Methods and systems for treating laundry appliances with disinfecting spray are provided. The laundry appliance includes a cabinet defining an opening and a laundry vessel mounted within the cabinet. The laundry vessel defines a treatment chamber. The laundry appliance also includes a spray nozzle positioned and operable to direct a spray of sanitizing agent into the treatment chamber. The laundry appliance may be treated by closing a water valve upstream of the treatment chamber, and, while the water valve is closed, activating a pump to pump the sanitizing agent from a tank to the spray nozzle. Thus, the spray of sanitizing agent is directed into the treatment chamber by and from the spray nozzle.

A laundry appliance includes a basket rotatably mounted within a tub, a motor assembly for selectively rotating the basket, and a wash fluid dispenser for selectively providing a flow of wash fluid into the tub. A controller is configured to receive a command to initiate a self-clean cycle, determine that a cycle adjustment condition exists based at least in part on historical data related to the performance of self-clean cycles, generate a set of adjusted self-clean cycle parameters by adjusting an operating parameter of the self-clean cycle, and perform the self-clean cycle in accordance with the adjusted self-clean cycle parameters.

An electronic device includes a camera to capture an image, and a processor to input an image acquired by photographing a detergent container into a trained model to acquire detergent information corresponding to the detergent container, and to guide an amount of detergent dispensed based on washing information corresponding to the detergent information. The trained model is a neural network trained using images of a plurality of detergent containers.

Disclosed are a washer for adjusting an amount of detergent and a method of driving the same, which controls an automatic detergent dispenser to decide the amount of detergent on an amount of contamination of laundry by using artificial intelligence or machine learning, and to input the determined amount of detergent to washing water.

A method for controlling a washing machine includes: detecting a first sensor value by a first set of electrode sensors and a second sensor value by a second set of electrode sensors having at least one electrode sensor which is placed vertically higher than the first set of electrode sensors, the first and second set of electrode sensors comprising at least three electrode sensors which are located at least one of cartridges and configured to detect an amount of detergent contained in the at least one the cartridges; comparing the first sensor value and the second sensor value with a first predetermined value, respectively; passing a predetermined time; redetecting the first sensor value and the second sensor value and calculating a difference sensor value; and determining that the detergent is filling to a height between the first set of electrode sensors and the second set of electrode sensors.

A method and system remotely controlling a cleaning cycle of a household appliance using transmission of wireless signals is provided. Signals are transmitted between a mobile dispensing device arranged in the household appliance and an electronic device. The method determines the strength of a signal received by the mobile dispensing device. The determined strength is compared to a threshold value. If the determined strength is above the threshold value transmission of wireless signals from the mobile dispensing device to the electronic device is enabled. If the determined strength is below the threshold value transmission of signals from the mobile dispensing device to the electronic device is disabled. When transmission of signals is enabled, a status wireless signal comprising status information about the cleaning cycle is sent to the electronic device.

Provided are an additive supply unit configured to supply a stored additive to a washing tub, and a washing machine including the same. The present disclosure provides an additive supply unit including a cartridge configured to accommodate a liquid additive therein and discharge the additive through a discharge port formed at one side thereof, a discharge tube connected to the cartridge and configured to provide a discharge path for the additive discharged from the cartridge, a check valve assembly interposed between the cartridge and the discharge tube and configured to selectively open or close the discharge tube and the discharge port of the cartridge depending on a change in pressure, a pump configured to generate a pressure to be provided to the check valve assembly, and a pressure tube configured to connect the check valve assembly and the pump and transmit the pressure generated from the pump to the check valve assembly, in which the check valve assembly has one end connected to the cartridge and the other end connected to the discharge tube.

A laundry treatment appliance includes a cabinet forming a receiving space therein, a tub provided within the receiving space, and a dispenser assembly provided within the receiving space, the dispenser assembly being in fluid communication with the tub. The dispenser assembly includes a dispenser body coupled to the cabinet and provided adjacent to the tub, a first drawer slidably inserted into the dispenser body along the transverse direction, and a second drawer slidably inserted into the dispenser body along the transverse direction, the second drawer being provided beneath the first drawer along the vertical direction.

A home appliance and a control method for the home appliance, which is operable in an IoT environment through a 5G communication network and uses a neural network model generated according to machine learning is provided. The home appliance may include a home appliance main body; a container mounted within the home appliance main body to accommodate a treatment target; a camera arranged to photograph the inside of the container; and one or more processors configured to control an operation of the home appliance, wherein the processor is configured to determine an amount of a treatment target based on feature shapes of the container identified in an image of the inside of the container photographed by the camera.

A water ozonation system (18) that receives source water (16) from a water source (14) and converts it to ozonated water (20) for use in a washing machine (12) includes a system body (30), an ozone generator (38), a sensor assembly (21), and a controller (46). The system body (30) receives the source water (16) from the water source (14). The ozone generator (38) is configured to generate ozone. The ozone generator (38) is coupled the system body (30). The sensor assembly (21) is also coupled to the system body (30). The sensor assembly (21) is configured to sense at least one ambient environmental condition and generate at least one electronic data signal based on the sensed at least one ambient environmental condition. The controller (46) receives the at least one electronic data signal from the sensor assembly (21) and regulates a level of ozone that is generated by the ozone generator (38) based at least in part on the at least one electronic data signal.