Patent Publication Number: US-2021174422-A1

Title: Smart apparatus

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. 10-2019-0159631, filed on Dec. 4, 2019, the contents of which are hereby incorporated by reference herein in its entirety. 
     BACKGROUND 
     The present disclosure describes an apparatus for generating a user profile, and recommending a product to a user based on the generated user profile and a recommended algorithm, with a method for recommending a customized product to the user. 
     The recommended algorithm is an algorithm of recommending what a subject will like. The recommended algorithm representatively used is collaborative filtering. The collaborative filtering, which is a likely mind, is made in a manner of recommending what other users having similar disposition to me like. 
     Meanwhile, the collaborative filtering cannot recommend a new product that the user does not inquire, and cannot perform a customized recommendation of an individual user because it makes a recommendation basically on the basis of popularity. 
     SUMMARY 
     The purpose of the present disclosure is to generate a user profile by using information of a smart apparatus, and recommend a customized recommendation of an individual user by using the generated user profile and a recommendation algorithm. 
     The present disclosure describes a smart apparatus comprising: a communication interface configured to communicate with a server; and one or more processors configured to: determine if an application is authorized to use a user profile, when the application installed on the smart apparatus requests the user profile; and transmit the user profile to the server corresponding to the application when the application is determined to have a user right, wherein the server is to recommend a product to the user based on the obtained user profile or recommended product information. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating an AI apparatus according to an embodiment of the present disclosure. 
         FIG. 2  is a block diagram illustrating an AI server according to an embodiment of the present disclosure. 
         FIG. 3  is a view illustrating an AI system according to an embodiment of the present disclosure. 
         FIG. 4  is a block diagram illustrating an AI apparatus according to an embodiment of the present disclosure. 
         FIG. 5  illustrates a process of generating a user profile of the present disclosure. 
         FIG. 6  is a flowchart of the present disclosure. 
         FIG. 7  is an embodiment of the present disclosure. 
         FIG. 8  is a view for explaining a process of generating recommended product information of the present disclosure. 
         FIG. 9  illustrates an example of obtaining the user profile of the present disclosure. 
         FIG. 10  illustrates an example of obtaining the user profile of the present disclosure. 
         FIG. 11  illustrates an example of obtaining the user profile of the present disclosure. 
         FIG. 12  illustrates an example of obtaining the user profile of the present disclosure. 
         FIG. 13  illustrates an example of obtaining the user profile of the present disclosure. 
         FIG. 14  illustrates an example of obtaining the user profile of the present disclosure. 
         FIG. 15  is an output example of the present disclosure. 
         FIG. 16  is an output example of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, details of the present invention will be described. An embodiment described below is only an example of the present invention, and the present invention can be deformed in various modes. Hence, specific configurations and functions disclosed below by no means limit the claims. 
     Hereinafter, embodiments of the present disclosure are described in more detail with reference to accompanying drawings and regardless of the drawings symbols, same or similar components are assigned with the same reference numerals and thus overlapping descriptions for those are omitted. The suffixes “module” and “interface” 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 disclosure in unnecessary detail. Additionally, the accompanying drawings are used to help easily understanding embodiments disclosed herein but the technical idea of the present disclosure is not limited thereto. It should be understood that all of variations, equivalents or substitutes contained in the concept and technical scope of the present disclosure are also included. 
     It will be understood that the terms “first” and “second” are used herein to describe various components but these components should not be limited by these terms. These terms are used only to distinguish one component from other components. 
     In this disclosure below, when one part (or element, device, etc.) is referred to as being ‘connected’ to another part (or element, device, etc.), it should be understood that the former can be ‘directly connected’ to the latter, or ‘electrically connected’ to the latter via an intervening part (or element, device, etc.). It will be further understood that when one component is referred to as being ‘directly connected’ or ‘directly linked’ to another component, it means that no intervening component is present. 
     &lt;Artificial Intelligence (AI)&gt; 
     Artificial intelligence refers to the field of studying artificial intelligence or methodology for making artificial intelligence, and machine learning refers to the field of defining various issues dealt with in the field of artificial intelligence and studying methodology for solving the various issues. Machine learning is defined as an algorithm that enhances the performance of a certain task through a steady experience with the certain task. 
     An artificial neural network (ANN) is a model used in machine learning and may mean a whole model of problem-solving ability which is composed of artificial neurons (nodes) that form a network by synaptic connections. The artificial neural network can be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating an output value. 
     The artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer includes one or more neurons, and the artificial neural network may include a synapse that links neurons to neurons. In the artificial neural network, each neuron may output the function value of the activation function for input signals, weights, and deflections input through the synapse. 
     Model parameters refer to parameters determined through learning and include a weight value of synaptic connection and deflection of neurons. A hyperparameter means a parameter to be set in the machine learning algorithm before learning, and includes a learning rate, a repetition number, a mini batch size, and an initialization function. 
     The purpose of the learning of the artificial neural network may be to determine the model parameters that minimize a loss function. The loss function may be used as an index to determine optimal model parameters in the learning process of the artificial neural network. 
     Machine learning may be classified into supervised learning, unsupervised learning, and reinforcement learning according to a learning method. 
     The supervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is given, and the label may mean the correct answer (or result value) that the artificial neural network must infer when the training data is input to the artificial neural network. The unsupervised learning may refer to a method of learning an artificial neural network in a state in which a label for training data is not given. The reinforcement learning may refer to a learning method in which an agent defined in a certain environment learns to select a behavior or a behavior sequence that maximizes cumulative compensation in each state. 
     Machine learning, which is implemented as a deep neural network (DNN) including a plurality of hidden layers among artificial neural networks, is also referred to as deep learning, and the deep learning is part of machine learning. In the following, machine learning is used to mean deep learning. 
     &lt;Robot&gt; 
     A robot may refer to a machine that automatically processes or operates a given task by its own ability. In particular, a robot having a function of recognizing an environment and performing a self-determination operation may be referred to as an intelligent robot. 
     Robots may be classified into industrial robots, medical robots, home robots, military robots, and the like according to the use purpose or field. 
     The robot includes a driving interface 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 interface, and may travel on the ground through the driving interface or fly in the air. 
     &lt;Self-Driving&gt; 
     Self-driving refers to a technique of driving for oneself, and a self-driving vehicle refers to a vehicle that travels without an operation of a user or with a minimum operation of a user. 
     For example, the self-driving may include a technology for maintaining a lane while driving, a technology for automatically adjusting a speed, such as adaptive cruise control, a technique for automatically traveling along a predetermined route, and a technology for automatically setting and traveling a route when a destination is set. 
     The vehicle may include a vehicle having only an internal combustion engine, a hybrid vehicle having an internal combustion engine and an electric motor together, and an electric vehicle having only an electric motor, and may include not only an automobile but also a train, a motorcycle, and the like. 
     Here, the self-driving vehicle may be regarded as a robot having a self-driving function. 
     &lt;eXtended Reality (XR)&gt; 
     Extended reality is collectively referred to as virtual reality (VR), augmented reality (AR), and mixed reality (MR). The VR technology provides a real-world object and background only as a CG image, the AR technology provides a virtual CG image on a real object image, and the MR technology is a computer graphic technology that mixes and combines virtual objects into the real world. 
     The MR technology is similar to the AR technology in that the real object and the virtual object are shown together. However, in the AR technology, the virtual object is used in the form that complements the real object, whereas in the MR technology, the virtual object and the real object are used in an equal manner. 
     The XR technology may be applied to a head-mount display (HMD), a head-up display (HUD), a mobile phone, a tablet PC, a laptop, a desktop, a TV, a digital signage, and the like. A device to which the XR technology is applied may be referred to as an XR device. 
       FIG. 1  is a block diagram illustrating an AI apparatus  100  according to an embodiment of the present disclosure. 
     Hereinafter, the AI apparatus  100  may be referred to as a terminal. 
     The AI apparatus (or an AI device)  100  may be implemented by a stationary device or a mobile device, such as a TV, a projector, a mobile phone, a smartphone, a desktop computer, a notebook, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a navigation device, a tablet PC, a wearable device, a set-top box (STB), a DMB receiver, a radio, a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, and the like. 
     Referring to  FIG. 1 , the AI apparatus  100  may include a communication interface  110 , an input interface  120 , a learning processor  130 , a sensing interface  140 , an output interface  150 , a memory  170 , and a processor  180 . 
     The communication interface  110  may transmit and receive data to and from external devices such as other  100   a  to  100   e  and the AI server  200  by using wire/wireless communication technology. For example, the communication interface  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 interface  110  includes GSM (Global System for Mobile communication), CDMA (Code Division Multi Access), LTE (Long Term Evolution), 5G, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Bluetooth™, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), and the like. 
     The input interface  120  may acquire various kinds of data. 
     Here, the input interface  120  may include a camera for inputting a video signal, a microphone for receiving an audio signal, and a user input interface 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 interface  120  may acquire a training data for model learning and an input data to be used when an output is acquired by using learning model. The input interface  120  may acquire raw input data. Here, the processor  180  or the learning processor  130  may extract an input feature by preprocessing the input data. 
     The learning processor  130  may learn a model composed of an artificial neural network by using training data. The learned artificial neural network may be referred to as a learning model. The learning model may be used to an infer result value for new input data rather than training data, and the inferred value may be used as a basis for determination to perform a certain operation. 
     Here, the learning processor  130  may perform AI processing together with the learning processor  240  of the AI server  200 . 
     Here, the learning processor  130  may include a memory integrated or implemented in the AI apparatus  100 . Alternatively, the learning processor  130  may be implemented by using the memory  170 , an external memory directly connected to the AI apparatus  100 , or a memory held in an external device. 
     The sensing interface  140  may acquire at least one of internal information about the AI apparatus  100 , ambient environment information about the AI apparatus  100 , and user information by using various sensors. 
     Examples of the sensors included in the sensing interface  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 interface  150  may generate an output related to a visual sense, an auditory sense, or a haptic sense. 
     Here, the output interface  150  may include a display interface for outputting time information, a speaker for outputting auditory information, and a haptic module for outputting haptic information. 
     The memory  170  may store data that supports various functions of the AI apparatus  100 . For example, the memory  170  may store input data acquired by the input interface  120 , training data, a learning model, a learning history, and the like. 
     The processor  180  may determine at least one executable operation of the AI apparatus  100  based on information determined or generated by using a data analysis algorithm or a machine learning algorithm. The processor  180  may control the components of the AI apparatus  100  to execute the determined operation. 
     To this end, the processor  180  may request, search, receive, or utilize data of the learning processor  130  or the memory  170 . The processor  180  may control the components of the AI apparatus  100  to execute the predicted operation or the operation determined to be desirable among the at least one executable operation. 
     When the connection of an external device is required to perform the determined operation, the processor  180  may generate a control signal for controlling the external device and may transmit the generated control signal to the external device. 
     The processor  180  may acquire intention information for the user input and may determine the user&#39;s requirements based on the acquired intention information. 
     The processor  180  may acquire the intention information corresponding to the user input by using at least one of a speech to text (STT) engine for converting speech input into a text string or a natural language processing (NLP) engine for acquiring intention information of a natural language. 
     At least one of the STT engine or the NLP engine may be configured as an artificial neural network, at least part of which is learned according to the machine learning algorithm. At least one of the STT engine or the NLP engine may be learned by the learning processor  130 , may be learned by the learning processor  240  of the AI server  200 , or may be learned by their distributed processing. 
     The processor  180  may collect history information including the operation contents of the AI apparatus  100  or the user&#39;s feedback on the operation and may store the collected history information in the memory  170  or the learning processor  130  or transmit the collected history information to the external device such as the AI server  200 . The collected history information may be used to update the learning model. 
     The processor  180  may control at least part of the components of AI apparatus  100  so as to drive an application program stored in memory  170 . Furthermore, the processor  180  may operate two or more of the components included in the AI apparatus  100  in combination so as to drive the application program. 
       FIG. 2  is a block diagram illustrating an AI server  200  according to an embodiment of the present 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. Here, the AI server  200  may be included as a partial configuration of the AI apparatus  100 , and may perform at least part of the AI processing together. 
     The AI server  200  may include a communication interface  210 , a memory  230 , a learning processor  240 , a processor  260 , and the like. 
     The communication interface  210  can transmit and receive data to and from an external device such as the AI apparatus  100 . 
     The memory  230  may include a model storage interface  231 . The model storage interface  231  may store a learning or learned model (or an artificial neural network  231   a ) through the learning processor  240 . 
     The learning processor  240  may learn the artificial neural network  231   a  by using the training data. The learning model may be used in a state of being mounted on the AI server  200  of the artificial neural network, or may be used in a state of being mounted on an external device such as the AI apparatus  100 . 
     The learning model may be implemented in hardware, software, or a combination of hardware and software. If all or part of the learning models are implemented in software, one or more instructions that constitute the learning model may be stored in memory  230 . 
     The processor  260  may infer the result value for new input data by using the learning model and may generate a response or a control command based on the inferred result value. 
       FIG. 3  is a view illustrating an AI system  1  according to an embodiment of the present 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 apparatuses  100   a  to  100   e.    
     The cloud network  10  may refer to a network that forms part of a cloud computing infrastructure or exists in a cloud computing infrastructure. The cloud network  10  may be configured by using a 3G network, a 4G or LTE network, or a 5G network. 
     That is, the devices  100   a  to  100   e  and  200  configuring the AI system  1  may be connected to each other through the cloud network  10 . In particular, each of the devices  100   a  to  100   e  and  200  may communicate with each other through a base station, but may directly communicate with each other without using a base station. 
     The AI server  200  may include a server that performs AI processing and a server that performs operations on big data. 
     The AI server  200  may be connected to at least one of the AI apparatuses constituting the AI system  1 , that is, the robot  100   a , the self-driving vehicle  100   b , the XR device  100   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 apparatuses  100   a  to  100   e.    
     Here, the AI server  200  may learn the artificial neural network according to the machine learning algorithm instead of the AI apparatuses  100   a  to  100   e , and may directly store the learning model or transmit the learning model to the AI apparatuses  100   a  to  100   e.    
     Here, the AI server  200  may receive input data from the AI apparatuses  100   a  to  100   e , may infer the result value for the received input data by using the learning model, may generate a response or a control command based on the inferred result value, and may transmit the response or the control command to the AI apparatuses  100   a  to  100   e.    
     Alternatively, the AI apparatuses  100   a  to  100   e  may infer the result value for the input data by directly using the learning model, and may generate the response or the control command based on the inference result. 
     Hereinafter, various embodiments of the AI apparatuses  100   a  to  100   e  to which the above-described technology is applied will be described. The AI apparatuses  100   a  to  100   e  illustrated in  FIG. 3  may be regarded as a specific embodiment of the AI apparatus  100  illustrated in  FIG. 1 . 
     &lt;AI+Robot&gt; 
     The robot  100   a , to which the AI technology is applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like. 
     The robot  100   a  may include a robot control module for controlling the operation, and the robot control module may refer to a software module or a chip implementing the software module by hardware. 
     The robot  100   a  may acquire state information about the robot  100   a  by using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, may determine the response to user interaction, or may determine the operation. 
     The robot  100   a  may use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan. 
     The robot  100   a  may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the robot  100   a  may recognize the surrounding environment and the objects by using the learning model, and may determine the operation by using the recognized surrounding information or object information. The learning model may be learned directly from the robot  100   a  or may be learned from an external device such as the AI server  200 . 
     Here, the robot  100   a  may perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server  200  and the generated result may be received to perform the operation. 
     The robot  100   a  may use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external device to determine the travel route and the travel plan, and may control the driving interface such that the robot  100   a  travels along the determined travel route and travel plan. 
     The map data may include object identification information about various objects arranged in the space in which the robot  100   a  moves. For example, the map data may include object identification information about fixed objects such as walls and doors and movable objects such as pollen and desks. The object identification information may include a name, a type, a distance, and a position. 
     In addition, the robot  100   a  may perform the operation or travel by controlling the driving interface based on the control/interaction of the user. Here, the robot  100   a  may acquire the intention information of the interaction due to the user&#39;s operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation. 
     &lt;AI+Self-Driving&gt; 
     The self-driving vehicle  100   b , to which the AI technology is applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like. 
     The self-driving vehicle  100   b  may include a self-driving control module for controlling a self-driving function, and the self-driving control module may refer to a software module or a chip implementing the software module by hardware. The self-driving control module may be included in the self-driving vehicle  100   b  as a component thereof, but may be implemented with separate hardware and connected to the outside of the self-driving vehicle  100   b.    
     The self-driving vehicle  100   b  may acquire state information about the self-driving vehicle  100   b  by using sensor information acquired from various kinds of sensors, may detect (recognize) surrounding environment and objects, may generate map data, may determine the route and the travel plan, or may determine the operation. 
     Like the robot  100   a , the self-driving vehicle  100   b  may use the sensor information acquired from at least one sensor among the lidar, the radar, and the camera so as to determine the travel route and the travel plan. 
     In particular, the self-driving vehicle  100   b  may recognize the environment or objects for an area covered by a field of view or an area over a certain distance by receiving the sensor information from external devices, or may receive directly recognized information from the external devices. 
     The self-driving vehicle  100   b  may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the self-driving vehicle  100   b  may recognize the surrounding environment and the objects by using the learning model, and may determine the traveling route by using the recognized surrounding information or object information. The learning model may be learned directly from the self-driving vehicle  100   a  or may be learned from an external device such as the AI server  200 . 
     Here, the self-driving vehicle  100   b  may perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server  200  and the generated result may be received to perform the operation. 
     The self-driving vehicle  100   b  may use at least one of the map data, the object information detected from the sensor information, or the object information acquired from the external device to determine the travel route and the travel plan, and may control the driving interface such that the self-driving vehicle  100   b  travels along the determined travel route and travel plan. 
     The map data may include object identification information about various objects arranged in the space (for example, road) in which the self-driving vehicle  100   b  travels. For example, the map data may include object identification information about fixed objects such as street lamps, rocks, and buildings and movable objects such as vehicles and pedestrians. The object identification information may include a name, a type, a distance, and a position. 
     In addition, the self-driving vehicle  100   b  may perform the operation or travel by controlling the driving interface based on the control/interaction of the user. Here, the self-driving vehicle  100   b  may acquire the intention information of the interaction due to the user&#39;s operation or speech utterance, and may determine the response based on the acquired intention information, and may perform the operation. 
     &lt;AI+XR&gt; 
     The XR device  100   c , to which the AI technology is applied, may be implemented by a head-mount display (HMD), a head-up display (HUD) provided in the vehicle, a television, a mobile phone, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a fixed robot, a mobile robot, or the like. 
     The XR device  100   c  may analyzes three-dimensional point cloud data or image data acquired from various sensors or the external devices, generate position data and attribute data for the three-dimensional points, acquire information about the surrounding space or the real object, and render to output the XR object to be output. For example, the XR device  100   c  may output an XR object including the additional information about the recognized object in correspondence to the recognized object. 
     The XR device  100   c  may perform the above-described operations by using the learning model composed of at least one artificial neural network. For example, the XR device  100   c  may recognize the real object from the three-dimensional point cloud data or the image data by using the learning model, and may provide information corresponding to the recognized real object. The learning model may be directly learned from the XR device  100   c , or may be learned from the external device such as the AI server  200 . 
     Here, the XR device  100   c  may perform the operation by generating the result by directly using the learning model, but the sensor information may be transmitted to the external device such as the AI server  200  and the generated result may be received to perform the operation. 
     &lt;AI+Robot+Self-Driving&gt; 
     The robot  100   a , to which the AI technology and the self-driving technology are applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, or the like. 
     The robot  100   a , to which the AI technology and the self-driving technology are applied, may refer to the robot itself having the self-driving function or the robot  100   a  interacting with the self-driving vehicle  100   b.    
     The robot  100   a  having the self-driving function may collectively refer to a device that moves for itself along the given route without the user&#39;s control or moves for itself by determining the route by itself. 
     The robot  100   a  and the self-driving vehicle  100   b  having the self-driving function may use a common sensing method so as to determine at least one of the travel route or the travel plan. For example, the robot  100   a  and the self-driving vehicle  100   b  having the self-driving function may determine at least one of the travel route or the travel plan by using the information sensed through the lidar, the radar, and the camera. 
     The robot  100   a  that interacts with the self-driving vehicle  100   b  exists separately from the self-driving vehicle  100   b  and may perform operations interworking with the self-driving function of the self-driving vehicle  100   b  or interworking with the user who rides on the self-driving vehicle  100   b.    
     Here, the robot  100   a  interacting with the self-driving vehicle  100   b  may control or assist the self-driving function of the self-driving vehicle  100   b  by acquiring sensor information on behalf of the self-driving vehicle  100   b  and providing the sensor information to the self-driving vehicle  100   b , or by acquiring sensor information, generating environment information or object information, and providing the information to the self-driving vehicle  100   b.    
     Alternatively, the robot  100   a  interacting with the self-driving vehicle  100   b  may monitor the user boarding the self-driving vehicle  100   b , or may control the function of the self-driving vehicle  100   b  through the interaction with the user. For example, when it is determined that the driver is in a drowsy state, the robot  100   a  may activate the self-driving function of the self-driving vehicle  100   b  or assist the control of the driving interface of the self-driving vehicle  100   b . The function of the self-driving vehicle  100   b  controlled by the robot  100   a  may include not only the self-driving function but also the function provided by the navigation system or the audio system provided in the self-driving vehicle  100   b.    
     Alternatively, the robot  100   a  that interacts with the self-driving vehicle  100   b  may provide information or assist the function to the self-driving vehicle  100   b  outside the self-driving vehicle  100   b . For example, the robot  100   a  may provide traffic information including signal information and the like, such as a smart signal, to the self-driving vehicle  100   b , and automatically connect an electric charger to a charging port by interacting with the self-driving vehicle  100   b  like an automatic electric charger of an electric vehicle. 
     &lt;AI+Robot+XR&gt; 
     The robot  100   a , to which the AI technology and the XR technology are applied, may be implemented as a guide robot, a carrying robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, or the like. 
     The robot  100   a , to which the XR technology is applied, may refer to a robot that is subjected to control/interaction in an XR image. In this case, the robot  100   a  may be separated from the XR device  100   c  and interwork with each other. 
     When the robot  100   a , which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the robot  100   a  or the XR device  100   c  may generate the XR image based on the sensor information, and the XR device  100   c  may output the generated XR image. The robot  100   a  may operate based on the control signal input through the XR device  100   c  or the user&#39;s interaction. 
     For example, the user can confirm the XR image corresponding to the time point of the robot  100   a  interworking remotely through the external device such as the XR device  100   c , adjust the self-driving travel path of the robot  100   a  through interaction, control the operation or driving, or confirm the information about the surrounding object. 
     &lt;AI+Self-Driving+XR&gt; 
     The self-driving vehicle  100   b , to which the AI technology and the XR technology are applied, may be implemented as a mobile robot, a vehicle, an unmanned flying vehicle, or the like. 
     The self-driving driving vehicle  100   b , to which the XR technology is applied, may refer to a self-driving vehicle having a means for providing an XR image or a self-driving vehicle that is subjected to control/interaction in an XR image. Particularly, the self-driving vehicle  100   b  that is subjected to control/interaction in the XR image may be distinguished from the XR device  100   c  and interwork with each other. 
     The self-driving vehicle  100   b  having the means for providing the XR image may acquire the sensor information from the sensors including the camera and output the generated XR image based on the acquired sensor information. For example, the self-driving vehicle  100   b  may include a HUD to output an XR image, thereby providing a passenger with a real object or an XR object corresponding to an object in the screen. 
     Here, when the XR object is output to the HUD, at least part of the XR object may be outputted so as to overlap the actual object to which the passenger&#39;s gaze is directed. Meanwhile, when the XR object is output to the display provided in the self-driving vehicle  100   b , at least part of the XR object may be output so as to overlap the object in the screen. For example, the self-driving vehicle  100   b  may output XR objects corresponding to objects such as a lane, another vehicle, a traffic light, a traffic sign, a two-wheeled vehicle, a pedestrian, a building, and the like. 
     When the self-driving vehicle  100   b , which is subjected to control/interaction in the XR image, may acquire the sensor information from the sensors including the camera, the self-driving vehicle  100   b  or the XR device  100   c  may generate the XR image based on the sensor information, and the XR device  100   c  may output the generated XR image. The self-driving vehicle  100   b  may operate based on the control signal input through the external device such as the XR device  100   c  or the user&#39;s interaction. 
       FIG. 4  is a block diagram illustrating an AI apparatus  100  according to an embodiment of the present disclosure. 
     The redundant repeat of  FIG. 1  will be omitted below. 
     In the present disclosure, the AI apparatus  100  may include an edge device. 
     The communication interface  110  may also be referred to as a communicator. 
     Referring to  FIG. 4 , the input interface  120  may include a camera  121  for image signal input, a microphone  122  for receiving audio signal input, and a user input interface  123  for receiving information from a user. 
     Voice data or image data collected by the input interface  120  are analyzed and processed as a user&#39;s control command. 
     Then, the input interface  120  is used for inputting image information (or signal), audio information (or signal), data, or information inputted from a user and the AI apparatus  100  may include at least one camera  121  in order for inputting image information. 
     The camera  121  processes image frames such as a still image or a video obtained by an image sensor in a video call mode or a capturing mode. The processed image frame may be displayed on the display interface  151  or stored in the memory  170 . 
     The microphone  122  processes external sound signals as electrical voice data. The processed voice data may be utilized variously according to a function (or an application program being executed) being performed in the AI apparatus  100 . Moreover, various noise canceling algorithms for removing noise occurring during the reception of external sound signals may be implemented in the microphone  122 . 
     The user input interface  123  is to receive information from a user and when information is inputted through the user input interface  123 , the processor  180  may control an operation of the AI apparatus  100  to correspond to the inputted information. 
     The user input interface  123  may include a mechanical input means (or a mechanical key, for example, a button, a dome switch, a jog wheel, and a jog switch at the front, back or side of the AI apparatus  100 ) and a touch type input means. As one example, a touch type input means may include a virtual key, a soft key, or a visual key, which is displayed on a touch screen through software processing or may include a touch key disposed at a portion other than the touch screen. 
     The sensing interface  140  may also be referred to as a sensor interface. 
     The output interface  150  may include at least one of a display interface  151 , a sound output module  152 , a haptic module  153 , or an optical output module  154 . 
     The display interface  151  may display (output) information processed in the AI apparatus  100 . For example, the display interface  151  may display execution screen information of an application program running on the AI apparatus  100  or user interface (UI) and graphic user interface (GUI) information according to such execution screen information. 
     The display interface  151  may be formed with a mutual layer structure with a touch sensor or formed integrally, so that a touch screen may be implemented. Such a touch screen may serve as the user input interface  123  providing an input interface between the AI apparatus  100  and a user, and an output interface between the AI apparatus  100  and a user at the same time. 
     The sound output module  152  may output audio data received from the wireless communication interface  110  or stored in the memory  170  in a call signal reception or call mode, a recording mode, a voice recognition mode, or a broadcast reception mode. 
     The sound output module  152  may include a receiver, a speaker, and a buzzer. 
     The haptic module  153  generates various haptic effects that a user can feel. A representative example of a haptic effect that the haptic module  153  generates is vibration. 
     The optical output module  154  outputs a signal for notifying event occurrence by using light of a light source of the AI apparatus  100 . An example of an event occurring in the AI apparatus  100  includes message reception, call signal reception, missed calls, alarm, schedule notification, e-mail reception, and information reception through an application. 
       FIG. 5  Illustrates a Process of Generating a User Profile of the Present Disclosure. 
     Ahead of explanation of the user profile, an intelligent service model for generating and utilizing the user profile may be implemented. The intelligent service model may include a collection engine, a categorization engine, and a service provider. The intelligent service model may be installed in an application shape and implemented through a processor  180 , or may be implemented through a server and the like connected to a user terminal and the like. 
     The collection engine may collect source data relative to user information through a terminal that a user uses. 
     The categorization engine may cluster and categorize the source data obtained from the collection engine. At this time, the clustering may mean grouping data with similar objects. The categorization engine may generate the user profile by clustering the obtained source data. 
     The service provider may recommend a server appropriate for the user by using the user profile generated in the categorization engine. 
     Such an intelligent service model can directly analyze, select and provide a service necessary for the user without the user&#39;s request, unlike a conventional service providing scheme. 
     The collection engine and the categorization engine of the present disclosure are included in the processor  180  of a smart apparatus  100 , or is implemented with software, and accordingly, the processor  180  can operations of the collection engine and the categorization engine. 
     In addition, an artificial intelligence (AI) apparatus  100  may be used in combination with the smart apparatus  100 . 
     In addition, the service provider may include the smart apparatus  100  of the present disclosure or an outer server. 
     Hereinafter, in  FIG. 5 , the process of generating the user profile will be described. 
     Referring to  FIG. 5 , the processor  180  of the smart apparatus  100  can obtain source data included in the smart apparatus  100  or another terminal, which the user possesses (S 510 ). At this time, the source data may mean data created by using the smart apparatus or another apparatus by the user. 
     For example, the source data may include position information, a message, an image and a video, calendar information, a call record, a memory, an app usage history or the like. 
     The processor  180  of the present disclosure may cluster the source data through a preset algorithm similar to the source data (S 520 ). 
     For the clustering, the processor  180  of the present disclosure may generate a set of data having a category relative to a service to be provided to the user. In addition, the set of data for clustering may be obtained from a server operated by external companies. The categorization engine may generate the user profile by clustering the obtained source data. 
     The processor  180  according to the present disclosure may generate the user profile (S 530 ). The user profile is information extracted based on the smart apparatus or another terminal which the user uses, and may include personal information of the user. 
     The generated user profile may include a status of children, a marital status, a presence of pets, a main means of transportation, a work status and the like. 
     According to the present disclosure, with a method for generating the user profile, the processor  180  may input the source data in an artificial neural network (ANN) model, and generate the user profile as a result value of the ANN model, and the source data may include application information installed in the smart apparatus, message information and contact address information. 
     The processor  180  according to the present disclosure may be transmitted to the outer server requesting the user profile. Herein, the outer server may mean a service provider, and may provide a service appropriate for the user based on the user profile (S 540 ). In addition, the processor  180  according to the present disclosure may provide a user-customized service based on the user profile. 
     Hereinafter, in  FIG. 6 , the process for recommending a user-customized product will be described. 
       FIG. 6  is a flowchart of the present disclosure. 
       FIG. 6  is a view for explaining an implementation of the user profile of the smart apparatus  100  of the present disclosure. A flowchart of  FIG. 6  may be implemented in the intelligent service model. 
     Referring to  FIG. 6 , the processor  180  of the smart apparatus  100  may obtain the source data included in the smart apparatus  100  or another terminal of the user (S 610 ). In addition, the processor  180  of the smart apparatus  100  may generate a cluster for generating the user profile (S 620 ), and may generate the user profile based on a clustering result (S 630 ). Steps S 610  to S 630  above may correspond to the process of  FIG. 5 . 
     The processor  180  of the smart apparatus  100  according to the present disclosure may generate the user profile, and generate product information to be recommended to the user of the smart apparatus  100  (S 640 ). 
     In detail, the product information to recommend may include product information generated based on recommendation algorithm and the user profile. At this time, the recommendation algorithm may be implemented by (1) User-Based Collaborative Filtering (UBCF) which provides a customized recommendation by analyzing the user&#39;s behavioral data. The UBCF may include a manner of recommending a product included in a product history in which another user purchased by calculating similarity between users. The similarity between users may be calculated by constituting a matrix when there is user evaluation data. In addition, the recommendation algorithm may be implemented by (2) Item-based Collaborative Filtering (IBCF) showing a similar product when the user inquires a product by calculating similarity between products. In addition, (3) Content-based Filtering may also be included in the recommendation algorithm. 
     A communication interface  110  of the smart apparatus  100  according to the present disclosure may transmit the user profile to the outer server when the outer server requests the user profile, by communicating with the outer server. In addition, when an application installed in the smart apparatus  100  requests the user profile, the processor  180  may confirm if the application is authorized to access the user profile (S 650 ). 
     The processor  180  may transmit the user profile to a server corresponding to the application, when the application the application is authorized to access the user profile (S 660 ). In addition, the outer server that obtains the user profile may transmit the product recommendation information to the smart apparatus  100  based on the obtained user profile. 
     As another example, the communication interface  110  of the smart apparatus  100  according to the present disclosure may transmit recommended product information generated based on the user profile and the recommendation algorithm in the smart apparatus  100  by communicating with the outer server (S 660 ). 
     Hereinafter,  FIG. 7  will describe the process of determining if the application of S 650  is authorized to access the user profile. 
       FIG. 7  is an Embodiment of the Present Disclosure. 
     Referring to  FIG. 7 , when the application installed in the smart apparatus  100  requests the user profile, the processor  180  may determine if the application is authorized to user the user profile, and may transmit the user profile to a server corresponding to the application, when the application has a user right. At this time, the server may be to recommend a product to the user based on the obtained user profile or recommended product information. 
     In addition, a plurality of applications are installed in the smart apparatus  100 , and a right to use the user profile may be differently assigned to each of the plurality of applications. 
     For example, referring to  FIG. 7 , suppose that there are application A and application B. The smart apparatus  100  may generate the user profile for providing a customized intelligent service of an individual user. In addition, the generated user profile may be stored in a memory  170 . 
     The processor  180  may store the generated user profile in the memory  170 , and update the user profile according to a subsequent application installation or use of the smart apparatus  100  of the user (S 710 ). 
     Suppose that the application A is an application not authorized to use the user profile, and the application B is an application authorized to use the user profile. 
     Servers corresponding to the applications A and B may call API of the smart apparatus  100  to provide the recommended product by using the user profile information (S 720 ). When an application installed in the smart apparatus  100  requests the user profile, the processor  180  may determine if the application is authorized to use the user profile. For example, it may be determined that the application A is not authorized to use the user profile, and the application B is authorized to use the user profile (S 730 ). 
     That is, the processor  180  may differently assign the right to use the user profile with regard to the application A and the application B, and may provide information only to an authorized application (S 740 ). 
     According to the present disclosure, the user profile includes at least one information of a status of children, a marital status, a presence of pets, a car status or a work status. In addition, the processor  180  may select information to be provided to each of the plurality of applications from information included in the user profile, and provide the selected information to each of the plurality of application. 
     At this time, the selected information may be individually determined as per each of the plurality of applications based on the information included in each of the plurality of applications. 
     The information included in each of the plurality of applications includes a function, use and a category of the application. 
     For example, suppose that a first application is an application for house furniture shopping, and a second application is an application that purchases car supplies. 
     The processor  180  may determine if the first application and the second application are authorized to use the user profile. 
     When the first application and the second application are authorized to use the user profile, the processor  180  may acquire information for the function, the use and the category included in the first application. 
     The processor  180  may select profile information to be provided to the first application, in the user profile according the acquired information. 
     For example, when the processor  180  acquires information for house furniture shopping acquired from the first application, the processor  180  may select information for choosing furniture, such as a status of children, a marital status, an age group and a gender, from a plurality of information included in the user profile, and transmit the information to a server corresponding to the first application. 
     The processor  180  may select information such as the type of car, a main transportation means, a gender or an age group, as information for choosing the car supplies in a case of the second application, and transmit the information to a server corresponding to the second application. 
     Such a processor  180  may select information to be provided to each of a plurality of applications from information included in the user profile, and provide the selected information to each of the plurality of applications. 
     According to the present disclosure, the processor  180  may extract an application authorized to user the user profile, generate a list including the application and the selected information corresponding to the application, and store the generated list in the memory  170 . 
     In detail, the processor  180  may generate a user profile list corresponding to each of the plurality of applications. The processor  180  may select the user profile to be provided to each of the plurality of applications installed in the smart apparatus  100 , and provide minimum personal information by generating the list corresponding to the application and efficiently manage the provided information. 
     The server corresponding to each of the applications may transmit product information to be recommended to the user based on the obtained user profile. 
     As another example, suppose that the smart apparatus  100  communicates with the server corresponding to the plurality of applications. 
     The communication interface  110  of the smart apparatus  100  may communicate with a first server and a second server. The processor  180  of the smart apparatus  100  may receive product purchase history information purchased through the first application, determine the product to recommend among a plurality of products included in the product purchase history information based on the user profile, and transmit information for the product to recommend to the second server corresponding to the second application. At this time, the product to recommend may be a product not included in a second product purchase history information purchased through the second application. 
     For example, suppose that clothing is purchased in the first application installed in the smart apparatus  100 , and clothing is not purchased in the second application. 
     The processor  180  may obtain clothing purchase history information purchased through the first application. The processor  180  may analyze information for clothing brands, materials and sizes, included in the purchase history. In addition, the processor  180  may determine the product to recommend among a plurality of products included in the clothing purchase history by reflecting the user profile. The communication interface  110  of the smart apparatus  100  may transmit clothing information to be recommended to the second server corresponding to the second application. At this time, the information for clothing is not included in purchase history of the second application. 
     Thereafter, though there is no purchase history for clothing from the second server corresponding to the second application, the communication interface  110  of the smart apparatus  100  may obtain recommend purchase information based on the user profile. 
     As described above, the plurality of applications are installed in the smart apparatus  100 , and the selected information in the user profile is provided to each of the plurality of applications, through which the recommended product information reflecting the user profile may be obtained from the outer server although the user profile information possessing or providing different purchase histories as per each of the plurality of applications is different from each other. Alternatively, the processor  180  of the smart apparatus  100  may generate the recommended product information. 
       FIG. 8  is a View for Explaining a Process of Generating Recommended Product Information of the Present Disclosure. 
     The process of  FIG. 8  may be performed in the outer server receiving the user profile, and when the process is performed in the outer server, product information to recommend, which the smart apparatus  100  obtains, may be generated through the process of  FIG. 8 . 
     In addition, the process of  FIG. 8  may be performed by the processor  180  of the smart apparatus  100  by using the user profile. 
     Referring to  FIG. 8 , the processor  180  of the smart apparatus  100  may generate the user profile by using the source data collected in the smart apparatus  100 , and generate the product information to recommend based on the generated user profile. 
     According to the present disclosure, a conventional product recommendation service  810  may mean a recommendation algorithm. 
     The processor  180  may generate the user profile based on personal data in order to provide an intelligent product recommendation service ( 821 ). In addition, the processor  180  may collect applications installed in the smart apparatus  100  or user activity information such as search histories and shopping information, and generate a product candidate group of interest to the user. 
     The processor  180  may classify the generated product candidate group to product candidates to recommend based on the user profile ( 822 ). After classifying the product candidates, the processor  180  may select the product to recommend based the profile and the product data through the user profile, the product purchase history information and the like. 
     The processor  180  may determine, as the product to recommend, a customized product determined to be proper among the recommended products in the conventional recommendation algorithm  810  and the products to recommend, which are selected based on the user profile. 
     The processor  180  may generate the recommended product information based on the recommendation algorithm and the user profile, and may provide the generated recommended product information through an application, or transmit the same to the outer server or an outer server corresponding to the application. 
     The present disclosure exemplifies the user profile, but is not limited to the examples, and may include a variety of user profiles generated as user personal information that can be obtained from the smart apparatus  100 . 
     Hereinafter,  FIGS. 9 to 14  illustrate an example of obtaining information in the user profile. 
       FIG. 9  Illustrates an Example of Obtaining the User Profile of the Present Disclosure. 
     Referring to  FIG. 9 , a method for determining if a child is present in information included in the user profile is illustrated. 
     The processor  180  of the smart apparatus  100  may collect source data for determining if the user&#39;s child is present. At this time, the source data may include a usage record of an application installed in the smart apparatus  100  and a use time of an application installed in the smart apparatus  100 . The processor  180  may acquire the usage record and the use time of the application (S 910 , S 920 ). At this time, application information may include information for an application history that the user purchases or installs, and include information provided from the outer server. 
     The processor  180  may collect keyword information relative to the child as clustering information for determining if the child is present (S 930 ). The keyword information may be provided from the outer server, and the processor  180  may internally generate the keyword information relative to the child. 
     When a keyword relative to the child is found by analyzing a name and a category of the installed application, the processor  180  may update the child&#39;s status of the user profile with “having a child”. 
     For example, when a word such as “kids” is included in the name of the application, the processor  180  may determine that the user has a child. 
     In addition, the processor  180  may derive a score for determining if the child is present, by analyzing the user time of the application. 
     In addition, the processor  180  may determine if the child is present by determining if keywords relative to the child are included in a message, contact information or text information included in the smart apparatus  100  of the user, in combination with the keywords relative to the child. 
     In an embodiment of the present disclosure, the processor  180  may input the obtained source data in the ANN model in order to determine if the child of the user profile is present, and update the child&#39;s status outputted by the ANN model to the user profile. At this time, the source data for determining the child&#39;s status may include application information, the message information or the contact information, which is installed in the smart apparatus. 
       FIG. 10  Illustrates an Example of Obtaining the User Profile of the Present Disclosure. 
     Referring to  FIG. 10 , a process of determining a marital status among the information included in the user profile is illustrated. 
     The processor  180  may obtain the source data in order to determine the marital status (S 1010 ). The processor may firstly determine the child&#39;s status determined in  FIG. 9  (S 1020 ). When the child is determined not to be present, the processor  180  may extract a marriage-related keyword from the contact information of the user&#39;s smart apparatus, and determine if the marriage-related keyword is included therein (S 1030 ). 
     When the contact information with the marriage-related keyword is not present, the processor  180  may determine that the user is a single man or a single woman. 
     When the child is determined to be present, in order to recognize the user&#39;s gender, the processor  180  may extract a keyword on men&#39;s marriage from the contact information of the smart apparatus  100 , and determine if the keyword on men&#39;s marriage is included therein (S 1040 ). 
     When the keyword on men&#39;s marriage is present, the processor  180  may determine that the user is a married man, and when the keyword on men&#39;s marriage is not present, the processor  180  may determine that the user is a married woman. 
     In addition, when the source data is changed according to the usage history of the smart apparatus  100  of the user, the processor  180  may update the user profile by analyzing the changed source data. 
       FIG. 11  Illustrates an Example of Obtaining the User Profile of the Present Disclosure. 
       FIG. 11  illustrates a process for determining a presence of pets among the information included in the user profile. 
     The processor  180  of the smart apparatus  100  may obtain the source data for determining the presence of pets (S 1110 ). At this time, the source data for determining the presence of pets may include an image or a video stored in the smart apparatus  100 . 
     The processor  180  may obtain the image or the video stored in a gallery the smart apparatus  100 , and select the image or the video including pets by using tag information in image or video information. 
     The processor  180  may obtain image creation time information, position information and animal information included in the tag information of the image or the video included in the pets. In addition, the processor  180  may determine if the image or the video was photographed in the smart apparatus of the user (S 1120 ). When the image or the video is created by using the smart apparatus that the user possesses or other apparatuses, etc., the processor  180  may determine if the image or the video was created around the user&#39;s residential area by using the position information of the corresponding image or video (S 1130 ). 
     For example, when the image or the video is created within a radius of 500 m around the user&#39;s residential area, the processor  180  may determine that the corresponding image or video is created in the user&#39;s house. 
     When the number of data that satisfies all of these conditions is more than a certain number, the processor  180  may determine that the user has a pet (S 1140 ). 
     In addition, when the source data is changed according to the usage history of the smart apparatus  100  of the user, the processor  180  may update the user profile by analyzing the changed source data. 
       FIG. 12  Illustrates an Example of Obtaining the User Profile of the Present Disclosure. 
       FIG. 12  illustrates the process for determining a work status among the information included in the user profile. 
     The processor  180  may obtain the source data for determining a work status (S 1210 ). The source data may include contact information including a keyword that can estimate the user&#39;s identity, and text information such as a message. 
     The processor  180  may determine if wage-related words are included in the source data (S 1220 ). The processor  180  may include the wage-related words, acquire application information installed in the smart apparatus  100 , and determine if the acquire information is a worker-enabled application (S 1230 ). 
     When the acquired information is the worker-enabled application, the processor  180  may classify the user as a workforce. 
     When the wage-related words are not included in the source data, the processor  180  may whether to install and use the worker-enabled application (S 1240 ), and may classify the user as an undergraduate when the user uses the application. For example, the wage-related words are not displayed in the source data, but the processor  180  may classify the user installing applications related to job seekers or career paths as the undergraduate. 
     When the user is not included in the worker or the undergraduate, the processor  180  may classify the user as a freelancer. 
     In addition, when the source data is changed according to the usage history of the smart apparatus  100  of the user, the processor  180  may update the user profile by analyzing the changed source data. 
       FIG. 13  Illustrates an Example of Obtaining the User Profile of the Present Disclosure. 
       FIG. 13  is a process of determining a car status among the information included in the user profile. 
     The processor  180  may acquire the source data for determining the car status (S 1310 ). 
     At this time, the source data for determining the car status may include database related to the user&#39;s Bluetooth use. 
     The processor  180  may acquire a usage record of Bluetooth connected in a certain period of time, which is information included in usage database of Bluetooth. 
     The processor  180  determines if there is data in which a type of a device to which Bluetooth included in the generated database is connected is a car audio (S 1320 ). If there is no data satisfying the condition, transportation that the user uses the most is public transportation. 
     When there is data in which the type of the device connected by using Bluetooth is a car audio, the processor  180  may determine whether the user takes more than a certain amount of time to walk to and from work (S 1330 ). 
     When the user takes more than a certain amount of time to walk to and from work, the processor  180  may determine that the user is a public transportation user who possesses the car. If the user does not take more than a certain amount of time, the processor may determine that the user is a car user who possesses the car. At this time, the time to walk to and from work may be acquired through AI techniques using the user&#39;s input, health-related applications or the source data. 
     In addition, when the source data is changed according to the usage history of the smart apparatus  100  of the user, the processor  180  may update the user profile by analyzing the changed source data. 
       FIG. 14  Illustrates an Example of Obtaining the User Profile of the Present Disclosure. 
       FIG. 14 . illustrates information relative to a brand or mart that the user uses frequently among the information included in the user profile. 
     The processor  180  may acquire the source data included in the smart apparatus  100  to determine a place of interest (S 1410 ). The acquire source data may include text information including shopping-related keywords. The processor  180  may classify a payment record among text data included in the source data, and extract a payment position in the payment record (S 1420 ). The processor  180  may determine if the number of settlement positions acquired is more than a certain number with the same name (S 1430 ). 
     For example, if there are more than 5 positions with the same name, a type or brand of a mart with the name may be determined as a type or brand of a mart that the user primarily uses. 
     According to the present disclosure, the user profile is not limited to the above-described example, and may be variously generated based on the user person information. 
     In addition, when the source data is changed according to the usage history of the smart apparatus  100  of the user, the processor  180  may update the user profile by analyzing the changed source data. 
       FIG. 15  and  FIG. 16  are Output Examples of the Present Dis Closure. 
       FIG. 15  and  FIG. 16  illustrate examples of product information to be recommended in an output interface  150  of the smart apparatus  100 . 
     Basically, the processor  180  may receive information for the recommended product by using the user profile and the recommendation algorithm, from the outer server, and the output interface  150  may output information for the recommended product. 
     In addition, when the processor  180  individually provides the user profile according to information of the application installed in the smart apparatus  100 , the processor  180  may receive the recommended product information from servers corresponding to each of a plurality of applications, and the output interface  150  may output the recommended product information received from the outer server. 
     At this time, by providing the user profile selected according to each of the outer servers, the recommended product information may be determined based on the selected user profile. 
     The smart apparatus  100  of the present disclosure may prevent a discharge of unnecessary person information, by acquiring information of the plurality of applications as described above and providing different user profiles as per each of the plurality of applications. In addition, an improved personal information management may be achieved by generating and managing a list in which the selected user profile is provided. 
     As another example, the smart apparatus  100  may receive a candidate group of the recommended product from the server, and the processor  180  may generate the recommended product information based on the recommendation algorithm and the user profile, and transmit the generated recommended product information to the outer server. Thereafter, the output interface  150  may output the recommended product information received from the outer server. 
     In the case above, a discharge to the outer server of the user profile is not achieved, the candidate group of the recommended product is received through the communication interface  110  of the smart apparatus  100 , and the processor  180  of the smart apparatus  100  may select the customized product among the candidate group based on the recommended algorithm and the user profile, and transmit the selected product information to the outer server. Therefore, the outer server may acquire the recommended product information reflecting the user profile, and the smart apparatus may receive product information to newly recommend. The outer interface  150  may output information for the recommended product. 
     Referring to  FIG. 15 , if the application installed in the smart apparatus  100  is authorized to use the user profile, the user profile information selected according to the information application may be displayed on the top ( 1510 ). 
     For example, the user profile provided in the application illustrated in  FIG. 15  includes a work status, a status of children, a presence of pets and a car status, and the user of the smart apparatus is a worker, but has the user profile that does not have the child, the pet and the car. 
     The user profile of  1510  above may be differently set through a touch input of the user and the like through the input interface  120  of the smart apparatus  100 , and this may be utilized when the user profile information is not correctly generated or when another user uses the smart apparatus  100 . 
     The output interface  150  may output a result in which the recommended product is analyzed, based on the recommendation algorithm and the selected user profile ( 1520 ). 
     Referring to  FIG. 16 , this figure illustrates an exemplified view of representing a case where the recommended product is changed before and after providing the user profile. 
     When using only the recommended algorithm, the smart apparatus  100  may generate and output the product information to recommended in purchase details as in  1610 , and generate and output the recommended information to recommend in a full list of popular products as in  1620 . 
     In addition, the product information to recommend may be received and outputted from the outer server. 
     In contrast, the product information to recommend, which is generated based on the user profile, may output “a suggestion of my own” tailored to the individual user as in  1630 . 
     The disclosure may provide a user-customized product by using minimum personal information in a smart apparatus by recommending a customized recommendation of an individual user based on a user profile and a recommendation algorithm. 
     Flowcharts according to the present disclosure may be performed regardless of the order or concurrently. That is, they are not constrained in time-series order. 
     Other implementations are within the scope of the following claims. 
     The present disclosure can be made in software, firmware or a combination of software and firmware. 
     According to an embodiment of the present disclosure, the above-described method may be implemented as a processor-readable code in a medium where a program is recorded. Examples of a processor-readable medium may include hard disk drive (HDD), solid state drive (SSD), silicon disk drive (SDD), read-only memory (ROM), random access memory (RAM), CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device.