Personalized Food Plan Generation with Ingredient Substitution
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Solution Overview
Problem
Amateur food preparers face difficulties in following recipes due to ingredient unavailability, cost constraints, and lack of specialized knowledge on ingredient substitution, leading to inconsistent and complex heuristic solutions.
Innovation Solution
A method and system for generating personalized food plans that include determining food substitution parameters, user preferences, and fulfillment parameters, using a recipe database and machine learning models to recommend ingredients and recipes, while considering availability and cost, and integrating inputs from health professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple heuristics are used for ingredient substitution, then ease of operation is improved, but reliability deteriorates due to complex considerations and frequent failures
Solution Approach 1:
The patent introduces an automated ingredient substitution system that acts as an intermediary between the recipe requirements and the amateur food preparer. This system uses machine learning models trained on expert culinary knowledge to automatically determine appropriate substitutions, eliminating the need for amateurs to manually navigate complex substitution heuristics while ensuring reliable, expert-level decisions.
Solution Approach 2:
The patent replaces the manual mechanical process of applying substitution heuristics with an automated computational system. Machine learning models process ingredient relationships and generate substitution recommendations automatically, replacing the need for human experts to manually evaluate and apply complex substitution rules.
2Reliability
If detailed substitution information is sought from multiple sources, then reliability is improved, but loss of time increases due to extensive searching
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models on extensive culinary knowledge and ingredient substitution data before actual use. This pre-processing of information allows the system to provide accurate substitution recommendations instantly during recipe preparation, eliminating the need for users to search multiple sources at the moment of need.
Solution Approach 2:
The patent creates a computational copy of expert culinary knowledge and substitution expertise within the machine learning system. This digital copy encapsulates years of expert experience and numerous substitution rules, allowing instant access to reliable substitution information without requiring users to consult multiple physical or online sources.
3Ease of operation
If ingredient substitution is attempted without specialized knowledge, then ease of operation is improved, but loss of information occurs due to inconsistent substitution guidelines
Solution Approach 1:
The patent transforms unstructured culinary knowledge and substitution guidelines into structured parameters and features that machine learning models can process. By converting expert knowledge into quantifiable parameters (ingredient properties, flavor profiles, functional characteristics), the system maintains consistency while remaining accessible to users without specialized knowledge.
Solution Approach 2:
The patent segments the complex task of ingredient substitution into distinct components: ingredient identification, property analysis, substitution candidate generation, and recommendation ranking. This segmentation allows the system to handle each aspect systematically, ensuring consistent application of substitution principles while maintaining ease of use for amateur preparers.
Data Source
AI summary
Systems and methods for improving food-related personalization for a user including generating a recipe database including a set of recipe data structures; deriving a recipe vector representation of the recipe data structures; determining a set of user food preferences; extracting a set of recipe vector constraints from the set of user food preferences; determining a personalized food plan for the user, including automatically selecting a subset of the set of recipe data structures associated with recipe vector representations that satisfy the set of recipe vector constraints; determining fulfillment parameters for grocery items associated with the personalized food plan; and automatically facilitating fulfillment of grocery items associated with the personalized food plan based on the fulfillment parameters.