Lg Electronics Inc.

Rnl Bio Co., Ltd.

Hyoeun Kim

Jaehong Kim

Hangil Jeong

Heeyeon Choi

Taeho Lee

Jongwoo Han

Hyejeong Jeon

Kamin Lee

Seungah Chae

Doyoung Lee

Yoojin Choi

Soungmin Im

Dami Choe

Yongsuk Lee

Jeong Chan Ra

Suyeon Kim

Sung Keun Kang

Hyunseok Shin

Beomsuk Moon

Ju Yeon Seo

Yeonjee Jung

The present disclosure relates to an artificial intelligence apparatus capable of classifying and detecting an item of an unseen class having high visual similarity to an item of a seen class that has been learned and known in advance, and a method for detecting an unseen class items thereof, and when the item image is input, generate encoded data by encoding the item image, generate decoded data by decoding the encoded data using a codebook of a plurality of codebooks in which encoded data for each item class is stored, corresponding to a pre-learned item class, and detect the unknown item by classifying the class of the item image based on the generated decoded data.

Artificial intelligence apparatus and method for detecting unseen class items thereof

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.

Refrigerator

An embodiment of the present invention provides, comprising: a communication unit configured to communicate with a plurality of external AI apparatuses; and a processor configured to receive sound signals of the user from the plurality of external AI apparatuses, calculate a distance and a variation of the distance from each of the plurality of external AI apparatuses to the user based on the received sound signals, determine a current path of the user based on the calculated distance and the calculated variation of the distance, and determine a future path of the user based on the current path.

Artificial intelligence apparatus for determining path of user and method for the same

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.

Artificial intelligence apparatus for recognizing speech of user and method for the same

Provided is an artificial intelligence air conditioner for calibrating sensor data. The artificial intelligence air conditioner includes a sensor unit configured to acquire sensor data; a communication unit configured to receive at least one of external sensor data or environment information from at least one of an external air conditioner or an internet of things (IoT) device; and a processor. The processor is configured to generate estimated sensor data corresponding to the sensor unit using a sensor data estimation model, the received external sensor data and the received environment information, determine whether the acquired sensor data is abnormal using the generated estimated sensor data, perform an air conditioning function using the acquired sensor data if the acquired sensor data is determined as normal, and perform the air conditioning function using the generated estimated sensor data if the acquired sensor data is determined as abnormal.

Artificial intelligence air conditioner and method for calibrating sensor data of air conditioner

An artificial intelligence device is provided. The artificial intelligence device according to an embodiment of the present disclosure includes: an input unit configured to receive a speech input; and a processor configured to operate in an interaction mode if a second wakeup word for setting an operation mode is recognized after a first wakeup word for calling the artificial intelligence device is recognized, and process one or more commands received after the second wakeup word according to the operation mode indicated by the second wakeup word.

Electronic device with wakeup word determined multi-mode operation

A robot cleaner for performing cleaning using artificial intelligence includes a suction unit configured to suction dust, a driving unit to drive the robot cleaner, a memory configured to store a compensation model for inferring optimal suction output and driving output for cleaning environment information for learning, and a processor configured to acquire cleaning environment information, determine a suction output value and a driving speed of the robot cleaner from the acquired cleaning environment information using the compensation model, control the suction unit to suction the dust with the determined suction output value, and control the driving unit to drive the robot cleaner at the determined driving speed.

Robot cleaner for performing cleaning using artificial intelligence and method of operating the same

An artificial intelligence apparatus for generating training data includes a memory configured to store a target artificial intelligence model, and a processor configured to receive sensor data, determine whether the received sensor data is irrelevant to a learning of the target artificial intelligence model, determine whether the received sensor data is useful for the learning if the received sensor data is determined to be relevant to the learning, extract a label from the received sensor data by using a label extractor if the received sensor data is determined to be useful for the learning, determine a confidence level of the extracted label, and generate training data including the received sensor data and the extracted label if the determined confidence level exceeds a first reference value.

Artificial intelligence apparatus for generating training data, artificial intelligence server, and method for the same

An artificial intelligence (AI) device may acquire user information, use information of a home appliance and weather information, acquire whether use of the home appliance is recommended and a recommended use time of the home appliance from the user information, the use information of the home appliance and the weather information, using an appliance use recommendation model, and output a recommendation comment indicating information on use of the home appliance based on the recommended use time.

Artificial intelligence device and operating method thereof

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.

Laundry treatment device and method of determining laundry course thereof

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.

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.

AI apparatus and operation method thereof

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.

Washing machine

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.

Growing community of inventors

Seoul, South Korea

Hyoeun Kim