AI Nutritional Needs Determination with Misreporting Weighting
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Solution Overview
Problem
The complexity and variability of nutritional data make it challenging to consistently determine individual nutritional needs and generate accurate supplementation plans, as existing systems struggle with subtle yet crucial factors that vary significantly between subjects.
Innovation Solution
A system utilizing artificial intelligence, including machine-learning processes, to receive biological extraction data, calculate nutritional needs, detect deficiencies, and determine supplement doses by weighting user-reported data with a misreporting factor, thereby generating a personalized supplementation plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional nutritional analysis systems are used, then the analysis process is simple, but the precision and reliability of determining individual nutritional needs deteriorates due to extreme complexity and variability of nutritional data
Solution Approach 1:
The patent introduces AI algorithms and machine learning models as intermediary components between raw nutritional data and analysis results. These intermediaries process the complex, variable nutritional data through multiple layers of computation, transforming unstructured data into precise nutritional need assessments while managing the inherent complexity of the analysis process.
Solution Approach 2:
The system dynamically adjusts analysis parameters and weighting factors based on individual user characteristics and data quality. By changing parameters such as misreporting factors, data weights, and model configuration based on the specific variability of each subject's nutritional data, the system achieves high precision without requiring a fixed complex structure.
2Reliability
If user-reported nutritional data is used directly, then the data collection is easy, but the reliability deteriorates due to user misreporting
Solution Approach 1:
The system implements feedback mechanisms where AI algorithms continuously evaluate the quality and consistency of user-reported data, calculate misreporting factors, and adjust the weighting of different data sources accordingly. This feedback loop maintains reliability by identifying and correcting potential misreporting while preserving the ease of direct user data entry.
Solution Approach 2:
An AI-based intermediary layer processes user-reported data before it is used for nutritional assessment. This intermediary validates, cleans, and weights the reported data based on multiple factors including consistency checks and misreporting probability, thereby maintaining data reliability without adding significant operational complexity for users.
3Measurement precision
If multiple factors and variables are considered in nutritional analysis, then the accuracy of supplementation plans improves, but the complexity of the analytical technique deteriorates
Solution Approach 1:
The patent segments the complex nutritional analysis into multiple independent processing stages: data collection, quality assessment, misreporting factor calculation, nutritional need determination, and supplementation plan generation. Each stage handles specific factors and variables independently, allowing the system to consider multiple variables for high accuracy while managing complexity through modular architecture.
Solution Approach 2:
The system dynamically changes parameters and weighting factors at each analytical stage based on the specific characteristics of the user's data and nutritional profile. By adjusting parameters such as data weights, misreporting factors, and model configurations, the system achieves high accuracy in supplementation plans without requiring a permanently complex analytical structure.
Data Source
AI summary
A system for determining a plurality of nutritional needs of a user and generating a nutrient supplementation plan using artificial intelligence includes at least a computing device designed and configured to receive, from a user, at least a biological extraction, generate, using the at least a biological extraction and a first machine-learning process, a plurality of nutritional needs of the user, determine a nutritional input to the user, detect at least a nutrition deficiency as a function of the plurality of nutritional needs and the nutritional input, and calculate at least a supplement dose from the plurality of nutritional needs and at the least a nutrition deficiency.