MULTI-PURPOSE SMART RICE COOKERS
First Claim
1. A rice cooker comprising:
- a chamber configured to hold multiple types of food;
a camera; and
a processing system configured to;
cause the camera to capture one or more images of the multiple types of food;
provide the captured images as an input to one or more machine learning models, the one or more machine learning models configured to classify a type of the multiple types of food; and
determine a mixture of the multiple types of food based on the classification, the mixture including at least two types of the multiple types of food.
1 Assignment
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Accused Products
Abstract
A rice cooker assembly uses machine learning models to identify and classify different types of food stored. The rice cooker has a chamber including different compartments for storing different types of food. A camera is positioned to view an interior of the chamber. The camera captures images of the contents of the chamber. From the images, the machine learning model classifies the different types of food stored. The rice cooker determines a mixture of different types of food based on nutrition value and/or taste. The rice cooker creates the mixture and controls the cooking process accordingly. The one or more machine learning models may be resident in the rice cooker or it may be accessed via a network.
7 Citations
21 Claims
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1. A rice cooker comprising:
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a chamber configured to hold multiple types of food; a camera; and a processing system configured to; cause the camera to capture one or more images of the multiple types of food; provide the captured images as an input to one or more machine learning models, the one or more machine learning models configured to classify a type of the multiple types of food; and determine a mixture of the multiple types of food based on the classification, the mixture including at least two types of the multiple types of food. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A computer-implemented method for controlling a rice cooker, comprising:
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capturing images of contents of a chamber over a period of time, the contents including multiple types of food; providing the captured images as an input to one or more machine learning models, the one or more machine learning models configured to classify each of the multiple types of food; and determining a mixture of the multiple types of food based on the classification, the mixture including at least two types of the multiple types of food. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21)
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Specification