Alimentary Provisioning System Using Biological Data Filtering
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Solution Overview
Problem
Existing solutions for alimentary provisioning often limit the selection of nutritional options, leading to frustration and under-utilization, as they fail to effectively utilize the multiplicity of possible solutions based on physiological dictates.
Innovation Solution
A system and method that utilize a computing device to record biological data from users, generate alimentary instruction sets, and select beneficial ingredient combinations by filtering and combining ingredients based on nutritional requirements and user goals, using machine learning and distance metrics to optimize nutritional matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing solutions limit sources or possible selections for alimentary provisioning, then the selection process becomes simpler, but user satisfaction and utilization decrease due to frustration from limited options
Solution Approach 1:
The system segments the complex selection process into multiple filtering stages. It first generates numerous possible ingredient combinations based on physiological data, then systematically filters these combinations through multiple criteria (nutritional requirements, user preferences, availability) to arrive at optimal selections. This segmentation allows the system to handle complexity by breaking it down into manageable steps.
Solution Approach 2:
The system dynamically changes filtering parameters based on user-specific physiological data, preferences, and contextual information. By adjusting parameters such as nutritional requirements, dietary restrictions, and preference weights for different ingredients, the system personalizes the selection process while maintaining the ability to consider multiple possible solutions throughout the filtering process.
2Device complexity
If existing solutions limit the number of ingredient combinations considered, then computational complexity decreases, but the quality of nutritional matching deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing ingredient data, nutritional information, and user profile data before the actual selection process. It pre-calculates nutritional profiles for all possible combinations and organizes them in a structured manner, enabling efficient filtering and selection without requiring complex real-time computations during the actual provisioning decision.
Solution Approach 2:
The system incorporates feedback mechanisms where user responses, physiological data updates, and nutritional analysis results are continuously fed back into the selection process. This allows the system to refine its nutritional matching accuracy iteratively, adjusting ingredient combinations based on actual user needs and outcomes rather than relying solely on static pre-computed results.
Data Source
AI summary
A system for alimentary provisioning may include a computing device configured to record at least a biological extraction from a user; generate an alimentary instruction set for the user as a function of the at least a biological extraction; receive a goal parameter from the user; generate a plurality of ingredient combinations, wherein each ingredient combination is a combination of at least two ingredients of a plurality of ingredients; filter the plurality of ingredient combinations according to the goal parameter; and select a plurality of beneficial ingredient combinations for the user from the plurality of ingredient combinations, wherein the plurality of beneficial ingredient combinations is selected as a function of the alimentary instruction set.