Abstract: A server may provide a voice recognition service. The server may include a memory configured for storing a plurality of voice recognition models, a communication device configured for communicating a plurality of voice recognition devices, and an artificial intelligence device configured for providing a voice recognition service to the plurality of voice recognition devices, acquiring use-related information regarding a first voice recognition device (from among the plurality of voice recognition devices), and changing a voice recognition model corresponding to the first voice recognition device from a first voice recognition model to a second voice recognition model based on the use-related 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: An AI device is provided. The AI device includes a memory to store data, a voice acquisition interface to acquire a voice signal, and a processor to perform preprocessing for the voice signal based on a parameter, to provide the preprocessed voice signal to a voice recognition model, to acquire a voice recognition result, to store a characteristic of the preprocessed voice signal in the memory, and to change the parameter using a distribution of characteristics of voice signals accumulated in the memory.

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: 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: 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: Embodiments provide an artificial intelligence apparatus for recognizing an utterance voice of a user. The artificial intelligence apparatus includes: a communication unit configured to communicate with at least one external artificial intelligence apparatus which obtains first sound data including the utterance voice of the user to generate a first speech recognition result from the first sound data; a microphone configured to obtain second sound data including the utterance voice; and a processor configured to receive first speech recognition results from each of the at least one external artificial intelligence apparatus, generate a second speech recognition result from the second sound data, generate a final speech recognition result for the utterance voice by using the first speech recognition results and the second speech recognition result, and perform a control corresponding to the final speech recognition result.

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: A voice recognition system is provided. The voice recognition system according to an embodiment of the present disclosure includes a voice recognition agent configured to receive voice data from a user and transmit the voice data to an artificial intelligence server, and the artificial intelligence server configured to input the voice data to a voice recognition model, transmit a recognition result based on the voice data to the voice recognition agent, and learn the voice data. When a voice recognition rate for the voice data is lower than a preset reference, the voice recognition agent is configured to request the user for additional data for learning voice data of a user.

Abstract: An artificial intelligence server is disclosed. The artificial intelligence server includes an input unit to which input data is inputted, and a processor, when a first output value outputted by an artificial intelligence model with respect to first input data is correct and a second output value outputted by the artificial intelligence model with respect to second input data is incorrect, configured to use the first input data and the second input data to obtain a first domain causing an incorrect answer, and train the artificial intelligence model to be domain-adapted for the first domain.

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 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: A laundry treatment device includes a wireless communication unit, at least one sensor, and a processor configured to apply a learning model learned through a supervised learning algorithm to sensing information including a sensing value collected from the at least one sensor and a measurement time of the sensing value, to acquire microorganism information including a type of microorganism and a proliferation rate of the microorganism, to acquire laundry guide information based on the acquired microorganism information, and to transmit the acquired laundry guide information to a terminal through the wireless communication unit.

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: 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.

Abstract: A server may provide a voice recognition service. The server may include a memory configured for storing a plurality of voice recognition models, a communication device configured for communicating a plurality of voice recognition devices, and an artificial intelligence device configured for providing a voice recognition service to the plurality of voice recognition devices, acquiring use-related information regarding a first voice recognition device (from among the plurality of voice recognition devices), and changing a voice recognition model corresponding to the first voice recognition device from a first voice recognition model to a second voice recognition model based on the use-related information.

Abstract: An AI device is provided. The AI device includes a memory to store data, a voice acquisition interface to acquire a voice signal, and a processor to perform preprocessing for the voice signal based on a parameter, to provide the preprocessed voice signal to a voice recognition model, to acquire a voice recognition result, to store a characteristic of the preprocessed voice signal in the memory, and to change the parameter using a distribution of characteristics of voice signals accumulated in the memory.

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: 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: Embodiments provide an artificial intelligence apparatus for recognizing an utterance voice of a user. The artificial intelligence apparatus includes: a communication unit configured to communicate with at least one external artificial intelligence apparatus which obtains first sound data including the utterance voice of the user to generate a first speech recognition result from the first sound data; a microphone configured to obtain second sound data including the utterance voice; and a processor configured to receive first speech recognition results from each of the at least one external artificial intelligence apparatus, generate a second speech recognition result from the second sound data, generate a final speech recognition result for the utterance voice by using the first speech recognition results and the second speech recognition result, and perform a control corresponding to the final speech recognition result.