Method and system for providing recipes for cooking
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Solution Overview
Problem
Existing cooking devices lack an efficient method to provide users with a vast array of creative and tailored recipes, often relying on limited downloadable options from the internet or social networks.
Innovation Solution
A method utilizing a trained recipe generator, which can be pre-trained and fine-tuned, to generate new recipes based on user triggers, such as voice requests or specific ingredient preferences, and adapt to user feedback and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If recipes are downloaded from the Internet or social networks, then the cooking device can provide pre-existing recipes to users, but the number of available recipes is limited
Solution Approach 1:
The system enables the cooking device to generate recipes autonomously using an AI model, eliminating the need to rely on externally downloaded recipes. The device serves itself by creating unlimited custom recipes based on user preferences, ingredients, and dietary requirements, thus resolving the contradiction between quantity and adaptability
Solution Approach 2:
The AI model dynamically adjusts recipe parameters such as ingredients, cooking methods, and flavor profiles based on user feedback and preferences. This allows the system to generate infinitely varied recipes tailored to individual users, transforming the static recipe database into a dynamic, adaptive system
2Productivity
If a trained recipe generator is used to generate creative recipes, then a vast number of recipes can be generated easily, but the system complexity increases
Solution Approach 1:
An AI language model acts as an intermediary between the user's simple input (e.g., desired ingredients or dietary preferences) and the complex task of recipe creation. The model handles the computational complexity internally while presenting simple, user-friendly interactions, thus maintaining high productivity without increasing perceived device complexity
Solution Approach 2:
The complex AI recipe generation functionality is extracted as a separate software module or service that can be integrated into the cooking device. This modular approach allows the core cooking device to remain relatively simple while incorporating advanced recipe generation capabilities through a dedicated component
3Adaptability or versatility
If the recipe generator is trained using thousands of recipes from the Internet, then the generator can provide suitable recipes for any taste and occasion, but data processing requirements increase
Solution Approach 1:
The AI model is pre-trained offline on extensive recipe databases before being deployed on the cooking device. This preliminary training phase, which requires significant computational resources, is performed separately using powerful servers or cloud infrastructure. Once trained, the model can operate efficiently on the device with minimal energy consumption, resolving the contradiction between comprehensive adaptability and low energy usage during operation
Data Source
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AI summary
The present invention relates to a method (M1, M2) for providing recipes (VR, TR) for cooking to a user (N1, N2, N3) of a cooking device (10), a related data processing program product, a control unit (1) of the cooking device, a related central entity (50) and a system (80). The method comprises the steps of providing a trained recipe generator and/or training a recipe generator (S1, S11); receiving a trigger, especially a request, a query or a prompt, from the user of the cooking device (U1, U11); generating, based on the trigger, a new recipe using the recipe generator (S3, U13); and providing the new recipe to the user (U4, U14).