Artificial intelligence cleaner and operating method thereof

An artificial intelligence cleaner may determine a length of a head from user cleaning information including a height of a user, a pressure sensing value applied to the head and a head angle between the head and the ground using a head length calculation model and control a head motor to have the determined length of the head.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. 119 and 365 to Korean Patent Application No. 10-2019-0156053, filed on 28 Nov. 2019, in the Korean Intellectual Property Office, the disclosure of which is incorporated herein by reference.

FIELD

The present disclosure relates to an artificial intelligence (AI) cleaner and, more particularly, to a handy stick AI cleaner.

BACKGROUND

A handy stick cleaner allows a user to clean a desired cleaning area via a handle.

A conventional handy stick cleaner used a method of allowing a user to manually increase or decrease the length of a head according to the height of the user and usability of the cleaner.

That is, the user needs to adjust the length of the head of the handy stick cleaner. However, the user who performs cleaning is not always the same and thus each user should adjust the length of the head of the handy stick cleaner.

Accordingly, each time the user is changed, the user should change the length of the head according to the height of the user and usability of the cleaner, thereby causing inconvenience.

That is, usability of the handy stick cleaner by multiple users may be impaired.

SUMMARY

An object of the present disclosure is to provide an artificial intelligence cleaner capable of automatically adjusting the length of a head according to the physical condition of a user.

Another object of the present disclosure is to provide an artificial intelligence cleaner capable of optimally adjusting the length of a head using a previously learned model even if a user is changed.

An artificial intelligence cleaner according to an embodiment of the present disclosure may determine a length of a head from user cleaning information including a height of a user, a pressure sensing value applied to the head and a head angle between the head and the ground using a head length calculation model and control a head motor to have the determined length of the head.

An artificial intelligence cleaner according to an embodiment of the present disclosure may determine a length of a head from user cleaning information using a head length calculation model learned via supervised learning.

DETAILED DESCRIPTION

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 may 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.

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 if 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.

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 device 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 device, and may travel on the ground through the driving device or fly in the air.

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 path, and a technology for automatically setting and traveling a path if a destination is set.

The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but also a train, a motorcycle, and the like.

In this case, the self-driving vehicle may be regarded as a robot having a self-driving function.

Extended reality is collectively referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The VR technology provides a real-world object and background only as a CG image, the AR technology provides a virtual CG image on a real object image, and the MR technology is a computer graphic technology that mixes and combines virtual objects into the real world.

The MR technology is similar to the AR technology in that the real object and the virtual object are illustrated together. However, in the AR technology, the virtual object is used in the form that complements the real object, whereas in the MR technology, the virtual object and the real object are used in an equal manner.

The XR technology may be applied to a head-mount display (HMD), a head-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop, a TV, a digital signage, and the like. A device to which the XR technology is applied may be referred to as an XR device.

FIG. 1illustrates an AI device100according to an embodiment of the present disclosure.

Referring toFIG. 1, the AI device100may include a communication unit110, an input unit120, a learning processor130, a sensing device140, an output device150, a memory170, and a processor180.

The communication unit110may transmit and receive data to and from external devices such as other AI devices100ato100eand the AI server200by using wire/wireless communication technology. For example, the communication unit110may 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 unit110includes 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 unit120may acquire various kinds of data.

In this case, the input unit120may 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 unit120may acquire a learning data for model learning and an input data to be used if an output is acquired by using learning model. The input unit120may acquire raw input data. In this case, the processor180or the learning processor130may extract an input feature by preprocessing the input data.

The learning processor130may 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.

In this case, the learning processor130may perform AI processing together with the learning processor240of the AI server200.

In this case, the learning processor130may include a memory integrated or implemented in the AI device100. Alternatively, the learning processor130may be implemented by using the memory170, an external memory directly connected to the AI device100, or a memory held in an external device.

The sensing device140may acquire at least one of internal information about the AI device100, ambient environment information about the AI device100, and user information by using various sensors.

Examples of the sensors included in the sensing device140may 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 device150may generate an output related to a visual sense, an auditory sense, or a haptic sense.

In this case, the output device150may include a display unit for outputting time information, a speaker for outputting auditory information, and a haptic module for outputting haptic information.

The memory170may store data that supports various functions of the AI device100. For example, the memory170may store input data acquired by the input unit120, learning data, a learning model, a learning history, and the like.

The processor180may determine at least one executable operation of the AI device100based on information determined or generated by using a data analysis algorithm or a machine learning algorithm. The processor180may control the components of the AI device100to execute the determined operation.

To this end, the processor180may request, search, receive, or utilize data of the learning processor130or the memory170. The processor180may control the components of the AI device100to execute the predicted operation or the operation determined to be desirable among the at least one executable operation.

If the connection of an external device is required to perform the determined operation, the processor180may generate a control signal for controlling the external device and may transmit the generated control signal to the external device.

The processor180may acquire intention information for the user input and may determine the user's requirements based on the acquired intention information.

The processor180may 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 processor130, may be learned by the learning processor240of the AI server200, or may be learned by their distributed processing.

The processor180may collect history information including the operation contents of the AI apparatus100or the user's feedback on the operation and may store the collected history information in the memory170or the learning processor130or transmit the collected history information to the external device such as the AI server200. The collected history information may be used to update the learning model.

The processor180may control at least part of the components of AI device100so as to drive an application program stored in memory170. Furthermore, the processor180may operate two or more of the components included in the AI device100in combination so as to drive the application program.

FIG. 2illustrates an AI server200according to an embodiment of the present disclosure.

Referring toFIG. 2, the AI server200may 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 server200may include a plurality of servers to perform distributed processing, or may be defined as a 5G network. In this case, the AI server200may be included as a partial configuration of the AI device100, and may perform at least part of the AI processing together.

The AI server200may include a communication unit210, a memory230, a learning processor240, a processor260, and the like.

The communication unit210may transmit and receive data to and from an external device such as the AI device100.

The memory230may include a model storage unit231. The model storage unit231may store a learning or learned model (or an artificial neural network231a) through the learning processor240.

The learning processor240may learn the artificial neural network231aby using the learning data. The learning model may be used in a state of being mounted on the AI server200of the artificial neural network, or may be used in a state of being mounted on an external device such as the AI device100.

The learning model may be implemented in hardware, software, or a combination of hardware and software. If all or part of the learning models is implemented in software, one or more instructions that constitute the learning model may be stored in memory230.

The processor260may 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. 3illustrates an AI system1according to an embodiment of the present disclosure.

Referring toFIG. 3, in the AI system1, at least one of an AI server200, a robot100a, a self-driving vehicle100b, an XR device100c, a smartphone100d, or a home appliance100eis connected to a cloud network10. The robot100a, the self-driving vehicle100b, the XR device100c, the smartphone100d, or the home appliance100e, to which the AI technology is applied, may be referred to as AI devices100ato100e.

The cloud network10may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure. The cloud network10may be configured by using a 3G network, a 4G or LTE network, or a 5G network.

In other words, the devices100ato100eand200configuring the AI system1may be connected to each other through the cloud network10. In particular, each of the devices100ato100eand200may communicate with each other through a base station, but may directly communicate with each other without using a base station.

The AI server200may include a server that performs AI processing and a server that performs operations on big data.

The AI server200may be connected to at least one of the AI devices constituting the AI system1, In other words, the robot100a, the self-driving vehicle100b, the XR device100c, the smartphone100d, or the home appliance100ethrough the cloud network10, and may assist at least part of AI processing of the connected AI devices100ato100e.

In this case, the AI server200may learn the artificial neural network according to the machine learning algorithm instead of the AI devices100ato100e, and may directly store the learning model or transmit the learning model to the AI devices100ato100e.

In this case, the AI server200may receive input data from the AI devices100ato100e, 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 devices100ato100e.

Alternatively, the AI devices100ato100emay 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 devices100ato100eto which the above-described technology is applied will be described. The AI devices100ato100eillustrated inFIG. 3may be regarded as a specific embodiment of the AI device100illustrated inFIG. 1.

The robot100a, 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 robot100amay 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 robot100amay acquire state information about the robot100aby using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the path and the travel plan, may determine the response to user interaction, or may determine the operation.

The robot100amay use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel path and the travel plan.

The robot100amay perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the robot100amay 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 robot100aor may be learned from an external device such as the AI server200.

In this case, the robot100amay 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 server200and the generated result may be received to perform the operation.

The robot100amay use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external apparatus to determine the travel path and the travel plan, and may control the driving device such that the robot100atravels along the determined travel path and travel plan.

The map data may include object identification information about various objects arranged in the space in which the robot100amoves. 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 robot100amay perform the operation or travel by controlling the driving device based on the control/interaction of the user. In this case, the robot100amay acquire the intention information of the interaction due to the user's operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation.

The self-driving vehicle100b, 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 vehicle100bmay 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 vehicle100bas a component thereof, but may be implemented with separate hardware and connected to the outside of the self-driving vehicle100b.

The self-driving vehicle100bmay acquire state information about the self-driving vehicle100bby using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the path and the travel plan, or may determine the operation.

Like the robot100a, the self-driving vehicle100bmay use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel path and the travel plan.

In particular, the self-driving vehicle100bmay recognize the environment or objects for an area covered by a field of view or an area over a certain distance by receiving the sensor information from external devices, or may receive directly recognized information from the external devices.

The self-driving vehicle100bmay perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the self-driving vehicle100bmay recognize the surrounding environment and the objects by using the learning model, and may determine the traveling movement line by using the recognized surrounding information or object information. The learning model may be learned directly from the self-driving vehicle100aor may be learned from an external device such as the AI server200.

In this case, the self-driving vehicle100bmay 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 server200and the generated result may be received to perform the operation.

The self-driving vehicle100bmay use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external apparatus to determine the travel path and the travel plan, and may control the driving device such that the self-driving vehicle100btravels along the determined travel path and travel plan.

The map data may include object identification information about various objects arranged in the space (for example, road) in which the self-driving vehicle100btravels. For example, the map data may include object identification information about fixed objects such as street lamps, rocks, and buildings and movable objects such as vehicles and pedestrians. The object identification information may include a name, a type, a distance, and a position.

In addition, the self-driving vehicle100bmay perform the operation or travel by controlling the driving device based on the control/interaction of the user. In this case, the self-driving vehicle100bmay acquire the intention information of the interaction due to the user's operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation.

The XR device100c, to which the AI technology is applied, may be implemented by a head-mount display (HMD), a head-up display (HUD) provided in the vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a fixed robot, a mobile robot, or the like.

The XR device100cmay analyzes three-dimensional point cloud data or image data acquired from various sensors or the external devices, generate position data and attribute data for the three-dimensional points, acquire information about the surrounding space or the real object, and render to output the XR object to be output. For example, the XR device100cmay output an XR object including the additional information about the recognized object in correspondence to the recognized object.

The XR device100cmay perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the XR device100cmay recognize the real object from the three-dimensional point cloud data or the image data by using the learning model, and may provide information corresponding to the recognized real object. The learning model may be directly learned from the XR device100c, or may be learned from the external device such as the AI server200.

In this case, the XR device100cmay 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 server200and the generated result may be received to perform the operation.

The robot100a, to which the AI technology and the self-driving technology are applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like.

The robot100a, to which the AI technology and the self-driving technology are applied, may refer to the robot itself having the self-driving function or the robot100ainteracting with the self-driving vehicle100b.

The robot100ahaving the self-driving function may collectively refer to a device that moves for itself along the given movement line without the user's control or moves for itself by determining the movement line by itself.

The robot100aand the self-driving vehicle100bhaving the self-driving function may use a common sensing method so as to determine at least one of the travel path or the travel plan. For example, the robot100aand the self-driving vehicle100bhaving the self-driving function may determine at least one of the travel path or the travel plan by using the information sensed through the lidar, the radar, and the camera.

The robot100athat interacts with the self-driving vehicle100bexists separately from the self-driving vehicle100band may perform operations interworking with the self-driving function of the self-driving vehicle100bor interworking with the user who rides on the self-driving vehicle100b.

In this case, the robot100ainteracting with the self-driving vehicle100bmay control or assist the self-driving function of the self-driving vehicle100bby acquiring sensor information on behalf of the self-driving vehicle100band providing the sensor information to the self-driving vehicle100b, or by acquiring sensor information, generating environment information or object information, and providing the information to the self-driving vehicle100b.

Alternatively, the robot100ainteracting with the self-driving vehicle100bmay monitor the user boarding the self-driving vehicle100b, or may control the function of the self-driving vehicle100bthrough the interaction with the user. For example, if it is determined that the driver is in a drowsy state, the robot100amay activate the self-driving function of the self-driving vehicle100bor assist the control of the driving device of the self-driving vehicle100b. The function of the self-driving vehicle100bcontrolled by the robot100amay include not only the self-driving function but also the function provided by the navigation system or the audio system provided in the self-driving vehicle100b.

Alternatively, the robot100athat interacts with the self-driving vehicle100bmay provide information or assist the function to the self-driving vehicle100boutside the self-driving vehicle100b. For example, the robot100amay provide traffic information including signal information and the like, such as a smart signal, to the self-driving vehicle100b, and automatically connect an electric charger to a charging port by interacting with the self-driving vehicle100blike an automatic electric charger of an electric vehicle.

The robot100a, to which the AI technology and the XR technology are applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, or the like.

The robot100a, to which the XR technology is applied, may refer to a robot In other words subjected to control/interaction in an XR image. In this case, the robot100amay be separated from the XR device100cand interwork with each other.

If the robot100a, which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the robot100aor the XR device100cmay generate the XR image based on the sensor information, and the XR device100cmay output the generated XR image. The robot100amay operate based on the control signal input through the XR device100cor the user's interaction.

For example, the user may confirm the XR image corresponding to the time point of the robot100ainterworking remotely through the external device such as the XR device100c, adjust the self-driving travel path of the robot100athrough interaction, control the operation or driving, or confirm the information about the surrounding object.

The self-driving vehicle100b, to which the AI technology and the XR technology are applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like.

The self-driving vehicle100b, to which the XR technology is applied, may refer to a self-driving vehicle having a means for providing an XR image or a self-driving vehicle In other words subjected to control/interaction in an XR image. Particularly, the self-driving vehicle100bIn other words subjected to control/interaction in the XR image may be distinguished from the XR device100cand interwork with each other.

The self-driving vehicle100bhaving the means for providing the XR image may acquire the sensor information from the sensors including the camera and output the generated XR image based on the acquired sensor information. For example, the self-driving vehicle100bmay include an HUD to output an XR image, thereby providing a passenger with a real object or an XR object corresponding to an object in the screen.

In this case, if the XR object is output to the HUD, at least part of the XR object may be outputted so as to overlap the actual object to which the passenger's gaze is directed. Meanwhile, if the XR object is output to the display provided in the self-driving vehicle100b, at least part of the XR object may be output so as to overlap the object in the screen. For example, the self-driving vehicle100bmay output XR objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, and the like.

If the self-driving vehicle100b, which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the self-driving vehicle100bor the XR device100cmay generate the XR image based on the sensor information, and the XR device100cmay output the generated XR image. The self-driving vehicle100bmay operate based on the control signal input through the external device such as the XR device100cor the user's interaction.

FIG. 4illustrates an AI device100according to an embodiment of the present disclosure.

The redundant repeat ofFIG. 1will be omitted below.

Referring toFIG. 4, the input unit120may include a camera121for image signal input, a microphone122for receiving audio signal input, and a user input unit123for receiving information from a user.

Voice data or image data collected by the input unit120are analyzed and processed as a user's control command.

Then, the input unit120is used for inputting image information (or signal), audio information (or signal), data, or information inputted from a user and the mobile terminal100may include at least one camera121in order for inputting image information.

The camera121processes image frames such as a still image or a video acquired by an image sensor in a video call mode or a capturing mode. The processed image frame may be displayed on the display unit151or stored in the memory170.

The microphone122processes external sound signals as electrical voice data. The processed voice data may be utilized variously according to a function (or an application program being executed) being performed in the mobile terminal100. Moreover, various noise canceling algorithms for removing noise occurring during the reception of external sound signals may be implemented in the microphone122.

The user input unit123is to receive information from a user and if information is inputted through the user input unit123, the processor180may control an operation of the mobile terminal100to correspond to the inputted information.

The user input unit123may include a mechanical input means (or a mechanical key, for example, a button, a dome switch, a jog wheel, and a jog switch at the front, back or side of the mobile terminal100) and a touch type input means. As one example, a touch type input means may include a virtual key, a soft key, or a visual key, which is displayed on a touch screen through software processing or may include a touch key disposed at a portion other than the touch screen.

The output device150may include at least one of a display unit151, a sound output module152, a haptic module153, or an optical output module154.

The display unit151may be formed with a mutual layer structure with a touch sensor or formed integrally, so that a touch screen may be implemented. Such a touch screen may serve as the user input unit123providing an input interface between the mobile terminal100and a user, and an output interface between the mobile terminal100and a user at the same time.

The sound output module152may output audio data received from the wireless communication unit110or stored in the memory170in a call signal reception or call mode, a recording mode, a voice recognition mode, or a broadcast reception mode.

The sound output module152may include a receiver, a speaker, and a buzzer.

The haptic module153generates various haptic effects that a user may feel. A representative example of a haptic effect that the haptic module153generates is vibration.

The optical output module154outputs a signal for notifying event occurrence by using light of a light source of the mobile terminal100. An example of an event occurring in the AI device100includes message reception, call signal reception, missed calls, alarm, schedule notification, e-mail reception, and information reception through an application.

FIG. 5is a view illustrating the configuration of a cleaner-type artificial intelligence device according to an embodiment of the present disclosure.

FIG. 5is a view illustrating the configuration of a handy stick cleaner type artificial intelligence device100.

The artificial intelligence device100may include a handle510, a body530, a head connector540, a head550and a dust suction unit570, in addition to the components ofFIG. 4.

The handle510is a part of the artificial intelligence device100capable of being gripped by a user's hand.

The handle510may be provided with a fingerprint recognition sensor for identifying a user via the fingerprint data501of the user.

The body530may include all the components ofFIG. 4.

The head connector540may be a member for connecting the body530with the head550.

The head connector540may include a head motor541capable of increasing or decreasing the length of the head550.

The head550connects the dust suction unit570with the body530and may extend downward from the body530.

The length of the head550may be automatically or manually adjusted according to user information. To this end, the body530may include a head motor for adjusting the length of the head550.

The length of the head550may be automatically adjusted by a length calculation model which will be described below.

The dust suction unit570may suck in the dust on a floor. To this end, the dust suction unit570may include a suction motor for sucking in air.

The dust suction unit570may be connected to an end of the head550.

FIG. 6is a view illustrating a method of operating an artificial intelligence device according to an embodiment of the present disclosure.

In particular,FIG. 6is a view illustrating an embodiment in which user information is registered and the length of the head550is automatically adjusted according to information on the registered user.

The processor180of the artificial intelligence device100registers user information (S601).

The user information may include the height and gender of the user, user identification information, a pressure sensing value and the angle of the head550.

The user identification information is to identify who the user is and may include one or more of the fingerprint of the user, the voice of the user and the name of the user.

The user may register the fingerprint or voice thereof in advance via the artificial intelligence device100for identifying the user.

The pressure sensing value may be a pressure value applied to the head550while the user performs cleaning via the artificial intelligence device100. To this end, the head550may be provided with a pressure sensor.

The angle of the head550may indicate an angle between the ground or the dust suction unit570and the head550while the user performs cleaning via the artificial intelligence device100.

To this end, the head550may be provided with an angle sensor.

The pressure sensing value and the angle of the head550may be measured by the artificial intelligence device100.

FIG. 7is a view illustrating an example of measuring a pressure sensing value and the angle of a head according to an embodiment of the present disclosure.

The head550may include a pressure sensor571and an angle sensor573.

The pressure sensor571and the angle sensor573may be disposed at an end of the head550in which the dust suction unit570is in contact with the head550.

The pressure sensor571may measure the pressure sensing value applied to the head550while the user performs cleaning.

The angle sensor573may measure a head angle a between the dust suction unit570or the ground and the head550.

The pressure sensing value measured by the pressure sensor571and the angle measured by the angle sensor573may be used to set the length of the head550later.

The pressure sensing value and the angle sensed by the angle sensor573, may be matched with the identification information of the user and stored in the memory170.

If the length of the head550is shorter than the height of the user, the pressure sensing value is increased and the angle of the head is closer to the vertical than the ground.

In contrast, if the length of the head550is longer than the height of the user, the pressure sensing value may be decreased and the angle of the head may be less than that of the vertical.

The head length calculation model may refer to a model in which a correlation among the height of the user, the pressure sensing value, the head angle and the head length is learned.

The processor180may receive the user information from an external device and store the received user information in the memory170.

The external device may be a mobile terminal such as a smartphone of a user, but this is merely an example.

The external device may receive the user information from the user via a cleaner application installed therein.

The external device may transmit the received user information to the artificial intelligence device100.

FIG. 8is a view illustrating a process of inputting user information via an external device according to an embodiment of the present disclosure.

Referring toFIG. 8, the display751of an external device700may display an execution screen710of the cleaner application. The execution screen710of the cleaner application may be a screen for receiving one or more of the height and gender of the user.

The external device700may transmit the information on the height and gender of the user to the artificial intelligence device100.

Information on the height and gender of the user transmitted from the external device700to the artificial intelligence device100may be transmitted before the user performs cleaning via the artificial intelligence device100.

The artificial intelligence device100may match the information on the height and gender of the user received from the external device700with the user identification information, the pressure sensing value and the angle of the head550and store the information on the height and gender of the user in the memory170.

The processor180of the artificial intelligence device100receives the user identification information (S603), and determines the length of the head550from the user information corresponding to the user identification information using the head length calculation model (S605).

The head length calculation model may be an artificial neural network based model subjected to supervised learning by a deep learning algorithm or a machine learning algorithm.

The head length calculation model may be learned by the learning processor130of the artificial intelligence device100or the learning processor240of the AI server200.

If the head length calculation model is learned by the learning processor240of the AI server200, the artificial intelligence device100may receive the head length calculation model from the AI server200via a communication unit110(also referred to as a communication interface) and store the head length calculation model in the memory170.

The head length calculation model may refer to a model for inferring the length of the head550optimized for the user from the height and gender of the user, the pressure sensing value and the angle of the head550.

Here, the gender of the user may be an optional parameter. That is, information on the gender of the user may not be used in the learning process of the head length calculation model, and only the height of the user, the pressure sensing value and the angle of the head may be used for learning.

The head length calculation model may refer to a model which is composed of an artificial neural network and is learned to infer the length (or the length range) of the head550indicating a feature point (or an output feature point) using the height and gender of the user for learning, the pressure sensing value and the angle of the head550as input data.

The head length calculation model may be learned via supervised learning. This will be described with reference to the following drawings.

FIG. 9is a view illustrating a supervised learning process of a head length calculation model based on an artificial neural network according to an embodiment of the present disclosure, andFIG. 10is a view illustrating a training data set for learning of a head length calculation model.

Referring toFIG. 9, the head length calculation model900may be learned with the goal of accurately inferring the labeled length of the head550from user cleaning information including the height and gender of the user, the pressure sensing value and the angle of the head550.

The user cleaning information indicating training data may be labeled with the length (or the length range) of the head550and the head length calculation model900may be learned using the labeled training data.

Referring toFIG. 10, a plurality of training data sets including the user cleaning information including the height and gender of the user the pressure sensing value and the angle of the head550and the length of the head550labeled in the user cleaning information is shown.

The head length calculation model900may be subjected to supervised learning using the plurality of training data sets.

The loss function or cost function of the head length calculation model900may be expressed by a square mean of a difference between the label indicating the length of the head550corresponding to each training data and the length of the head550inferred from each training data.

In addition, the head length calculation model900may determine model parameters included in the artificial neural network to minimize the cost function via learning.

When an input feature vector is extracted from the user cleaning information for learning and is input, a result of determining the length of the head550may be output as a target feature vector, and the head length calculation model900may be learned to minimize the loss function corresponding to the difference between the output target feature vector and the labeled length of the head550.

The processor180may extract the user cleaning information including the height and gender of the user, the pressure sensing value and the angle of the head matching the user identification information from the memory170, after receiving the user identification information via fingerprint recognition.

The processor180may determine the length of the head550from the extracted user cleaning information using the head length calculation model900.

The determined length of the head550may be matched with the user identification information and stored in the memory170. When the user inputs, to the artificial intelligence device100, the user identification information later, the length of the head550may be automatically adjusted to a length matching the user identification information.

InFIG. 6, assume that the pressure sensing value and the angle of the head corresponding to the user identification information is acquired in advance in a previous cleaning process.

However, the present disclosure is not limited thereto and the pressure sensing value and the angle of the head may be acquired in a current cleaning process and, based on this, the length of the head550may be determined in real time.

The processor180of the artificial intelligence device100controls the head motor541to have the determined length of the head550(S607).

The processor180may control the head motor541to have the determined length of the head550via the head length calculation model900.

That is, the processor180may transmit a control command (or current) for increasing or decreasing the length of the head550to the head motor541.

According to the embodiment of the present disclosure, the height of the cleaner required by the user may be automatically controlled, thereby obtaining a user-friendly cleaner capable of being easily used.

FIG. 11is a flowchart illustrating a method of operating an artificial intelligence device according to another embodiment of the present disclosure.

In particular,FIG. 11is a view illustrating an embodiment in which, when the user uses the artificial intelligence device100in a state in which the head length calculation model900is installed in the artificial intelligence device100, the length of the head550is automatically adjusted based on the measured pressure sensing value and the angle of the head.

Referring toFIG. 11, the processor180of the artificial intelligence device100receives the user identification information (S1101).

In one embodiment, the user identification information may be fingerprint data indicating the fingerprint of the user.

In another example, the user identification information may be voice data indicating the voice of the user.

The user identification information may include a variety of information capable of identifying the user, such as face image data indicating the face of the user, iris data, etc.

The processor180may compare the received user identification information with the user identification information stored in the memory170to search for matching identification information.

The processor180may extract the height and gender of the user corresponding to the matching user identification information from the memory170. That is, the height and gender of the user matching the user identification information may be stored in the memory170.

Thereafter, the processor180of the artificial intelligence device100measures the pressure sensing value applied to the head550and the head angle (S1103).

The processor180may measure pressure applied to the head550via the pressure sensor571, after identifying the user via the user identification information. Simultaneously, the processor180may measure the head angle between the head550and the ground or between the head550and the dust suction unit570via the angle sensor573.

The processor180of the artificial intelligence device100determines the length of the head550from the user cleaning information corresponding to the user identification information using the head length calculation model900(S1105).

The processor180may acquire user cleaning information including the height and gender of the user corresponding to the user identification information and the pressure sensing value and head angle measured in step S1103.

The processor180may determine the length of the head550from the user cleaning information using the head length calculation model900.

The processor180may determine the determined length of the head550as the length optimized for the identified user.

For the detailed description of the head length calculation model900, refer to the description ofFIGS. 9 and 10.

The processor180of the artificial intelligence device100controls the head motor541to have the determined length of the head550(S1107).

Thereafter, the user may perform cleaning using the head550having the adjusted length.

According to the embodiment of the present disclosure, it is possible to provide the head550adjusted to have the length optimized for the user using the pre-learned head length calculation model900.

Therefore, it is possible to greatly improve the cleaning convenience of the user.

Meanwhile, when the length of the head550is readjusted by the user in a state of being adjusted to the determined length of the head550, the processor180may update the head length calculation model900using the height of the user, the pressure sensing value, the head angle and the readjusted length of the head550.

FIG. 12is a view illustrating a head length table in which head lengths respectively match a plurality of users.

The head length table1200may refer to a table in which optimized head lengths respectively matching a plurality of users are stored.

Each head length may be obtained based on the head length calculation model900.

The head length table1200may be stored in the memory170. When the user identification information is received, the artificial intelligence device100may extract the head length corresponding to the user identification information and adjust the height of the head550to the extracted head length.

For example, the artificial intelligence device100may adjust the length of the head550to 30 cm when User A is identified, adjust the length of the head550to 25 cm when User B is identified, and adjust the length of the head550to 50 cm when User C is identified.

According to the embodiment of the present disclosure, when the user is identified, it is possible to control the height of the head550to have an appropriate length via the head length table1200.

FIG. 13is a flowchart illustrating a method of operating an artificial intelligence device according to another embodiment of the present disclosure.

In particular,FIG. 13is a view illustrating an embodiment in which the length of the head550is automatically adjusted based on the pressure sensing value, when the user does not input the height of the user via an application.

Referring toFIG. 13, the processor180of the artificial intelligence device100receives user identification information (S1301).

The user identification information may include one or more of fingerprint data indicating the fingerprint of the user or voice data indicating the voice of the user.

The processor180of the artificial intelligence device100measures the pressure sensing value applied to the head550via the pressure sensor571(S1303).

Step S1303may be performed after the user starts cleaning via the artificial intelligence device100.

The processor180of the artificial intelligence device100determines whether the measured pressure sensing value is greater than a reference value (S1305).

The reference value may be a threshold value used to adjust the length of the head550.

If the length of the head550is shorter than the height of the user, the pressure sensing value may increase, and, if the length of the head550is longer than the height of the user, the pressure sensing value may decrease.

When the measured pressure sensing value is greater than the reference value, the processor180of the artificial intelligence device100controls the head motor541to increase the length of the head550(S1307).

When the measured pressure sensing value is less than the reference value, the processor180of the artificial intelligence device100controls the head motor541to decrease the length of the head550(S1309).

That is, even if the height information of the user is not known, the processor180may adjust the length of the head550to be appropriate to the user using the pressure sensing value applied to the head550. Therefore, it is possible to greatly improve cleaning convenience of the user.

Thereafter, the processor180of the artificial intelligence device100matches the adjusted length of the head550with the user identification information and stores the adjusted length of the head550in the memory170(S1311).

The processor180may match the adjusted length of the head550with the user identification information and store the adjusted length of the head550in the memory170, as in the head length table1200shown inFIG. 12.

The processor180may automatically adjust the length of the head550to the length value of the head550appropriate to the identified user using the head length table1200later.

Therefore, since the user does not need to manually adjust the length of the head, it is possible to improve convenience.

According to the embodiment of the present disclosure, the height of the cleaner required by the user may be automatically controlled, thereby obtaining a user-friendly cleaner capable of being easily used.

According to the embodiment of the present disclosure, since the user does not need to manually adjust the length of the head, it is possible to greatly improve convenience.

The present disclosure may also be embodied as computer readable codes on a medium having a program recorded thereon. The computer readable medium is any data storage device that may store data which may be thereafter read by a computer system. Examples of the computer readable medium include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, or the like. In addition, the computer may include the processor180of the AI device.