Alimentary Scheduling System Using Nutrient Distance Metrics
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Solution Overview
Problem
Existing solutions for selecting alimentary provisioning based on physiological dictates often limit sources or selections, leading to frustration and under-utilization, as they fail to effectively manage the multiplicity of possible solutions.
Innovation Solution
A system and method that utilize a computing device to provide an alimentary instruction set, determine per-meal nutrient requirements, receive and rank ingredient combinations from multiple providers based on nutritional matching, and allow user selection to generate a personalized list of ingredient combinations that minimize nutritional distance, enabling iterative refinement of meal schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple alimentary provider devices and ingredient combinations are considered, then the comprehensiveness and personalization of meal planning is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of meal planning into distinct components: receiving ingredient combinations from multiple providers, determining nutrient listings for each combination, calculating distance metrics against target nutrients, and generating ranked lists. This segmentation allows each component to be processed independently, managing system complexity while maintaining comprehensive personalization capabilities.
Solution Approach 2:
The system introduces a new dimension of nutritional analysis by calculating distance metrics between ingredient combinations and target nutrient profiles. This dimensional approach transforms the selection problem from simple provider comparison to multi-dimensional nutritional optimization, enabling personalized meal planning across multiple nutrient parameters simultaneously.
2Measurement precision
If nutrient distance metrics are calculated for all provider ingredient combinations, then the precision of nutritional matching is improved, but the computational time and processing resources increase
Solution Approach 1:
The system performs preliminary actions by receiving and storing ingredient combinations from all alimentary providers before the actual meal selection process. Nutrient listings are determined in advance for all combinations, and distance metrics are pre-calculated against target nutrients. This preliminary processing organizes data structureally, enabling faster retrieval and ranking when users need meal recommendations, thus reducing real-time computational time while maintaining high precision.
Data Source
AI summary
A system for scheduling alimentary combinations includes a computing device configured to provide an alimentary instruction set including a plurality of target nutrient quantities corresponding to a plurality of scheduled meals, determine a per-meal 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 ingredient combinations as a function of the plurality of provider ingredient combinations, receive a user selection of a provider ingredient combination corresponding to a meal of the plurality of scheduled meals, and generate a modified ranked list of ingredient combinations as a function of the user selection and the alimentary instruction set.