Cognitive Recipe Modification for Chromatic Identity Prediction
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Solution Overview
Problem
Current cooking applications and recipe-based computing systems lack the cognitive capabilities to adequately evaluate the complex interactions of ingredients and determine proper quantities and preparation methods for modifying recipes to achieve specific colors in food dishes, especially when replacing ingredients significantly different from the original recipe.
Innovation Solution
A cognitive system that identifies and learns the color contributions of each ingredient based on its introduction, quantity, action, and timing in a recipe, using support vector machines and machine learning algorithms to predict and modify the color profile of a dish by selecting substitution candidates that pair best with other ingredients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning algorithms are used to predict and modify color profiles of dishes, then the ability to achieve desired colors is improved, but the system complexity increases
Solution Approach 1:
The system segments the recipe modification task into distinct components: color prediction module, substitution candidate identification module, and recipe modification module. Each component handles a specific aspect of the problem, making the overall complex system manageable and maintainable while achieving accurate color prediction through specialized machine learning models
Solution Approach 2:
The patent introduces an intermediary color prediction model that acts as a bridge between ingredient composition and final dish color. This intermediary layer processes ingredient data through trained machine learning algorithms to predict color outcomes, enabling the system to handle color prediction without requiring direct complex physical chemistry calculations
2Manufacturing precision
If the system evaluates complex interactions of ingredients to determine proper quantities and preparation methods, then the recipe modification accuracy is improved, but the computational time increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive datasets of ingredient combinations and their color outcomes. This pre-processing allows the system to make accurate predictions during actual recipe modification without performing exhaustive real-time calculations, significantly reducing computational time while maintaining high accuracy
Solution Approach 2:
The patent transforms the complex multi-parameter recipe evaluation problem into a more manageable form by using machine learning models that have learned optimal parameter relationships during training. The system changes the approach from explicit calculation of all ingredient interactions to using learned parameter mappings that capture complex relationships efficiently
Data Source
AI summary
A mechanism is provided for modifying an existing recipe to meet a set of desired colors for a final food dish. Responsive to receiving a request to modify the existing recipe to meet the set of desired colors, at least one of the set of existing colors to be changed to meet the desired set of colors is identified. An ingredient-action-sequence triplet associated with each at least one existing color to be changed is identified and, from a corpus of ingredient-action-sequence triplets associated with other existing recipes, one or more substitution candidates that can produce the at least one target color are identified. The one or more substitution candidates are ranked based on how each candidate pairs best with other ingredients in the existing recipe. Based on a selection of a substitution candidate from the one or more substitution candidates, the existing recipe is modified with the substitute candidate.