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公开(公告)号：US20190313833A1
公开(公告)日：2019-10-17
申请号：US15770731
申请日：2018-04-16
发明人： Xiaochun Li , Linnan Zhu , Hua Zhou
摘要： 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.
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2.发明授权公开(公告)号：US11406121B2
公开(公告)日：2022-08-09
申请号：US15760841
申请日：2018-03-08
发明人： Dongyan Wang , Linnan Zhu , Xiaochun Li , Junyang Zhou
摘要： A rice cooker assembly uses machine learning models to identify and classify content in grain mixtures thereby to provide better automation of the cooking process. As one example, a rice cooker has a chamber storing grains. 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 determines whether the contents of the chamber includes one type or multiple types of grain or whether the contents of the chamber includes any inedible objects. The machine learning model further classifies the one or more types of grains and inedible objects if any. The cooking process may be controlled accordingly. The machine learning model may be resident in the rice cooker or it may be accessed via a network.
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公开(公告)号：US20190274337A1
公开(公告)日：2019-09-12
申请号：US15760841
申请日：2018-03-08
发明人： Dongyan Wang , Linnan Zhu , Xiaochun Li , Junyang Zhou
摘要： A rice cooker assembly uses machine learning models to identify and classify content in grain mixtures thereby to provide better automation of the cooking process. As one example, a rice cooker has a chamber storing grains. 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 determines whether the contents of the chamber includes one type or multiple types of grain or whether the contents of the chamber includes any inedible objects. The machine learning model further classifies the one or more types of grains and inedible objects if any. The cooking process may be controlled accordingly. The machine learning model may be resident in the rice cooker or it may be accessed via a network.
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公开(公告)号：US20190187634A1
公开(公告)日：2019-06-20
申请号：US15843580
申请日：2017-12-15
申请人： MIDEA GROUP CO., LTD
发明人： Yi Fan , Xiaochun Li
IPC分类号： G05B13/02 , G05B19/042 , F24F11/64 , F24F11/65 , F24F11/56
CPC分类号： G05B13/0265 , F24F11/56 , F24F11/64 , F24F11/65 , F24F2120/12 , F24F2120/20 , F24F2140/50 , F24F2140/60 , G05B19/0426 , G05B2219/2614 , G05B2219/2642 , H05B37/0218
摘要： Machine learning is used to control environmental systems for a building or other man-made structure. In one approach, environmental data is collected by sensors for an environment within the man-made structure. The environmental data is used as input to a machine learning model that predicts at least one attribute affecting control of the environment within the man-made structure. For example, the machine learning model might predict load on the environmental system, resource consumption by the environmental system, or cost of operating the environmental system. The environmental system for the man-made structure is controlled based on the predicted attribute.
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公开(公告)号：US20190187635A1
公开(公告)日：2019-06-20
申请号：US15844071
申请日：2017-12-15
申请人： MIDEA GROUP CO., LTD
发明人： Yi Fan , Xiaochun Li
CPC分类号： G05B13/0265 , F24F11/56 , F24F11/64 , F24F11/65 , F24F2120/12 , F24F2120/20 , F24F2140/50 , F24F2140/60 , G05B19/0426 , G05B2219/2614 , G05B2219/2642 , H05B37/0218
摘要： Machine learning is used to control environmental systems for a building or other man-made structure. In one approach, environmental data is collected by sensors for an environment within the man-made structure. The environmental data is used as input to a machine learning model that predicts at least one attribute affecting control of the environment within the man-made structure. For example, the machine learning model might predict load on the environmental system, resource consumption by the environmental system, or cost of operating the environmental system. The environmental system for the man-made structure is controlled based on the predicted attribute.
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公开(公告)号：US20190110638A1
公开(公告)日：2019-04-18
申请号：US15578677
申请日：2017-10-16
申请人： MIDEA GROUP CO., LTD
发明人： Xiaochun Li , Hua Zhou , Jianliang Ma , Yue Zhang
摘要： A cooking appliance uses machine learning models to provide better automation of the cooking process. As one example, a cooking appliance has a cook chamber in which food is placed for cooking. A camera is positioned to view an interior of the cook chamber. When food is placed inside the cook chamber, the camera captures images of the food. From the images, the machine learning model determines various attributes of the food, such as the type of food and/or the amount of food, and the cooking process is controlled accordingly. The machine learning model may be resident in the cooking appliance or it may be accessed via a network.
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