ABSTRACT
Abstract
The present disclosure provides an artificial intelligence apparatus includes a haptic module configured to output a vibration notification, a microphone configured to obtain noise data generated by the vibration notification, a gyro sensor configured to obtain equilibrium state information of the artificial intelligence apparatus, and a processor configured to obtain floor strength information output by a floor strength prediction model by providing the equilibrium state information and the noise data to the floor strength prediction model which outputs the floor strength information of a floor on which the artificial intelligence apparatus is placed, and determine vibration notification strength based on the floor strength 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. 10-2019-0144556, filed on Nov. 12, 2019, the contents of which are all hereby incorporated by reference herein in its entirety.
BACKGROUND
The present disclosure relates to an artificial intelligence apparatus for providing a notification and a method for the same.
Artificial intelligence is a field of computer engineering and information technology for researching a method of enabling a computer to do thinking, learning and self-development that can be done by human intelligence, and means that a computer can imitate a human intelligent action.
In addition, artificial intelligence does not exist in itself but has many direct and indirect associations with the other fields of computer science. In particular, today, attempts to introduce artificial intelligent elements to various fields of information technology to deal with issues of the fields have been actively made.
Meanwhile, technology for recognizing and learning a surrounding situation using artificial intelligence and providing information desired by a user in a desired form or performing a function or operation desired by the user is actively being studied.
An electronic device for providing such operations and functions may be referred to as an artificial intelligence apparatus.
Meanwhile, a device such as a smartphone provides a notification to let a user know when a message, a call, or the like is received.
However, if a notification is provided regardless of the situation where a device such as a smartphone is placed, the notification may cause damage.
Accordingly, the necessity of a function that can determine the situation where the smartphone is placed and provide an optimal notification is increasing.
SUMMARY
An object of the present disclosure is to solve the above and other problems.
An object of the present disclosure is to provide an artificial intelligence apparatus which adjusts a notification to prevent falling due to the notification and damage caused by the falling.
An object of the present disclosure is to provide an artificial intelligence apparatus that prevents the falling by adjusting the vibration notification by itself by the artificial intelligence apparatus which is placed in a non-horizontal position such as an inclined table.
An object of the present disclosure is to provide an artificial intelligence apparatus which adjusts a notification by determining a situation where an artificial intelligence apparatus is placed using noise data generated as a vibration notification.
An object of the present disclosure is to provide an artificial intelligence apparatus which adjusts a notification according to a situation where an artificial intelligence apparatus is placed.
An object of the present disclosure is to provide an artificial intelligence apparatus which adjusts the notification by predicting the movement of the artificial intelligence apparatus due to the notification.
An embodiment of the present disclosure provides an artificial intelligence apparatus including: a haptic module configured to output a vibration notification; a microphone configured to obtain noise data generated by the vibration notification; a gyro sensor configured to obtain equilibrium state information of the artificial intelligence apparatus; and a processor configured to: obtain floor strength information output by a floor strength prediction model by providing the equilibrium state information and the noise data to the floor strength prediction model which outputs the floor strength information of a floor on which the artificial intelligence apparatus is placed, and determine vibration notification strength based on the floor strength information.
In addition, an embodiment of the present disclosure provides a method for providing a notification including: outputting a vibration notification; obtaining noise data generated due to the vibration notification; obtaining equilibrium state information of the artificial intelligence apparatus; obtaining the floor strength information output by a floor strength prediction model by providing the equilibrium state information and the noise data to a floor strength prediction model which outputs floor strength information of a floor on which the artificial intelligence apparatus is placed; and determining the vibration notification strength based on the floor strength information.
According to an embodiment of the present disclosure, it is possible to prevent the falling due to the notification and the damage caused due to the falling by adjusting the notification strength.
In addition, according to various embodiments of the present disclosure, an artificial intelligence apparatus placed in a non-horizontal position such as an inclined table may prevent from falling by adjusting the vibration notification by itself.
In addition, according to various embodiments of the present disclosure, the falling of the artificial intelligence apparatus may be prevented by adjusting the notification by determining a situation where the artificial intelligence apparatus is placed using the noise data generated as the vibration notification.
In addition, according to various embodiments of the present disclosure, the falling of the artificial intelligence apparatus may be prevented by adjusting the notification according to the situation where the artificial intelligence apparatus is placed.
In addition, according to various embodiments of the present disclosure, the falling of the artificial intelligence apparatus may be prevented by adjusting the notification by predicting the movement of the artificial intelligence apparatus due to the notification.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an AI device according to an embodiment of the present disclosure.
FIG. 2 illustrates an AI server according to an embodiment of the present disclosure.
FIG. 3 illustrates an AI system according to an embodiment of the present disclosure.
FIG. 4 is a block diagram illustrating an artificial intelligence apparatus according to the present disclosure.
FIG. 5 is an operation flowchart illustrating a method for determining a vibration notification strength using floor strength information according to an embodiment of the present disclosure.
FIG. 6 is a view illustrating a floor strength prediction model according to an embodiment of the present disclosure.
FIG. 7 is an operation flowchart illustrating a method for determining a vibration notification strength using floor state change information according to an embodiment of the present disclosure.
FIG. 8 is a view illustrating a floor state change prediction model according to an embodiment of the present disclosure.
FIG. 9 is an operation flowchart illustrating a method for determining the vibration notification strength by using the movement prediction information according to an embodiment of the present disclosure.
FIG. 10 is a view illustrating a movement information prediction model according to an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings, symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes âmoduleâ and âunitâ for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included.
It will be understood that the terms âfirstâ and âsecondâ are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components.
In this disclosure below, when one part (or element, device, etc.) is referred to as being âconnectedâ to another part (or element, device, etc.), it should be understood that the former can be âdirectly connectedâ to the latter, or âelectrically connectedâ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being âdirectly connectedâ or âdirectly linkedâ to another component, it means that no intervening component is present.
<Artificial Intelligence (AI)>
Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.
An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value.
The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse.
Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function.
The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network.
Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.
The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for 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 learning is part of machine learning. In the following, machine learning is used to mean deep learning.
<Robot>
A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot.
Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field.
The robot includes a driving unit may include an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a driving unit, and may travel on the ground through the driving unit or fly in the air.
<Self-Driving>
Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user.
For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a preset 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
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. 10-2019-0144556, filed on Nov. 12, 2019, the contents of which are all hereby incorporated by reference herein in its entirety.
BACKGROUND
The present disclosure relates to an artificial intelligence apparatus for providing a notification and a method for the same.
Artificial intelligence is a field of computer engineering and information technology for researching a method of enabling a computer to do thinking, learning and self-development that can be done by human intelligence, and means that a computer can imitate a human intelligent action.
In addition, artificial intelligence does not exist in itself but has many direct and indirect associations with the other fields of computer science. In particular, today, attempts to introduce artificial intelligent elements to various fields of information technology to deal with issues of the fields have been actively made.
Meanwhile, technology for recognizing and learning a surrounding situation using artificial intelligence and providing information desired by a user in a desired form or performing a function or operation desired by the user is actively being studied.
An electronic device for providing such operations and functions may be referred to as an artificial intelligence apparatus.
Meanwhile, a device such as a smartphone provides a notification to let a user know when a message, a call, or the like is received.
However, if a notification is provided regardless of the situation where a device such as a smartphone is placed, the notification may cause damage.
Accordingly, the necessity of a function that can determine the situation where the smartphone is placed and provide an optimal notification is increasing.
SUMMARY
An object of the present disclosure is to solve the above and other problems.
An object of the present disclosure is to provide an artificial intelligence apparatus which adjusts a notification to prevent falling due to the notification and damage caused by the falling.
An object of the present disclosure is to provide an artificial intelligence apparatus that prevents the falling by adjusting the vibration notification by itself by the artificial intelligence apparatus which is placed in a non-horizontal position such as an inclined table.
An object of the present disclosure is to provide an artificial intelligence apparatus which adjusts a notification by determining a situation where an artificial intelligence apparatus is placed using noise data generated as a vibration notification.
An object of the present disclosure is to provide an artificial intelligence apparatus which adjusts a notification according to a situation where an artificial intelligence apparatus is placed.
An object of the present disclosure is to provide an artificial intelligence apparatus which adjusts the notification by predicting the movement of the artificial intelligence apparatus due to the notification.
An embodiment of the present disclosure provides an artificial intelligence apparatus including: a haptic module configured to output a vibration notification; a microphone configured to obtain noise data generated by the vibration notification; a gyro sensor configured to obtain equilibrium state information of the artificial intelligence apparatus; and a processor configured to: obtain floor strength information output by a floor strength prediction model by providing the equilibrium state information and the noise data to the floor strength prediction model which outputs the floor strength information of a floor on which the artificial intelligence apparatus is placed, and determine vibration notification strength based on the floor strength information.
In addition, an embodiment of the present disclosure provides a method for providing a notification including: outputting a vibration notification; obtaining noise data generated due to the vibration notification; obtaining equilibrium state information of the artificial intelligence apparatus; obtaining the floor strength information output by a floor strength prediction model by providing the equilibrium state information and the noise data to a floor strength prediction model which outputs floor strength information of a floor on which the artificial intelligence apparatus is placed; and determining the vibration notification strength based on the floor strength information.
According to an embodiment of the present disclosure, it is possible to prevent the falling due to the notification and the damage caused due to the falling by adjusting the notification strength.
In addition, according to various embodiments of the present disclosure, an artificial intelligence apparatus placed in a non-horizontal position such as an inclined table may prevent from falling by adjusting the vibration notification by itself.
In addition, according to various embodiments of the present disclosure, the falling of the artificial intelligence apparatus may be prevented by adjusting the notification by determining a situation where the artificial intelligence apparatus is placed using the noise data generated as the vibration notification.
In addition, according to various embodiments of the present disclosure, the falling of the artificial intelligence apparatus may be prevented by adjusting the notification according to the situation where the artificial intelligence apparatus is placed.
In addition, according to various embodiments of the present disclosure, the falling of the artificial intelligence apparatus may be prevented by adjusting the notification by predicting the movement of the artificial intelligence apparatus due to the notification.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an AI device according to an embodiment of the present disclosure.
FIG. 2 illustrates an AI server according to an embodiment of the present disclosure.
FIG. 3 illustrates an AI system according to an embodiment of the present disclosure.
FIG. 4 is a block diagram illustrating an artificial intelligence apparatus according to the present disclosure.
FIG. 5 is an operation flowchart illustrating a method for determining a vibration notification strength using floor strength information according to an embodiment of the present disclosure.
FIG. 6 is a view illustrating a floor strength prediction model according to an embodiment of the present disclosure.
FIG. 7 is an operation flowchart illustrating a method for determining a vibration notification strength using floor state change information according to an embodiment of the present disclosure.
FIG. 8 is a view illustrating a floor state change prediction model according to an embodiment of the present disclosure.
FIG. 9 is an operation flowchart illustrating a method for determining the vibration notification strength by using the movement prediction information according to an embodiment of the present disclosure.
FIG. 10 is a view illustrating a movement information prediction model according to an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings, symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes âmoduleâ and âunitâ for components used in the description below are assigned or mixed in consideration of easiness in writing the specification and do not have distinctive meanings or roles by themselves. In the following description, detailed descriptions of well-known functions or constructions will be omitted since they would obscure the invention in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included.
It will be understood that the terms âfirstâ and âsecondâ are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components.
In this disclosure below, when one part (or element, device, etc.) is referred to as being âconnectedâ to another part (or element, device, etc.), it should be understood that the former can be âdirectly connectedâ to the latter, or âelectrically connectedâ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being âdirectly connectedâ or âdirectly linkedâ to another component, it means that no intervening component is present.
<Artificial Intelligence (AI)>
Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task.
An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value.
The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse.
Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function.
The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network.
Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method.
The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for 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 learning is part of machine learning. In the following, machine learning is used to mean deep learning.
<Robot>
A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot.
Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field.
The robot includes a driving unit may include an actuator or a motor and may perform various physical operations such as moving a robot joint. In addition, a movable robot may include a wheel, a brake, a propeller, and the like in a driving unit, and may travel on the ground through the driving unit or fly in the air.
<Self-Driving>
Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user.
For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a preset 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 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 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), Bluetoothâ¢, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like.
The input unit 120 may obtain 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 obtained from the camera or the microphone may be referred to as sensing data or sensor information.
The input unit 120 may obtain a learning data for model learning and an input data to be used when an output is obtained by using learning model. The input unit 120 may obtain 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 obtain at least one of internal information on the AI device 100 , ambient environment information on 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 obtained 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 obtain intention information for the user input and may determine the user's requirements based on the obtained intention information.
The processor 180 may obtain 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 obtaining intention information of a natural language.
At least one of the STT engine or the NLP engine may be configured as an artificial neural network, at least part of which is learned according to the machine learning algorithm. At least one of the STT engine or the NLP engine may be learned by the learning processor 130 , may be learned by the learning processor 240 of the AI server 200 , or may be learned by their distributed processing.
The processor 180 may collect history information including the operation contents of the AI apparatus 100 or the user's feedback on the operation and may store the collected history information in the memory 170 or the learning processor 130 or transmit the collected history information to the external device such as the AI server 200 . The collected history information may be used to update the learning model.
The processor 180 may control at least part of the components of AI 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. 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 disclosure.
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.
In other words, 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 .
<AI+Robot>
The robot 100 a , to which the AI technology is applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like.
The robot 100 a may include a robot control module for controlling the operation, and the robot control module may refer to a software module or a chip implementing the software module by hardware.
The robot 100 a may obtain state information on the robot 100 a by using sensor information obtained 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 obtained 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 obtained 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 on various objects arranged in the space in which the robot 100 a moves. For example, the map data may include object identification information on 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 obtain the intention information of the interaction due to the user's operation or speech utterance, and may determine the response based on the obtained intention information, and may perform the operation.
<AI+Self-Driving>
The self-driving vehicle 100 b , to which the AI technology is applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like.
The self-driving vehicle 100 b may include a self-driving control module for controlling a self-driving function, and the self-driving control module may refer to a software module or a chip implementing the software module by hardware. The self-driving control module may be included in the self-driving vehicle 100 b as a component thereof, but may be implemented with separate hardware and connected to the outside of the self-driving vehicle 100 b.
The self-driving vehicle</figur
CLAIMS
Claims ( 18 )
What is claimed is:
1. An artificial intelligence apparatus comprising:
a haptic device configured to output a vibration notification;
a microphone configured to obtain noise data generated due to the vibration notification;
a gyro sensor configured to obtain equilibrium state information of the artificial intelligence apparatus; and
a processor configured to:
obtain surface strength information of a surface on which the artificial intelligence apparatus is resting on, wherein the surface strength information is obtained by inputting the equilibrium state information and the noise data into a surface strength prediction model, wherein the surface strength prediction model outputs the surface strength information of the surface, and
determine a strength of the vibration notification based on the obtained surface strength information.
2. The artificial intelligence apparatus of claim 1 ,
wherein the surface strength prediction model is an artificial neural network which is trained based on learning data labeled with predetermined surface strength information on predetermined equilibrium state information and predetermined noise data.
3. The artificial intelligence apparatus of claim 1 ,
wherein the processor is further configured to:
obtain equilibrium state change information on a change of the equilibrium state information for a preset time period,
obtain noise data change information on a change of the noise data during the preset time period,
input the obtained equilibrium state change information and the obtained noise data change information into a surface state change prediction model for outputting state change information of the surface on which the artificial intelligence apparatus is resting on,
obtain a surface state change information using the surface state change prediction model, and
determine the strength of the vibration notification based on the obtained surface state change information.
4. The artificial intelligence apparatus of claim 3 ,
wherein the processor is further configured to determine to lower the strength of the vibration notification upon a determination that a portion of the artificial intelligence apparatus is not resting on the surface based on the obtained surface state change information.
5. The artificial intelligence apparatus of claim 3 ,
wherein the surface state change prediction model is an artificial neural network which is trained based on learning data labeled with predetermined surface state change information on predetermined equilibrium state change information and predetermined noise data change information.
6. The artificial intelligence apparatus of claim 5 ,
wherein the surface state change prediction model is an artificial neural network model which is trained based on a plurality of noise data change information, wherein the plurality of noise data change information is obtained from a plurality of microphones.
7. The artificial intelligence apparatus of claim 1 , further comprising:
an acceleration sensor configured to obtain movement information on a movement distance and a movement direction of the artificial intelligence apparatus; and
a memory configured to store notification information on a number of notifications, wherein the notifications information corresponds to the number of notifications until each of at least one notification is stopped for each of at least one notification;
wherein the processor is further configured to:
obtain movement prediction information using a movement prediction model by inputting at least the movement information, the equilibrium state information, and the notification information into the movement prediction model, wherein the movement prediction model outputs the movement prediction information on a distance and a direction to be moved by the artificial intelligence apparatus due to a predetermined vibration notification, and
wherein the determination of the strength of the vibration notification is further based on the movement prediction information.
8. The artificial intelligence apparatus of claim 7 ,
wherein the processor is further configured to determine to lower the strength of the vibration notification upon a determination that the artificial intelligence apparatus is predicted to move at least a preset distance based on the movement prediction information.
9. The artificial intelligence apparatus of claim 7 ,
wherein the processor is further configured to:
obtain an average number of vibrations until the vibration notification is stopped when the artificial intelligence apparatus receives a call, a text, or generates a notification, wherein the average number of vibrations is obtained based on the notification information, and
determine the strength of the vibration notification based on the movement prediction information and the average number of vibrations.
10. A method comprising:
outputting a vibration notification;
obtaining noise data generated due to the vibration notification;
obtaining equilibrium state information of an artificial intelligence apparatus;
obtaining surface strength information of a surface on which the artificial intelligence apparatus is resting on, wherein the surface strength information is obtained by inputting the equilibrium state information and the noise data into a surface strength prediction model, wherein the surface strength prediction model outputs the surface strength information; and
determining a strength of the vibration notification based on the obtained surface strength information.
11. The method of claim 10 ,
wherein the surface strength prediction model is an artificial neural network which is trained based on learning data labeled with predetermined surface strength information on predetermined equilibrium state information and predetermined noise data.
12. The method of claim 10 ,
wherein the strength of the vibration notification is determined based on:
obtaining equilibrium state change information on a change of the equilibrium state information for a preset time period,
obtaining noise data change information on the change of the noise data during the preset time period,
inputting the obtained equilibrium state change information and the obtained noise data change information into a surface state change prediction model for outputting state change information of the surface on which the artificial intelligence apparatus is resting on,
obtaining a surface state change information using the surface state change prediction model and
determining the strength of the vibration notification based on the surface state change information.
13. The method of claim 12 ,
wherein the strength of the vibration notification is determined based on:
determining lower the strength of the vibration notification upon a determination that a portion of the artificial intelligence apparatus is not resting on the surface based on the obtained surface state change information.
14. The method of claim 12 ,
wherein the surface state change prediction model is an artificial neural network which is trained based on learning data labeled with predetermined surface state change information on predetermined equilibrium state change information and predetermined noise data change information.
15. The method of claim 14 ,
wherein the surface state change prediction model is an artificial neural network model which is trained based on a plurality of noise data change information, wherein the plurality of noise data change information is obtained from each of a plurality of microphones.
16. The method of claim 10 ,
wherein the strength of the vibration notification is determined based on:
obtaining movement information on a movement distance and a movement direction of the artificial intelligence apparatus;
obtaining notification information on a number of notifications corresponding to the vibration notification from a memory configured to store notification information on the number of notifications until each of at least one notification is stopped for each of at least one notification;
obtaining movement prediction information using a movement prediction model by inputting at least the movement information, the equilibrium state information, and the notification information into the movement prediction model, wherein the movement prediction model outputs the movement prediction information on a distance and a direction to be moved by the artificial intelligence apparatus due to a preset vibration notification; and
determining the strength of the vibration notification based on the movement prediction information.
17. The method of claim 16 ,
wherein the strength of the vibration notification is determined based on:
determining whether to lower the strength of the vibration notification upon a determination that the artificial intelligence apparatus is predicted to move at least a preset distance based on the movement prediction information.
18. The method of claim 16 ,
wherein the strength of the vibration notification is determined based on:
obtaining an average number of vibrations until the vibration notification is stopped based on the notification information; and
determining the strength of the vibration notification based on the movement prediction information and the average number of vibrations.
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