Ingredient Vector Matching for Personalized Menu Recommendations

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Solution Overview

Problem

Existing food recommendation systems fail to provide personalized suggestions that account for nuanced individual taste preferences, dietary restrictions, and ingredient compositions, complicating meal choices and limiting dining options.

Innovation Solution

A machine learning-based system that transforms sparse menu descriptions into comprehensive ingredient profiles, using vector-based matching technology to analyze user preferences and menu items, applying multi-stage filtering and real-time processing to deliver dish-level personalization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional restaurant reviews and broad categorizations are used for food recommendations, then the system is simple to operate, but the recommendation precision and personalization are insufficient

Engineering Contradiction:

Improverecommendation precisionVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the food recommendation system into multiple independent components: user profile module, menu item database, dietary restriction database, ingredient database, and recommendation engine. Each module handles specific data types and processing tasks, enabling precise recommendations while maintaining manageable system complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces multi-dimensional analysis by evaluating menu items across numerous attributes simultaneously (ingredients, dietary restrictions, allergens, flavor profiles, cooking methods). This dimensional expansion transforms simple rating systems into comprehensive recommendation capabilities that consider nuanced user preferences and requirements.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Measurement precision

If comprehensive ingredient analysis and multi-stage filtering are applied, then the recommendation accuracy improves, but the processing time increases

Engineering Contradiction:

Improvedietary matching accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary processing by pre-tagging all menu items with comprehensive metadata (ingredients, dietary restrictions, allergens, nutritional information) and pre-processing user profiles with their preferences and restrictions. This advance preparation enables rapid real-time matching without repetitive analysis, achieving both accuracy and speed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical filtering approaches with machine learning algorithms and vector-based similarity matching. The recommendation engine uses computational models to rapidly compare user profiles against menu items, substituting complex multi-stage manual filtering with efficient algorithmic processing that maintains accuracy while reducing time.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If the system analyzes multiple data sources including user preferences, dietary restrictions, and ingredient compositions, then the personalization quality improves, but the data processing complexity increases

Engineering Contradiction:

Improvepersonalization capabilityVSAvoiddata processing complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent creates a universal data structure and processing framework that handles multiple data types (user preferences, dietary restrictions, ingredient compositions, allergen information) through a single integrated recommendation engine. This multi-functional approach enables personalized recommendations across diverse data sources without requiring separate processing systems for each data type.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The patent transforms diverse data sources into standardized parameters and features that the recommendation engine can process uniformly. User preferences, dietary restrictions, and ingredient compositions are converted into comparable vectors and attributes, enabling the system to analyze and integrate multiple data sources through consistent parameter transformations rather than complex heterogeneous processing.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260050836A1Systems, methods, and devices for personalized food recommendation using machine learning

Publication Date: 2026.02.19 FLAVORHAWK INC

AI summary

A method for generating food recommendations includes obtaining food item files from online sources. Ingredients are identified from the food items. The food item files are tagged with metadata identifying the ingredients. A first set of vectors is generated that represent values for each of the food items and the ingredients. Food preferences are received from a user input. Ingredients are identified from the user input food preferences. A second set of vectors is generated that represent the user preferences based on the user input. The first set of vectors are compared to the second set of vectors. Vectors are selected from the first set of vectors based on similarity scores between the vector sets that meet or exceed a similarity threshold. Menu items are identified with food items that match the selected vectors. The identified menu items are provided as a recommendation.