutritional parameters are highlighted and how they are weighted, transforming the same comprehensive database into customized information sets for different users and contexts.
3Measurement precision
If a machine-learning process is trained with extensive edible training data to improve score accuracy, then the edible score becomes more reliable, but the computational complexity and processing time increase
Solution Approach 1:
The machine learning model performs preliminary training with extensive edible training data in advance, building a pre-trained repository of nutritional patterns and health outcome correlations. This pre-training phase consolidates computational efforts before actual use, allowing the model to quickly apply learned patterns to new food queries without requiring extensive real-time computation, thus achieving both high accuracy and fast response times.
Data Source
AI summary
A system for calculating an edible score in a display interface, including a computing device configured to initiate, a display interface; retrieve, a performance profile relating to a user; determine, an edible of interest; receive, nourishment information relating to the edible of interest; generate, a score machine-learning process to output an edible score; and display, the edible score within the display interface.