Machine Learning Nutritional Calculator for Meal Selection
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Solution Overview
Problem
Selecting meal options that meet nutritional requirements can be challenging due to the overwhelming number of choices, making informed decisions difficult.
Innovation Solution
A system and method using a computing device to initiate a display interface, retrieve input credentials, generate a training set, calculate nutritional requirements of meal options using machine-learning processes, and display these requirements within the interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If numerous meal options are provided to users, then choice variety is improved, but decision-making difficulty increases
Solution Approach 1:
The patent introduces an intermediary system (computing device with machine learning model) that mediates between the numerous meal options and the user. This intermediary automatically calculates nutritional requirements, compares meal options against nutritional targets, and presents ranked recommendations, thereby resolving the contradiction by filtering complexity while preserving variety.
Solution Approach 2:
The patent replaces the mechanical/manual process of nutritional calculation and meal evaluation with an automated machine learning system. The ML model automatically processes meal composition data, calculates nutritional content, and generates recommendations without requiring manual user computation, thus improving ease of operation while maintaining extensive meal option variety.
2Measurement precision
If manual nutritional calculation is performed for each meal option, then measurement precision is improved, but time consumption increases
Solution Approach 1:
The patent implements preliminary action by pre-training the machine learning model with extensive nutritional data and meal composition information before actual use. The model is预先 prepared with knowledge of nutritional requirements, meal components, and calculation methodologies, enabling rapid and accurate nutritional assessment without time-consuming manual calculations during meal selection.
Solution Approach 2:
The patent substitutes manual nutritional calculation mechanics with an automated machine learning system. The ML model performs nutritional computations instantaneously by processing meal input data through trained algorithms, achieving both high measurement precision and rapid calculation speed, thereby resolving the contradiction between accuracy and time consumption.
3Adaptability or versatility
If personalized nutritional requirements are calculated for each user profile, then adaptability is improved, but system complexity increases
Solution Approach 1:
The patent applies universality by designing a single machine learning model that handles multiple functions: it processes diverse user profiles (age, gender, activity level, dietary restrictions), calculates various nutritional requirements (calories, macronutrients, micronutrients), and evaluates different meal types. This multi-functional approach achieves high personalization capability while avoiding the complexity of separate specialized systems for each function.
Data Source
AI summary
A system for calculating nutritional requirements in a display interface the system including a computing device configured to initiate a display interface within the computing device; retrieve an input, including an input credential, and wherein the input relates a representative profile to a nutritional requirement; generate a training set using the input; receive a meal option; calculate using a machine-learning process, a nutritional requirement of the meal option using the training set; determine the nutritional requirement of the meal option as a function of the machine-learning process; and display the nutritional requirement within the display interface.