A laundry treatment device includes a washing module configured to perform operation related to washing, a camera configured to capture an image of a tag attached to a laundry, and a processor configured to acquire laundry information of a plurality of laundries, to convert the acquired laundry information into encoding data, and to acquire values of laundry control variables corresponding to the converted encoding data based on a laundry course learning model learned using a plurality of reference data through a deep learning algorithm.

A clothes drying device, including an air channel, an air path switching assembly, multiple clothes drying apparatuses, and multiple heating apparatuses. The air path switching assembly includes a first multi-position switching component and a second multi-position switching component. The first multi-position switching component and the second multi-position switching component are respectively provided between a hot air input section and intermediate heat source sections and between a hot air output section and the intermediate heat source sections; the first multi-position switching component and the second multi-position switching component have the same number of switching positions and are configured to work in conjunction to each other so that at least one clothes drying apparatus is selectively communicated with the corresponding hot air input section, the hot air output section, and the intermediate heat source sections so as to form a circulation loop.

In particular, a method performed by one or more devices is disclosed, the method comprising: obtaining a first intensity information item (210) representative of a spectral image (208) resulting from an soiling (202, 302) of a textile (200, 304); obtaining a second intensity information item (212, 214) representative of a spectral image (216) characteristic of at least one property of at least one part of the textile (200, 304); determining at least one treatment parameter, wherein the determination of the treatment parameter takes place dependent both on the composition of the soiling (202, 302) from the first intensity information item (210) and on the at least one property of at least the part of the textile (200, 304) from the second Intensity information item (212, 214); and outputting or triggering an outputting of the at least one treatment parameter.

A washing machine has a rotatable drum and drum drive and a drum container for it, an inlet device to introduce water into the drum onto laundry therein, a pump and water pipes, several temperature sensors on the drum container, a heater for heating water and a washing machine controller connected to everything. Laundry in the drum is moistened with a defined quantity of water, wherein the moisture penetration varies depending on the fiber type of the laundry. Then a predefined quantity of water with a predefined temperature is applied to this laundry. The washing machine controller can deduce the fiber type of the laundry from the way how quickly and to what extent heat is extracted from this quantity of water.

Devices, systems, and methods for autonomously sorting dirty laundry articles into batched loads for washing are described. For example, an autonomous sorting system includes an enclosed channel including a stationary floor extending between an inlet end and an outlet end of the channel, a plurality of arms disposed in series along the enclosed channel for selectively grasping at least one of the plurality of deformable articles in sequence. The system includes an outlet orifice adjacent the outlet end through which each separated deformable article exits the enclosed channel upon release by the terminal gripper of the one of the plurality of arms, and one or more conveyors disposed adjacent the outlet end configured for receiving thereon a plurality of bins for collecting for washing together two or more articles of the plurality of deformable articles released through the outlet orifice having a common sensor-detected one or more characteristics.

Disclosed is an artificial intelligence (AI)-based laundry treatment apparatus comprising: a washing module; a camera to acquire an image of laundry; a memory for storing therein a laundry recognition model, wherein the model is trained using a machine learning or deep learning algorithm, wherein the model is configured to recognize laundry information about the laundry; and a processor configured to apply image data from the acquired image to the laundry recognition model to acquire the laundry information.

A dryer appliance includes a microphone for monitoring sound generated during operation of the dryer appliance and a controller is operably coupled to the microphone. The controller is configured for obtaining a sound signal generated during operation of the dryer appliance and converting the sound signal into a spectrogram that represents a sound frequency and a sound amplitude over time. An artificial intelligence image recognition process is used to analyze the spectrogram to identify one or more sound signatures that are associated with particular operating conditions, and operation of the dryer appliance is adjusted based at least in part on the identification of the sound signature.

A method of operating a dryer appliance is provided that includes obtaining washer operating parameters from a washing machine appliance that is linked to the dryer appliance and obtaining dryer operating parameters of the dryer appliance. These parameters are transmitted to a remote server along with a user input such as a selected dryness level. The method further includes receiving operating instructions from the remote server and adjusting at least one operating parameter of the dryer appliance based on the operating instructions.

Disclosed is an artificial intelligence (AI)-based self-control air conditioner. The AI-based self-control air conditioner includes a communication unit configured to receive an image including member data for identifying the member from an image acquisition apparatus corresponding to a group including at least one member, and a processor configured to recognize the member data from the received image, to acquire operation data including an operation condition of an air conditioner, which is desired by the member, based on the recognized data, to store member information including the member data and the operation data in a database, and to acquire and analyze the operation condition of the air conditioner, which is desired by the member, with respect to at least one member from a plurality of pieces of member information corresponding to the group stored in the database, wherein the air conditioner is autonomously driven according to control of the processor. Accordingly, the air conditioner learns members itself and controls an operation in an optimum state to reduce power consumption and is driven according to an operation condition set for each member to enhance personal convenience of the member.

A dryer appliance includes a drum rotatably mounted within a cabinet for receiving a load of clothes during a drying cycle. A temperature sensor measures a temperature of air circulated through the drum and a controller obtains a remaining moisture content of the load of clothes from a washing machine appliance, obtains a measured air temperature using the temperature sensor, determines a restriction level of a vent based at least in part on the measured air temperature, and adjusts at least one operating parameter of the dryer appliance based at least in part on the remaining moisture content and the restriction level.