ABSTRACT
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.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims priority under 35 U.S.C. 119 and 35 U.S.C. 365 to Korean Patent Application No. 10-2019-0102695 filed on Aug. 21, 2019, which is hereby incorporated by reference in its entirety.
BACKGROUND
The present disclosure relates to an artificial intelligence apparatus and a method for recognizing an utterance voice of a user. More particularly, the present disclosure relates to an artificial intelligence apparatus and a method for recognizing an utterance voice of a user by attempting to recognize the utterance voice of the user through a plurality of artificial intelligence apparatuses, and collecting speech recognition results from each of the artificial intelligence apparatuses.
Recently, there is a trend that services that employ a speech recognition technology such as artificial intelligence speakers, voice control, and voice secretary. In addition, users having a plurality of artificial intelligence apparatuses in one space are increasing.
However, in the related art, even if a user speaks in a space provided with the artificial intelligence apparatuses, each of the artificial intelligence apparatuses individually attempts speech recognition.
If speech recognition results of the artificial intelligence apparatuses are properly collected, an utterance voice of the user may be recognized with higher accuracy.
SUMMARY
Embodiments provide an artificial intelligence apparatus and a method thereof, in which weights between artificial intelligence apparatuses are determined in consideration of an environment at an utterance time of a user, and speech recognition results for an utterance voice of the user that are obtained from each of the artificial intelligence apparatuses are collected based on the determined weights so as to generate a final speech recognition result.
In one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which a speech recognition result generated from voice data corresponding to an utterance voice of a user is received from at least one artificial intelligence apparatus, a speech recognition result is directly generated from the voice data corresponding to the utterance voice of the user, a final speech recognition result is generated by using the received speech recognition result and the generated speech recognition result, and a control corresponding to the generated final speech recognition result is performed.
In addition, in one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which an environment variable corresponding to an utterance time is determined based on the received voice data, a weight for each of the artificial intelligence apparatuses is determined based on the determined environment variable, and the final speech recognition result is generated from the received speech recognition result and the generated speech recognition result based on the determined weight.
In addition, in one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which a noise type included in sound data is determined, and the weight for each of the artificial intelligence apparatuses is determined based on the determined noise type.
The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram illustrating an AI apparatus according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating an AI server according to an embodiment of the present invention.
FIG. 3 is a view illustrating an AI system according to an embodiment of the present invention.
FIG. 4 is a block diagram illustrating an AI apparatus according to an embodiment of the present invention.
FIG. 5 is a view illustrating an AI system according to an embodiment of the present invention.
FIG. 6 is a view illustrating an AI system according to an embodiment of the present invention.
FIG. 7 is a flowchart illustrating a method of recognizing a voice of a user according to one embodiment.
FIG. 8 is a flowchart illustrating the method of recognizing the voice of the user according to one embodiment.
FIG. 9 is a view illustrating a method of generating a final speech recognition result according to one embodiment.
FIGS. 10 and 11 are views illustrating examples of a noise classification model according to one embodiment.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes âmoduleâ and âunitâ for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included.
It will be understood that the terms âfirstâ and âsecondâ are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components.
In this disclosure below, when one part (or element, device, etc.) is referred to as being âconnectedâ to another part (or element, device, etc.), it should be understood that the former can be âdirectly connectedâ to the latter, or âelectrically connectedâ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being âdirectly connectedâ or âdirectly linkedâ to another component, it means that no intervening component is present.
<Artificial Intelligence (AI)>
Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.
An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value.
The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse.
Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function.
The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network.
Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.
The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the training data is input to the artificial neural network. The unsupervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is not given. The reinforcement learning may refer to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state.
Machine learning, which is implemented as a deep neural network (DNN) including a plurality of hidden layers among artificial neural networks, is also referred to as deep learning, and the deep learning is part of machine learning. In the following, machine learning is used to mean deep learning.
<Robot>
A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot.
Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field.
The robot includes a driving unit may include an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a driving unit, and may travel on the ground through the driving unit or fly in the air.
<Self-Driving>
Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user.
For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a predetermined route, and a technology for automatically setting and traveling a route when a destination is set.
The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but also a train, a motorcycle, and the like.
Here, the self-driving vehicle may be regarded as a robot having a self-driving function.
<eXtended Reality (XR)>
Extended reality is collectively referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The VR technology provides a real-world object and background only as a CG image, the AR technology provides a virtual CG image on a real object image, and the MR technology is a computer graphic technology that mixes and combines virtual objects into the real world.
The MR technology is similar to the AR technology in that the real object and the virtual object are shown together. However, in the AR technology, the virtual object is used in the form that complements the real object, whereas in the MR technology, the virtual object and the real object are used in an equal manner.
The XR technology may be applied to a head-mount display (HMD), a head-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop, a TV, a digital signage, and the like. A device to which the XR technology is applied may be referred to as an XR device.
FIG. 1 is a block diagram illustrating an AI apparatus 100 according to an embodiment of the present invention.
Hereinafter, the AI apparatus 100 may be referred to as a terminal.
The AI apparatus (or an AI device) 100 may be implemented by a stationary device or a mobile device, such as a TV, a projector, a mobile phone, a smartphone, a desktop computer, a notebook, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, a tablet PC, a wearable device, a set-top box (STB), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, and the like.
Referring to FIG. 1 , the AI apparatus 100 may include a communication unit 110 , an input unit 120 , a learning processor 130 , a sensing unit 140 , an output unit 150 , a memory 170 , and a <figure-callout id="180" label="processor" filenames="US11164586-202
CROSS-REFERENCE TO RELATED APPLICATIONS
The present application claims priority under 35 U.S.C. 119 and 35 U.S.C. 365 to Korean Patent Application No. 10-2019-0102695 filed on Aug. 21, 2019, which is hereby incorporated by reference in its entirety.
BACKGROUND
The present disclosure relates to an artificial intelligence apparatus and a method for recognizing an utterance voice of a user. More particularly, the present disclosure relates to an artificial intelligence apparatus and a method for recognizing an utterance voice of a user by attempting to recognize the utterance voice of the user through a plurality of artificial intelligence apparatuses, and collecting speech recognition results from each of the artificial intelligence apparatuses.
Recently, there is a trend that services that employ a speech recognition technology such as artificial intelligence speakers, voice control, and voice secretary. In addition, users having a plurality of artificial intelligence apparatuses in one space are increasing.
However, in the related art, even if a user speaks in a space provided with the artificial intelligence apparatuses, each of the artificial intelligence apparatuses individually attempts speech recognition.
If speech recognition results of the artificial intelligence apparatuses are properly collected, an utterance voice of the user may be recognized with higher accuracy.
SUMMARY
Embodiments provide an artificial intelligence apparatus and a method thereof, in which weights between artificial intelligence apparatuses are determined in consideration of an environment at an utterance time of a user, and speech recognition results for an utterance voice of the user that are obtained from each of the artificial intelligence apparatuses are collected based on the determined weights so as to generate a final speech recognition result.
In one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which a speech recognition result generated from voice data corresponding to an utterance voice of a user is received from at least one artificial intelligence apparatus, a speech recognition result is directly generated from the voice data corresponding to the utterance voice of the user, a final speech recognition result is generated by using the received speech recognition result and the generated speech recognition result, and a control corresponding to the generated final speech recognition result is performed.
In addition, in one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which an environment variable corresponding to an utterance time is determined based on the received voice data, a weight for each of the artificial intelligence apparatuses is determined based on the determined environment variable, and the final speech recognition result is generated from the received speech recognition result and the generated speech recognition result based on the determined weight.
In addition, in one embodiment, there are provided an artificial intelligence apparatus and a method thereof, in which a noise type included in sound data is determined, and the weight for each of the artificial intelligence apparatuses is determined based on the determined noise type.
The details of one or more embodiments are set forth in the accompanying drawings and the description below. Other features will be apparent from the description and drawings, and from the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram illustrating an AI apparatus according to an embodiment of the present invention.
FIG. 2 is a block diagram illustrating an AI server according to an embodiment of the present invention.
FIG. 3 is a view illustrating an AI system according to an embodiment of the present invention.
FIG. 4 is a block diagram illustrating an AI apparatus according to an embodiment of the present invention.
FIG. 5 is a view illustrating an AI system according to an embodiment of the present invention.
FIG. 6 is a view illustrating an AI system according to an embodiment of the present invention.
FIG. 7 is a flowchart illustrating a method of recognizing a voice of a user according to one embodiment.
FIG. 8 is a flowchart illustrating the method of recognizing the voice of the user according to one embodiment.
FIG. 9 is a view illustrating a method of generating a final speech recognition result according to one embodiment.
FIGS. 10 and 11 are views illustrating examples of a noise classification model according to one embodiment.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes âmoduleâ and âunitâ for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included.
It will be understood that the terms âfirstâ and âsecondâ are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components.
In this disclosure below, when one part (or element, device, etc.) is referred to as being âconnectedâ to another part (or element, device, etc.), it should be understood that the former can be âdirectly connectedâ to the latter, or âelectrically connectedâ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being âdirectly connectedâ or âdirectly linkedâ to another component, it means that no intervening component is present.
<Artificial Intelligence (AI)>
Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.
An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value.
The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse.
Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function.
The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network.
Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.
The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the training data is input to the artificial neural network. The unsupervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is not given. The reinforcement learning may refer to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state.
Machine learning, which is implemented as a deep neural network (DNN) including a plurality of hidden layers among artificial neural networks, is also referred to as deep learning, and the deep learning is part of machine learning. In the following, machine learning is used to mean deep learning.
<Robot>
A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot.
Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field.
The robot includes a driving unit may include an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a driving unit, and may travel on the ground through the driving unit or fly in the air.
<Self-Driving>
Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user.
For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a predetermined route, and a technology for automatically setting and traveling a route when a destination is set.
The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but also a train, a motorcycle, and the like.
Here, the self-driving vehicle may be regarded as a robot having a self-driving function.
<eXtended Reality (XR)>
Extended reality is collectively referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The VR technology provides a real-world object and background only as a CG image, the AR technology provides a virtual CG image on a real object image, and the MR technology is a computer graphic technology that mixes and combines virtual objects into the real world.
The MR technology is similar to the AR technology in that the real object and the virtual object are shown together. However, in the AR technology, the virtual object is used in the form that complements the real object, whereas in the MR technology, the virtual object and the real object are used in an equal manner.
The XR technology may be applied to a head-mount display (HMD), a head-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop, a TV, a digital signage, and the like. A device to which the XR technology is applied may be referred to as an XR device.
FIG. 1 is a block diagram illustrating an AI apparatus 100 according to an embodiment of the present invention.
Hereinafter, the AI apparatus 100 may be referred to as a terminal.
The AI apparatus (or an AI device) 100 may be implemented by a stationary device or a mobile device, such as a TV, a projector, a mobile phone, a smartphone, a desktop computer, a notebook, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, a tablet PC, a wearable device, a set-top box (STB), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, and the like.
Referring to FIG. 1 , the AI apparatus 100 may include a communication unit 110 , an input unit 120 , a learning processor 130 , a sensing unit 140 , an output unit 150 , a memory 170 , and a processor 180 .
The communication unit 110 may transmit and receive data to and from external devices such as other AI apparatuses 100 a to 100 e and the AI server 200 by using wire/wireless communication technology. For example, the communication unit 110 may transmit and receive sensor information, a user input, a learning model, and a control signal to and from external devices.
The communication technology used by the communication unit 110 includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetoothâ¢, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.
The input unit 120 may acquire various kinds of data.
Here, the input unit 120 may include a camera for inputting a video signal, a microphone for receiving an audio signal, and a user input unit for receiving information from a user. The camera or the microphone may be treated as a sensor, and the signal acquired from the camera or the microphone may be referred to as sensing data or sensor information.
The input unit 120 may acquire training data for model learning and input data to be used when an output is acquired by using learning model. The input unit 120 may acquire raw input data. Here, the processor 180 or the learning processor 130 may extract an input feature by preprocessing the input data.
The learning processor 130 may learn a model composed of an artificial neural network by using training data. The learned artificial neural network may be referred to as a learning model. The learning model may be used to an infer result value for new input data rather than training data, and the inferred value may be used as a basis for determination to perform a certain operation.
Here, the learning processor 130 may perform AI processing together with the learning processor 240 of the AI server 200 .
Here, the learning processor 130 may include a memory integrated or implemented in the AI apparatus 100 . Alternatively, the learning processor 130 may be implemented by using the memory 170 , an external memory directly connected to the AI apparatus 100 , or a memory held in an external device.
The sensing unit 140 may acquire at least one of internal information about the AI apparatus 100 , ambient environment information about the AI apparatus 100 , and user information by using various sensors.
Examples of the sensors included in the sensing unit 140 may include a proximity sensor, an illuminance sensor, an acceleration sensor, a magnetic sensor, a gyro sensor, an inertial sensor, an RGB sensor, an IR sensor, a fingerprint recognition sensor, an ultrasonic sensor, an optical sensor, a microphone, a lidar, and a radar.
The output unit 150 may generate an output related to a visual sense, an auditory sense, or a haptic sense.
Here, the output unit 150 may include a display unit for outputting time information, a speaker for outputting auditory information, and a haptic module for outputting haptic information.
The memory 170 may store data that supports various functions of the AI apparatus 100 . For example, the memory 170 may store input data acquired by the input unit 120 , training data, a learning model, a learning history, and the like.
The processor 180 may determine at least one executable operation of the AI apparatus 100 based on information determined or generated by using a data analysis algorithm or a machine learning algorithm. The processor 180 may control the components of the AI apparatus 100 to execute the determined operation.
To this end, the processor 180 may request, search, receive, or utilize data of the learning processor 130 or the memory 170 . The processor 180 may control the components of the AI apparatus 100 to execute the predicted operation or the operation determined to be desirable among the at least one executable operation.
When the connection of an external device is required to perform the determined operation, the processor 180 may generate a control signal for controlling the external device and may transmit the generated control signal to the external device.
The processor 180 may acquire intention information for the user input and may determine the user's requirements based on the acquired intention information.
The processor 180 may acquire the intention information corresponding to the user input by using at least one of a speech to text (STT) engine for converting speech input into a text string or a natural language processing (NLP) engine for acquiring intention information of a natural language.
At least one of the STT engine or the NLP engine may be configured as an artificial neural network, at least part of which is learned according to the machine learning algorithm. At least one of the STT engine or the NLP engine may be learned by the learning processor 130 , may be learned by the learning processor 240 of the AI server 200 , or may be learned by their distributed processing.
The processor 180 may collect history information including the operation contents of the AI apparatus 100 or the user's feedback on the operation and may store the collected history information in the memory 170 or the learning processor 130 or transmit the collected history information to the external device such as the AI server 200 . The collected history information may be used to update the learning model.
The processor 180 may control at least part of the components of AI apparatus 100 so as to drive an application program stored in memory 170 . Furthermore, the processor 180 may operate two or more of the components included in the AI apparatus 100 in combination so as to drive the application program.
FIG. 2 is a block diagram illustrating an AI server 200 according to an embodiment of the present invention.
Referring to FIG. 2 , the AI server 200 may refer to a device that learns an artificial neural network by using a machine learning algorithm or uses a learned artificial neural network. The AI server 200 may include a plurality of servers to perform distributed processing, or may be defined as a 5G network. Here, the AI server 200 may be included as a partial configuration of the AI apparatus 100 , and may perform at least part of the AI processing together.
The AI server 200 may include a communication unit 210 , a memory 230 , a learning processor 240 , a processor 260 , and the like.
The communication unit 210 can transmit and receive data to and from an external device such as the AI apparatus 100 .
The memory 230 may include a model storage unit 231 . The model storage unit 231 may store a learning or learned model (or an artificial neural network 231 a ) through the learning processor 240 .
The learning processor 240 may learn the artificial neural network 231 a by using the training data. The learning model may be used in a state of being mounted on the AI server 200 of the artificial neural network, or may be used in a state of being mounted on an external device such as the AI apparatus 100 .
The learning model may be implemented in hardware, software, or a combination of hardware and software. If all or part of the learning models are implemented in software, one or more instructions that constitute the learning model may be stored in memory 230 .
The processor 260 may infer the result value for new input data by using the learning model and may generate a response or a control command based on the inferred result value.
FIG. 3 is a view illustrating an AI system 1 according to an embodiment of the present invention.
Referring to FIG. 3 , in the AI system 1 , at least one of an AI server 200 , a robot 100 a , a self-driving vehicle 100 b , an XR device 100 c , a smartphone 100 d , or a home appliance 100 e is connected to a cloud network 10 . The robot 100 a , the self-driving vehicle 100 b , the XR device 100 c , the smartphone 100 d , or the home appliance 100 e , to which the AI technology is applied, may be referred to as AI apparatuses 100 a to 100 e.
The cloud network 10 may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure. The cloud network 10 may be configured by using a 3G network, a 4G or LTE network, or a 5G network.
That is, the devices 100 a to 100 e and 200 configuring the AI system 1 may be connected to each other through the cloud network 10 . In particular, each of the devices 100 a to 100 e and 200 may communicate with each other through a base station, but may directly communicate with each other without using a base station.
The AI server 200 may include a server that performs AI processing and a server that performs operations on big data.
The AI server 200 may be connected to at least one of the AI apparatuses constituting the AI system 1 , that is, the robot 100 a , the self-driving vehicle 100 b , the XR device 100 e , the smartphone 100 d , or the home appliance 100 e through the cloud network 10 , and may assist at least part of AI processing of the connected AI apparatuses 100 a to 100 e.
Here, the AI server 200 may learn the artificial neural network according to the machine learning algorithm instead of the AI apparatuses 100 a to 100 e , and may directly store the learning model or transmit the learning model to the AI apparatuses 100 a to 100 e.
Here, the AI server 200 may receive input data from the AI apparatuses 100 a to 100 e , may infer the result value for the received input data by using the learning model, may generate a response or a control command based on the inferred result value, and may transmit the response or the control command to the AI apparatuses 100 a to 100 e.
Alternatively, the AI apparatuses 100 a to 100 e may infer the result value for the input data by directly using the learning model, and may generate the response or the control command based on the inference result.
Hereinafter, various embodiments of the AI apparatuses 100 a to 100 e to which the above-described technology is applied will be described. The AI apparatuses 100 a to 100 e illustrated in FIG. 3 may be regarded as a specific embodiment of the AI apparatus 100 illustrated in FIG. 1 .
<AI+Robot>
The robot 100 a , to which the AI technology is applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like.
The robot 100 a may include a robot control module for controlling the operation, and the robot control module may refer to a software module or a chip implementing the software module by hardware.
The robot 100 a may acquire state information about the robot 100 a by using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, may determine the response to user interaction, or may determine the operation.
The robot 100 a may use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan.
The robot 100 a may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the robot 100 a may recognize the surrounding environment and the objects by using the learning model, and may determine the operation by using the recognized surrounding information or object information. The learning model may be learned directly from the robot 100 a or may be learned from an external device such as the AI server 200 .
Here, the robot 100 a may perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server 200 and the generated result may be received to perform the operation.
The robot 100 a may use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external device to determine the travel route and the travel plan, and may control the driving unit such that the robot 100 a travels along the determined travel route and travel plan.
The map data may include object identification information about various objects arranged in the space in which the robot 100 a moves. For example, the map data may include object identification information about fixed objects such as walls and doors and movable objects such as pollen and desks. The object identification information may include a name, a type, a distance, and a position.
In addition, the robot 100 a may perform the operation or travel by controlling the driving unit based on the control/interaction of the user. Here, the robot 100 a may acquire the intention information of the interaction due to the user's operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation.
<AI+Self-Driving>
The self-driving vehicle 100 b , to which the AI technology is applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like.
The self-driving vehicle 100 b may include a self-driving control module for controlling a self-driving function, and the self-driving control module may refer to a software module or a chip implementing the software module by hardware. The self-driving control module may be included in the self-driving vehicle 100 b as a component thereof, but may be implemented with separate hardware and connected to the outside of the self-driving vehicle 100 b.
The self-driving vehicle 100 b may acquire state information about the self-driving vehicle 100 b by using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, or may determine the operation.
Like the robot 100 a , the self-driving vehicle 100 b may use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan.
In particular, the self-driving vehicle 100 b may recognize the environment or objects for an area covered by a field of view or an area over a certain distance by receiving the sensor information from external devices, or may receive directly recognized information from the external devices.
The self-driving vehicle 100 b may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the self-driving vehicle 100 b may recognize the surrounding environment and the objects by using the learning model, and may determine the traveling route by using the recognized surrounding information or object information. The learning model may be learned directly from the self-driving vehicle 100 a or may be learned from an external device such as the AI server 200 .
Here, the self-driving <figure-callout id="100b" label="vehicle" filenames="US11164586-
CLAIMS
Claims ( 12 )
What is claimed is:
1. An artificial intelligence apparatus for recognizing an utterance voice of a user, the artificial intelligence apparatus comprising:
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 first speech recognition results from the first sound data;
a microphone configured to obtain second sound data including the utterance voice; and
a processor configured to:
receive the 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 based on the first speech recognition results, the second speech recognition result and an environment variable; and
perform a control corresponding to the final speech recognition result,
wherein the processor generating the final speech recognition result is further configured to:
determine the environment variable corresponding to an utterance time point of the user by using the second sound data,
input the environment variable to a weight determination model,
obtain weights for speech recognition results that output from the weight determination model,
assign one or more weight from among the weights to the first speech recognition results and assign another weight from among the weights to the second speech recognition result,
sum weighted first speech recognition results and a weighted second speech recognition result, and
generate the final speech recognition results based on the sum,
wherein the environment variable includes at least one of a noise level, a noise type, an utterance level, or positional relation, and
wherein the weight determination model includes an artificial neural network.
2. The artificial intelligence apparatus according to claim 1 , wherein the at least one external artificial intelligence apparatus generates the first speech recognition results from the first sound data by using a first speech recognition model corresponding to each of the at least one external artificial intelligence apparatus, and
the processor is configured to generate the second speech recognition result from the second sound data by using a second speech recognition model.
3. The artificial intelligence apparatus according to claim 2 , wherein the first speech recognition model and the second speech recognition model include an artificial neural network and are learned by using a machine learning algorithm or a deep learning algorithm.
4. The artificial intelligence apparatus according to claim 3 , wherein each of the first speech recognition model and the second speech recognition model is learned by using training data corresponding to an application environment.
5. The artificial intelligence apparatus according to claim 1 , wherein the noise type includes at least one of media, a vibration, a conversation, a wind, or a daily life noise.
6. The artificial intelligence apparatus according to claim 5 , wherein the processor is configured to:
increase weights for a TV, a radio, and a speaker if the noise type includes the media;
increase weights for a refrigerator and a washing machine if the noise type includes the vibration; and
increase weights for a cleaner, an air purifier, a fan, and an air conditioner if the noise type includes the wind.
7. The artificial intelligence apparatus according to claim 1 , wherein the processor is configured to determine the noise type from the second sound data by using a noise classification model.
8. The artificial intelligence apparatus according to claim 7 , wherein the noise classification model includes an artificial neural network and is learned by using a machine learning algorithm or a deep learning algorithm.
9. The artificial intelligence apparatus according to claim 1 , wherein the processor is configured to:
determine a distance from each of the artificial intelligence apparatuses to the user based on the positional relation; and
increase a weight for each of the artificial intelligence apparatuses as the distance is shorter.
10. The artificial intelligence apparatus according to claim 1 , wherein the processor is configured to:
receive a first timestamp for a time point of receiving the first sound data from each of the at least one external artificial intelligence apparatus;
obtain a second timestamp for a time point of receiving the second sound data;
calculate a reception time difference in the at least one external artificial intelligence apparatus based on the first timestamp and the second timestamp;
determine a location of the user based on the reception time difference; and
determine the positional relation based on the determined location of the user.
11. A method for recognizing an utterance voice of a user, the method comprising:
receiving first speech recognition results from each of at least one external artificial intelligence apparatus which obtains first sound data including the utterance voice of the user to generate the first speech recognition results from the first sound data;
obtaining second sound data including the utterance voice;
generating a second speech recognition result from the second sound data;
generating a final speech recognition result for the utterance voice based on the first speech recognition results, the second speech recognition result and an environment variable; and
performing a control corresponding to the final speech recognition result,
wherein the method further comprises:
determining the environment variable corresponding to an utterance time point of the user by using the second sound data;
inputting the environment variable to a weight determination model;
obtaining weights for speech recognition results that output from the weight determination model;
assigning one or more weights from among the weights to the first speech recognition results and assigning another weight from among the weights to the second speech recognition result;
summing weighted first speech recognition results and a weighted second speech recognition result to generate a sum; and
generating the final speech recognition results based on the sum,
wherein the environment variable includes at least one of a noise level, a noise type, an utterance level, or positional relation, and
wherein the weight determination model includes an artificial neural network.
12. A non-transitory recording medium recorded with a program to perform a method for recognizing an utterance voice of a user, wherein the method includes:
receiving first speech recognition results from each of at, least one external artificial intelligence apparatus which obtains first sound data including the utterance voice of the user to generate the first speech recognition results from the first sound data;
obtaining second sound data including the utterance voice;
generating a second speech recognition result from the second sound data;
generating a final speech recognition result for the utterance voice based on the first speech recognition results, the second speech recognition result and an environment variable; and
performing a control corresponding to the final speech recognition result,
wherein the method further comprises:
determining the environment variable corresponding to an utterance time point of the user by using the second sound data;
inputting the environment variable to a weight determination model;
obtaining weights for speech recognition results that output from the weight determination model;
assigning one or more weights from among the weights to the first speech recognition results and assigning another weight from among the weights to the second speech recognition result;
summing weighted first speech recognition results and a weighted second speech recognition result to generate a sum; and
generating the final speech recognition results based on the sum,
wherein the environment variable includes at least one of a noise level, a noise type, an utterance level, or positional relation, and
wherein the weight determination model includes an artificial neural network.
US16/557,377
2019-08-21
2019-08-30
Artificial intelligence apparatus and method for recognizing utterance voice of user
Active
2040-01-23
US11164586B2
( en )
Applications Claiming Priority (2)
Application Number
Priority Date
Filing Date
Title
KR1020190102695A
KR102281602B1
( en )
2019-08-21
2019-08-21
Artificial intelligence apparatus and method for recognizing utterance voice of user
KR10-2019-0102695
2019-08-21
Publications (2)
Publication Number
Publication Date
US20190385614A1
US20190385614A1 ( en )
2019-12-19
US11164586B2
true
US11164586B2 ( en )
2021-11-02
Family
ID=67950922
Family Applications (1)
Application Number
Title
Priority Date
Filing Date
US16/557,377
Active
2040-01-23
US11164586B2
( en )
2019-08-21
2019-08-30
Artificial intelligence apparatus and method for recognizing utterance voice of user
Country Status (2)
Country
Link
US
( 1 )
US11164586B2
( en )
KR
( 1 )
KR102281602B1
( en )
Cited By (2)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20250095645A1
( en )
*
2023-09-19
2025-03-20
Hyundai Motor Company
Method and apparatus for inferring user intention in vehicle voice recognition system
US12370680B2
( en )
*
2022-07-18
2025-07-29
Samsung Electronics Co., Ltd.
Robot for acquiring learning data and method for controlling thereof
Families Citing this family (14)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
KR102912749B1
( en )
*
2019-09-30
2026-01-16
ìì§ì ì 주ìíì¬
Artificial intelligence apparatus and method for recognizing speech in consideration of utterance style
KR102344044B1
( en )
*
2020-01-22
2021-12-27
주ìíì¬ ìì§ì íë¬ì¤
Settop terminal and operating method of thereof
US11430445B2
( en )
2020-01-30
2022-08-30
Walmart Apollo, Llc
Detecting voice grocery concepts from catalog items
US11966964B2
( en )
2020-01-31
2024-04-23
Walmart Apollo, Llc
Voice-enabled recipe selection
KR102836970B1
( en )
*
2020-01-31
2025-07-24
ì¼ì±ì ì주ìíì¬
Electronic device and Method for controlling the electronic device thereof
KR102319013B1
( en )
*
2020-01-31
2021-10-29
íêµê³¼í기ì ì
Method and system for personality recognition from dialogues
WO2021177486A1
( en )
*
2020-03-05
2021-09-10
(주)ì¤ë§í¸ë ì´ëìì¤í
Radar-based voice recognition service device and method
US20220165291A1
( en )
*
2020-11-20
2022-05-26
Samsung Electronics Co., Ltd.
Electronic apparatus, control method thereof and electronic system
CN112652310B
( en )
*
2020-12-31
2024-08-09
ä¹é«ä¿¡æ¯ç§æï¼ä¸æµ·ï¼è¡ä»½æéå ¬å¸
Distributed speech processing system and method
CN114400006B
( en )
*
2022-01-24
2024-03-15
è ¾è®¯ç§æï¼æ·±å³ï¼æéå ¬å¸
Speech recognition method and device
KR20240011047A
( en )
*
2022-07-18
2024-01-25
ì¼ì±ì ì주ìíì¬
A Robot for acquiring learning data and Method for controlling thereof
CN115203690B
( en )
*
2022-08-04
2025-07-18
æµæ±å·¥ä¸å¤§å¦
Deep learning model safety reinforcement method based on abnormal deviation type neurons
KR20240053154A
( en )
2022-10-17
2024-04-24
ì¡ìì¸
Speech recognition media playback device and method
WO2026038671A1
( en )
*
2024-08-12
2026-02-19
ì¼ì±ì ì주ìíì¬
Electronic device for improving voice recognition rate and operation method thereof
Citations (24)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
KR20040010860A
( en )
2002-07-25
2004-02-05
주ìíì¬ ììí í¬
Surrounding-condition-adaptive voice recognition device including multiple recognition module and the method thereof
US20130073293A1
( en )
*
2011-09-20
2013-03-21
Lg Electronics Inc.
Electronic device and method for controlling the same
US20150120288A1
( en )
*
2013-10-29
2015-04-30
At&T Intellectual Property I, L.P.
System and method of performing automatic speech recognition using local private data
US9384736B2
( en )
*
2012-08-21
2016-07-05
Nuance Communications, Inc.
Method to provide incremental UI response based on multiple asynchronous evidence about user input
US20170064427A1
( en )
*
2014-04-21
2017-03-02
Apple Inc.
Wireless earphone
US20170133000A1
( en )
*
2015-11-06
2017-05-11
Cirrus Logic International Semiconductor Ltd.
Feedback howl management in adaptive noise cancellation system
US9674328B2
( en )
*
2011-02-22
2017-06-06
Speak With Me, Inc.
Hybridized client-server speech recognition
US10032455B2
( en )
*
2011-01-07
2018-07-24
Nuance Communications, Inc.
Configurable speech recognition system using a pronunciation alignment between multiple recognizers
US20180268837A1
( en )
*
2017-03-20
2018-09-20
Bose Corporation
Audio signal processing for noise reduction
US20180286425A1
( en )
*
2017-03-31
2018-10-04
Samsung Electronics Co., Ltd.
Method and device for removing noise using neural network model
WO2018189031A1
( en )
2017-04-14
2018-10-18
Philips Lighting Holding B.V.
A positioning system for determining a location of an object
KR20180119070A
( en )
2017-04-24
2018-11-01
ìì§ì ì 주ìíì¬
electronic device
US20180350381A1
( en )
*
2017-05-31
2018-12-06
Apple Inc.
System and method of noise reduction for a mobile device
US20190206403A1
( en )
*
2017-12-28
2019-07-04
Samsung Electronics Co., Ltd.
Sound output system and voice processing method
US20190212976A1
( en )
*
2017-05-03
2019-07-11
The Reverie Group, Llc
Enhanced control and security of a voice controlled device
US20200146097A1
( en )
*
2018-11-02
2020-05-07
Plantronics, Inc.
Discontinuous Transmission on Short-Range Packet-Based Radio Links
US20200160838A1
( en )
*
2018-11-21
2020-05-21
Samsung Electronics Co., Ltd.
Speech recognition method and apparatus
US20200213729A1
( en )
*
2018-12-20
2020-07-02
Sonos, Inc.
Optimization of network microphone devices using noise classification
US10714072B1
( en )
*
2019-04-01
2020-07-14
Cirrus Logic, Inc.
On-demand adaptive active noise cancellation
US10748529B1
( en )
*
2013-03-15
2020-08-18
Apple Inc.
Voice activated device for use with a voice-based digital assistant
US20200336846A1
( en )
*
2019-04-17
2020-10-22
Oticon A/S
Hearing device comprising a keyword detector and an own voice detector and/or a transmitter
US20200374269A1
( en )
*
2019-05-22
2020-11-26
Synaptics Incorporated
Secure audio systems and methods
US10896675B1
( en )
*
2018-06-29
2021-01-19
X Development Llc
Multi-tiered command processing
US20210044889A1
( en )
*
2019-08-06
2021-02-11
Plantronics, Inc.
Neckband Headset with Noise Rejection
2019
2019-08-21
KR
KR1020190102695A
patent/KR102281602B1/en
active
Active
2019-08-30
US
US16/557,377
patent/US11164586B2/en
active
Active
Patent Citations (25)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
KR20040010860A
( en )
2002-07-25
2004-02-05
주ìíì¬ ììí í¬
Surrounding-condition-adaptive voice recognition device including multiple recognition module and the method thereof
US10032455B2
( en )
*
2011-01-07
2018-07-24
Nuance Communications, Inc.
Configurable speech recognition system using a pronunciation alignment between multiple recognizers
US9674328B2
( en )
*
2011-02-22
2017-06-06
Speak With Me, Inc.
Hybridized client-server speech recognition
US20130073293A1
( en )
*
2011-09-20
2013-03-21
Lg Electronics Inc.
Electronic device and method for controlling the same
US9384736B2
( en )
*
2012-08-21
2016-07-05
Nuance Communications, Inc.
Method to provide incremental UI response based on multiple asynchronous evidence about user input
US10748529B1
( en )
*
2013-03-15
2020-08-18
Apple Inc.
Voice activated device for use with a voice-based digital assistant
US20150120288A1
( en )
*
2013-10-29
2015-04-30
At&T Intellectual Property I, L.P.
System and method of performing automatic speech recognition using local private data
US20170064427A1
( en )
*
2014-04-21
2017-03-02
Apple Inc.
Wireless earphone
US20170133000A1
( en )
*
2015-11-06
2017-05-11
Cirrus Logic International Semiconductor Ltd.
Feedback howl management in adaptive noise cancellation system
US20180268837A1
( en )
*
2017-03-20
2018-09-20
Bose Corporation
Audio signal processing for noise reduction
US20180286425A1
( en )
*
2017-03-31
2018-10-04
Samsung Electronics Co., Ltd.
Method and device for removing noise using neural network model
WO2018189031A1
( en )
2017-04-14
2018-10-18
Philips Lighting Holding B.V.
A positioning system for determining a location of an object
US20200135194A1
( en )
2017-04-24
2020-04-30
Lg Electronics Inc.
Electronic device
KR20180119070A
( en )
2017-04-24
2018-11-01
ìì§ì ì 주ìíì¬
electronic device
US20190212976A1
( en )
*
2017-05-03
2019-07-11
The Reverie Group, Llc
Enhanced control and security of a voice controlled device
US20180350381A1
( en )
*
2017-05-31
2018-12-06
Apple Inc.
System and method of noise reduction for a mobile device
US20190206403A1
( en )
*
2017-12-28
2019-07-04
Samsung Electronics Co., Ltd.
Sound output system and voice processing method
US10896675B1
( en )
*
2018-06-29
2021-01-19
X Development Llc
Multi-tiered command processing
US20200146097A1
( en )
*
2018-11-02
2020-05-07
Plantronics, Inc.
Discontinuous Transmission on Short-Range Packet-Based Radio Links
US20200160838A1
( en )
*
2018-11-21
2020-05-21
Samsung Electronics Co., Ltd.
Speech recognition method and apparatus
US20200213729A1
( en )
*
2018-12-20
2020-07-02
Sonos, Inc.
Optimization of network microphone devices using noise classification
US10714072B1
( en )
*
2019-04-01
2020-07-14
Cirrus Logic, Inc.
On-demand adaptive active noise cancellation
US20200336846A1
( en )
*
2019-04-17
2020-10-22
Oticon A/S
Hearing device comprising a keyword detector and an own voice detector and/or a transmitter
US20200374269A1
( en )
*
2019-05-22
2020-11-26
Synaptics Incorporated
Secure audio systems and methods
US20210044889A1
( en )
*
2019-08-06
2021-02-11
Plantronics, Inc.
Neckband Headset with Noise Rejection
Cited By (3)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US12370680B2
( en )
*
2022-07-18
2025-07-29
Samsung Electronics Co., Ltd.
Robot for acquiring learning data and method for controlling thereof
US20250095645A1
( en )
*
2023-09-19
2025-03-20
Hyundai Motor Company
Method and apparatus for inferring user intention in vehicle voice recognition system
US12609118B2
( en )
*
2023-09-19
2026-04-21
Hyundai Motor Company
Method and apparatus for inferring user intention in vehicle voice recognition system
Also Published As
Publication number
Publication date
US20190385614A1
( en )
2019-12-19
KR102281602B1
( en )
2021-07-29
KR20190104490A
( en )
2019-09-10
Similar Documents
Publication
Publication Date
Title
US11663516B2
( en )
2023-05-30
Artificial intelligence apparatus and method for updating artificial intelligence model
US20190385614A1
( en )
2019-12-19
Artificial intelligence apparatus and method for recognizing utterance voice of user
US11669781B2
( en )
2023-06-06
Artificial intelligence server and method for updating artificial intelligence model by merging plurality of pieces of update information
US11495214B2
( en )
2022-11-08
Artificial intelligence device for providing voice recognition service and method of operating the same
US10872438B2
( en )
2020-12-22
Artificial intelligence device capable of being controlled according to user&#39;s gaze and method of operating the same
US11568239B2
( en )
2023-01-31
Artificial intelligence server and method for providing information to user
US11383379B2
( en )
2022-07-12
Artificial intelligence server for controlling plurality of robots and method for the same
US11507825B2
( en )
2022-11-22
AI apparatus and method for managing operation of artificial intelligence system
US11501757B2
( en )
2022-11-15
Artificial intelligence apparatus
US11769047B2
( en )
2023-09-26
Artificial intelligence apparatus using a plurality of output layers and method for same
US11398222B2
( en )
2022-07-26
Artificial intelligence apparatus and method for recognizing speech of user in consideration of user&#39;s application usage log
US11322134B2
( en )
2022-05-03
Artificial intelligence device and operating method thereof
US11508358B2
( en )
2022-11-22
Artificial intelligence apparatus and method for recognizing speech in consideration of utterance style
US11449074B2
( en )
2022-09-20
Robot for providing guidance service using artificial intelligence and method of operating the same
US11182922B2
( en )
2021-11-23
AI apparatus and method for determining location of user
US11755033B2
( en )
2023-09-12
Artificial intelligence device installed in vehicle and method therefor
US11210567B2
( en )
2021-12-28
Artificial intelligence apparatus and method for recognizing object
US11977384B2
( en )
2024-05-07
Control system for controlling a plurality of robots using artificial intelligence
US11423877B2
( en )
2022-08-23
Robot for providing guidance service using artificial intelligence and method of operating the same
US11867791B2
( en )
2024-01-09
Artificial intelligence apparatus for determining path of user and method for the same
US20210334461A1
( en )
2021-10-28
Artificial intelligence apparatus and method for generating named entity table
US20210407517A1
( en )
2021-12-30
Artificial intelligence robot for providing voice recognition function and method of operating the same
US11863627B2
( en )
2024-01-02
Smart home device and method
EP4113905B1
( en )
2026-01-28
Artificial intelligence device for providing device control function based on interworking between devices and method therefor
US11464380B2
( en )
2022-10-11
Artificial intelligence cleaner and operating method thereof
Legal Events
Date
Code
Title
Description
2019-08-30
FEPP
Fee payment procedure
Free format text : ENTITY STATUS SET TO UNDISCOUNTED (ORIGINAL EVENT CODE: BIG.); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY
2019-09-10
AS
Assignment
Owner name : LG ELECTRONICS INC., KOREA, REPUBLIC OF
Free format text : ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KIM, JAEHONG;KIM, HYOEUN;JEONG, HANGIL;REEL/FRAME:050324/0513
Effective date : 20190828
2019-11-05
STPP
Information on status: patent application and granting procedure in general
Free format text : DOCKETED NEW CASE - READY FOR EXAMINATION
2021-03-23
STPP
Information on status: patent application and granting procedure in general
Free format text : NON FINAL ACTION MAILED
2021-06-28
STPP
Information on status: patent application and granting procedure in general
Free format text : RESPONSE TO NON-FINAL OFFICE ACTION ENTERED AND FORWARDED TO EXAMINER
2021-07-08
STPP
Information on status: patent application and granting procedure in general
Free format text : NOTICE OF ALLOWANCE MAILED -- APPLICATION RECEIVED IN OFFICE OF PUBLICATIONS
2021-10-01
STPP
Information on status: patent application and granting procedure in general
Free format text : PUBLICATIONS -- ISSUE FEE PAYMENT RECEIVED
2021-10-04
STPP
Information on status: patent application and granting procedure in general
Free format text : PUBLICATIONS -- ISSUE FEE PAYMENT VERIFIED
2021-10-13
STCF
Information on status: patent grant
Free format text : PATENTED CASE
2025-04-07
MAFP
Maintenance fee payment
Free format text : PAYMENT OF MAINTENANCE FEE, 4TH YEAR, LARGE ENTITY (ORIGINAL EVENT CODE: M1551); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY
Year of fee payment : 4