A microwave oven system, which includes: a microwave oven having a controllable power setting; a thermal camera at the microwave oven and which provides temperature data of one or more cooking items in the microwave oven; an optical camera at the microwave oven and which provides optical data of the one or more cooking items in the microwave oven; and a controller. A processor is configured to: receive the optical data, identify using a machine learning model, the one or more cooking items and their quantities at the microwave using the received optical data of the optical camera, access a recipe data bank, determine, using the recipe data bank, one or more steps for cooking of the one or more cooking items in the microwave oven.

In an embodiment, a method includes: transmitting radar signals; receiving reflected radar signals; transmitting radio-frequency signal (RF) signals with a first antenna site towards a second antenna site, where the first antenna site is disposed at an edge of a loading opening of a laundry machine, and where the second antenna site is disposed opposite to the first antenna site at the edge of the loading opening; receiving propagated RF signals with the second antenna site; determining a presence of a first object at the loading opening based on the reflected radar signals; determining a first property of the first object based on the reflected radar signals, and the propagated RF signals; and determining a first configuration of an operating cycle of the laundry machine based on the first property or alerting a user of the first property before the operating cycle of the laundry machine begins.

A washing machine appliance includes a wash basket that is rotatably mounted within a wash tub and that defines a wash chamber for receiving a load of clothes. A motor assembly is mechanically coupled to the wash basket for selectively rotating the wash basket and a camera assembly is mounted within the cabinet in view of the wash chamber. A controller is configured to obtain a series of image frames using the camera assembly during a wash cycle, identify a tracking point on a clothing article, generate a motion path of the tracking point over the series of image frames using an optical flow technique, estimate a turnover rate of the load of clothes based at least in part on the motion path of the clothing article, and implement responsive action in response to determining that the turnover rate falls below a predetermined threshold rate.

A method of controlling load volume in a laundry drying appliance where a load of wet laundry is place in a drum of the laundry drying appliance and the load volume of the wet laundry is sensed or determined according to the method. A condition of the drum of the laundry drying appliance is actively monitored and a determination is made if a proper load volume is present in laundry drying appliance.

A method may include receiving a two-dimensional image of a laundry chamber and identifying a laundry article based on the received two-dimensional image. The method may further include selecting a prior-user profile corresponding to the laundry appliance in response to identifying the laundry article and directing a found-article message to the prior-user profile following selecting the prior-user profile.

An artificial intelligent laundry treatment apparatus according to an embodiment of the present invention includes: a door including an external cover and an internal glass and configured to open and close a laundry entrance; a gasket formed on an inner circumferential surface of the laundry entrance; a door imaging sensor disposed to face the internal glass and configured to acquire a door image; a gasket imaging sensor configured to acquire a gasket image including a region of the gasket; and a processor configured to classify a state of the door on the basis of the door image, to acquire a gasket contamination degree on the basis of the gasket image, and to determine whether inside cleansing is required for a region including an inside of a drum on the basis of at least one of the classification result of the state of the door or the acquired gasket contamination degree.

The present disclosure relates to a dryer. A dryer according to an embodiment of the present disclosure includes a drum, at least one lifter formed to protrude from an inner peripheral surface of the drum, an anode disposed in the lifter to output a RF signal, and a RF output device configured to output the RF signal. Therefore, it is possible to uniformly dry laundry in the drum.

The present disclosure relates to a method and to a textile piece device which allows storing and/or monitoring the load on a textile piece during one or more treatment cycles.

Disclosed is a washing machine. The washing machine includes an image capturer, a storage, and a controller. When the washing machine is in a standby state, if photographed data is identified to be registered user data, the controller recognizes a photographed user as a registered user and performs control such that a door is opened. If the photographed data is identified to be child data, the controller performs control such that, when the door is in an open state, the door is not closed and such that, when the door is in a closed state, the door is not opened.

A laundry appliance includes a basket that is rotatably mounted within a cabinet and that defines a chamber for receiving a load of clothes. A camera assembly is mounted within the cabinet for taking images of the chamber and a controller obtains an image of the chamber when the basket is empty and not rotating. The image is analyzed using an autoencoder neural network process to determine an image quality and a responsive action is implemented when an image quality drops below a quality threshold.