Patent Publication Number: US-2021173614-A1

Title: Artificial intelligence device and method for operating the same

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     Pursuant to 35 U.S.C. § 119(a), this application claims the benefit of earlier filing date and right of priority to Korean Patent Application No. 10-2019-0160620, filed on Dec. 5, 2019, the contents of which are all hereby incorporated by reference herein in its entirety. 
     BACKGROUND 
     The present disclosure relates to an artificial intelligence device, and more particularly, to an artificial intelligence device that recognizes an object with a video and controls an audio output of the recognized object. 
     When playing a video, most of objects contained in the video and the person who wants to hear by zooming in to the sound are actually limited. 
     That is, after recording a sound source including a video by using a product capable of acquiring a multi-channel sound source including a mobile device, when the corresponding video is played, a user&#39;s need to listen more closely to a specific target sound among various sound sources of the video occurs. 
     If the user provides a means that focuses on an audio generated from a specific object among the plurality of objects contained in the image, the user may watch the video more usefully. 
     SUMMARY 
     Embodiments provide a function of selecting one or more objects capable of audio zoom-in among a plurality of objects on a playback screen when a video is played so as to allow a user to select the objects. 
     Embodiments also provide a function of distinguishing a plurality of objects contained in a video by using an image recognition technique, distinguishing one or more objects capable of generating sound among the plurality of objects, and audio zoom-in of the one or more distinguished objects. 
     In one embodiment, an artificial intelligence device may identify a plurality of objects contained in the video, acquires one or more objects, which are capable of outputting an audio, of the plurality of identified objects, display one or more volume adjustment items for adjusting a volume of the audio output from each of the one or more acquired objects on a display, and adjust the volume of the audio output from the corresponding object according to an operation command of each of the volume adjustment items. 
     In another embodiment, an artificial intelligence device may control one or more speakers to increase in output of an audio generated by a selected object among one or more objects capable of outputting the audio. 
     Effects of the Invention 
     According to the exemplary embodiment, when the user plays the video, the user may focus on the desired object to watch the video, thereby feeling the improved video viewing experience. 
     In addition, the user may conveniently adjust the audio output of the desired object when playing the video. 
     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 view of an AI device according to an embodiment. 
         FIG. 2  is a view of an AI server according to an embodiment. 
         FIG. 3  is a view of an AI system according to an embodiment. 
         FIG. 4  is a view of an AI device according to another embodiment. 
         FIG. 5  is a flowchart for explaining a method for operating an artificial intelligence device according to an embodiment. 
         FIGS. 6 and 7  are views for explaining a process of training an object detection model according to an embodiment. 
         FIG. 8  is a view illustrating a process of training an object identification model according to an embodiment. 
         FIG. 9  is a view illustrating an example of identifying an utterable object and adjusting an audio output of the identified object when playing a video according to an embodiment. 
         FIG. 10  is a view illustrating a process of adjusting an audio output of a selected object among a plurality of objects contained in a video according to an embodiment. 
         FIG. 11  is a flowchart illustrating a method for adjusting an audio volume of an object according to an embodiment. 
         FIG. 12  is a view illustrating an example of clustering (grouping) a plurality of objects when the plurality of utterable objects contained in a video are provided according to an embodiment. 
         FIG. 13  is a view illustrating a result of clustering the plurality of objects into a plurality of clusters according to an embodiment. 
         FIG. 14  is a view illustrating an example of clustering a plurality of objects contained in the video according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     &lt;Artificial Intelligence (AI)&gt; 
     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 training an artificial neural network in a state in which a label for learning data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the learning data is input to the artificial neural network. The unsupervised learning may refer to a method of training an artificial neural network in a state in which a label for learning 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. 
     &lt;Robot&gt; 
     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. 
     &lt;Self-Driving&gt; 
     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. 
     In this case, the self-driving vehicle may be regarded as a robot having a self-driving function. 
     &lt;eXtended Reality (XR)&gt; 
     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 illustrated 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  illustrates an AI device  100  according to an embodiment of the present disclosure. 
     The AI device (or an AI apparatus)  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 device  100  may include a communication unit  110 , an input unit  120 , a learning processor  130 , a sensing device  140 , an output device  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 devices  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), Bluetooth™, 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. 
     In this case, 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 a learning data for model learning and an input data to be used if an output is acquired by using learning model. The input unit  120  may acquire raw input data. In this case, 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 learning 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 learning data, and the inferred value may be used as a basis for determination to perform a certain operation. 
     At this time, the learning processor  130  may perform AI processing together with the learning processor  240  of the AI server  200 . 
     At this time, the learning processor  130  may include a memory integrated or implemented in the AI device  100 . Alternatively, the learning processor  130  may be implemented by using the memory  170 , an external memory directly connected to the AI device  100 , or a memory held in an external device. 
     The sensing device  140  may acquire at least one of internal information about the AI device  100 , ambient environment information about the AI device  100 , and user information by using various sensors. 
     Examples of the sensors included in the sensing device  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 device  150  may generate an output related to a visual sense, an auditory sense, or a haptic sense. 
     on, 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 device  100 . For example, the memory  170  may store input data acquired by the input unit  120 , learning data, a learning model, a learning history, and the like. 
     The processor  180  may determine at least one executable operation of the AI device  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 device  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 device  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&#39;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&#39;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 device  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 device  100  in combination so as to drive the application program. 
       FIG. 2  illustrates an AI server  200  according to an embodiment of the present disclosure. 
     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. In this case, the AI server  200  may be included as a partial configuration of the AI device  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  may transmit and receive data to and from an external device such as the AI device  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 learning 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 device  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 of 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 devices  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 devices constituting the AI system  1 , that is, 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  through the cloud network  10 , and may assist at least part of AI processing of the connected AI devices  100   a  to  100   e.    
     At this time, the AI server  200  may learn the artificial neural network according to the machine learning algorithm instead of the AI devices  100   a  to  100   e , and may directly store the learning model or transmit the learning model to the AI devices  100   a  to  100   e.    
     At this time, the AI server  200  may receive input data from the AI devices  100   a  to  100   e , may infer the result value for the accommodated 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 devices  100   a  to  100   e.    
     Alternatively, the AI devices  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 devices  100   a  to  100   e  to which the above-described technology is applied will be described. The AI devices  100   a  to  100   e  illustrated in  FIG. 3  may be regarded as a specific embodiment of the AI device  100  illustrated in  FIG. 1 . 
     &lt;AI+Robot&gt; 
     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 provided as 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 . 
     At this time, 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 accommodated 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 apparatus 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. At this time, the robot  100   a  may acquire the intention information of the interaction due to the user&#39;s operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation. 
     &lt;AI+Self-Driving&gt; 
     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 path 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 path 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 movement line 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 . 
     In this case, the self-driving vehicle  100   b  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 self-driving vehicle  100   b  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 apparatus to determine the travel path and the travel plan, and may control the driving device such that the self-driving vehicle  100   b  travels along the determined travel path and travel plan. 
     The map data may include object identification information about various objects arranged in the space (for example, road) in which the self-driving vehicle  100   b  travels. For example, the map data may include object identification information about fixed objects such as street lamps, rocks, and buildings and movable objects such as vehicles and pedestrians. The object identification information may include a name, a type, a distance, and a position. 
     In addition, the self-driving vehicle  100   b  may perform the operation or travel by controlling the driving device based on the control/interaction of the user. In this case, the self-driving vehicle  100   b  may acquire the intention information of the interaction due to the user&#39;s operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation. 
     &lt;AI+XR&gt; 
     The XR device  100   c , to which the AI technology is applied, may be implemented by a head-mount display (HMD), a head-up display (HUD) provided in the vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a fixed robot, a mobile robot, or the like. 
     The XR device  100   c  may analyzes three-dimensional point cloud data or image data acquired from various sensors or the external devices, generate position data and attribute data for the three-dimensional points, acquire information about the surrounding space or the real object, and render to output the XR object to be output. For example, the XR device  100   c  may output an XR object including the additional information about the recognized object in correspondence to the recognized object. 
     The XR device  100   c  may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the XR device  100   c  may recognize the real object from the three-dimensional point cloud data or the image data by using the learning model, and may provide information corresponding to the recognized real object. The learning model may be directly learned from the XR device  100   c , or may be learned from the external device such as the AI server  200 . 
     In this case, the XR device  100   c  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. 
     &lt;AI+Robot+Self-Driving&gt; 
     The robot  100   a , to which the AI technology and the self-driving technology are 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 , to which the AI technology and the self-driving technology are applied, may refer to the robot itself having the self-driving function or the robot  100   a  interacting with the self-driving vehicle  100   b.    
     The robot  100   a  having the self-driving function may collectively refer to a device that moves for itself along the given movement line without the user&#39;s control or moves for itself by determining the movement line by itself. 
     The robot  100   a  and the self-driving vehicle  100   b  having the self-driving function may use a common sensing method so as to determine at least one of the travel route or the travel plan. For example, the robot  100   a  and the self-driving vehicle  100   b  having the self-driving function may determine at least one of the travel route or the travel plan by using the information sensed through the lidar, the radar, and the camera. 
     The robot  100   a  that interacts with the self-driving vehicle  100   b  exists separately from the self-driving vehicle  100   b  and may perform operations interworking with the self-driving function of the self-driving vehicle  100   b  or interworking with the user who rides on the self-driving vehicle  100   b.    
     At this time, the robot  100   a  interacting with the self-driving vehicle  100   b  may control or assist the self-driving function of the self-driving vehicle  100   b  by acquiring sensor information on behalf of the self-driving vehicle  100   b  and providing the sensor information to the self-driving vehicle  100   b , or by acquiring sensor information, generating environment information or object information, and providing the information to the self-driving vehicle  100   b.    
     Alternatively, the robot  100   a  interacting with the self-driving vehicle  100   b  may monitor the user boarding the self-driving vehicle  100   b , or may control the function of the self-driving vehicle  100   b  through the interaction with the user. For example, when it is determined that the driver is in a drowsy state, the robot  100   a  may activate the self-driving function of the self-driving vehicle  100   b  or assist the control of the driving unit of the self-driving vehicle  100   b . The function of the self-driving vehicle  100   b  controlled by the robot  100   a  may include not only the self-driving function but also the function provided by the navigation system or the audio system provided in the self-driving vehicle  100   b.    
     Alternatively, the robot  100   a  that interacts with the self-driving vehicle  100   b  may provide information or assist the function to the self-driving vehicle  100   b  outside the self-driving vehicle  100   b . For example, the robot  100   a  may provide traffic information including signal information and the like, such as a smart signal, to the self-driving vehicle  100   b , and automatically connect an electric charger to a charging port by interacting with the self-driving vehicle  100   b  like an automatic electric charger of an electric vehicle. 
     &lt;AI+Robot+XR&gt; 
     The robot  100   a , to which the AI technology and the XR technology are 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, a drone, or the like. 
     The robot  100   a , to which the XR technology is applied, may refer to a robot subjected to control/interaction in an XR image. In this case, the robot  100   a  may be separated from the XR device  100   c  and interwork with each other. 
     If the robot  100   a , which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the robot  100   a  or the XR device  100   c  may generate the XR image based on the sensor information, and the XR device  100   c  may output the generated XR image. The robot  100   a  may operate based on the control signal input through the XR device  100   c  or the user&#39;s interaction. 
     For example, the user may confirm the XR image corresponding to the time point of the robot  100   a  interworking remotely through the external device such as the XR device  100   c , adjust the self-driving travel path of the robot  100   a  through interaction, control the operation or driving, or confirm the information about the surrounding object. 
     &lt;AI+Self-Driving+XR&gt; 
     The self-driving vehicle  100   b , to which the AI technology and the XR technology are applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like. 
     The self-driving vehicle  100   b , to which the XR technology is applied, may refer to a self-driving vehicle having a means for providing an XR image or a self-driving vehicle subjected to control/interaction in an XR image. Particularly, the self-driving vehicle  100   b  that is subjected to control/interaction in the XR image may be distinguished from the XR device  100   c  and interlocked with each other. 
     The self-driving vehicle  100   b  having the means for providing the XR image may acquire the sensor information from the sensors including the camera and output the generated XR image based on the acquired sensor information. For example, the self-driving vehicle  100   b  may include a HUD to output an XR image, thereby providing a passenger with a real object or an XR object corresponding to an object in the screen. 
     In this case, if the XR object is output to the HUD, at least part of the XR object may be outputted so as to overlap the actual object to which the passenger&#39;s gaze is directed. Meanwhile, if the XR object is output to the display provided in the self-driving vehicle  100   b , at least part of the XR object may be output so as to overlap the object in the screen. For example, the self-driving vehicle  100   b  may output XR objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, and the like. 
     If the self-driving vehicle  100   b , which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the self-driving vehicle  100   b  or the XR device  100   c  may generate the XR image based on the sensor information, and the XR device  100   c  may output the generated XR image. The self-driving vehicle  100   b  may operate based on the control signal input through the external device such as the XR device  100   c  or the user&#39;s interaction. 
       FIG. 4  illustrates an AI device  100  according to an embodiment of the present disclosure. 
     The redundant repeat of  FIG. 1  will be omitted below. 
     Referring to  FIG. 4 , the input unit  120  may include a camera  121  for image signal input, a microphone  122  for receiving audio signal input, and a user input unit  123  for receiving information from a user. 
     Voice data or image data collected by the input unit  120  are analyzed and processed as a user&#39;s control command. 
     Then, the input unit  120  is used for inputting image information (or signal), audio information (or signal), data, or information inputted from a user and the mobile terminal  100  may include at least one camera  121  in order for inputting image information. 
     The camera  121  processes image frames such as a still image or a video acquired by an image sensor in a video call mode or a capturing mode. The processed image frame may be displayed on the display unit  151  or stored in the memory  170 . 
     The microphone  122  processes external sound signals as electrical voice data. The processed voice data may be utilized variously according to a function (or an application program being executed) being performed in the mobile terminal  100 . Moreover, various noise canceling algorithms for removing noise occurring during the reception of external sound signals may be implemented in the microphone  122 . 
     The user input unit  123  is to receive information from a user and if information is inputted through the user input unit  123 , the processor  180  may control an operation of the mobile terminal  100  to correspond to the inputted information. 
     The user input unit  123  may include a mechanical input means (or a mechanical key, for example, a button, a dome switch, a jog wheel, and a jog switch at the front, back or side of the mobile terminal  100 ) and a touch type input means. As one example, a touch type input means may include a virtual key, a soft key, or a visual key, which is displayed on a touch screen through software processing or may include a touch key disposed at a portion other than the touch screen. 
     The output device  150  may include at least one of a display unit  151 , a sound output module  152 , a haptic module  153 , or an optical output module  154 . 
     The display unit  151  may display (output) information processed in the mobile terminal  100 . For example, the display unit  151  may display execution screen information of an application program running on the mobile terminal  100  or user interface (UI) and graphic user interface (GUI) information according to such execution screen information. 
     The display unit  151  may be formed with a mutual layer structure with a touch sensor or formed integrally, so that a touch screen may be implemented. Such a touch screen may serve as the user input unit  123  providing an input interface between the mobile terminal  100  and a user, and an output interface between the mobile terminal  100  and a user at the same time. 
     The sound output module  152  may output audio data received from the wireless communication unit  110  or stored in the memory  170  in a call signal reception or call mode, a recording mode, a voice recognition mode, or a broadcast reception mode. 
     The sound output module  152  may include a receiver, a speaker, and a buzzer. 
     The haptic module  153  generates various haptic effects that a user may feel. A representative example of a haptic effect that the haptic module  153  generates is vibration. 
     The optical output module  154  outputs a signal for notifying event occurrence by using light of a light source of the mobile terminal  100 . An example of an event occurring in the AI device  100  includes message reception, call signal reception, missed calls, alarm, schedule notification, e-mail reception, and information reception through an application. 
       FIG. 5  is a flowchart for explaining a method for operating an artificial intelligence device according to an embodiment. 
     Referring to  FIG. 5 , a processor  180  of the artificial intelligence apparatus  100  plays a video through the display unit  151  (S 501 ). 
     The processor  180  may separate a video and a sound source corresponding to the video to store the video and the sound source in the memory  170 . 
     The processor  180  of the artificial intelligence device  100  identifies a plurality of objects contained in the video (S 503 ). 
     The processor  180  may identify the plurality of objects from image data at a specific time point of the video or from image data in a specific playback section. 
     The processor  180  may detect the plurality of objects from the video and identify each of the plurality of detected objects based on an object detection model. 
     The object detection model may be a model for detecting the plurality of objects from an image. 
     The object detection model may be an artificial neural network based model trained by a deep learning algorithm or a machine learning algorithm. 
     The object detection model may be a model that is trained by the running processor  130  of the artificial intelligence device  100  and stored in the memory  170 . 
     For another example, the object detection model may be a model trained by the running processor  240  of the AI server  200  and transmitted from the AI server  200  to the artificial intelligence device  100 . 
     An example of detecting the plurality of objects from the image using the object detection model will be described with reference to the following drawings. 
       FIGS. 6 and 7  are views for explaining a process of learning the object detection model according to an embodiment. 
     Referring to  FIG. 6 , the object detection model  600  uses a training image data set  610  including a plurality of image data and acquires an object bounding box set  630  including a plurality of objects from each training image data. 
     The object bounding box set  630  may be a set of bounding boxes containing an object. 
     The object detection model  600  may detect the plurality of objects from the image data by using a YOLO (You Only Look Once) algorithm. 
     The YOLO (You Only Look Once) algorithm may be constituted by a plurality of CNNs. 
     The YOLO (You Only Look Once) algorithm will be described with reference to  FIG. 7 . 
     The YOLO (You Only Look Once) algorithm may include a grid division process, a prediction process, a reliability calculation process, and an object selection process. 
     The grid division process may be a process of dividing the image data  700  into a plurality of grids. The plurality of grids  701  may be the same size. 
     The prediction process may be a process of predicting the number of bounding boxes  710  designated in a predefined shape with respect to a center of the grid for each grid. 
     The bounding box designated as a predefined shape may be generated from data by the K-average algorithm and may contain dictionary information on the size and shape of the object. 
     Each of the bounding boxes may be designed to detect objects having different sizes and shapes. 
     Each of the bounding boxes may represent a shape or boundary of the object. 
     The reliability calculation process may be a process of calculating reliability of the bounding box according to whether the object is included in each of the obtained bounding boxes or only the background is alone. 
     The object determination process may be a process of determining that an object exists in a bounding box having reliability equal to or greater than a preset value according to the reliability calculation process. 
     The plurality of bounding boxes  730  and  750  included in the image data  700  may be extracted through the object determination process. 
       FIG. 5  will be described again. 
     The processor  180  may acquire identification information of each of the objects from the plurality of bounding boxes extracted through the object detection model  600 . 
     The processor  180  may identify an object existing in the bounding box from the image data corresponding to each of the bounding boxes using the object identification model. 
     The object identification model may be a learned artificial neural network based model using the deep learning algorithm or the machine learning algorithm. 
     The object identification model may be a model learned through supervised learning. 
     The object identification model may be a model for inferring identification information of the object from image data. The identification information of the object may be information for identifying the object such as a name of the object, an identifier of the object, and the like. 
     The object identification model may be a model that outputs identification information of the object using training data sets including the training image data and labeling data labeled in the training image data as input data. 
       FIG. 8  is a view illustrating a process of training an object identification model according to an embodiment. 
     Referring to  FIG. 8 , the object identification model  800  may infer object identification information using the training data set including the training image data and the labeling data labeled thereon. 
     The labeling data is correct answer data and may be object identification information. 
     The object identification model  800  may be trained to minimize a cost function corresponding to a difference between the labeling data and the object identification information. 
     The cost function of the object identification model  800  may be expressed as a squared mean of a difference between the label for the object identification information corresponding to each image data and the object identification information inferred from each image data. 
     When an input feature vector is extracted from the training image data and is input, the object identification result is output as a target feature vector, and the object identification model  800  is learned to minimize a loss function corresponding to a difference between the output target feature vector and the labeled object identification information. 
     The object identification model  800  may be trained by the running processor  130  of the artificial intelligence device  100  or the running processor  240  of the AI server  200  and mounted on the artificial intelligence device  100 . 
     The object identification model  800  may determine first object identification information from first image data corresponding to the first bounding box  730  illustrated in  FIG. 7 . For example, the first object identification information may be a dog. 
     The object identification model  800  may determine second object identification information from second image data corresponding to the second bounding box  750 . For example, the second object identification information may be a person. 
     As described above, it may be identified which object is the object from the image data through the object identification model  800 . 
       FIG. 5  will be described again. 
     The processor  180  of the artificial intelligence device  100  acquires one or more objects capable of speaking a voice among the identified plurality of objects (S 505 ). 
     The processor  180  may determine whether the corresponding object is an object capable of speaking a voice based on the object identification information acquired through the object identification model  800 . 
     The processor  180  may store an object list representing the object capable of speaking a voice in the memory  170 . For example, the object list may include objects capable of outputting a voice, like animals such as people, cars, dogs, and cats. 
     The processor  180  may compare the object list stored in the memory  170  with the object identification information to determine whether the identified object is an utterable object from the video. 
     As the result of the comparison, when the object identification information is included in the object list, the processor  180  may determine that the identified object is the utterable object. 
     As the result of the comparison, when the object identification information is not included in the object list, the processor  180  may determine that the identified object is an object that is not spoken. 
     The processor  180  of the artificial intelligence device  100  displays a volume adjustment item for adjusting the audio volume of the one or more obtained objects on the display unit  151  (S 507 ). 
     The processor  180  may display the volume adjustment item for adjusting the volume of an audio output by the utterable object at a position adjacent to the object. 
     When the processor  180  receives a command for adjusting the volume of the audio of the object, the processor  180  may display the volume adjustment item on the display unit  151 . 
     The command for adjusting the audio volume of the object may be a voice command such as &lt;adjust object sound!&gt; or a command for selecting an utterable object. 
     The processor  180  of the artificial intelligence device  100  adjusts and outputs the volume of the audio spoken by the corresponding object according to an operation command of the volume adjustment item (S 509 ). 
     The processor  180  may control the sound output unit  152  to adjust the volume of the audio spoken by the object according to the manipulation command of the volume adjustment item. 
     The sound output unit  152  may include one or more speakers. 
     The processor  180  may control the one or more speakers to adjust an output of the audio spoken by the object according to the operation command. 
     The processor  180  may control the one or more speakers to increase in volume of an audio output by an object selected by the user. 
     The processor  180  may adjust the output of the speaker corresponding to a position of the object by tracking a position of the selected object. 
     For example, when the object is disposed at a left side, the processor  180  may increase in audio output of the speaker disposed at the left side and decrease in audio output of the speaker disposed on a right side. 
     An embodiment of adjusting the audio volume of the object will be described with reference to the following drawings. 
       FIG. 9  is a view illustrating an example of identifying the utterable object and adjusting the audio output of the identified object when playing the video according to an embodiment. 
       FIG. 9  illustrates a video  900  being played through the display unit  151  of the artificial intelligence device  100 . 
     The processor  180  may identify a plurality of spoken objects  911 ,  913 , and  915  contained in the video  900  by using the object detection model  600  and the object identification model  800 . 
     The plurality of objects  911 ,  913 , and  915  may all be humans. 
     The processor  180  may display indicators  912 ,  914 , and  916  for distinguishing each of the plurality of objects  911 ,  913 , and  915 . 
     That is, the first indicator  912  is for distinguishing the first object  911 , the second indicator  914  is for distinguishing the second object  913 , and the third indicator  916  is for distinguishing the third object  915 . 
     The processor  180  may display a volume icon indicating that the audio output of the object is adjustable and a volume adjustment item for adjusting the audio output of the object, which are adjacent to each of the plurality of objects  911 ,  913 , and  915 . 
     For example, the first volume icon  901  and the first volume adjustment item  921  may be displayed near the first object  911 . The first volume adjustment item  921  may be a bar-shaped item for adjusting the volume of audio output from the first object  911  according to a user&#39;s manipulation command. 
     Similarly, the second volume icon  903  and the second volume adjustment item  923  may be displayed near the second object  913 . The third volume icon  905  and the third volume control item  925  may be displayed near the third object  915 . 
     The processor  180  may activate or deactivate an output of an audio spoken by the object according to the selection of the volume icon. For example, when the second volume icon  903  that is in the activated state is selected, the processor  180  may mute the output of the audio spoken by the second object  913 . 
     When any one of the plurality of objects  911 ,  913 , and  915  is selected, only an indicator, a volume icon, and a volume control item, which correspond to the selected object, may be displayed, and the indicator, volume icon, and volume adjustment item, which correspond to the remaining objects, may disappear. 
       FIG. 10  is a view illustrating a process of adjusting an audio output of a selected object among the plurality of objects contained in the video according to an embodiment. 
     In  FIG. 9 , when the processor  180  receives a command for selecting the first object  911 , as illustrated in  FIG. 10 , only the first indicator  912 , the first volume icon  901 , and the first volume adjustment item  921 , which correspond to the first object  911 , may be displayed, and the indicators, the volume icons, and the volume adjustment items, which correspond to the remaining objects  913  and  915  may not be displayed. 
     The user may manipulate the adjustment bar included in the first volume adjustment item  921  to adjust the volume of the audio spoken by the first object  911 . 
     In another embodiment, when the first object  911  is selected, the processor  180  enlarges the size of the first object  911  while controlling the volume of the audio spoken by the first object  911  to play the video  900 . 
     As described above, according to an embodiment, the user may watch a video by focusing on a desired object when playing the video. In addition, the user may conveniently adjust the audio output of the desired object when playing the video. 
     Meanwhile, in order to adjust the audio output of a selected object among the plurality of utterable objects when playing the video, a method of distinguishing the audio output for each object is required. This is because a position of the object may change with time when the video is played. 
       FIG. 11  is a flowchart illustrating a method for adjusting an audio volume of an object according to an embodiment. 
       FIG. 11  may be a view for describing an operation S 509  of  FIG. 5  in detail. 
     The processor  180  of the artificial intelligence device  100  receives a command for selecting any one of one or more utterable objects (S 1101 ). 
     The user may select an object by touching the object inside the indicator. 
     The processor  180  of the artificial intelligence device  100  performs beamforming and object tracking in a direction of the selected object according to the received command (S 1103 ). 
     The input unit  120  of the artificial intelligence device  100  may include a plurality of microphones. 
     The beamforming may be a signal processing technique for receiving an audio signal corresponding to the selected object among the audio signals received by each of the one or more microphones in comparison with other signals. 
     That is, the processor  180  may reinforce the audio signal output by the selected object by using the beamforming. 
     The object tracking may be a technique for continuously tracking an object of interest from the video. 
     After the object is selected, the processor  180  may compare the previous frame with the current frame and track the corresponding object when the selected object is included in the same frame. 
     The processor  180  of the artificial intelligence device  100  adjusts and outputs an audio volume of the selected object according to the beamforming and the object tracking (S 1105 ). 
     The processor  180  may adjust a degree (or intensity) of the beamforming by using the object tracking according to reception of an operation command for the volume adjustment item. Accordingly, the output of the audio volume of the object selected by the user may be adjusted. 
     According to another embodiment, the processor  180  may classify an audio signal output by the object by using prior information of the object. 
     The dictionary information of the object may include one or more of the frequency distribution according to types of object and characteristics of the object. 
     The types of object may indicate whether the object is a human, an animal, or a machine. 
     The characteristics of the object may be a gender indicating whether the object is a man or a woman. 
     The characteristics of the object may indicate whether the object is a cat, a dog, or a horse when the object is an animal. 
     The processor  180  may separate sound source signals corresponding to each object from sound source signals of the video by using dictionary information of each of the plurality of objects. 
     When the plurality of objects in the video are disposed at the same position or direction, and it is difficult to distinguish the audio of the objects by the above methods, a known sound separation technique may be used. 
     The known sound separation techniques may be any one of source separation, blind signal separation, and blind source separation. 
     Each sound separation technique may be a technique of separating a plurality of sound signals from frequency characteristics of each of the plurality of sound signals. 
     When recognizing the object through the video, a number of objects for audio zoom-in may be recognized according to the number of frames constituting the video. When the user selects the recognized object, an image and audio to which the audio zoom-in is applied should be output for each selected object. However, actually, when the objects are in close proximity to each other, the user may not notice a big difference. 
     Therefore, according to an embodiment, it is necessary to cluster the objects in close proximity into one group. 
       FIG. 12  is a view illustrating an example of clustering (grouping) the plurality of objects when the plurality of utterable objects contained in the video are provided according to an embodiment. 
     The processor  180  of the artificial intelligence device  100  acquires a plurality of utterable objects from the video (S 1201 ). 
     For this, an embodiment related to an operation S 503  illustrated in  FIG. 5  may be applied to  FIG. 12 . 
     That is, the processor  180  may identify the plurality of objects from the video using the object detection model  600  and the object identification model  800 . 
     The processor  180  of the artificial intelligence device  100  clusters the plurality of acquired objects into a plurality of clusters (S 1203 ). 
     One cluster may form one cluster. 
     In an embodiment, the processor  180  may cluster the plurality of objects into a plurality of clusters by using a K-nearest neighbor algorithm. 
     The K-nearest neighbor algorithm may be an algorithm for clustering K objects disposed in the neighbor in the cluster in a feature space. 
     The processor  180  may adjust the number of objects included in one cluster by setting a threshold at a distance between the objects in one cluster using the K-nearest neighbor algorithm. 
     The feature space may be a space for vectorizing each object to indicate at which each object is disposed. 
       FIG. 13  is a view illustrating a result of clustering the plurality of objects into the plurality of clusters according to an embodiment. 
     Referring to  FIG. 13 , a plurality of objects included in one scene are clustered into three clusters  1310 ,  1330 , and  1350 . 
     Each cluster may be treated as an object. That is, the user may select the object included in the cluster and adjust the audio volume of the selected object according to the selection of the cluster and the audio adjustment of the cluster. 
       FIG. 14  is a view illustrating an example of clustering the plurality of objects contained in the video according to an embodiment. 
     Referring to  FIG. 14 , the processor  180  of the artificial intelligence device  100  plays the video  1400  through the display unit  151 . 
     The processor  180  classifies the plurality of utterable objects  1411  to  1415  included in the video  1400  into a plurality of clusters  1410 ,  1430 ,  1450 , and  1470  using the K-nearest neighbor algorithm. 
     The processor  180  may recognize each of the plurality of clusters  1410 ,  1430 ,  1450 , and  1470  as one object. 
     That is, the first object  1411  and the second object  1412  belonging to the first cluster  1410  may be treated as one object. 
     When the first cluster  1410  is selected, the processor  180  may determine that the first object  1411  and the second object  1412  are selected. 
     A volume adjustment item (not shown) for adjusting a volume of audio output from the cluster and a volume icon (not shown) indicating that the volume is adjustable may be displayed at adjacent positions of each cluster. 
     The embodiment of  FIG. 9  will be described by deriving the volume icon and the volume control item. 
     The processor  180  may mute the audio output by the objects in the cluster through a manipulation command of the volume icon. 
     The processor  180  may increase or decrease in volume of the audio output by the object in the cluster as the operation command of the volume adjustment item is received. 
     When the first cluster  1410  is selected, the processor  180  may enlarge and play the first object  1411  and the second object  1413  included in the first cluster  1410 . 
     When there are a plurality of objects in one cluster, one of the source separation, the blind signal separation, and the blind source separation, which are the beamforming method or the known sound separation technique described in  FIG. 11 , may be used as the audio output by each object. 
     As described above, according to an embodiment, when the plurality of objects are disposed in the vicinity of the video, the plurality of objects are clustered into the one group, and as the audio is output, the user may watch the video while focusing on the corresponding objects. 
     The above-described present disclosure may be implemented as a computer-readable code on a computer-readable medium in which a program is stored. The computer readable recording medium includes all types of recording devices in which data readable by a computer system is stored. Examples of the computer-readable recording medium include hard disk drives (HDD), solid state disks (SSD), silicon disk drives (SDD), read only memories (ROMs), random access memories (RAMs), compact disc read only memories (CD-ROMs), magnetic tapes, floppy discs, and optical data storage devices. Also, the computer may include the processor  180  of the artificial intelligence server. 
     Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.