Symptomatic Food Preference Menu System
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Solution Overview
Problem
Existing methods fail to effectively integrate food preferences with symptomatic inputs to recommend food elements that minimize symptomatic exacerbation or alleviation, lacking a systematic approach to personalize dietary suggestions based on user-specific data.
Innovation Solution
A system and method utilizing a computing device to retrieve a user's food profile, select food elements, create a food preference menu, and modify it based on entries from a symptomatic database, incorporating machine-learning processes to identify symptomatic neutralizers and rank food elements according to their impact on user symptoms, while considering genetic, social, and prior food preference data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a systematic approach integrates food preferences with symptomatic inputs to personalize dietary recommendations, then the effectiveness of food element selection in minimizing symptomatic exacerbation is improved, but the device complexity increases
Solution Approach 1:
The system segments the complex task of dietary recommendation into distinct functional modules: a food profile module that stores user-specific food preferences and characteristics, a symptomatic database module that contains symptom-food relationships, and a processing module that integrates these data sources. This segmentation allows each module to handle specific aspects of the problem independently, improving overall system reliability while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces a computing device as an intermediary that mediates between the food profile data, symptomatic database entries, and the final food element recommendations. This intermediary processes and integrates information from multiple sources, applying algorithms to determine optimal food selections that minimize symptomatic exacerbation. The intermediary handles the complexity of data integration and presentation, shielding users from system complexity while delivering reliable personalized recommendations.
2Measurement precision
If machine-learning processes are used to identify symptomatic neutralizers and rank food elements, then the precision of food recommendations is improved, but the loss of time in processing and analyzing data increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing food profile data and symptomatic database entries before they are needed for recommendations. Food elements are pre-ranked and categorized based on their potential impact on symptoms, and the machine-learning models are pre-trained on historical data. This preliminary preparation reduces the computational burden during actual recommendation generation, maintaining high precision while minimizing real-time processing time.
Solution Approach 2:
The patent applies partial action by focusing machine-learning processing on the most relevant food elements and symptom relationships rather than analyzing all possible combinations. The system identifies and prioritizes key food elements that have the greatest impact on user symptoms, applying complex analysis only where needed. This selective approach maintains recommendation precision for critical food items while reducing overall processing time by avoiding exhaustive analysis of less relevant data.
Data Source
AI summary
A system for ordered food preferences accompanying symptomatic inputs, the system including a computing device, the computing device designed and configured to retrieve a food profile pertaining to a user; select a first food element as a function of the food profile; select a second food element as a function of the first food element; create a food preference menu wherein the food preference menu contains the first food element and the second food element; and modify the food preference menu as a function of an entry contained within a symptomatic database.