KR102653992B1 - An artificial intelligence apparatus for providing customized local service based on user's residential area and method thereof - Google Patents

Abstract

The present disclosure provides a communication unit that receives a place search command from the user's artificial intelligence device, obtains the user's profile information from a user profile information database in response to the place search command, and provides a predetermined location based on the residential information included in the user profile information. The business information of each of the multiple local service businesses located within the radius is acquired from the local service business information database, and based on the business location information, subscription information, business popularity information, and business advertisement information included in the acquired multiple business information. Provides a local service providing device that includes a processor that sets an exposure ranking for each of the plurality of company information obtained and transmits the plurality of company information reflecting the exposure ranking to an artificial intelligence device through a communication unit.

Description

Local service provision device and method for providing local services based on the user's residence {AN ARTIFICIAL INTELLIGENCE APPARATUS FOR PROVIDING CUSTOMIZED LOCAL SERVICE BASED ON USER'S RESIDENTIAL AREA AND METHOD THEREOF}

This disclosure relates to a local service provision device and method for providing local services based on a user's residence. Specifically, it relates to a local service provision device and method that provides service provider information related to the user's residence and allows the user to use the service provider.

Artificial intelligence is a field of computer science and information technology that studies ways to enable computers to do things like thinking, learning, and self-development that can be done with human intelligence. This means enabling imitation.

Additionally, artificial intelligence does not exist by itself, but is directly or indirectly related to other fields of computer science. In particular, in modern times, attempts are being made very actively to introduce artificial intelligence elements in various fields of information technology and use them to solve problems in those fields.

Meanwhile, technologies that use artificial intelligence to recognize and learn surrounding situations, provide information desired by the user in the desired form, or perform actions or functions desired by the user are being actively researched.

And electronic devices that provide these various operations and functions can be called artificial intelligence devices.

Meanwhile, with recent developments in IT technology, IT technologies are being incorporated into the overall lives of users. However, the reality is that IT services related to users' residences are still lacking.

For example, users who have moved to a specific area require a lot of time to acquire information related to that area, and there is a problem in that they cannot properly use services provided in their residential area. In addition, since various information related to people's residences are delivered through paper printouts or paper mail, there is an urgent need for improvement.

The present disclosure aims to solve the above-described problems and other problems.

The purpose of this disclosure is to provide a local service provision device and method that provides customized local service information based on the user's residence.

The purpose of this disclosure is to provide a local service provision device and method that provides various information related to the user's residence.

The purpose of this disclosure is to provide an artificial intelligence device and method that enable users to search for and use information on customized local service providers related to their residence.

An embodiment of the present disclosure includes a communication unit that receives a place search command from the user's artificial intelligence device, obtains the user's profile information from a user profile information database in response to the place search command, and obtains residential information included in the user profile information. Based on this, the company information of each of the plurality of local service companies located within a predetermined radius is acquired from the local service company information database, and the company location information, subscription information, company popularity information, and company included in the obtained plurality of company information are obtained. Provides a local service providing device including a processor that sets an exposure ranking for each of the plurality of company information obtained based on advertising information and transmits the plurality of company information reflecting the exposure ranking to an artificial intelligence device through the communication department. .

In addition, the present disclosure includes receiving a place search command from a user's artificial intelligence device, obtaining the user's profile information from a user profile information database in response to the place search command, and based on residential information included in the user profile information. Obtaining business information for each of a plurality of local service businesses located within a predetermined radius from a local service business information database, including location information, subscription information, business popularity information, and business information for each business included in the obtained plurality of business information. Provided is a local service provision method including the steps of setting exposure rankings for each of a plurality of company information obtained based on advertising information and transmitting the plurality of company information reflecting the exposure rankings to an artificial intelligence device.

According to an embodiment of the present disclosure, customized local service information can be provided based on the user's residence.

Additionally, according to various embodiments of the present disclosure, various information related to the user's residence may be provided.

Additionally, according to various embodiments of the present disclosure, users can search for and use information on customized local service providers related to their residence.

Additionally, according to various embodiments of the present disclosure, users can search for and use information on customized local service providers related to their residence.

Figure 1 shows an AI device according to an embodiment of the present disclosure.
Figure 2 shows an AI server according to an embodiment of the present disclosure.
Figure 3 shows an AI system according to an embodiment of the present disclosure.
Figure 4 is a block diagram showing an AI device according to an embodiment of the present disclosure.
Figure 5 is a diagram illustrating a local service provision system according to an embodiment of the present disclosure.
Figure 6 is a flow chart illustrating a method of providing local service information according to an embodiment of the present disclosure.
Figure 7 is a diagram illustrating a screen on which local service information is provided according to an embodiment of the present invention.
Figures 8 and 9 are diagrams for explaining a method of registering local service provider subscription information according to an embodiment of the present disclosure.
Figure 10 is a diagram for explaining a method of brokering the purchase of products sold by a local service company according to an embodiment of the present disclosure.
Figure 11 is an example diagram for explaining a screen on which advertisement information of a local service company is displayed according to an embodiment of the present disclosure.
Figure 12 is an example diagram for explaining a screen displaying order processing status information according to an embodiment of the present disclosure.
Figure 13 is a flowchart for explaining a method of providing local event information according to an embodiment of the present disclosure.
14 and 15 are exemplary diagrams showing an event search interface according to an embodiment of the present disclosure.
Figure 16 is a flowchart for explaining a method of providing residence management information according to an embodiment of the present disclosure.
Figure 17 is an example diagram for explaining a management information search interface according to an embodiment of the present disclosure.
18 and 19 are exemplary diagrams for explaining an e-mail information interface according to an embodiment of the present disclosure.
Figures 20 and 21 are exemplary diagrams for explaining a residential news interface according to an embodiment of the present disclosure.
22 and 23 are exemplary diagrams for explaining a cost information interface according to an embodiment of the present disclosure.
Figure 24 is an example diagram for explaining a residential service provider interface according to an embodiment of the present disclosure.

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the attached drawings. However, identical or similar components will be assigned the same reference numbers regardless of reference numerals, and duplicate descriptions thereof will be omitted. The suffixes “module” and “part” for components used in the following description are given or used interchangeably only for the ease of preparing the specification, and do not have distinct meanings or roles in themselves. Additionally, in describing the embodiments disclosed in this specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed descriptions will be omitted. In addition, the attached drawings are only for easy understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the attached drawings, and all changes included in the spirit and technical scope of the present disclosure are not limited. , should be understood to include equivalents or substitutes.

Terms containing ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

<Artificial Intelligence (AI)>

Artificial intelligence refers to the field of researching artificial intelligence or methodologies to create it, and machine learning refers to the field of defining various problems dealt with in the field of artificial intelligence and researching methodologies to solve them. do. Machine learning is also defined as an algorithm that improves the performance of a certain task through consistent experience.

Artificial Neural Network (ANN) is a model used in machine learning. It can refer to an overall model with problem-solving capabilities that is composed of artificial neurons (nodes) that form a network through the combination of synapses. Artificial neural networks can be defined by connection patterns between neurons in different layers, a learning process that updates model parameters, and an activation function that generates output values.

An 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 synapses connecting neurons. In an artificial neural network, each neuron can output the activation function value for the input signals, weight, and bias input through the synapse.

Model parameters refer to parameters determined through learning and include the weight of synaptic connections and the bias of neurons. Hyperparameters refer to parameters that must be set before learning in a machine learning algorithm and include learning rate, number of repetitions, mini-batch size, initialization function, etc.

The purpose of artificial neural network learning can be seen as determining model parameters that minimize the loss function. The loss function can be used as an indicator to determine optimal model parameters in the learning process of an artificial neural network.

Machine learning can be classified into supervised learning, unsupervised learning, and reinforcement learning depending on the learning method.

Supervised learning refers to a method of training an artificial neural network with a given label for the learning data. A label refers to the correct answer (or result value) that the artificial neural network must infer when learning data is input to the artificial neural network. It can mean. Unsupervised learning can refer to a method of training an artificial neural network in a state where no labels for training data are given. Reinforcement learning can refer to a learning method in which an agent defined within an environment learns to select an action or action sequence that maximizes the cumulative reward in each state.

Among artificial neural networks, machine learning implemented with a deep neural network (DNN) that includes multiple hidden layers is also called deep learning, and deep learning is a part of machine learning. Hereinafter, machine learning is used to include deep learning.

<Robot>

A robot can refer to a machine that automatically processes or operates a given task based on its own capabilities. In particular, a robot that has the ability to recognize the environment, make decisions on its own, and perform actions can be called an intelligent robot.

Robots can be classified into industrial, medical, household, military, etc. depending on their purpose or field of use.

A robot is equipped with a driving unit including an actuator or motor and can perform various physical movements such as moving robot joints. In addition, a mobile robot includes wheels, brakes, and propellers in the driving part, and can travel on the ground or fly in the air through the driving part.

<Self-Driving>

Autonomous driving refers to technology that drives on its own, and an autonomous vehicle refers to a vehicle that drives without user intervention or with minimal user intervention.

For example, autonomous driving includes technology that maintains the driving lane, technology that automatically adjusts speed such as adaptive cruise control, technology that automatically drives along a set route, technology that automatically sets the route and drives once the destination is set, etc. All of these can be included.

Vehicles include vehicles equipped only with an internal combustion engine, hybrid vehicles equipped with both an internal combustion engine and an electric motor, and electric vehicles equipped with only an electric motor, and may include not only cars but also trains and motorcycles.

At this time, the self-driving vehicle can be viewed as a robot with self-driving functions.

*40<Extended Reality (XR: eXtended Reality)>

Extended reality refers collectively to virtual reality (VR), augmented reality (AR), and mixed reality (MR). VR technology provides objects and backgrounds in the real world only as CG images, AR technology provides virtual CG images on top of images of real objects, and MR technology provides computer technology that mixes and combines virtual objects in the real world. It is a graphic technology.

MR technology is similar to AR technology in that it shows real objects and virtual objects together. However, in AR technology, virtual objects are used to complement real objects, whereas in MR technology, virtual objects and real objects are used equally.

XR technology can be applied to HMD (Head-Mount Display), HUD (Head-Up Display), mobile phones, tablet PCs, laptops, desktops, TVs, digital signage, etc., and devices with XR technology applied are called XR Devices. It can be called.

Figure 1 shows an AI device 100 according to an embodiment of the present disclosure.

The AI device 100 includes TVs, projectors, mobile phones, smartphones, desktop computers, laptops, digital broadcasting terminals, PDAs (personal digital assistants), PMPs (portable multimedia players), navigation, tablet PCs, wearable devices, and set-top boxes (STBs). ), DMB receivers, radios, washing machines, refrigerators, desktop computers, digital signage, robots, vehicles, etc., can be implemented as fixed or movable devices.

Referring to FIG. 1, the terminal 100 includes a communication unit 110, an input unit 120, a learning processor 130, a sensing unit 140, an output unit 150, a memory 170, and a processor 180. It can be included.

The communication unit 110 can transmit and receive data with external devices such as other AI devices 100a to 100e or the AI server 200 using wired or wireless communication technology. For example, the communication unit 110 may transmit and receive sensor information, user input, learning models, and control signals with external devices.

At this time, the communication technologies used by the communication unit 110 include Global System for Mobile communication (GSM), Code Division Multi Access (CDMA), Long Term Evolution (LTE), 5G, Wireless LAN (WLAN), and Wireless-Fidelity (Wi-Fi). ), Bluetooth??, RFID (Radio Frequency Identification), Infrared Data Association (IrDA), ZigBee, NFC (Near Field Communication), etc.

The input unit 120 can acquire various types of data.

At this time, the input unit 120 may include a camera for inputting video signals, a microphone for receiving audio signals, and a user input unit for receiving information from the user. Here, the camera or microphone may be treated as a sensor, and the signal obtained from the camera or microphone may be referred to as sensing data or sensor information.

The input unit 120 may acquire training data for model learning and input data to be used when obtaining an output using the learning model. The input unit 120 may acquire unprocessed input data, and in this case, the processor 180 or the learning processor 130 may extract input features by preprocessing the input data.

The learning processor 130 can train a model composed of an artificial neural network using training data. Here, the learned artificial neural network may be referred to as a learning model. A learning model can be used to infer a result value for new input data other than learning data, and the inferred value can be used as the basis for a decision to perform an operation.

At this time, the learning processor 130 may perform AI processing together with the learning processor 240 of the AI server 200.

At this time, the learning processor 130 may include memory integrated or implemented in the AI device 100. Alternatively, the learning processor 130 may be implemented using the memory 170, an external memory directly coupled to the AI device 100, or a memory maintained in an external device.

The sensing unit 140 may use various sensors to obtain at least one of internal information of the AI device 100, information about the surrounding environment of the AI device 100, and user information.

At this time, the sensors included in the sensing unit 140 include a proximity sensor, illuminance sensor, acceleration sensor, magnetic sensor, gyro sensor, inertial sensor, RGB sensor, IR sensor, fingerprint recognition sensor, ultrasonic sensor, light sensor, microphone, and lidar. , radar, etc.

The output unit 150 may generate output related to vision, hearing, or tactile sensation.

At this time, the output unit 150 may include a display unit that outputs visual information, a speaker that outputs auditory information, and a haptic module that outputs tactile information.

The memory 170 may store data supporting various functions of the AI device 100. For example, the memory 170 may store input data, learning data, learning models, learning history, etc. obtained from the input unit 120.

The processor 180 may determine at least one executable operation of the AI device 100 based on information determined or generated using a data analysis algorithm or a machine learning algorithm. Additionally, the processor 180 may control the components of the AI device 100 to perform the determined operation.

To this end, the processor 180 may request, retrieve, receive, or utilize data from the learning processor 130 or the memory 170, and perform an operation that is predicted or determined to be desirable among the at least one executable operation. Components of the AI device 100 can be controlled to execute.

At this time, if linkage with an external device is necessary to perform the determined operation, the processor 180 may generate a control signal to control the external device and transmit the generated control signal to the external device.

The processor 180 may obtain intent information regarding user input and determine the user's request based on the obtained intent information.

At this time, the processor 180 uses at least one of a STT (Speech To Text) engine for converting voice input into a character string or a Natural Language Processing (NLP) engine for acquiring intent information of natural language, so that the user Intent information corresponding to the input can be obtained.

At this time, at least one of the STT engine or the NLP engine may be composed of at least a portion of an artificial neural network learned according to a machine learning algorithm. And, at least one of the STT engine or the NLP engine is learned by the learning processor 130, learned by the learning processor 240 of the AI server 200, or learned by distributed processing thereof. It may be.

The processor 180 collects history information including the user's feedback on the operation or operation of the AI device 100 and stores it in the memory 170 or the learning processor 130, or in the AI server 200, etc. Can be transmitted to an external device. The collected historical information can be used to update the learning model.

The processor 180 may control at least some of the components of the AI device 100 to run an application program stored in the memory 170. Furthermore, the processor 180 may operate two or more of the components included in the AI device 100 in combination with each other to run the application program.

Figure 2 shows 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 trains an artificial neural network using a machine learning algorithm or uses a learned artificial neural network. Here, the AI server 200 may be composed of a plurality of servers to perform distributed processing, and may be defined as a 5G network. At this time, the AI server 200 may be included as a part of the AI device 100 and may perform at least part of the AI processing.

The AI server 200 may include a communication unit 210, a memory 230, a learning processor 240, and a processor 260.

The communication unit 210 can transmit and receive data with an external device such as the AI device 100.

Memory 230 may include a model storage unit 231. The model storage unit 231 may store a model (or artificial neural network, 231a) that is being trained or has been learned through the learning processor 240.

The learning processor 240 can train the artificial neural network 231a using training data. The learning model may be used while mounted on the AI server 200 of the artificial neural network, or may be mounted and used on an external device such as the AI device 100.

Learning models can be implemented in hardware, software, or a combination of hardware and software. When part or all of the learning model is implemented as software, one or more instructions constituting the learning model may be stored in the memory 230.

The processor 260 may infer a result value for new input data using a learning model and generate a response or control command based on the inferred result value.

Figure 3 shows an AI system 1 according to an embodiment of the present disclosure.

Referring to FIG. 3, the AI system 1 includes at least one of an AI server 200, a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e. It is connected to this cloud network (10). Here, a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e to which AI technology is applied may be referred to as AI devices 100a to 100e.

The cloud network 10 may constitute part of a cloud computing infrastructure or may refer to a network that exists within the cloud computing infrastructure. Here, the cloud network 10 may be configured using a 3G network, 4G, Long Term Evolution (LTE) network, or 5G network.

That is, each device (100a to 100e, 200) constituting the AI system 1 may be connected to each other through the cloud network 10. In particular, the devices 100a to 100e and 200 may communicate with each other through a base station, but may also communicate directly with each other without going through the base station.

The AI server 200 may include a server that performs AI processing and a server that performs calculations on big data.

The AI server 200 is connected to at least one of the AI devices constituting the AI system 1: a robot 100a, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e. It is connected through the cloud network 10 and can assist at least some of the AI processing of the connected AI devices 100a to 100e.

At this time, the AI server 200 can train an artificial neural network according to a machine learning algorithm on behalf of the AI devices 100a to 100e, and directly store or transmit the learning model to the AI devices 100a to 100e.

At this time, the AI server 200 receives input data from the AI devices 100a to 100e, infers a result value for the received input data using a learning model, and provides a response or control command based on the inferred result value. It can be generated and transmitted to AI devices (100a to 100e).

Alternatively, the AI devices 100a to 100e may infer a result value for input data using a direct learning model and generate a response or control command based on the inferred result value.

Below, various embodiments of AI devices 100a to 100e to which the above-described technology is applied will be described. Here, the AI devices 100a to 100e shown in FIG. 3 can be viewed as specific examples of the AI device 100 shown in FIG. 1.

<AI+Robot>

The robot 100a applies AI technology and can be implemented as a guidance robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc.

The robot 100a may include a robot control module for controlling operations, and the robot control module may mean a software module or a chip implementing it as hardware.

The robot 100a uses sensor information obtained from various types of sensors to acquire status information of the robot 100a, detect (recognize) the surrounding environment and objects, generate map data, or determine movement path and driving. It can determine a plan, determine a response to user interaction, or determine an action.

Here, the robot 100a may use sensor information acquired from at least one sensor among lidar, radar, and camera to determine the movement path and driving plan.

The robot 100a may perform the above operations using a learning model composed of at least one artificial neural network. For example, the robot 100a can recognize the surrounding environment and objects using a learning model, and can determine an operation using the recognized surrounding environment information or object information. Here, the learning model may be learned directly from the robot 100a or from an external device such as the AI server 200.

At this time, the robot 100a may perform an operation by generating a result using a direct learning model, but performs the operation by transmitting sensor information to an external device such as the AI server 200 and receiving the result generated accordingly. You may.

The robot 100a determines the movement path and driving plan using at least one of map data, object information detected from sensor information, or object information acquired from an external device, and controls the driving unit to follow the determined movement path and driving plan. The robot 100a can be driven accordingly.

The map data may include object identification information about various objects arranged in the space where the robot 100a moves. For example, map data may include object identification information for fixed objects such as walls and doors and movable objects such as flower pots and desks. Additionally, object identification information may include name, type, distance, location, etc.

Additionally, the robot 100a can perform actions or drive by controlling the driving unit based on the user's control/interaction. At this time, the robot 100a may acquire interaction intention information according to the user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

<AI+Autonomous Driving>

The self-driving vehicle 100b can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying AI technology.

The autonomous vehicle 100b may include an autonomous driving control module for controlling autonomous driving functions, and the autonomous driving control module may refer to a software module or a chip implementing it as hardware. The self-driving control module may be included internally as a component of the self-driving vehicle 100b, but may also be configured as separate hardware and connected to the outside of the self-driving vehicle 100b.

The self-driving vehicle 100b uses sensor information obtained from various types of sensors to obtain status information of the self-driving vehicle 100b, detect (recognize) the surrounding environment and objects, generate map data, or You can determine the movement route and driving plan, or determine the action.

Here, the autonomous vehicle 100b, like the robot 100a, may use sensor information acquired from at least one sensor among lidar, radar, and camera to determine the movement path and driving plan.

In particular, the autonomous vehicle 100b can recognize the environment or objects in an area where the view is obscured or an area over a certain distance by receiving sensor information from external devices, or receive recognized information directly from external devices. .

The autonomous vehicle 100b may perform the above operations using a learning model composed of at least one artificial neural network. For example, the self-driving vehicle 100b can recognize the surrounding environment and objects using a learning model, and determine the driving route using the recognized surrounding environment information or object information. Here, the learning model may be learned directly from the autonomous vehicle 100b or from an external device such as the AI server 200.

At this time, the autonomous vehicle 100b may perform operations by generating results using a direct learning model, but operates by transmitting sensor information to an external device such as the AI server 200 and receiving the results generated accordingly. You can also perform .

The autonomous vehicle 100b determines the movement path and driving plan using at least one of map data, object information detected from sensor information, or object information acquired from an external device, and controls the driving unit to maintain the determined movement path and driving. The autonomous vehicle 100b can be driven according to a plan.

The map data may include object identification information about various objects placed in the space (eg, road) where the autonomous vehicle 100b drives. For example, map data may include object identification information for fixed objects such as streetlights, rocks, and buildings, and movable objects such as vehicles and pedestrians. Additionally, object identification information may include name, type, distance, location, etc.

Additionally, the autonomous vehicle 100b can perform operations or drive by controlling the driving unit based on the user's control/interaction. At this time, the autonomous vehicle 100b may acquire interaction intention information according to the user's motion or voice utterance, determine a response based on the acquired intention information, and perform the operation.

<AI+XR>

The XR device (100c) is equipped with AI technology and can be used for HMD (Head-Mount Display), HUD (Head-Up Display) installed in vehicles, televisions, mobile phones, smart phones, computers, wearable devices, home appliances, and digital signage. , it can be implemented as a vehicle, a fixed robot, or a mobile robot.

The XR device 100c analyzes 3D point cloud data or image data acquired through various sensors or from external devices to generate location data and attribute data for 3D points, thereby providing information about surrounding space or real objects. The XR object to be acquired and output can be rendered and output. For example, the XR device 100c may output an XR object containing additional information about the recognized object in correspondence to the recognized object.

The XR device 100c may perform the above operations using a learning model composed of at least one artificial neural network. For example, the XR device 100c can recognize a real object from 3D point cloud data or image data using a learning model, and provide information corresponding to the recognized real object. Here, the learning model may be learned directly from the XR device 100c or may be learned from an external device such as the AI server 200.

At this time, the XR device 100c may perform an operation by generating a result using a direct learning model, but may perform the operation by transmitting sensor information to an external device such as the AI server 200 and receiving the result generated accordingly. It can also be done.

<AI+Robot+Autonomous Driving>

The robot 100a applies AI technology and autonomous driving technology, and can be implemented as a guidance robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc.

The robot 100a to which AI technology and autonomous driving technology is applied may refer to a robot itself with autonomous driving functions or a robot 100a that interacts with an autonomous vehicle 100b.

The robot 100a with an autonomous driving function may refer to devices that move on their own along a given route without user control or move by determining the route on their own.

A robot 100a and an autonomous vehicle 100b with autonomous driving functions may use a common sensing method to determine one or more of a movement path or a driving plan. For example, the robot 100a and the autonomous vehicle 100b with autonomous driving functions can determine one or more of a movement path or a driving plan using information sensed through lidar, radar, and cameras.

The robot 100a that interacts with the self-driving vehicle 100b exists separately from the self-driving vehicle 100b and is linked to the self-driving function inside the self-driving vehicle 100b or is connected to the self-driving vehicle 100b. You can perform actions linked to the user on board.

At this time, the robot 100a interacting with the self-driving vehicle 100b acquires sensor information on behalf of the self-driving vehicle 100b and provides it to the self-driving vehicle 100b, or acquires sensor information and provides surrounding environment information or By generating object information and providing it to the autonomous vehicle 100b, the autonomous driving function of the autonomous vehicle 100b can be controlled or assisted.

Alternatively, the robot 100a interacting with the self-driving vehicle 100b may monitor the user riding the self-driving vehicle 100b or control the functions of the self-driving vehicle 100b through interaction with the user. . For example, when it is determined that the driver is drowsy, the robot 100a may activate the autonomous driving function of the autonomous vehicle 100b or assist in controlling the driving unit of the autonomous vehicle 100b. Here, the functions of the autonomous vehicle 100b controlled by the robot 100a may include not only the autonomous driving function but also functions provided by a navigation system or audio system provided inside the autonomous vehicle 100b.

Alternatively, the robot 100a interacting with the self-driving vehicle 100b may provide information to the self-driving vehicle 100b or assist its functions from outside the self-driving vehicle 100b. For example, the robot 100a may provide traffic information including signal information to the autonomous vehicle 100b, such as a smart traffic light, and may interact with the autonomous vehicle 100b, such as an automatic electric charger for an electric vehicle. You can also automatically connect an electric charger to the charging port.

<AI+Robot+XR>

The robot 100a applies AI technology and XR technology and can be implemented as a guidance robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, etc.

The robot 100a to which XR technology is applied may refer to a robot that is subject to control/interaction within an XR image. In this case, the robot 100a is distinct from the XR device 100c and may be interoperable with each other.

When the robot 100a, which is the object of control/interaction within the XR image, acquires sensor information from sensors including a camera, the robot 100a or the XR device 100c generates an XR image based on the sensor information. And, the XR device 100c can output the generated XR image. And, this robot 100a may operate based on a control signal input through the XR device 100c or user interaction.

For example, the user can check the XR image corresponding to the viewpoint of the remotely linked robot 100a through an external device such as the XR device 100c, and adjust the autonomous driving path of the robot 100a through interaction. , you can control movement or driving, or check information about surrounding objects.

<AI+Autonomous Driving+XR>

The self-driving vehicle 100b can be implemented as a mobile robot, vehicle, unmanned aerial vehicle, etc. by applying AI technology and XR technology.

The autonomous vehicle 100b to which XR technology is applied may refer to an autonomous vehicle equipped with a means for providing XR images or an autonomous vehicle that is subject to control/interaction within XR images. In particular, the autonomous vehicle 100b, which is the subject of control/interaction within the XR image, is distinct from the XR device 100c and may be interoperable with each other.

An autonomous vehicle 100b equipped with a means for providing an XR image may acquire sensor information from sensors including a camera and output an XR image generated based on the acquired sensor information. For example, the self-driving vehicle 100b may be equipped with a HUD and output XR images, thereby providing occupants with XR objects corresponding to real objects or objects on the screen.

At this time, when the XR object is output to the HUD, at least a portion of the XR object may be output to overlap the actual object toward which the passenger's gaze is directed. On the other hand, when the XR object is output to a display provided inside the autonomous vehicle 100b, at least part of the XR object may be output to overlap the object in the screen. For example, the autonomous vehicle 100b may output XR objects corresponding to objects such as lanes, other vehicles, traffic lights, traffic signs, two-wheeled vehicles, pedestrians, buildings, etc.

When the autonomous vehicle 100b, which is the subject of control/interaction within the XR image, acquires sensor information from sensors including a camera, the autonomous vehicle 100b or the XR device 100c detects sensor information based on the sensor information. An XR image is generated, and the XR device 100c can output the generated XR image. In addition, this autonomous vehicle 100b may operate based on a control signal input through an external device such as the XR device 100c or user interaction.

Figure 4 is a block diagram for explaining an artificial intelligence device related to the present invention.

Descriptions overlapping with FIG. 1 are omitted.

The communication unit 110 may include at least one of a broadcast reception module 111, a mobile communication module 112, a wireless Internet module 113, a short-range communication module 114, and a location information module 115.

The broadcast reception module 111 receives broadcast signals and/or broadcast-related information from an external broadcast management server through a broadcast channel.

The mobile communication module 112 uses technical standards or communication methods for mobile communication (e.g., Global System for Mobile communication (GSM), Code Division Multi Access (CDMA), Code Division Multi Access 2000 (CDMA2000), EV -DO (Enhanced Voice-Data Optimized or Enhanced Voice-Data Only), WCDMA (Wideband CDMA), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), LTE (Long Term Evolution), LTE-A (Long Term Evolution-Advanced, etc.), wireless signals are transmitted and received with at least one of a base station, an external terminal, and a server on a mobile communication network constructed according to (Long Term Evolution-Advanced), etc.).

The wireless Internet module 113 refers to a module for wireless Internet access and may be built into or external to the artificial intelligence device 100. The wireless Internet module 113 is configured to transmit and receive wireless signals in a communication network based on wireless Internet technologies.

Wireless Internet technologies include, for example, WLAN (Wireless LAN), Wi-Fi (Wireless-Fidelity), Wi-Fi (Wireless Fidelity) Direct, DLNA (Digital Living Network Alliance), WiBro (Wireless Broadband), and WiMAX (Worldwide). These include Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access), HSUPA (High Speed Uplink Packet Access), LTE (Long Term Evolution), and LTE-A (Long Term Evolution-Advanced).

The short-range communication module 114 is for short-range communication, including Bluetooth™, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and NFC. Short-distance communication can be supported using at least one of (Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and Wireless USB (Wireless Universal Serial Bus) technologies.

The location information module 115 is a module for acquiring the location (or current location) of a mobile artificial intelligence device, and representative examples thereof include a Global Positioning System (GPS) module or a Wireless Fidelity (WiFi) module. For example, if an artificial intelligence device uses a GPS module, it can acquire the location of the mobile artificial intelligence device using signals sent from GPS satellites.

The input unit 120 may include a camera 121 for inputting video signals, a microphone 122 for receiving audio signals, and a user input unit 123 for receiving information from a user.

The camera 121 processes image frames such as still images or moving images obtained by an image sensor in video call mode or shooting mode. The processed image frame may be displayed on the display unit 151 or stored in the memory 170.

The microphone 122 processes external acoustic signals into electrical voice data. The processed voice data can be utilized in various ways depending on the function (or application being executed) being performed by the artificial intelligence device 100. Meanwhile, various noise removal algorithms may be implemented in the microphone 122 to remove noise generated in the process of receiving an external acoustic signal.

The user input unit 123 is for receiving information from the user. When information is input through the user input unit 123, the processor 180 can control the operation of the artificial intelligence device 100 to correspond to the input information. there is.

The user input unit 123 is a mechanical input means (or mechanical key, for example, a button located on the front/rear or side of the artificial intelligence device 100, a dome switch, a jog wheel, a jog switches, etc.) and touch-type input means. As an example, the touch input means consists of a virtual key, soft key, or visual key displayed on the touch screen through software processing, or a part other than the touch screen. It can be done with a touch key placed in .

The output unit 150 is for generating output related to vision, hearing, or tactile sense, and includes at least one of a display unit 151, an audio output unit 152, a haptip module 153, and an optical output unit 154. can do.

The display unit 151 displays (outputs) information processed by the artificial intelligence device 100. For example, the display unit 151 may display execution screen information of an application running on the artificial intelligence device 100, or UI (User Interface) and GUI (Graphic User Interface) information according to such execution screen information. there is.

The display unit 151 can implement a touch screen by forming a layered structure or being integrated with the touch sensor. This touch screen functions as a user input unit 123 that provides an input interface between the artificial intelligence device 100 and the user, and can simultaneously provide an output interface between the artificial intelligence device 100 and the user.

The audio output unit 152 may output audio data received from the communication unit 110 or stored in the memory 170 in call signal reception, call mode or recording mode, voice recognition mode, broadcast reception mode, etc.

The sound output unit 152 may include at least one of a receiver, a speaker, and a buzzer.

The haptic module 153 generates various tactile effects that the user can feel. A representative example of a tactile effect generated by the haptic module 153 may be vibration.

The optical output unit 154 uses light from the light source of the artificial intelligence device 100 to output a signal to notify that an event has occurred. Examples of events that occur in the artificial intelligence device 100 may include receiving a message, receiving a call signal, a missed call, an alarm, a schedule notification, receiving an email, receiving information through an application, etc.

The interface unit 160 serves as a passageway for various types of external devices connected to the artificial intelligence device 100. This interface unit 160 connects devices equipped with a wired/wireless headset port, an external charger port, a wired/wireless data port, a memory card port, and an identification module. It may include at least one of a port, an audio input/output (I/O) port, a video input/output (I/O) port, and an earphone port. The artificial intelligence device 100 may perform appropriate control related to the connected external device in response to the external device being connected to the interface unit 160.

Meanwhile, the identification module is a chip that stores various information for authenticating the authority to use the artificial intelligence device 100, including a user identification module (UIM), a subscriber identity module (SIM), and a general user. It may include an authentication module (universal subscriber identity module (USIM)), etc. A device equipped with an identification module (hereinafter referred to as an 'identification device') may be manufactured in a smart card format. Therefore, the identification device can be connected to the artificial intelligence device 100 through the interface unit 160.

The power supply unit 190 receives external power and internal power under the control of the processor 180 and supplies power to each component included in the artificial intelligence device 100. This power supply unit 190 includes a battery, and the battery may be a built-in battery or a replaceable battery.

Meanwhile, as discussed above, the processor 180 controls operations related to application programs and generally controls the overall operation of the artificial intelligence device 100. For example, if the state of the artificial intelligence device satisfies a set condition, the processor 180 may execute or release a lock state that restricts the user's input of control commands to applications.

Meanwhile, the artificial intelligence server 200 may be called a local service providing device 200.

FIG. 5 is a diagram illustrating a local service providing system 2 according to an embodiment of the present disclosure.

The local service provision system 2 may include an artificial intelligence device 100, a local service provision device 200, and a business terminal 300.

The artificial intelligence device 100 may be a device installed in the user's residence. The artificial intelligence device 100 can receive various local information from the local service providing device 200 and display various local information through the display unit 151, and provides an interface that allows each user to use various local services. can do.

Meanwhile, the memory 230 of the local service providing device 200 may include a plurality of databases that store various local information related to the user's residence.

For example, memory 230 may include local service provider database 232.

The local service company information database 232 may include business information about a plurality of local service companies. For example, business information may include at least one of business name information, business location information, category information, business contact information, business hours information, business homepage information, business event information, and business other information. Company event information may include company event progress information, event progress date information, company popularity information, company advertisement information, and event tag information.

Business popularity information may be information calculated by indicators such as evaluation scores left by users who visit local service businesses or use services, the number of visits, or the number of service uses.

Additionally, the business advertisement information may include information on whether local service businesses run advertisements used to increase rankings displayed in search results and the unit price paid for advertisements.

Meanwhile, the memory 230 may include a user profile information database 233.

The user profile information database 233 may store a plurality of user profile information.

User profile information may include user ID, name, gender, age, residential address, family member information, and subscription information.

Subscription information may include information about a company that the user has registered as a company of interest among a plurality of local service companies. For example, a user can receive information about new news or discount events by registering a local service company as a company of interest.

Meanwhile, the local service providing device 200 may include a residence management information database 234.

Additionally, residence management information may include e-mail information sent to an electronic address associated with the user's residence. The electronic address associated with the user's residence may be an email address generated based on the physical address of the user's residence. E-mail information may include e-mail information sent and received to the user's e-mail address. Additionally, residence management information may include residence news information related to the user's residence. For example, if the user's residence is an apartment, residence news information may include news about electing an apartment representative, etc. Additionally, residence management information may include information on costs incurred in relation to the residence. Information on expenses incurred in relation to residence may include information about billing companies, expense details, expense payment accounts, etc. Additionally, residence management information may include information on local service companies that provide services related to residence. Local service companies that provide residential-related services may also be named residential service companies. For example, residential service companies may include companies that clean residential areas, companies that collect laundry from residential areas and do laundry, etc. Additionally, residence management information may include residence advertisement information related to the residence. For example, residential advertisement information may include job advertisement information for seeking people related to residential housing.

Meanwhile, the company terminal 300 may be a terminal 300 used by a local service company that provides services related to the user's residence or provides services around the user's residence.

Local service companies may include companies that provide services corresponding to a predetermined category in the area surrounding the user's residence. Service categories may include categories such as shopping, restaurants, cafes, hospitals/pharmacies, educational facilities, and government offices.

Figure 6 is a flow chart illustrating a method of providing local service information according to an embodiment of the present disclosure.

The local service providing device 200 may receive a place search command from the artificial intelligence device 100 through the communication unit 210 (S601).

The location search command may be a command to search for local service providers located within a predetermined radius around the user's residence.

Additionally, the location search command may be a command to search for local service providers by service category. Service categories may include categories such as shopping, restaurants, cafes, hospitals/pharmacies, educational facilities, and government offices.

The processor 260 of the local service providing device 200 may obtain user profile information from the user profile information database 233 based on the identification information of the user who entered the place search command (S602).

The user's identification information may be the ID with which the user logs into the local service providing device 200.

The processor 260 may obtain information on one or more companies registered as local service companies within a predetermined radius from the local service company database 232 based on the residential information included in the user profile information (S603).

Local service companies may include companies that provide services corresponding to a predetermined category in the area surrounding the user's residence.

Additionally, the processor 260 may set the exposure ranking of the obtained company information based on local service company location information, subscription information, company popularity information, and company advertisement information (S604).

The processor 260 may set the exposure ranking of company information in order of distance from the user's residential address based on the location information of the local service company.

Additionally, the processor 260 may obtain subscription information included in the user profile information and determine which company the user has subscribed to among local service companies obtained based on the user's residential address. Among the extracted company information, the processor 260 may set a higher exposure ranking for company information of companies to which the user has subscribed than to company information of companies to which the user has not subscribed.

Additionally, the processor 260 may set the exposure ranking of the extracted company information based on the popularity of the extracted local service company.

The popularity of a local service company may be a score calculated by combining the number of subscribers, number of user visits, number of user orders, etc.

The processor 260 may set the exposure ranking of the business information of each of the plurality of local service companies to be high in order of popularity based on the business popularity information of each of the plurality of local service companies.

Additionally, the processor 260 may determine the exposure ranking of company information based on the extracted advertisement information of the local service company.

For example, the processor 260 may set the business information with the highest advertising amount among the business information of a plurality of local service companies as the highest exposure priority.

Additionally, for example, the processor 260 may set the exposure ranking of company information based on the advertising amount, advertising unit price, advertising quality, advertising click-through rate, etc. presented by the local service company.

Meanwhile, the processor 260 may transmit company information reflecting exposure ranking information to the artificial intelligence device 100 (S606).

Figure 7 is a diagram illustrating a screen on which local service information is provided according to an embodiment of the present disclosure.

The artificial intelligence device 100 can output and provide a place search interface 701. The place search interface 700 may be a screen interface displayed through the display unit 151 of the artificial intelligence device 100.

Additionally, the place search interface 701 may be a screen interface called by the processor 180 when the place search button 702 is selected by the user.

Meanwhile, the place search interface 701 may include an event search button 703 that can be switched to an event search interface that displays event schedule information of local service companies around the user's residence.

Meanwhile, the place search interface 701 may include a management information search button 704 that can switch to a management information search interface that displays management information related to the user's residential building (eg, apartment).

Meanwhile, the place search interface 701 may display service category information 705. Service category information is information about the categories into which each of a plurality of local service companies is classified, including the 'subscription' category, 'shopping' category, 'restaurant' category, 'cafe' category, depending on whether the user has registered as a company of interest. It may include the ‘Hospital/Pharmacy’ category, the ‘Educational Facility’ category, and the ‘Government Office’ category.

Meanwhile, the place search interface 701 may display the user's residence 706 on a map image.

Additionally, the place search interface 701 may display a plurality of company information reflecting the exposure ranking information received from the local service providing device 200.

For example, the company name “CC Elementary School”, company schedule information, and company subscription information of the searched first local service company 707 may be displayed.

Additionally, the company name “BB Cafe”, company event information, and company subscription information of the second local service company 708 can be displayed.

Additionally, the location search interface 701 may display the exposure ranking of each local service company. For example, an exposure ranking icon 709 may be displayed. The place search interface 701 may draw the user's attention to local service companies with high exposure rankings by displaying the exposure ranking icon 709.

8 and 9 are diagrams for explaining a method of registering subscription information for a local service provider according to an embodiment of the present disclosure.

Referring to FIG. 8, the processor 180 of the artificial intelligence device 100 may display a place search interface 801 through the display unit 151.

The place search interface 801 may display a residential area 802 based on the user's residential address. Additionally, the place search interface 801 may display a map image 803 around the user's residence.

For example, the place search interface 801 may include a category classification button 804 that receives a command to classify the service category as 'cafe'. When the category classification button 804 is input through the place search interface 801, the processor 180 obtains information on local service companies having a service category corresponding to the category classification button 804 and displays the place search interface 801. ) can be displayed through.

Meanwhile, referring to FIG. 9, when one service company 805 among a plurality of local service companies displayed on the place search interface 801 is selected through the input unit 120, the processor 180 provides information on the selected company. The detailed information interface 806 can be displayed through the output unit 150.

Meanwhile, the detailed information interface 806 may include a detailed information display interface 808 that displays business location information, business contact information, business hours information, business homepage information, business event information, and other business information. there is.

Additionally, the detailed information interface 806 may include a subscription button 807 that allows the user to subscribe to the selected local service provider.

For example, the processor 180 may detect that the subscription button 807 is input through the input unit 120. The processor 180 may transmit business information for which a subscription has been applied to the local service providing device 200 through the communication unit 110 . The local service providing device 200 may receive subscription information about a local service company from the artificial intelligence device 100 and update subscription information about the local service company in association with user profile information.

Figure 10 is a diagram for explaining a method of brokering the purchase of products sold by a local service company according to an embodiment of the present disclosure.

The processor 260 of the local service providing device 200 may determine the local service provider to which the user subscribes among a plurality of local service providers. For example, the processor 260 obtains business information for each of a plurality of local service businesses located within a predetermined radius from the local service business information database 232 based on the residential address included in the user's profile information, and each Based on the subscription information included in the company information, it can be determined whether it is a local service company to which the user subscribes.

Additionally, the processor 260 may transmit advertising information of a local service company to which the user has subscribed among a plurality of local service companies to the artificial intelligence device 100 based on whether the user has subscribed (S1001). Advertising information may include information such as product or service information, price, rating, cumulative purchase quantity, etc. provided by local service providers.

Figure 11 is an example diagram for explaining a screen on which advertisement information of a local service company is displayed according to an embodiment of the present disclosure.

Referring to FIG. 11, the artificial intelligence device 100 may display an advertising interface 1101 including advertising information received from the local service providing device 200 through the display unit 151. The advertising interface 1101 may be displayed in a pop-up form on top of the place search interface.

Meanwhile, the advertising interface 1101 may include a purchase button 1102 that allows the user to purchase the advertising product included in the advertising information.

*220 Meanwhile, the artificial intelligence device 100 can detect that the user has clicked the purchase button 1102 through the input unit 120. The artificial intelligence device 100 may transmit order information to the local service providing device 200.

Accordingly, the local service providing device 200 may receive order information related to advertisement information from the artificial intelligence device 100 through the communication unit 210 (S1002).

Meanwhile, the local service providing device 200 may transmit order information to the business terminal 300 corresponding to the order information received through the communication unit 210 (S1003).

Additionally, the business terminal 300 may process order information received from the local service providing device 200. Meanwhile, the business terminal 300 may transmit order processing status information, which is information about the status of order information being processed, to the local service providing device 200. Additionally, the local service providing device 200 may receive order processing status information corresponding to order information from the business terminal 300 (S1004).

Order processing status information may include information about the processing stage in which the order is being processed, real-time delivery status information, etc. For example, the order processing stage can be divided into stages such as order completion, preparing for delivery, delivery departure, and delivery completion. Additionally, real-time delivery status information may include information about the delivery route, location of the delivery driver, etc.

Additionally, the local service providing device 200 may transmit order processing status information corresponding to the order information to the artificial intelligence device 100 (S1005).

The artificial intelligence device 100 may display order processing status information received from the local service providing device 200 through the display unit 151.

Figure 12 is an example diagram for explaining a screen displaying order processing status information according to an embodiment of the present disclosure.

Referring to FIG. 12, the artificial intelligence device 100 may display order processing step information 1201 included in the received order processing status information on the place search interface. Additionally, the artificial intelligence device 100 may display real-time delivery status information included in the received order processing status information on the location search interface. For example, a location search interface may display a delivery route 1202 and a delivery driver's location 1203.

Accordingly, the user can use the artificial intelligence device 100 to search for local service companies around the residence, purchase products for sale, and have them delivered.

Figure 13 is a flowchart for explaining a method of providing local event information according to an embodiment of the present disclosure.

The communication unit 210 of the local service providing device 200 may receive an event search command from the artificial intelligence device 100 (S1301).

The event search command may be a command that allows the user to search for event information provided by local service providers around the user's residence using a predetermined search term.

Meanwhile, the processor 260 may obtain user profile information associated with the user who entered the event search command from the user profile information database 233 (S1302).

The processor 260 may obtain business event information of a local service company located within a predetermined radius from the local service company information database 232 based on the user's residence information included in the user profile information (S1303).

Meanwhile, the processor 260 may classify the business event information by each event tag information based on the event tag information included in the business event information.

Event tag information may be tag information that summarizes company event information and is expressed within a predetermined number of characters.

Additionally, the processor 260 may determine the frequency of occurrence of each event tag information. For example, if the first event tag information is 'discount event' and there are 5 company event information including the event tag 'discount event', the processor 260 sets the frequency of occurrence of the first event tag information to 5. It can be judged as raw fish.

The processor 260 may transmit a plurality of event tag information, a frequency of occurrence of each of the plurality of event tag information, and company event information classified into each of the plurality of event tag information to the artificial intelligence device 100 through the communication unit 210. .

14 and 15 are exemplary diagrams showing an event search interface according to an embodiment of the present disclosure.

Referring to FIG. 14, the processor 180 of the artificial intelligence device 100 may display an event search interface 1401 through the display unit 151.

The processor 180 may receive business event information being conducted by a plurality of local service companies from the local service providing device 200 through the communication unit 110. Additionally, the processor 180 may receive, through the communication unit 110, a plurality of event tag information, a frequency of occurrence of each of the plurality of event tag information, and company event information classified into each of the plurality of event tag information. Company event information may include company event progress information, event progress date information, company popularity information, company advertisement information, and company event tag information.

The processor 180 may display business event information held by a plurality of local service companies received from the local service providing device 200 through the event search interface 1401.

The event search interface 1402 may include an event search word input interface 1402. The processor 180 may transmit the inputted search word to the local service providing device 200 through the event search word input interface 1402 and receive business event information related to the search word from the local service providing device 200.

Additionally, the event search interface 1401 may include an event tag information interface 1403. The processor 180 may display an event tag information interface 1403 that displays each of a plurality of event tag information received from the local service providing device 200. The event tag information interface 1403 can display event tag information sorted in order of frequency of occurrence.

Additionally, the event search interface 1401 may include an advertisement information interface 1404. The processor 180 may display the business advertisement information through the advertisement information interface 1404 based on the business advertisement information included in the business event information received from the local service providing device 200.

Additionally, event search interface 1401 may include a schedule of interest information interface 1405.

The interest schedule information interface 1405 is an interface that can display company event information for each of the plurality of local service companies subscribed to by the user by company or schedule.

For example, the interest schedule information interface 1405 may display each of a plurality of local service providers subscribed to by the user as an icon 1406 of a different color.

Additionally, the interest schedule information interface 1405 may display the event schedules of each of a plurality of local service companies in different colors in a calendar format.

Additionally, the interest schedule information interface 1405 may display a detailed view icon 1407 through which event information for each of a plurality of local service providers can be viewed in detail. The processor 180 may detect that the detailed view icon 1407 is selected through the input unit 120 and display event information of the local service provider in the form of a list.

Referring to FIG. 15, the event search interface 1401 may display the event detailed information interface 1408 in the form of a list, which displays the event schedule, event contents, etc. included in the event information of the local service company.

Figure 16 is a flowchart for explaining a method of providing residence management information according to an embodiment of the present disclosure.

The communication unit 210 of the local service providing device 200 may receive a residence management information search command from the artificial intelligence device 100 (S1601).

Meanwhile, the processor 260 may obtain user profile information associated with the user who entered the residence management information search command from the user profile information database 233 (S1502).

The processor 260 may obtain residence management information related to the user's residence from the residence management information database 233 based on the user's residence information included in the user profile information (S1503).

Meanwhile, residence management information may include e-mail information, residence news information, cost information, residence service company information, and residence advertisement information.

The processor 260 may transmit user profile information and residence management information to the artificial intelligence device 100 through the communication unit 210.

Figure 17 is an example diagram for explaining a management information search interface according to an embodiment of the present disclosure.

Referring to FIG. 17, the processor 180 of the artificial intelligence device 100 may display a management search interface 1601 through the display unit 151.

The processor 180 may receive user profile information and residence management information from the local service providing device 200 through the communication unit 110.

The processor 180 may display the user profile information and residence management information received from the local service providing device 200 through the residence management information search interface 1701.

The residence management information search interface 1701 may include a member information interface 1702 that displays family member information included in user profile information.

The residence management information search interface 1701 may include a residence information interface 1703 that displays residence information included in user profile information.

The residence management information search interface 1701 may include an e-mail information interface 1704 that displays e-mail information included in the residence management information.

Referring to FIG. 18, the e-mail information interface 1704 may provide an interface that allows the user to check and manage a plurality of e-mail information received from a plurality of company terminals 300 to the user's e-mail address. there is.

Additionally, referring to FIG. 19, the e-mail information interface 1704 may provide an interface that allows the user to apply for a business from which he or she wishes to receive e-mail.

Additionally, the residence management information search interface 1701 may include a residence news interface 1705 that displays residence news information included in residence management information.

Referring to FIG. 20, the residential news interface 1705 may provide an interface through which a user can check residential news information.

Additionally, referring to FIG. 21, the residence news interface 1705 may provide an interface for checking information about facilities used at the residence.

Additionally, the residence management information search interface 1701 may include a cost information interface 1706 that displays cost information included in the residence management information.

Referring to FIGS. 22 and 23, the cost information interface 1706 can provide an interface to check the status of costs related to a residence, check costs charged by residential service companies, and pay the costs. there is.

Additionally, the residence management information search interface 1701 may include a residence service company interface 1707 that displays residence service companies included in the residence management information.

Referring to FIG. 24, the residential service company interface 1707 can display information on the residential service company and e-mail information sent by the residential service company.

The present disclosure described above can be implemented as computer-readable code on a program-recorded medium. Computer-readable media includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is. Additionally, the computer may include a processor 180 of an artificial intelligence device.

Claims (14)

a communication unit that receives a place search command from the user's artificial intelligence device;
Obtaining the user's profile information from a user profile information database in response to the place search command,
Obtaining business information for each of a plurality of local service businesses located within a predetermined radius from the local service business information database based on the residential information included in the user profile information,
Setting an exposure ranking for each of the obtained plurality of business information based on each business location information, subscription information, business popularity information, and business advertisement information included in the obtained plurality of business information,
Comprising a processor that transmits a plurality of company information reflecting the exposure ranking to the artificial intelligence device through the communication unit,
The processor,
Determine which local service provider the user has subscribed to among the plurality of local service providers,
Setting the exposure ranking of the business information of the local service company to which the user has subscribed to higher than that of the business information of the local service company to which the user has not subscribed,
Local service delivery device. According to claim 1,
The processor,
Based on the business popularity information of each of the plurality of local service companies, setting the exposure ranking of the business information of each of the plurality of local service companies high in order of the company's popularity,
Local service delivery device. According to claim 1,
The processor,
Based on the advertising information of each of the plurality of local service companies, the company information with the highest advertising amount is set as the highest exposure ranking,
Local service delivery device. According to claim 1,
The processor,
Determine which local service provider the user has subscribed to among the plurality of local service providers,
Transmitting advertising information of local service providers subscribed to by the user to the artificial intelligence device through the communication unit,
Local service delivery device. According to clause 4,
The processor,
Obtaining order information associated with the advertisement information from the artificial intelligence device through the communication unit,
Transmitting the order information to a company terminal corresponding to the order information through the communication unit,
Local service delivery device. According to clause 5,
The processor,
Receiving order processing status information, which is information about the status of the order information being processed, from the company terminal through the communication unit, and transmitting the order processing status information to the artificial intelligence device,
Local service delivery device. According to claim 1,
The Department of Communications,
Receive an event search command from the artificial intelligence device,
The processor,
Based on the user's residential information, company event information for each of a plurality of local service companies located within a predetermined radius is obtained from the service company information database, and the event information is acquired based on the event tag information included in each of the company event information. The business event information is classified by tag information, the frequency of occurrence is determined by the event tag information, and the business event information classified by the event tag information, the frequency of occurrence of the event tag information, and the event tag information is generated through the communication unit. transmitting to the artificial intelligence device,
Local service delivery device. According to claim 1,
The Department of Communications,
Receiving a residence management information search command from the artificial intelligence device,
The processor,
Obtaining residence management information associated with the user's residence from a residence management information database based on the user's residence information, and transmitting the residence management information to the artificial intelligence device through the communication unit,
Local service delivery device. Receiving a place search command from the user's artificial intelligence device;
Obtaining profile information of the user from a user profile information database in response to the place search command;
Obtaining business information for each of a plurality of local service businesses located within a predetermined radius from a local service business information database based on residential information included in the user profile information;
Setting an exposure ranking for each of the obtained plurality of business information based on each business location information, subscription information, business popularity information, and business advertisement information included in the acquired plurality of business information; and
A step of transmitting a plurality of company information reflecting the exposure ranking to the artificial intelligence device,
The step of setting the exposure ranking for each of the obtained plural company information is
determining a local service provider to which the user has subscribed among the plurality of local service providers;
Including the step of setting a higher exposure ranking for business information of a local service company to which the user has subscribed than to business information of a local service company to which the user has not subscribed.
How to provide local services. According to clause 9,
Further comprising transmitting advertising information of a local service provider subscribed to by the user to the artificial intelligence device,
How to provide local services. According to clause 10,
Receiving order information associated with the advertisement information from the artificial intelligence device; and
Further comprising transmitting the order information to a company terminal corresponding to the order information,
How to provide local services. According to claim 11,
Receiving order processing status information, which is information about the status of the order information being processed, from the company terminal; and
Further comprising transmitting the order processing status information to the artificial intelligence device,
How to provide local services. According to clause 9,
Receiving an event search command from the artificial intelligence device;
Obtaining business event information for each of a plurality of local service companies located within a predetermined radius based on the user's residential information from a service company information database;
Classifying the business event information according to the event tag information based on event tag information included in each of the business event information;
determining a frequency of occurrence for each event tag information; and
Further comprising transmitting the event tag information, the frequency of occurrence of the event tag information, and the company event information classified by the event tag information to the artificial intelligence device,
How to provide local services. According to clause 9,
Receiving a residence management information search command from the artificial intelligence device;
Obtaining residence management information associated with the user's residence based on the user's residence information from a residence management information database; and
Further comprising transmitting the residence management information to the artificial intelligence device,
How to provide local services.