Lg Electronics Inc.

Heeyeon Choi

Jaehong Kim

Hyoeun Kim

Hangil Jeong

Hyejeong Jeon

Jongwoo Han

Kamin Lee

Taeho Lee

Soungmin Im

Yongsuk Lee

Seungah Chae

Hyunseok Shin

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 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

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.

AI-based laundry treatment apparatus and operation method thereof

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 device mounted on a vehicle is provided. A sensing unit acquires a gyroscope sensor value, an acceleration sensor value, a GPS sensor value, and a proximity sensor value. If the acquired data satisfies a predetermined reference value, a processor inputs the acquired sensor values to an artificial intelligence model, acquires whether an impact requiring self-diagnosis occurs and impact direction information as a result value, selects an ECU module to perform self-diagnosis according to the acquired result value, and performs self-diagnosis.

Artificial intelligence device mounted on vehicle to perform self-diagnosis, and method for the same

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 apparatus that includes: a sensing unit that includes a gyroscope sensor and an acceleration sensor; an output unit that outputs information; and a processor that acquires a resultant value output from a deep learning model by inputting data acquired from the gyroscope sensor and the acceleration sensor into the deep learning model, performs self-diagnosis when the resultant value shows a shock requiring self-diagnosis, and controls the output unit to output a result of the self diagnosis, in which the deep learning model is a neural network having an adjusted weight by being trained using whether a shock is a shock requiring the self-diagnosis as a resultant value and sensing values of the gyroscope sensor and the acceleration sensor as input values.

Artificial intelligence apparatus for performing self diagnosis and method for the same

Provided is an artificial intelligence (AI) apparatus for managing an operation of an artificial intelligence (AI) system. The AI apparatus includes: a communication unit that receives state information from at least one member AI apparatus included in the AI system, respectively; a memory that stores apparatus information on the at least one member AI apparatus, respectively; and a processor that: upon acquiring a control command of a user, determines a target member AI apparatus to perform the control command; determines whether the target member AI apparatus is capable of performing the control command or not; transmits the control command to the target member AI apparatus if the target member AI apparatus is capable of performing the control command, and outputs a response indicating that the target member AI apparatus is not capable of performing the control command if the target member AI apparatus is not capable of performing the control command.

AI apparatus and method for managing operation of artificial intelligence system

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.

Artificial intelligence device capable of controlling operation of another device and method of operating the same

An artificial intelligence apparatus for recognizing speech by correcting misrecognized word includes a microphone and a processor. The processor is configured to obtain, via the microphone, speech data including speech of a user, convert the speech data into text by using an acoustic model and a language model, determine whether an uncertain recognition exists in an acoustic recognition result according to the acoustic model, determine whether the converted text is a normal sentence by using a natural language processing model if an uncertain recognition exists in the acoustic recognition result, determine a sentence most similar to the converted text among sentences pre-learned by using the language model if the converted text is not a normal sentence, replace the converted text with the determined most similar sentence, and generate a speech recognition result corresponding to the speech data by using the converted text.

Artificial intelligence apparatus and method for recognizing speech by correcting misrecognized word

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.

Artificial intelligence device with a voice recognition

Disclosed herein is an artificial intelligence apparatus for recognizing at least one object, comprising: a memory configured to store a plurality of recognition models for generating identification information corresponding to the object from image data; and a processor configured to: obtain image data for the object, generate first identification information corresponding to the object from the image data using a default recognition model composed of at least one or more of the plurality of recognition models, measure a confidence level for the first identification information, obtain the first identification information as a recognition result of the object if the confidence level is equal to or greater than a first reference value, and obtain second identification information corresponding to the object from the image data as a recognition result of the object using a compound recognition model composed of at least one or more of the plurality of recognition models if the measured confidence level is less than the first reference value, wherein the default recognition model is a model defined by first weights for the plurality of recognition models, and wherein the compound recognition model is a model defined by second weights for the plurality of recognition models.

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Seoul, South Korea

Heeyeon Choi