Cooking Appliance Parameter Adaptation for Cross-Recipe Compatibility
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Solution Overview
Problem
Existing cooking appliance operation methods restrict users to specific recipes and do not account for variations in cooking results across different appliances, requiring manual adaptation of parameters.
Innovation Solution
A method that utilizes a database with appliance-specific parameters, extracts cooking process parameters from recipe data, adapts them based on user input and appliance type, and employs machine learning to optimize settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users search for recipes online and apply them directly to their cooking appliance, then the recipe selection is expanded, but the cooking parameters may not be suitable for the specific appliance type leading to poor cooking results
Solution Approach 1:
The system automatically adjusts cooking parameters (temperature, time, power level) based on the appliance type detected from the user's device information. Recipe parameters are transformed to match the specific characteristics of different appliance models, ensuring reliable cooking results across multiple appliance types while maintaining broad recipe compatibility.
Solution Approach 2:
The cooking appliance acts as an intermediary between the recipe source and the actual cooking process. It receives recipe parameters, compares them against its own capabilities stored in the database, and automatically adapts the parameters before execution. This intermediary function ensures that recipes from any source can be reliably executed on any supported appliance type.
2Measurement precision
If the system stores extensive appliance-specific data in a database, then the accuracy of parameter adaptation is improved, but the system complexity increases
Solution Approach 1:
The database is segmented into distinct modules: appliance type identification data, heating method characteristics, and parameter conversion rules. This segmentation allows the system to query only relevant data portions based on the detected appliance type, improving adaptation accuracy while managing database complexity through organized, modular data structures.
Solution Approach 2:
The database is designed with a universal structure that can accommodate multiple appliance types and heating methods using a common data model. By creating a flexible schema that handles various appliance configurations through standardized fields and relationships, the system achieves high adaptation accuracy without proportionally increasing structural complexity.
3Ease of operation
If the system automatically adapts cooking parameters based on appliance type, then the ease of operation is improved, but the loss of information about original recipe parameters occurs
Solution Approach 1:
The system provides feedback to the user by displaying both the original recipe parameters and the automatically adapted parameters. This transparency allows users to understand what changes were made and why, preserving information about the original recipe intent while maintaining the ease of automatic adaptation. Users can review and, if needed, manually adjust the adapted parameters.
Data Source
AI summary
A method for operating a cooking appliance, comprising: (a) providing a database with operating parameters of the cooking appliance; (b) obtaining recipe data from a recipe source; (c) extracting cooking process parameters from the obtained recipe data; (d) determining adapted cooking parameters by evaluating the extracted cooking process parameters in light of data from the database, and optionally of settings made by a user; and (e) operating the cooking appliance based on the adapted cooking parameters.