Cooking Device Image Recognition for Automatic Program Selection
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Solution Overview
Problem
Premium cooking devices require complex user interactions and limited automatic programs, leading to time-consuming and error-prone manual input processes for cooking various dishes, especially when preparing foods not available as automated options.
Innovation Solution
A method using machine learning-based visual object recognition to identify food through recorded images, selecting an appropriate cooking program based on probability thresholds, and continuously improving the recognition algorithm through user feedback, allowing for reduced user interaction and improved cooking results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic cooking programs are provided for various dishes, then cooking quality is improved, but device complexity and user interaction steps increase
Solution Approach 1:
The cooking device automatically identifies the food item using image recognition technology and selects the appropriate cooking program without requiring the user to manually input food information or navigate through menu structures. The system serves itself by autonomously determining cooking parameters based on the recorded image of the food.
Solution Approach 2:
The patent replaces the mechanical interaction system (manual menu navigation and input steps) with an optical recognition system (image recording and machine learning-based food identification). This substitution eliminates the need for users to physically interact with complex menu structures while maintaining high cooking quality.
2Adaptability or versatility
If manual input is required for dishes not available as automatic programs, then cooking flexibility is improved, but time consumption and error probability increase
Solution Approach 1:
The system automatically handles the entire process of food identification and cooking program selection, eliminating the need for users to manually research cooking information or input multiple parameters. The device independently determines the appropriate cooking settings based on image recognition, thereby reducing both time consumption and potential for user error while maintaining cooking flexibility.
3Device complexity
If standardization of cooking programs is implemented, then device complexity is reduced, but adaptability to individual foods decreases
Solution Approach 1:
The patent implements a dynamic system where the cooking device adapts its program selection based on real-time image recognition results. Rather than relying on fixed, pre-defined categories, the system dynamically identifies specific food items and selects or creates appropriate cooking programs, thereby maintaining program structure simplicity while achieving high adaptability to individual foods.
Solution Approach 2:
The system changes the fundamental parameter for program selection from manual user input to automated image-based identification. This parameter change enables the device to maintain a standardized program structure while simultaneously adapting to a wide variety of individual food items through automatic recognition and parameter adjustment.
Data Source
AI summary
A method for controlling a cooking device includes the following steps: recording an image of food; implementing a recognition of the recorded image of the food to determine the likelihood that the food corresponds to known foods. If a probability value exceeds a default threshold value, a cooking program associated with the known food is selected and specified to the cooking device. The probability value for the recorded image and the associated food is increased in response to a user subsequently starting the selected cooking program, and reduced in response to the user discarding the selected cooking program. A cooking device includes a primary control unit for controlling the cooking functions; a network interface with a second control unit, and a camera for recording images of the cooking space. The second control unit processes recorded images independently of the primary control unit and transmits them over the network interface.