Abstract: An operating method of a nonvolatile memory device includes receiving, at the nonvolatile memory device, a suspend command, suspending, at the nonvolatile memory device, a program operation being performed, in response to the suspend command, receiving, at the nonvolatile memory device, a resume command, and resuming, at the nonvolatile memory device, the suspended program operation in response to the resume command. The program operation includes program loops, each of which includes a bit line setup interval, a program interval, and a verify interval. In the program interval of each of the program loops, a level of a program voltage to be applied to selected memory cells of the nonvolatile memory device increases as much as a first voltage. A difference between a level of the program voltage finally applied s suspend and a level of the program voltage applied first after resume is different from the first voltage.

Abstract: An embodiment of the present invention provides an artificial intelligence (AI) apparatus for recognizing a speech of a user, the artificial intelligence apparatus includes a memory to store a speech recognition model and a processor to obtain a speech signal for a user speech, to convert the speech signal into a text using the speech recognition model, to measure a confidence level for the conversion, to perform a control operation corresponding to the converted text if the measured confidence level is greater than or equal to a reference value, and to provide feedback for the conversion if the measured confidence level is less than the reference value.

Abstract: An operating method of a nonvolatile memory device includes receiving, at the nonvolatile memory device, a suspend command, suspending, at the nonvolatile memory device, a program operation being performed, in response to the suspend command, receiving, at the nonvolatile memory device, a resume command, and resuming, at the nonvolatile memory device, the suspended program operation in response to the resume command. The program operation includes program loops, each of which includes a bit line setup interval, a program interval, and a verify interval. In the program interval of each of the program loops, a level of a program voltage to be applied to selected memory cells of the nonvolatile memory device increases as much as a first voltage. A difference between a level of the program voltage finally applied s suspend and a level of the program voltage applied first after resume is different from the first voltage.

Abstract: Disclosed herein an artificial intelligence apparatus for updating an artificial intelligence model including a memory configured to store an artificial intelligence model and training data for the artificial intelligence model and a processor configured to receive sensor data, calculate a confidence level of the received sensor data for the stored artificial intelligence model, label the received sensor data if the calculated confidence level is less than a reference value, store the labeled received sensor data in the memory as the training data or test data, and update the stored artificial intelligence model using the stored training data.

Abstract: An operating method of a nonvolatile memory device includes receiving, at the nonvolatile memory device, a suspend command, suspending, at the nonvolatile memory device, a program operation being performed, in response to the suspend command, receiving, at the nonvolatile memory device, a resume command, and resuming, at the nonvolatile memory device, the suspended program operation in response to the resume command. The program operation includes program loops, each of which includes a bit line setup interval, a program interval, and a verify interval. In the program interval of each of the program loops, a level of a program voltage to be applied to selected memory cells of the nonvolatile memory device increases as much as a first voltage. A difference between a level of the program voltage finally applied s suspend and a level of the program voltage applied first after resume is different from the first voltage.

Abstract: A method for controlling an AI apparatus mounted on home appliance, the method comprising: receiving a sound signal at a predetermined time; removing noises of the received signal and separating the signal from which the noises are removed, into a signal by a user and a device signal; acquiring a result value outputted by an AI model by inputting the separated signal to the AI model using a multi-class separation; and executing a sleep mode of the home appliance and outputting a sleep mode switch notice when the result value is a user sleep, and requesting a feedback from the user when the result value is an unclear signal and updating the AI model using the feedback.

Abstract: An artificial intelligence device includes a microphone configured to receive a speech command, a speaker, a communication unit configured to perform communication with an external artificial intelligence device, and a processor configured to receive a wake-up command through the microphone, acquire a first speech quality level of the received wake-up command, receive a second speech quality level of the wake-up command input to the external artificial intelligence device from the external artificial intelligence device through the communication unit, output a notification indicating that the artificial intelligence device is selected as an object to be controlled through the speaker, when the first speech quality level is larger than the second speech quality level, receive an operation command through the microphone, acquire an intention of the received operation command and transmit the operation command to an external artificial intelligence device which will perform operation corresponding to the operati

Abstract: 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.

Abstract: Disclosed is an artificial intelligence (AI) server. The AI server includes a communication unit configured to communicate with an AI device; and an AI unit configured to receive feature data from the AI device, wherein the received feature data is generated by the AI device by obtaining sensing data and compressing the sensing data while preserving a feature of the sensing data; and input the received feature data to a deep learning model to obtain second sensing data for use in a recognition model related to an AI function of the AI device.

Abstract: According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, a door, one or more cameras provided in the door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment, obtain one or more food identification items from the photographed internal image, and if the obtained one or more food identification items are stored in the local DB, recognize one or more product names of the obtained one or more food identification items as product names of the food identification items stored in the local DB.

Abstract: Disclosed is an artificial intelligence (AI) apparatus comprising: a short-range communication module configured for sensing a plurality of laundry treatment apparatuses positioned around the AI apparatus; and a processor configured for: acquiring laundry information about laundry and characteristic information of each of the detected laundry treatment apparatuses; determining a laundry group corresponding to the laundry, based on the acquired laundry information; comparing characteristic information of the determined laundry group and characteristic information of each of the plurality of laundry treatment apparatuses with each other; and determining a laundry treatment apparatus for washing the laundry among the plurality of laundry treatment apparatuses based on the comparison result.

Abstract: According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, an outer door, one or more cameras provided in the outer door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment through the one or more cameras, obtain a food identification item from the photographed internal image, determine whether the obtained food identification item is stored in the local DB, and when the food identification item is not stored in the local DB, determine whether the obtained food identification item is stored in the global DB.

Abstract: According to an embodiment of the present disclosure, a refrigerator may include a storage compartment, a door, one or more cameras provided in the door, a global DB configured to store a plurality of default food identification items and a plurality of default product names respectively corresponding to the plurality of default food identification items, and a local DB configured to store edited product names and a food identification item corresponding to the edited product names, and a processor configured to photograph an internal image of the storage compartment, obtain one or more food identification items from the photographed internal image, and if the obtained one or more food identification items are stored in the local DB, recognize one or more product names of the obtained one or more food identification items as product names of the food identification items stored in the local DB.

Abstract: An embodiment of the present invention provides an artificial intelligence (AI) apparatus for recognizing a speech of a user, the artificial intelligence apparatus includes a memory to store a speech recognition model and a processor to obtain a speech signal for a user speech, to convert the speech signal into a text using the speech recognition model, to measure a confidence level for the conversion, to perform a control operation corresponding to the converted text if the measured confidence level is greater than or equal to a reference value, and to provide feedback for the conversion if the measured confidence level is less than the reference value.

Abstract: An artificial intelligence device includes a microphone configured to receive a command uttered by a user, a wireless communication unit configured to perform communication with an external artificial intelligence device, and a processor configured to receive a first operation command through the microphone, acquire a first speech quality level and a first intention of the received first operation command, determine a first external artificial intelligence device to perform the acquired first intention, transmit a first control command corresponding to the first intention to the determined first external artificial intelligence device, receive a second operation command through the microphone, acquire a second speech quality level and a second intention of the received second operation command, and determine that a device to be controlled is changed when a difference between the first speech quality level and the second speech quality level is equal to or greater than a predetermined level range.

Abstract: Disclosed herein is a washing machine including a first data acquirer configured to collect data related to a laundry pattern of a user, a second data acquirer configured to collect data related to context information, and a processor configured to provide the laundry pattern of the user and the context information to a reinforcement learning model as an environment and to train the reinforcement learning model using feedback of the user on a recommended laundry course when the reinforcement learning model recommends the laundry course.

Abstract: Disclosed is a laundry scheduling apparatus. The apparatus includes a communication unit, an output unit, and a processor configured to pair with at least one washing machine via the communication unit, obtain laundry preference parameters of a user generated by learning based on at least one of a deep learning algorithm or a machine learning algorithm, using at least one of a laundry log of the user or laundry satisfaction information of the user as input data, generate laundry scheduling information by using washing machine information about the paired at least one washing machine, the laundry preference parameters, and laundry item information obtained via at least one of a user input unit, an interface unit, or a sensor, and cause the output unit to output the laundry scheduling information.

Abstract: An embodiment of the present invention provides an artificial intelligence (AI) apparatus for recognizing a speech of a user, the artificial intelligence apparatus includes a memory to store a speech recognition model and a processor to obtain a speech signal for a user speech, to convert the speech signal into a text using the speech recognition model, to measure a confidence level for the conversion, to perform a control operation corresponding to the converted text if the measured confidence level is greater than or equal to a reference value, and to provide feedback for the conversion if the measured confidence level is less than the reference value.

Abstract: A light output device for caring for a skin of a user using artificial intelligence includes a plurality of light sources configured to irradiate light, a memory configured to store a skin care model learned using a deep learning algorithm to infer a facial skin state of the user, a camera configured to capture an image of a face of the user, and a processor configured to acquire a skin state of each part of the face based on a first-type face image captured through the camera and the skin care model, and control light output of the plurality of light sources based on the acquired skin state.

Abstract: A laundry scheduling device according to an embodiment of the present invention includes an input interface that receives speech including a plurality of words respectively representing a plurality of laundry items from a use, and a processor that acquires one or more features represented by each of the plurality of words by inputting speech data corresponding to the received speech to a learning model and performing word embedding, and generates a laundry schedule of the plurality of laundry items based on the one or more features represented by each of the plurality of words.