AI Dietary Instruction System for Personalized Meal Planning
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Solution Overview
Problem
The complexity of data in generating instruction sets poses challenges, leading to inaccurate results, and existing solutions fail to combine reliable and accurate information effectively.
Innovation Solution
A system and method using artificial intelligence that includes a processor and memory to receive training data, record expanded biological extraction data, and generate a dietary instruction set using machine-learning algorithms, incorporating physiological state data, prognostic labels, and ameliorative process labels to create a structured meal plan tailored to individual users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing solutions are used to generate instruction sets, then the process is simpler, but the accuracy and reliability of results deteriorate due to data complexity
Solution Approach 1:
The system segments the complex data analysis process into distinct functional modules: data collection module, data processing module, machine learning model module, and instruction generation module. Each module handles specific aspects of the analysis, making the overall complex process manageable and improving accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces trained machine learning models as intermediary components between raw biological extraction data and final instruction sets. These models act as mediators that process complex data through learned patterns, transforming difficult-to-analyze biological data into reliable instructional outputs without requiring direct complex analysis by the system.
2Reliability
If more comprehensive training data is collected to improve accuracy, then the reliability of diagnostic outputs improves, but the time and resources required for training increase
Solution Approach 1:
The system performs preliminary actions by collecting and preparing comprehensive training data in advance, including diverse biological extraction data from multiple sources. The machine learning models are pre-trained on this extensive dataset before deployment, so that when the system operates, the training work is already completed, reducing real-time processing time while maintaining high reliability.
Solution Approach 2:
The system implements continuous improvement of training data and model performance through ongoing data collection and iterative training. As more data becomes available, the models continue to learn and improve, maintaining reliability over time while the training process operates continuously in the background rather than interrupting service.
Data Source
AI summary
A system for generating a dietary instruction set using artificial intelligence and a method related thereto include a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive training data, record expanded biological extraction data from a first user, the expanded biological extraction data including physiological state data and at least a user behavior, generate a diagnostic output based on the expanded biological extraction data using at least a machine-learning algorithm iteratively trained as a function of the training data, and generate a dietary instruction set associated with the user as a function of the expanded biological extraction data and the diagnostic output, the dietary instruction set including at least structured meal plan.