公开(公告)号：US20210287128A1

公开(公告)日：2021-09-16

申请号：US16496365

申请日：2019-08-08

Applicant: LG ELECTRONICS INC.

Inventor： Jongwoo HAN ,  Jaehong KIM ,  Hyoeun KIM ,  Taeho LEE ,  Hyejeong JEON ,  Hangil JEONG ,  Heeyeon CHOI

IPC: G06N20/00 ,  G06N3/04 ,  G06K9/62 ,  G06N5/00

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.

公开(公告)号：US20210118436A1

公开(公告)日：2021-04-22

申请号：US16693132

申请日：2019-11-22

Applicant: LG ELECTRONICS INC.

Inventor： Jaehong KIM ,  Heeyeon CHOI

IPC: G10L15/197 ,  G10L15/02 ,  G10L15/22 ,  G10L15/187 ,  G10L15/18 ,  G10L15/16 ,  G06N3/04 ,  G06N3/08

Abstract: 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.

公开(公告)号：US20200051571A1

公开(公告)日：2020-02-13

申请号：US16657137

申请日：2019-10-18

Applicant: LG ELECTRONICS INC.

Inventor： Jongwoo HAN ,  Jaehong KIM ,  Hyoeun KIM ,  Taeho LEE ,  Hangil JEONG ,  Heeyeon CHOI

IPC: G10L17/02 ,  G10L17/04 ,  G10L21/0208 ,  G10L17/18

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.

公开(公告)号：US20200035045A1

公开(公告)日：2020-01-30

申请号：US16591231

申请日：2019-10-02

Applicant: LG Electronics Inc.

Inventor： Jaehong KIM ,  Heeyeon CHOI

IPC: G07C5/08 ,  G06N3/04 ,  G06N3/08

Abstract: 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.

公开(公告)号：US20200034661A1

公开(公告)日：2020-01-30

申请号：US16593928

申请日：2019-10-04

Applicant: LG Electronics Inc.

Inventor： Jaehong KIM ,  Hyoeun KIM ,  Hyejeong JEON ,  Heeyeon CHOI

IPC: G06K9/62 ,  G06N3/08 ,  G06N5/04 ,  G06N20/00

Abstract: 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.