Neuromorphic Food Preference Analysis Using Hyperspectral Input
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Solution Overview
Problem
Current methods for determining individualized culinary preferences fail to comprehensively consider health conditions, allergies, and personal preferences, often resulting in unpleasant eating experiences and lack of specific recommendations for meal preparation.
Innovation Solution
A deep learning-based system that utilizes hyperspectral neuromorphic computing to analyze somatic and cognitive responses to various food samples, associating ingredients and preparation techniques with user preferences through unsupervised and supervised learning, optimizing ingredient and technique selection for personalized meal preparation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional methods are used to determine food preferences, then the system is simple and easy to operate, but it cannot comprehensively consider health conditions, allergies, and personal preferences
Solution Approach 1:
The system segments the food preference determination process into multiple independent modules: health condition analysis module, allergy detection module, personal preference analysis module, and meal recommendation module. Each module processes specific input data and contributes to the overall recommendation, allowing comprehensive consideration of multiple factors while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system employs a universal deep learning framework that can handle multiple types of input data (health conditions, allergies, personal preferences) and generate comprehensive meal recommendations. The same core algorithm processes different input formats and produces tailored recommendations, making the system adaptable to various user needs without requiring separate specialized systems for each function.
2Measurement precision
If comprehensive data collection on user responses is implemented, then preference determination accuracy is improved, but data processing complexity and time increase
Solution Approach 1:
The system performs preliminary action by pre-processing and storing user response data in structured formats before actual preference determination. Historical data is pre-organized in databases with appropriate schemas, and feature extraction algorithms are pre-trained, enabling rapid query processing during actual meal recommendations without re-processing all historical data from scratch.
Solution Approach 2:
The system extracts only the relevant features and information needed for preference determination from the comprehensive collected data. Through feature selection and dimensionality reduction techniques, the system identifies and processes only the critical patterns and relationships, filtering out redundant information and reducing processing time while maintaining high accuracy in preference determination.
Data Source
AI summary
One or more processors receive hyperspectral band input, biometric input, and cognitive input as response input, from a user sampling a plurality of base foods, each base food prepared with a subset of ingredients and preparation techniques. The response input is transformed to a numeric representation of the respective input. Deep learning techniques are used to train an algorithm using the response data. A probabilistic ranking of base food is generated using unsupervised learning. Probability values of base food, ingredients, and preparation technique, associations preferred by the user, are generated, along with rules which define constraints associated with conditions for base food, ingredient, and preparation techniques, of user preferences. An objective function is generated that includes decision variables respectively aligned with constraints, and in response to optimizing the objective function, a preferred base food and ingredients, with preferred conditions of the user, is determined.