Machine Learning Nutrient Ranking System
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Solution Overview
Problem
Current alimentary design systems do not optimize nutritional delivery based on the physiological state of an individual, leading to inefficient nutrition delivery.
Innovation Solution
A machine-learning based system that provides a nutrient instruction set tailored to specific user afflictions, ranks alimentary combinations by minimizing the distance to target nutrient quantities, and generates a modified ranked list based on user-specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current alimentary design systems are used, then the system is simple to operate, but nutritional delivery is not optimized based on individual physiological state
Solution Approach 1:
The patent replaces traditional rule-based alimentary design systems with a machine learning-based system that uses neural networks to optimize nutritional delivery. The system processes user physiological data and affliiction information through machine learning models to generate personalized alimentary combinations, substituting simple mechanical rules with intelligent automated decision-making.
Solution Approach 2:
The system dynamically adjusts alimentary combinations based on changing parameters such as user affliiction state, physiological data, and nutrient requirements. The machine learning model continuously optimizes nutrient quantities and food selections based on real-time parameter changes, enabling adaptive nutritional delivery that responds to individual needs.
2Reliability
If personalized nutrient delivery is implemented, then nutritional efficacy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user physiological data, affliiction history, and nutrient requirement profiles before actual alimentary combination generation. The machine learning model is pre-trained on extensive datasets, enabling rapid inference when generating personalized recommendations without requiring time-consuming real-time analysis.
Solution Approach 2:
The system creates copies of optimized alimentary combinations from training data and pre-computed models. Instead of calculating optimal combinations from scratch for each user, the system retrieves and adapts pre-computedalimentary patterns that match user profiles, significantly reducing processing time while maintaining nutritional efficacy.
3Productivity
If machine learning-based optimization is applied, then nutrient delivery is optimized, but system complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components between raw user data and final alimentary recommendations. The neural networks serve as mediators that process complex physiological data and affliiction information, transforming them into optimized nutrient delivery plans without requiring direct complex calculations in the final system architecture.
Solution Approach 2:
The system implements self-service through automated machine learning processes that independently optimize alimentary combinations without requiring manual intervention. The machine learning models automatically adapt to user data, generate personalized recommendations, and adjust nutrient delivery strategies, reducing the need for human expertise and simplifying operational complexity.
Data Source
AI summary
A system for ranking alimentary combinations includes a computing device configured to provide a nutrient instruction set including a plurality of target nutrient quantities corresponding to a plurality of candidate alimentary combinations, determine a per-combination alimentary instruction set as a function of the plurality of target nutrient quantities, receive, from each alimentary provider device of a plurality of alimentary provider devices, a plurality of provider ingredient combinations, generate a ranked list of alimentary combinations as a function of the plurality of provider alimentary combinations, receive a user selection of a candidate alimentary combination corresponding to an edible of the plurality of candidate alimentary combinations, and generate a modified ranked list of alimentary combinations as a function of the user selection and the nutrient instruction set.