Google Patentsopen access

Artificial intelligence moving agent — Lg Electronics Inc. (US11397871B2)

ABSTRACT

Abstract

An artificial intelligence moving agent is provided. The artificial intelligence moving agent includes: a camera configured to photograph an image, and a processor configured to photograph an object, acquire type information of the object by providing an image of the photographed object to an artificial intelligence model, acquire correction type information designated by a user with respect to the image of the photographed object, and train the artificial intelligence model by using the correction type information.

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. 10-2019-0100577, filed on Aug. 16, 2019, the contents of which are hereby incorporated by reference herein in its entirety.

BACKGROUND

The present disclosure relates to an artificial intelligence moving agent, which is a moving agent capable of object recognition, to train an artificial intelligence model performing the object recognition by using labeling data directly inputted by a user.

Artificial intelligence (AI) is one field of computer engineering and information technology for studying a method of enabling a computer to perform thinking, learning, and self-development that can be performed by human intelligence and may denote that a computer imitates an intelligent action of a human.

In addition, the artificial intelligence is directly or indirectly associated with other fields of computer engineering without being individually provided. Particularly, at present, in various fields of information technology, an attempt to introduce AI components and use the AI components in solving a problem of a corresponding field is being actively done.

Meanwhile, technologies have been actively studied to recognize and learn surrounding situations by using the artificial intelligence, and provide information desired by the user in a desired format or perform operations or functions desired by the user.

In addition, an electronic device for providing such various operations and functions may be referred to as an artificial intelligence device.

Meanwhile, recently, a robot cleaner in addition to an inherent cleaning function may recognize an object in a space using a mounted camera and may perform an additional function such as collision avoidance, optimal path setting, and crime prevention using the recognition result.

Recently, in order to improve performance of the object recognition, various objects have been recognized by using an artificial intelligence model generated using a deep learning algorithm.

The artificial intelligence model is released as a product after trained using various objects to set parameters. In addition, the robot cleaner equipped with the artificial intelligence model performs an object recognition function in an indoor space of the user. An object to learn in advance may be different from an object actually existing in the indoor space, and thus, the performance of the object recognition by the artificial intelligence model may be lowered.

SUMMARY

To solve the above problems, embodiments provides an artificial intelligence moving agent to train an artificial intelligence model performing the object recognition by using labeling data directly inputted by a user. The artificial intelligence moving agent according to one embodiment includes: a camera configured to photograph an image, and a processor configured to photograph an object, acquire type information of the object by providing an image of the photographed object to an artificial intelligence model, acquire correction type information designated by a user with respect to the image of the photographed object, and train the artificial intelligence model by using the correction type information. 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 illustrates an AI device 100 according to an embodiment.

FIG. 2 illustrates an AI server 200 according to an embodiment.

FIG. 3 illustrates an AI system 1 according to an embodiment.

FIG. 4A is a perspective view of a robot cleaner according to an embodiment.

FIG. 4B illustrates a horizontal angle of view of the robot cleaner of FIG. 4A .

FIG. 4C is a front view of the robot cleaner of FIG. 4A .

FIG. 4D illustrates a bottom surface of the robot cleaner of FIG. 4A .

FIG. 4E is a block diagram illustrating a main parts of the robot cleaner according to an embodiment.

FIG. 5 is a view describing a method of operating a moving agent 100 according to the embodiments.

FIG. 6 is a view describing a method of generating an artificial intelligence model according to the embodiments.

FIGS. 7 and 8 are views describing a method of acquiring type information of an object by photographing the object and by using an image obtained by photographing the object, according to the embodiments.

FIG. 9 is a view describing a method of receiving correction type information according to the embodiments.

FIG. 10 is a view describing a method of training an artificial intelligence model 810 by using a received feedback.

FIG. 11 is a view describing a condition for transmitting the image obtained by photographing the object and type information of the object, according to the embodiments.

FIGS. 12 and 13 are views describing a situation of re-training the artificial intelligence model by using a feedback, according to the embodiments.

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 “module” and “unit” 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 “first” and “second” 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 ‘connected’ to another part (or element, device, etc.), it should be understood that the former can be ‘directly connected’ to the latter, or ‘electrically connected’ 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 ‘directly connected’ or ‘directly linked’ 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 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 learning 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 running is part of machine running. In the following, machine learning is used to mean deep running.

<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.

At this time, 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 illustrates an AI device 100 according to an embodiment of the present invention.

The 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 <figur

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. 10-2019-0100577, filed on Aug. 16, 2019, the contents of which are hereby incorporated by reference herein in its entirety.

BACKGROUND

The present disclosure relates to an artificial intelligence moving agent, which is a moving agent capable of object recognition, to train an artificial intelligence model performing the object recognition by using labeling data directly inputted by a user.

Artificial intelligence (AI) is one field of computer engineering and information technology for studying a method of enabling a computer to perform thinking, learning, and self-development that can be performed by human intelligence and may denote that a computer imitates an intelligent action of a human.

In addition, the artificial intelligence is directly or indirectly associated with other fields of computer engineering without being individually provided. Particularly, at present, in various fields of information technology, an attempt to introduce AI components and use the AI components in solving a problem of a corresponding field is being actively done.

Meanwhile, technologies have been actively studied to recognize and learn surrounding situations by using the artificial intelligence, and provide information desired by the user in a desired format or perform operations or functions desired by the user.

In addition, an electronic device for providing such various operations and functions may be referred to as an artificial intelligence device.

Meanwhile, recently, a robot cleaner in addition to an inherent cleaning function may recognize an object in a space using a mounted camera and may perform an additional function such as collision avoidance, optimal path setting, and crime prevention using the recognition result.

Recently, in order to improve performance of the object recognition, various objects have been recognized by using an artificial intelligence model generated using a deep learning algorithm.

The artificial intelligence model is released as a product after trained using various objects to set parameters. In addition, the robot cleaner equipped with the artificial intelligence model performs an object recognition function in an indoor space of the user. An object to learn in advance may be different from an object actually existing in the indoor space, and thus, the performance of the object recognition by the artificial intelligence model may be lowered.

SUMMARY

To solve the above problems, embodiments provides an artificial intelligence moving agent to train an artificial intelligence model performing the object recognition by using labeling data directly inputted by a user. The artificial intelligence moving agent according to one embodiment includes: a camera configured to photograph an image, and a processor configured to photograph an object, acquire type information of the object by providing an image of the photographed object to an artificial intelligence model, acquire correction type information designated by a user with respect to the image of the photographed object, and train the artificial intelligence model by using the correction type information. 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 illustrates an AI device 100 according to an embodiment.

FIG. 2 illustrates an AI server 200 according to an embodiment.

FIG. 3 illustrates an AI system 1 according to an embodiment.

FIG. 4A is a perspective view of a robot cleaner according to an embodiment.

FIG. 4B illustrates a horizontal angle of view of the robot cleaner of FIG. 4A .

FIG. 4C is a front view of the robot cleaner of FIG. 4A .

FIG. 4D illustrates a bottom surface of the robot cleaner of FIG. 4A .

FIG. 4E is a block diagram illustrating a main parts of the robot cleaner according to an embodiment.

FIG. 5 is a view describing a method of operating a moving agent 100 according to the embodiments.

FIG. 6 is a view describing a method of generating an artificial intelligence model according to the embodiments.

FIGS. 7 and 8 are views describing a method of acquiring type information of an object by photographing the object and by using an image obtained by photographing the object, according to the embodiments.

FIG. 9 is a view describing a method of receiving correction type information according to the embodiments.

FIG. 10 is a view describing a method of training an artificial intelligence model 810 by using a received feedback.

FIG. 11 is a view describing a condition for transmitting the image obtained by photographing the object and type information of the object, according to the embodiments.

FIGS. 12 and 13 are views describing a situation of re-training the artificial intelligence model by using a feedback, according to the embodiments.

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 “module” and “unit” 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 “first” and “second” 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 ‘connected’ to another part (or element, device, etc.), it should be understood that the former can be ‘directly connected’ to the latter, or ‘electrically connected’ 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 ‘directly connected’ or ‘directly linked’ to another component, it means that no intervening component is present.

&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 learning 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 learning 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 running is part of machine running. In the following, machine learning is used to mean deep running.

&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.

At this time, 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 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 illustrates an AI device 100 according to an embodiment of the present invention.

The 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 device 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 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.

At this time, 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 when 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 unit 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 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.

At this time, 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 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 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. At this time, 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 can 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 illustrates 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 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 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 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 .

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 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 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 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 s

CLAIMS

Claims ( 8 )

What is claimed is:

1. An artificial intelligence moving agent comprising:

a camera;

a memory configured to store data; and

a processor configured to:

cause the camera to capture an image of an object,

acquire type information of the object by providing, to an artificial intelligence model, the image captured by the camera, wherein the artificial intelligence model outputs a result value including the type information of the object and a confidence score of the object, and

based on the confidence score of the object being lower than a preset value,

acquire a corrected type information with respect to the image, wherein the corrected type information is acquired by transmitting, to a terminal of a user, the image and the type information of the object and receiving, from the terminal, the corrected type information labeled by a user with respect to the image, and

train the artificial intelligence model by using the image and the corrected type information labeled by the user, wherein the processor is further configured to:

store a result value for at least one object existing in a specific space of the image in the memory, and

re-train the trained artificial intelligence model by transmitting an additional image obtained by capturing the additional image of the at least one object existing in the specific space of the image to the terminal based on a determination that the result value has changed, wherein the trained artificial intelligence model is further re-trained by using a feedback received from the terminal.

2. The artificial intelligence moving agent according to claim 1 , wherein the artificial intelligence model includes a neural network trained using images of various training objects and type information labeled on each of the images of the various training objects.

3. The artificial intelligence moving agent according to claim 1 , wherein, before the artificial intelligence model is coupled on the artificial intelligence moving agent, the artificial intelligence model extracts a feature vector for a training object, outputs a result value corresponding to the training object by using the feature vector extracted from the training object, and sets a parameter by using the result value corresponding to the training object and type information labeled on the training object; and wherein, after the artificial intelligence model is coupled on the artificial intelligence moving agent, the artificial intelligence model extracts a feature vector for the object, outputs a result value corresponding to the object by using the feature vector extracted from the object, and sets a parameter by using the result value corresponding to the object and the corrected type information.

4. The artificial intelligence moving agent according to claim 1 , wherein the processor is further configured to: provide, to the trained artificial intelligence model, a second image of the object captured by the camera, and acquire the corrected type information outputted by the trained artificial intelligence model.

5. A method of operating an artificial intelligence moving agent, the method comprising:

capturing an image of an object;

acquiring type information of the object by providing to an artificial intelligence model, the image, wherein the artificial intelligence model outputs a result value including the type information of the object and a confidence score of the object; and

based on the confidence score of the object being lower than a preset value,

acquiring a corrected type information with respect to the image, wherein the corrected type information is acquired by transmitting, to a terminal of a user, the image and the type information of the object and receiving, from the terminal, the corrected type information labeled by a user with respect to the image;

training the artificial intelligence model by using the image and the corrected type information labeled by the user;

store a result value for at least one object existing in a specific space of the image in a memory; and

re-train the trained artificial intelligence model by transmitting an additional image obtained by capturing the additional image of the at least one object existing in the specific space of the image to the terminal based on a determination that the result value has changed, wherein the trained artificial intelligence model is further re-trained by using a feedback received from the terminal.

6. The method according to claim 5 , wherein the artificial intelligence model includes a neural network trained using images of various training objects and type information labeled on each of the images of the various training objects.

7. The method according to claim 5 , wherein, before the artificial intelligence model is coupled on the artificial intelligence moving agent, the artificial intelligence model extracts a feature vector for a training object, outputs a result value corresponding to the training object by using the feature vector extracted from the training object, and sets a parameter by using the result value corresponding to the training object and type information labeled on the training object; and, after the artificial intelligence model is coupled on the artificial intelligence moving agent, the artificial intelligence model extracts a feature vector for the object, outputs a result value corresponding to the object using the feature vector extracted from the object, and sets a parameter using the result value corresponding to the object and the corrected type information.

8. The method according to claim 7 , further comprising:

providing a second image obtained by photographing the object to the trained artificial intelligence model, and

acquiring the corrected type information outputted by the trained artificial intelligence model.

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