KR102514128B1 - An artificial intelligence apparatus for providing a connection between home devices and method thereof - Google Patents

Abstract

The present invention relates to an artificial intelligence device that provides connection between a plurality of home devices, a communication unit that receives a visitor image from a door lock device including a door camera that photographs the visitor, and recognizes the visitor by inputting the visitor image into a visitor recognition model. and a processor for acquiring visitor information output from the model and selecting a home device to receive the visitor image and visitor information from among a plurality of home devices, and a communication unit configured to transmit the visitor image and visitor information to the selected home device. provide the device.

Description

Artificial intelligence device providing connection between multiple home devices and its method

The present disclosure relates to an artificial intelligence device and method for providing connection between a plurality of home devices.

Artificial intelligence is a field of computer science and information technology that studies ways to enable computers to do thinking, learning, and self-development that can be done with human intelligence. It means to be able to imitate.

Also, 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 to introduce artificial intelligence elements in various fields of information technology and use them to solve problems in those fields are being actively made.

Meanwhile, technologies for recognizing and learning surrounding situations using artificial intelligence, providing information desired by a user in a desired form, or performing an operation or function desired by a user are being actively researched.

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

Meanwhile, numerous artificial devices exist inside and outside houses, offices, transportation facilities, cultural facilities, sports facilities, and various facilities.

Each device may acquire or generate various data.

Accordingly, there is an increasing need to share or manage data acquired by each device with other devices.

In addition, the need to connect each device to share or control data between each device is increasing.

The present disclosure aims to solve the foregoing and other problems.

An object of the present disclosure is to provide an artificial intelligence device and method for providing connection between a plurality of home devices.

An object of the present disclosure is to provide an artificial intelligence device and method applicable to a smart home system.

An object of the present disclosure is to provide an artificial intelligence device and method capable of strengthening access security by providing connection between devices.

An object of the present disclosure is to provide an artificial intelligence apparatus and method for enabling a user to conveniently control each device by providing connection between devices.

An object of the present disclosure is to provide an artificial intelligence apparatus and method for enabling a user to conveniently control each device by providing connection between devices.

An embodiment of the present disclosure is an artificial intelligence device for providing connection between a plurality of home devices, a communication unit receiving a visitor image from a door locking device including a door camera for photographing the visitor, and a visitor recognition model for the visitor image. and a processor for acquiring visitor information output from the visitor recognition model by inputting the input and selecting a home device from among a plurality of home devices to receive the visitor image and visitor information, and the communication unit transmits the visitor image and visitor information to the selected home device. An artificial intelligence device that transmits is provided.

In addition, the step of receiving a visitor image from a door locking device including a door camera for photographing the visitor of the present disclosure, inputting the visitor image into a visitor recognition model to obtain visitor information output from the visitor recognition model, a plurality of home A method for providing a connection between a plurality of home devices includes selecting a home device to receive a visitor image and visitor information from among devices, and transmitting the visitor image and visitor information to the selected home device.

According to an embodiment of the present disclosure, a plurality of home devices may be collectively controlled by connecting a plurality of home devices.

Also, according to various embodiments of the present disclosure, access security for visitors may be strengthened by connecting a plurality of home devices.

Also, according to various embodiments of the present disclosure, members may be identified and abnormal activities of the members may be detected using information collected from a plurality of home devices.

1 shows an AI device according to an embodiment of the present disclosure.
2 shows an AI server according to an embodiment of the present disclosure.
3 shows an AI system according to an embodiment of the present disclosure.
4 is a block diagram illustrating an AI device according to an embodiment of the present disclosure.
5 shows a smart home system according to an embodiment of the present invention.
6 is a diagram showing a smart home security system according to an embodiment of the present invention.
7 is a flowchart illustrating a method for providing a connection between devices for home security according to an embodiment of the present invention.
8 is a diagram illustrating a home security interface according to an embodiment of the present invention.
9 is a diagram for explaining a method for providing a connection between home devices according to an embodiment of the present invention.
10 is an exemplary diagram of an integrated information interface according to an embodiment of the present invention.
11 is an exemplary view of a lighting control interface according to an embodiment of the present invention.
12 is a block diagram of a home mail system according to an embodiment of the present invention.
13 is an exemplary view of an integrated convenience application according to an embodiment of the present invention.
14 is an exemplary diagram of a guidance system according to an embodiment of the present invention.
15 is an exemplary view of a guidance system according to an embodiment of the present invention.

Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the accompanying drawings, but the same or similar elements are given the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "unit" for components used in the following description are given or used together in consideration of ease of writing the specification, and do not have meanings or roles that are distinct from each other by themselves. In addition, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of a related known technology may obscure the gist of the embodiment disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in this specification, the technical idea disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and technical scope of the present disclosure , it should be understood to include equivalents or substitutes.

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

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

<Artificial Intelligence (AI)>

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

An artificial neural network (ANN) is a model used in machine learning, and may refer to an overall model that has problem-solving capabilities and is composed of artificial neurons (nodes) that form a network by synaptic coupling. An 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 output values.

An artificial neural network may include an input layer, an output layer, and optionally one or more hidden layers. Each layer may include one or more neurons, and the artificial neural network may include neurons and synapses connecting the neurons. In an artificial neural network, each neuron may output a function value of an activation function for input signals, weights, and biases input through a synapse.

Model parameters refer to parameters determined through learning, and include weights of synaptic connections and biases of neurons. In addition, hyperparameters mean parameters that must be set before learning in a machine learning algorithm, and include a learning rate, number of iterations, mini-batch size, initialization function, and the like.

The purpose of learning an artificial neural network can be seen as determining model parameters that minimize the loss function. The loss function may be used as an index for determining 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 according to learning methods.

Supervised learning refers to a method of training an artificial neural network given a label for training data, and a label is the correct answer (or result value) that the artificial neural network must infer when learning data is input to the artificial neural network. can mean Unsupervised learning may refer to a method of training an artificial neural network in a state in which a label for training data is not given. Reinforcement learning may refer to a learning method in which an agent defined in an environment learns to select an action or action sequence that maximizes a cumulative reward in each state.

Among artificial neural networks, machine learning implemented as a deep neural network (DNN) including a plurality of 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 may refer to a machine that automatically processes or operates a given task based on its own abilities. In particular, a robot having a function of recognizing an environment and performing an operation based on self-determination may be referred to as an intelligent robot.

Robots can be classified into industrial, medical, household, military, etc. according to the purpose or field of use.

The robot may perform various physical operations such as moving a robot joint by having a driving unit including an actuator or a motor. In addition, the movable robot includes wheels, brakes, propellers, and the like in the driving unit, and can run on the ground or fly in the air through the driving unit.

<Self-Driving>

Autonomous driving refers to a technology that drives by itself, and an autonomous vehicle refers to a vehicle that travels without a user's manipulation or with a user's minimal manipulation.

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 a route when a destination is set, and so on. All of these can be included.

A vehicle includes a vehicle having only an internal combustion engine, a hybrid vehicle having both an internal combustion engine and an electric motor, and an electric vehicle having only an electric motor, and may include not only automobiles but also trains and motorcycles.

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

<XR: eXtended Reality>

Extended reality is a generic term for virtual reality (VR), augmented reality (AR), and mixed reality (MR). VR technology provides only CG images of objects or backgrounds in the real world, AR technology provides CG images created virtually on top of images of real objects, and MR technology provides a computer that mixes and combines virtual objects in the real world. It is a graphic technique.

MR technology is similar to AR technology in that it shows real and virtual objects together. However, there is a difference in that virtual objects are used to supplement real objects in AR technology, whereas virtual objects and real objects are used with equal characteristics in MR technology.

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

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

The AI device 100 is a TV, projector, mobile phone, smart phone, desktop computer, notebook, digital broadcasting terminal, PDA (personal digital assistants), PMP (portable multimedia player), navigation, tablet PC, wearable device, 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, and the like.

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

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

At this time, 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), and the like.

The input unit 120 may acquire various types of data.

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

The input unit 120 may obtain learning data for model learning and input data to be used when obtaining an output using the learning model. The input unit 120 may obtain raw input data, and in this case, the processor 180 or the learning processor 130 may extract input features as preprocessing of the input data.

The learning processor 130 may learn 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. The learning model may be used to infer a result value for new input data other than learning data, and the inferred value may be used as a basis for a decision to perform a certain operation.

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

At this time, the learning processor 130 may include a memory integrated or implemented in the AI device 100. Alternatively, the learning processor 130 may be implemented using a 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 obtain at least one of internal information of the AI device 100, surrounding environment information of the AI device 100, and user information using various sensors.

At this time, the sensors included in the sensing unit 140 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, and a LiDAR sensor. , radar, etc.

The output unit 150 may generate an output related to sight, hearing, or touch.

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 obtained from the input unit 120, learning data, a learning model, a learning history, and the like.

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

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

In this case, the processor 180 may generate a control signal for controlling the external device and transmit the generated control signal to the external device when it is necessary to link the external device to perform the determined operation.

The processor 180 may obtain intention information for a user input and determine a user's requirement based on the acquired intention information.

At this time, the processor 180 uses at least one of a STT (Speech To Text) engine for converting a voice input into a character string and a Natural Language Processing (NLP) engine for obtaining intention information of a natural language, and Intent information corresponding to the input may be obtained.

At this time, at least one or more of the STT engine or NLP engine may be composed of an artificial neural network at least partially trained according to a machine learning algorithm. In addition, at least one or more 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 could be

The processor 180 collects history information including user feedback on the operation contents or operation of the AI device 100 and stores it in the memory 170 or the learning processor 130, or the AI server 200, etc. Can be transmitted to an external device. The collected history 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 in order to drive an application program stored in the memory 170 . Furthermore, the processor 180 may combine and operate two or more of the components included in the AI device 100 to drive the application program.

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

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

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

The memory 230 may include a model storage unit 231 . The model storage unit 231 may store a model being learned or learned through the learning processor 240 (or artificial neural network, 231a).

The learning processor 240 may learn the artificial neural network 231a using the learning data. The learning model may be used while loaded in the AI server 200 of the artificial neural network, or may be loaded and used in an external device such as the AI device 100.

A learning model 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 the learning model, and generate a response or control command based on the inferred result value.

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 with this cloud network 10 . Here, a robot 100a to which AI technology is applied, an autonomous vehicle 100b, an XR device 100c, a smartphone 100d, or a home appliance 100e may be referred to as AI devices 100a to 100e.

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

That is, each of the devices 100a to 100e and 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 directly communicate with each other without going through a 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 an AI device constituting the AI system 1, such as 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 may assist at least part of the AI processing of the connected AI devices 100a to 100e.

At this time, the AI server 200 may train the 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 result values for the received input data using a learning model, and issues a response or control command based on the inferred result values. It can be generated and transmitted to the AI devices 100a to 100e.

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

Hereinafter, various embodiments of the 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 may be regarded as specific examples of the AI device 100 shown in FIG. 1 .

<AI+Robot>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. by applying AI technology.

The robot 100a may include a robot control module for controlling an operation, and the robot control module may mean a software module or a chip implemented as hardware.

The robot 100a acquires state information of the robot 100a using sensor information acquired from various types of sensors, detects (recognizes) surrounding environments and objects, creates map data, moves and travels It can determine a plan, determine a response to a user interaction, or determine an action.

Here, the robot 100a may use sensor information obtained from at least one sensor among lidar, radar, and camera to determine a moving path and a 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 may recognize a surrounding environment and an object using a learning model, and may determine an operation using the recognized surrounding environment information or object information. Here, the learning model may be directly learned in the robot 100a or learned in 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 transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation. You may.

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

The map data may include object identification information about various objects disposed in the space in which the robot 100a moves. For example, the map data may include object identification information on fixed objects such as walls and doors and movable objects such as flower pots and desks. In addition, the object identification information may include a name, type, distance, location, and the like.

In addition, the robot 100a may perform an operation or drive by controlling a drive unit based on a user's control/interaction. In this case, the robot 100a may obtain intention information of an interaction according to a user's motion or voice utterance, determine a response based on the obtained intention information, and perform an operation.

<AI+autonomous driving>

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

The autonomous vehicle 100b may include an autonomous driving control module for controlling an autonomous driving function, and the autonomous driving control module may mean a software module or a chip implemented with hardware. The self-driving control module may be included inside as a component of the self-driving vehicle 100b, but may be configured as separate hardware and connected to the outside of the self-driving vehicle 100b.

The self-driving vehicle 100b obtains state information of the self-driving vehicle 100b using sensor information obtained from various types of sensors, detects (recognizes) surrounding environments and objects, generates map data, A movement route and travel plan may be determined, or an action may be determined.

Here, the self-driving vehicle 100b may use sensor information obtained from at least one sensor among lidar, radar, and camera, like the robot 100a, in order to determine a moving route and a driving plan.

In particular, the self-driving vehicle 100b may receive sensor information from external devices to recognize an environment or object in an area where the field of view is obscured or an area over a certain distance, or receive directly recognized information from external devices. .

The self-driving 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 may recognize surrounding environments and objects using a learning model, and may determine a driving route using the recognized surrounding environment information or object information. Here, the learning model may be directly learned in the self-driving vehicle 100b or learned in an external device such as the AI server 200.

At this time, the self-driving vehicle 100b may perform an operation by generating a result using a direct learning model, but operates by transmitting sensor information to an external device such as the AI server 200 and receiving a result generated accordingly. can also be performed.

The self-driving vehicle 100b determines a movement route and driving plan using at least one of map data, object information detected from sensor information, or object information obtained from an external device, and controls the driving unit to determine the movement route and driving. The autonomous vehicle 100b may be driven according to a plan.

The map data may include object identification information about various objects disposed in a space (eg, a road) in which the autonomous vehicle 100b travels. For example, the map data may include object identification information on fixed objects such as streetlights, rocks, and buildings and movable objects such as vehicles and pedestrians. In addition, the object identification information may include a name, type, distance, location, and the like.

In addition, the autonomous vehicle 100b may perform an operation or drive by controlling a driving unit based on a user's control/interaction. In this case, the self-driving vehicle 100b may obtain intention information of an interaction according to a user's motion or voice utterance, determine a response based on the acquired intention information, and perform an operation.

<AI+XR>

The XR device (100c) is applied with AI technology, HMD (Head-Mount Display), HUD (Head-Up Display) equipped in the vehicle, television, mobile phone, smart phone, computer, wearable device, home appliances, 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 obtained through various sensors or from an external device to generate location data and attribute data for 3D points, thereby generating information about surrounding space or real objects. The XR object to be acquired and output can be rendered and output. For example, the XR apparatus 100c may output an XR object including additional information about the recognized object in correspondence with the recognized object.

The XR device 100c may perform the above operations using a learning model composed of one or more artificial neural networks. For example, the XR apparatus 100c may recognize a real object in 3D point cloud data or image data by using the learning model, and may provide information corresponding to the recognized real object. Here, the learning model may be directly learned in the XR device 100c or learned in 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 transmits sensor information to an external device such as the AI server 200 and receives the result generated accordingly to perform the operation. can also be done

<AI+robot+autonomous driving>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, etc. by applying AI technology and autonomous driving technology.

The robot 100a to which AI technology and autonomous driving technology are applied may refer to a robot itself having an autonomous driving function or a robot 100a interacting with an autonomous vehicle 100b.

The robot 100a having an autonomous driving function may collectively refer to devices that move on their own according to a given movement line without user control or determine and move a movement line by themselves.

The robot 100a and the autonomous vehicle 100b having an autonomous driving function may use a common sensing method to determine one or more of a moving route or driving plan. For example, the robot 100a and the autonomous vehicle 100b having an autonomous driving function may determine one or more of a moving route or driving plan using information sensed through lidar, radar, and a camera.

The robot 100a interacting with the self-driving vehicle 100b exists separately from the self-driving vehicle 100b, is linked to the self-driving function inside the self-driving vehicle 100b, or is connected to the self-driving vehicle 100b. An operation associated with the boarding user may be performed.

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

Alternatively, the robot 100a interacting with the autonomous vehicle 100b may monitor a user riding in the autonomous vehicle 100b or control functions of the autonomous vehicle 100b through interaction with the user. . For example, when it is determined that the driver is in a drowsy state, the robot 100a may activate an autonomous driving function of the autonomous vehicle 100b or assist in controlling a driving unit of the autonomous vehicle 100b. Here, the functions of the self-driving vehicle 100b controlled by the robot 100a may include functions provided by a navigation system or an audio system installed inside the self-driving vehicle 100b as well as a simple self-driving function.

Alternatively, the robot 100a interacting with the autonomous vehicle 100b may provide information or assist functions to the autonomous vehicle 100b outside the autonomous 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, or interact with the autonomous vehicle 100b, such as an automatic electric charger of an electric vehicle. You can also automatically connect the electric charger to the charging port.

<AI+Robot+XR>

The robot 100a may be implemented as a guide robot, a transport robot, a cleaning robot, a wearable robot, an entertainment robot, a pet robot, an unmanned flying robot, a drone, etc. by applying AI technology and XR technology.

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

When the robot 100a, which is a target of control/interaction within the XR image, obtains sensor information from sensors including cameras, the robot 100a or the XR device 100c generates an XR image based on the sensor information. And, the XR device 100c may output the generated XR image. In addition, the robot 100a may operate based on a control signal input through the XR device 100c or a user's interaction.

For example, the user may check an XR image corresponding to the point of view of the robot 100a remotely linked through an external device such as the XR device 100c, and adjust the autonomous driving path of the robot 100a through interaction. , operation or driving can be controlled, or information of surrounding objects can be checked.

<AI+Autonomous Driving+XR>

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

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

The self-driving vehicle 100b equipped with a means for providing an XR image may obtain sensor information from sensors including cameras, and output an XR image generated based on the obtained sensor information. For example, the self-driving vehicle 100b may provide an XR object corresponding to a real object or an object in a screen to a passenger by outputting an XR image with a HUD.

In this case, when the XR object is output to the HUD, at least a part of the XR object may be output to overlap the real object toward which the passenger's gaze is directed. On the other hand, when an XR object is output to a display provided inside the self-driving vehicle 100b, at least a 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, and buildings.

When the self-driving vehicle 100b, which is a target of control/interaction within the XR image, acquires sensor information from sensors including cameras, the self-driving vehicle 100b or the XR device 100c obtains sensor information based on the sensor information. An XR image is generated, and the XR apparatus 100c may output the generated XR image. In addition, the self-driving vehicle 100b may operate based on a control signal input through an external device such as the XR device 100c or a user's interaction.

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

Description overlapping with FIG. 1 will be 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-distance communication module 114, and a location information module 115.

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

The mobile communication module 112 complies with technical standards or communication methods for mobile communication (eg, 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.) to transmit and receive radio signals with at least one of a base station, an external terminal, and a server on a mobile communication network.

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 radio signals in a communication network according to 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), WiMAX (World Interoperability for Microwave Access), High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), Long Term Evolution (LTE), Long Term Evolution-Advanced (LTE-A), and the like.

The short-range communication module 114 is for short-range communication, and includes Bluetooth™, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, and NFC. Near Field Communication (Near Field Communication), Wi-Fi (Wireless-Fidelity), Wi-Fi Direct, and wireless USB (Wireless Universal Serial Bus) technology may be used to support short-distance communication.

The location information module 115 is a module for acquiring the location (or current location) of the mobile artificial intelligence device, and representative examples thereof include a Global Positioning System (GPS) module or a Wireless Fidelity (WiFi) module. For example, if the artificial intelligence device utilizes a GPS module, the position of the mobile artificial intelligence device may be obtained using a signal transmitted from a GPS satellite.

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

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

The microphone 122 processes external sound signals into electrical voice data. The processed voice data may be utilized in various ways according to the function (or application program being executed) being performed by the artificial intelligence device 100 . Meanwhile, various noise cancellation algorithms for removing noise generated in the process of receiving an external sound signal may be implemented in the microphone 122 .

The user input unit 123 is for receiving information from a 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 a 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 input means. As an example, the touch input means consists of a virtual key, soft key, or visual key displayed on a touch screen through software processing, or a part other than the touch screen. It can be made of a touch key (touch key) disposed on.

The output unit 150 is for generating an output related to sight, hearing, or touch, and includes at least one of a display unit 151, a sound output unit 152, a haptic 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 program driven by 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 may implement a touch screen by forming a mutual layer structure or integrally with the touch sensor. Such a touch screen may function as a user input unit 123 providing an input interface between the artificial intelligence device 100 and the user, and may 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 reception of a call signal, communication mode or recording mode, voice recognition mode, or broadcast reception mode.

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

The light output unit 154 outputs a signal for notifying the occurrence of an event using light from a light source of the artificial intelligence device 100 . Examples of events generated by the artificial intelligence device 100 may include message reception, call signal reception, missed calls, alarms, schedule notifications, e-mail reception, and information reception through applications.

The interface unit 160 serves as a passage for various types of external devices connected to the artificial intelligence device 100 . The interface unit 160 connects a device 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. In response to an external device being connected to the interface unit 160, the artificial intelligence device 100 may perform appropriate control related to the connected external device.

On the other hand, the identification module is a chip that stores various information for authenticating the right to use the artificial intelligence device 100, and includes a user identification module (UIM), a subscriber identity module (SIM), and a general-purpose user. An authentication module (universal subscriber identity module; USIM) and the like may be included. A device equipped with an identification module (hereinafter referred to as 'identification device') may be manufactured in the form of a smart card. Accordingly, the identification device may 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 . The power supply unit 190 includes a battery, and the battery may be a built-in battery or a replaceable battery.

Meanwhile, as described above, the processor 180 controls the operation related to the application program and the general operation of the artificial intelligence device 100 in general. For example, if the state of the artificial intelligence device satisfies a set condition, the processor 180 may execute or release a locked state that restricts a user's control command input to applications.

5 shows a smart home system 10 according to an embodiment of the present invention.

The smart home system 10 may be an embodiment of the AI system 1.

The smart home system 10 is a system that can be applied to the inside and outside of various buildings, and is integrated with devices and sensors present inside and outside houses, offices, transportation facilities, cultural facilities, sports facilities, and various facilities. It is a system that can be applied to

In addition, the smart home system 10 is a system that uses various data collected from a plurality of devices or sensors, allows various devices or sensors to share, and provides benefits to users by controlling various devices or sensors. can

Also, the smart home system 10 may be a system for building a smart city. A smart city may refer to a city that provides the information needed to efficiently manage assets and resources using devices (or sensors) that collect various types of electronic data.

Meanwhile, the smart home system 10 may include one or more home devices. Each of the home devices 100_1, 100_2, 100_3, 100_4, 100_5, 100_6, and 100_7 may be an embodiment of the artificial intelligence device 100. Home devices include smart wall pads, smart switches, TVs, door lock devices, remote controllers, cameras, home security cameras, lighting devices, temperature control devices, lighting control devices, air conditioners, wired and wireless video, audio or text data transceivers, air conditioning and heating devices. , wired/wireless gateways, temperature/humidity/illuminance measurement devices, projectors, mobile phones, smart phones, desktop computers, laptops, digital broadcasting terminals, PDA(personal digital assistants), PMP(portable multimedia player), navigation, tablet PCs, wearable devices , set-top boxes (STBs), DMB receivers, radios, washing machines, refrigerators, desktop computers, digital signage, robots, vehicles, etc., fixed or movable devices, etc. may be included.

6 is a diagram showing a smart home security system 20 according to an embodiment of the present invention.

Smart home security system 20 may be one embodiment of smart home system 10 .

The smart home security system 20 may be configured by connecting the artificial intelligence device 100, the door lock device 300, and a plurality of home devices 100_1, 100_2, 100_N-1, and 100_N through a wired or wireless network.

The door locking device 300 may be a device installed on a door of a building to control opening and closing of the door. Also, the door device 300 may be a type of home device.

In addition, the door locking device 300 may include a door camera 301 that photographs a visitor. The door camera 301 may be installed to take pictures around the door. The door camera 301 may be set to start recording when an event of operating a door bell occurs, or may be set to always take pictures around the door.

In addition, the door locking device 300 may include a door speaker 302 capable of outputting an audio signal.

Meanwhile, the artificial intelligence device 100 may be a smart wall pad. A smart wall pad may be a device installed on a wall of a building to collect and display data from various devices and to control various types of devices.

7 is a flowchart illustrating a method for providing a connection between devices for home security according to an embodiment of the present invention.

The communication unit 110 may receive a visitor image from the door locking device 300 including the door camera 301 for photographing the visitor (S7001).

The door locking device 300 may transmit an image around the door captured by the door camera 301 to the artificial intelligence device 100 .

For example, the door locking device 300 causes the door camera 301 to start photographing the visitor when an event in which the visitor operates the door bell, and transmits the photographed visitor image to the artificial intelligence device 100. can be sent to

Meanwhile, the processor 180 may obtain visitor information output from the face recognition model by inputting the visitor image to the face recognition model (S7002).

The processor 180 may obtain visitor information of a person included in the visitor image by providing the visitor image to the visitor recognition model.

The visitor recognition model may compare a face of a visitor included in an input image with a face of a previously registered visitor, identify a visitor, and output visitor information.

The visitor information may include at least one of visitor face information, visitor identity information, visit time information, and visit number information.

For example, the visitor identity information is information for specifying the identity of the visitor, and information such as a family member, a security guard, and a courier may be specified. If the identity of the visitor is not verified, it may be identified as an unidentified visitor.

Meanwhile, the visitor recognition model may be an Artificial Neural Network (ANN) model used in machine learning. The visitor recognition model may be composed of artificial neurons (nodes) that form a network through synaptic coupling. An image recognition model may be defined by a connection pattern between neurons in different layers, a learning process for updating model parameters, and an activation function for generating output values.

The visitor recognition model may include an input layer, an output layer, and optionally one or more hidden layers. Each layer may include one or more neurons, and the artificial neural network may include neurons and synapses connecting the neurons. In an artificial neural network, each neuron may output a function value of an activation function for input signals, weights, and biases input through a synapse.

The visitor recognition model can be created through supervised learning, unsupervised learning, or reinforcement learning, depending on the learning method.

For example, when a visitor recognition model is generated through supervised learning, it may be learned in a given state of a label for learning data. The label may mean a correct answer (or a result value) that the artificial neural network should infer when learning data is input to the artificial neural network.

The learning processor 130 may designate a label that specifies the visitor's face image. For example, each of a face image of a family member, a face image of a security guard, a face image of a courier, and other unidentified face images may be labeled and designated.

Accordingly, the learning processor 130 may train a visitor recognition model to identify visitors by labeling the face image input when registering visitor information. Accordingly, when a new image file is input, the visitor's face can be identified by determining the label of the face image included in the corresponding image file.

In addition, the visitor recognition model is an unsupervised learning that trains an artificial neural network in a state where labels for training data are not given, or an agent defined in an environment learns to select an action or action sequence that maximizes the cumulative reward in each state. It can also be learned through reinforcement learning.

Meanwhile, the processor 180 may select a home device to receive a visitor image and visitor information from among a plurality of home devices (S7003).

When there are two or more home devices, the processor 180 may select a home device to transmit a visitor image and visitor information. Therefore, it is possible to prevent a situation in which the operation of all devices is hindered by transmitting the visitor image and visitor information to a specific home device without the need to transmit the visitor image and visitor information to all of the plurality of home devices included in the smart home security system 20. can

Also, the processor 180 may select a home device to receive a visitor image and visitor information from among a plurality of home devices based on an operating state of each of the plurality of home devices.

For example, the processor 180 determines a home device being used by a user based on whether each of a plurality of home devices is operating, and sets the home device being used by the user as a home device to receive a visitor image and visitor information. You can choose.

For example, when a parcel delivery arrives while the user is watching TV, which is one of a plurality of home devices, the door locking device 300 may transmit a visitor image of the parcel delivery man to the artificial intelligence device 100 . The processor 180 may obtain visitor information output through a visitor recognition model. If the courier is a pre-registered courier, visitor information may be acquired. The processor 180 may select a TV, which is a home device used by a user, among a plurality of home devices as a home device to receive a visitor image and visitor information.

Also, when there are a plurality of home devices being used by the user, the processor 180 may select a home device to receive the visitor image and the visitor information based on the display size of each home device being used by the user.

The processor 180 may determine a home device having a display of a predetermined size or larger among a plurality of home devices, and select a home device having a display of a predetermined size or larger as a home device to receive a visitor image and visitor information.

Also, the processor 180 may select a home device having the largest display among a plurality of home devices as a home device to receive a visitor image and visitor information.

Meanwhile, the communication unit 110 may transmit the visitor image and visitor information to the selected device (S7004).

The processor 180 may transmit the visitor image and visitor information to the selected home device through the communication unit 110 .

Accordingly, the selected home device may provide a home security interface displaying the received visitor image and visitor information so that the user can identify the visitor.

8 is a diagram illustrating a home security interface according to an embodiment of the present invention.

The home device 400 may receive a visitor image 406 and visitor information 407 from the artificial intelligence device 100 . The visitor information 407 may include information on the number of visitors. The home device 400 may include a TV.

The home device 400 may provide a home security interface 410 through a display. The home device 400 may receive a door control command for the door lock device 300 input through the home security interface 410 . The home device 400 may transmit a door control command for the door lock device 300 to the artificial intelligence device 100 .

The home security interface 410 may include control buttons 401 , 402 , 403 , and 404 for controlling the operation of the door locking device 300 . Also, the home security interface 410 may include a screen displaying a visitor image 406 and visitor information 407 . In addition, the home security interface 410 may include a box button 405 for selecting settings for the door locking device 300 to perform the same operation for the same visitor.

Meanwhile, the home device 400 may be controlled by the remote control device 500 .

The remote control device 500 may include physical buttons 501 , 502 , 503 , and 504 corresponding to the respective control buttons 401 , 402 , 403 , and 404 of the home security interface 410 .

For example, the control buttons for controlling the operation of the door locking device 300 include a door open button 401 for unlocking, a call button 402 for making a call with a visitor, and outputting a pre-stored voice. It may include an automatic answering button 403 and an ignore button 404 for stopping the door bell sound and exiting the home security interface 410 after a predetermined period of time. The first physical button 501 of the remote control device may correspond to the door open button 401, the second physical button 502 may correspond to the call button 402, and the third physical button 503 may correspond to the automatic It may correspond to the response button 403, and the fourth physical button 504 may correspond to the ignore button 404. The home device 400 may receive an input for a control button corresponding to a physical button from the remote control device 500 and may receive a control command for controlling the door locking device 300 .

Meanwhile, the processor 180 may receive a control command for the door lock device 300 from the home device 400 that provides the home security interface 410 through the communication unit 110 .

When receiving a lock release control command for the door lock device 300 from the home device 400, the processor 180 may control the door lock device 300 to unlock the door.

In addition, when the processor 180 receives a call mode control command from the home device 400 to allow the door lock device 300 to enter a call mode in which it can communicate with a visitor, the door speaker of the door lock device 300 302 and door microphone 303 can be activated.

In addition, the processor 180 may control the door locking device 300 to output voice data received from the selected home device 400 through the door speaker 300 included in the door locking device 300. there is.

In addition, when the processor 180 receives an automatic response control command for causing the door lock device 300 to output pre-stored voice data, the processor 180 transmits the pre-stored voice data to the door lock device 300 so that the door speaker 302 ) may control the door locking device 300 to output pre-stored voice data.

Meanwhile, when the ignore button 404 is input in the home security interface 410, the processor 180 may control the door lock device 300 to stop the door bell sound.

On the other hand, when a setting for the door fund device 300 to perform the same operation with respect to the same visitor is input in the home security interface 410, the processor 180 matches the door control command received with the visitor information to the memory 170. ) can be stored.

The processor 180 inputs the visitor image into the visitor recognition model, obtains visitor information output from the visitor recognition model, determines whether a door control command matching the visitor information exists, and determines whether a door control command matching the visitor information exists. If present, the door locking device 300 may be controlled with a corresponding door control command.

Meanwhile, the processor 100 may control the locking device to enter the safe mode for a predetermined period of time when the visitor's face is not identified from the visitor's image and visitor information cannot be obtained.

The safe mode is a mode that outputs a warning notification or sends a warning notification to an external server when it is determined that a visitor whose face cannot be identified stays for a preset period of time or revisits more than a predetermined number of times within a predetermined period of time. can be

After entering the safe mode, the processor 180 may obtain a stay time of a visitor whose face is not identified based on the visitor image.

In addition, the processor 180 may control an emergency notification to be output through a door speaker when a visitor whose face cannot be identified stays for a predetermined period or longer.

In addition, the communication unit 110 receives audio data acquired by the door microphone of the door locking device, and the processor 180 sends an emergency notification to a speaker when a sound higher than a preset decibel is detected from the received audio data after entering the safe mode. It can be controlled to output through .

Meanwhile, the processor 180 may obtain visitor information output from the visitor recognition model and obtain visit history information. The visit history information may be information stored such as the visit time of each visitor and whether or not the visitor is a registered visitor who is permitted to visit.

The processor 180 may determine whether the visitor is a pre-registered visitor based on the visitor information. If the visitor is not a pre-registered visitor, the processor 180 may determine whether there is a history of repeated visits for a preset number of times during a preset period of time. Accordingly, the processor 180 may determine a case in which a visitor who is not permitted to visit visits several times. The processor 180 may control the door locking device to enter the safe mode for a predetermined period of time when determining an unregistered user who repeatedly visits a predetermined number of times for a predetermined period of time.

Meanwhile, when a plurality of door locking devices are installed in a group building facility such as an apartment, the artificial intelligence device 100 may receive and store visitor information from each external door locking device, and may utilize the security service.

For example, the communication unit 110 may receive face information of a visitor whose visit is rejected from the first external door locking device.

The memory 170 may store face information of a visitor whose visit is rejected.

The processor 180 may determine whether facial information obtained from the visitor image received from the door locking device 300 matches face information of the visitor whose visit has been rejected. Accordingly, the processor 180 may determine whether or not the person whose visit is rejected in another place.

The processor 180 may control the door locking device to enter the safe mode for a predetermined period of time when face information obtained from the visitor image matches face information of the rejected visitor.

Also, the communication unit 110 may transmit face information of the rejected visitor to the second external door locking device. Accordingly, the artificial intelligence device 100 can share information about a person in danger between devices.

Meanwhile, the processor 180 may determine whether the visitor is a member whose access is permitted based on the visitor information. Members allowed access may refer to members allowed access, such as family members, friends, relatives, and employees.

The artificial intelligence apparatus 100, based on data received from a plurality of home devices, member information (eg, family information), entry/exit history information, usage information of each home device, sleep information of members, and home environment information. information about can be obtained.

For example, the artificial intelligence device 100 may obtain member information based on the number of portable devices accessing the home network, visitor information photographed during entry and exit, and the like. Member information may be input by the user. Persons registered as members may be allowed access.

In addition, the processor 180 may update a member's entry/exit history when the visitor is a member whose access is permitted. The processor 180 may store the input/output time of each member in the memory 170 .

The processor 180 may obtain member activity information based on the updated input/output history and operation information of a plurality of home devices.

The activity information may mean the time/period/pattern of a member staying at home and the usage time/period/pattern of each home device.

For example, the processor 180 may generate an input/output pattern for each month, day, hour, weather, or predetermined period based on the input/output history of each member. Accordingly, the processor 180 may obtain activity information of each member.

In addition, the processor 180 may obtain activity information of each member based on the type and time of home devices used when each member stays in the house.

For example, if the home device is a TV, activity information of the member may be obtained based on operation time information of the member's TV.

The processor 180 may detect an abnormal activity based on the member's activity information.

The processor 180 compares the previously obtained member's activity information with the current member's activity information, and when it does not match the previously obtained member's activity information, it may detect an abnormal activity.

For example, the processor 180 may detect that an abnormal activity has occurred when a member who goes out once a week does not go out and does not have time to use the TV even though he is not going out.

The processor 180 may provide a notification to an external device when an abnormal activity is detected.

9 is a diagram for explaining a method for providing a connection between home devices according to an embodiment of the present invention.

A plurality of home devices may be connected to the artificial intelligence device 100 through a wired or wireless network to form a home network system.

The artificial intelligence device 100 may be a gateway device connected to a plurality of home devices. Accordingly, it is possible to receive data from each of a plurality of home devices and transmit data to each of a plurality of other home devices.

The artificial intelligence device 100 may receive a request for data transmission from the first home device 100_1 located in the underground space 701 of the building to the second home device 100_2 located in the second floor space 702 of the building. there is.

The communication unit 110 may receive a connection request from the first home device 100_1 to the second home device 100_2.

The processor 180 may obtain a first communication protocol used by the first home device 100_1 and obtain a second communication protocol used by the second home device 100_2.

For example, when the first home device 100_1 uses the Bluetooth protocol and the second home device 100_2 uses the Wi-fi protocol, the second home device 100_2 is transmitted from the first home device 100_1. ) cannot transmit data directly. Accordingly, the processor 180 of the artificial intelligence device 100 may transmit data received from the first home device 100_1 to the second home device 100_2 using a Wi-fi communication protocol.

Accordingly, the artificial intelligence device 100 may provide a connection between a plurality of home devices in the smart home system 10 including a plurality of devices using different communication protocols.

Accordingly, the artificial intelligence apparatus 100 may perform a function of converting one protocol into another protocol among a plurality of home devices using a single communication protocol.

Meanwhile, the artificial intelligence device 100 may display data collected from a plurality of home devices or display the integrated information interface 1000 capable of manipulating each of the plurality of home devices through the display unit 151 .

10 is an exemplary diagram of an integrated information interface according to an embodiment of the present invention.

The integrated information interface 1000 may include a temperature display interface 1001 displaying temperature information and humidity information collected from a home device capable of measuring and adjusting temperature and humidity.

In addition, the integrated information interface 1000 includes an indoor temperature display interface 1002 that displays indoor temperature information collected from a home device capable of measuring and adjusting indoor temperature and a home device functioning as a boiler and information on a boiler operating state. can include

In addition, the integrated information interface 1000 may include a visitor display interface 1003 displaying a visitor image collected from a home device that captures the visitor.

In addition, the integrated information interface 1000 may display operation mode interfaces 1004 and 1005 displaying operation modes of indoor home devices. The operation mode interfaces 1004 and 1005 may include a return mode interface 1004 indicating that the user has entered home and a wake up mode interface 1005 indicating that it is time to wake up in the morning.

In addition, the unified information interface 1000 may include a lighting information interface 1006 capable of specifying a location of a home device having a lighting unit and controlling the home device having a lighting unit.

In addition, the integrated information interface 1000 may include a message information interface 1007 capable of displaying a message notification when a home device capable of receiving a message receives a message.

Meanwhile, the artificial intelligence device 100 may display a lighting control interface 1100 capable of controlling a plurality of home devices having lighting means through the display unit 151 .

11 is an exemplary view of a lighting control interface according to an embodiment of the present invention.

The lighting control interface 1100 may provide an interface capable of displaying on/off states of lighting devices installed in each room and setting the intensity of illumination of all lighting devices.

For example, the lighting control interface 1100 may include an interface 1101 displaying current total illumination setting information in the form of a bar. In addition, when the user lowers or increases the total illumination setting, the display of the bar may be displayed differently (1102, 1103).

Also, the lighting control interface 1100 may provide an interface 1104 capable of setting the intensity of illumination of a lighting device installed in a specific room.

Meanwhile, the artificial intelligence device 100 may interwork with the home mail system 30 .

12 is a block diagram of a home mail system 30 according to one embodiment of the present invention.

The home mail system 30 may include a sender device 1201 , a home mail transmission server 1202 , a customer information database 1203 and an artificial intelligence device 100 .

The home mail system 30 may be a system for replacing paper mail delivered to a house where the artificial intelligence device 100 is installed with electronic mail.

The mail transmission server 1202 may generate a unique identification mail address based on the address of the place where the artificial intelligence device 100 is installed. The unique identification mail address may be an email address that can be distinguished for each address based on a plurality of offline addresses. Accordingly, the mail transmission server 1202 may assign a distinct email address to each address where the artificial intelligence device 100 is installed.

The customer information database 1203 may store the address of the place where the artificial intelligence device 100 is installed, a unique identification e-mail address corresponding to the address, and information on whether or not to consent to reception.

The sender device 1201 may be a device used by a sender who wants to send digital mail to the artificial intelligence device 100 . For example, the sender may be a mobile communication company, a securities company, an insurance company, or an organization. On the other hand, digital mail can replace the existing paper mail. Existing paper mail may include various promotional materials in the apartment complex, maintenance fee statements, and announcements in the apartment complex. In addition, digital mail may be produced in a form capable of receiving residents' votes on various issues within an apartment complex, and may be in a form capable of receiving participation in a used trading market. In addition, the digital postal mail may include promotional materials of a shopping mall in the vicinity of the address where the artificial intelligence device 100 is installed, promotional materials of a store newly established in the vicinity, or discount information. In addition, the digital postal mail may include information about goods delivered to the address where the artificial intelligence device 100 is installed, delivery status, arrival information, and the like.

For example, the sender device 1201 sends a digital mail that replaces the contents of the paper mail with a unique identification mail address corresponding to an offline address to receive the paper mail through the mail transmission server 1202, the artificial intelligence device 100. ) can be sent to

The mail transmission server 1202 receives a digital mail delivery request from the sender device 1201, and the unique identification mail address corresponding to the offline address of the place where the artificial intelligence device 100 is installed is provided from the customer information database 1203. obtained, and can send digital mail to the artificial intelligence device 100 based on the unique identification mail address. Therefore, the sender can send digital mail only with the offline address of the place where the artificial intelligence device 100 is installed.

Therefore, the sender can increase customer satisfaction by using digital content that has a better content delivery effect than paper mail, and can reduce cost waste by replacing paper mail.

Meanwhile, the mail transmission server 1202 may receive feedback information about the digital mail sent by the sender device 1201 from the artificial intelligence device 100 . The feedback information may be an opt-out request for digital mail. When receiving a rejection request from the artificial intelligence device 100, the mail transmission server 1202 may update the customer information database 1203 and block future transmission of digital mail sent by the sender device 1201.

13 is an exemplary view of an integrated convenience application according to an embodiment of the present invention.

Meanwhile, the smart home system 10 may be connected to one or more user devices 1300 . The user device 1300 may be a device capable of accessing various services provided within the area where the artificial intelligence apparatus 100 is installed. For example, user devices may include smartphones and vehicles.

The user device 1300 may be a device capable of executing an integrated convenience application. The integrated convenience application may be an application that provides a service through which users can conveniently use cultural, medical, and shopping facilities within their residential area.

The user device 1300 may execute an integrated convenience application 1301 that provides information on hospitals or restaurants that can be reserved in the vicinity, provides information on cultural events in the vicinity, and provides information on health care centers and life support centers. there is.

In addition, the user device 1300 provides a service that allows anyone, including the elderly and the handicapped, to move more conveniently within the town, such as a taxi, a taxi for the disabled, or a car-sharing service, which can be reserved or easily called. It is possible to execute an integration convenience application 1302 that does. In addition, the integrated convenience application 1302 may recognize the user device 1300 and enable automatic parking payment for the user device 1300 .

In addition, the user device 1300 includes an integrated convenience application 1303 that provides an online space where residents in a residential area can purchase baby products, toys, books, household items, groceries, etc. as a group, trade second-hand items, or share them free of charge. can run

In addition, the user device 1300 supports various meetings or activities for residents in the residential area, and conveniently handles various inconveniences or suggestions/inquiries that occur not only in the residential space in the town, but also in commercial and office spaces, neighborhood facilities, etc. An integrated convenience application 1304 providing a sharable online communication space may be executed.

Meanwhile, when the user device 1300 is a vehicle, a payment system for a vehicle is installed so that payment can be made even in the vehicle. For example, a beacon device of a store may detect the user device 1300 and provide a menu corresponding to the corresponding store so that the user can pay for an order.

Meanwhile, when the user device 1300 is a vehicle, a vehicle security service may be provided. For example, when a person other than the owner of the vehicle parks the vehicle, a service capable of preventing vehicle theft and accidents may be provided. The user device 1300 may operate a valet parking mode. In the valet parking mode, the vehicle can be parked without a key. When the gear of the vehicle is shifted to P mode, an interface for entering valet parking mode may be displayed on the display. The vehicle may store an image of a driver providing valet parking service. In addition, the speed of the vehicle may be limited to a certain speed or less. In addition, a camera to photograph the interior of the vehicle and a microphone to record sound may be activated. In addition, the captured image can be transmitted to an external server to detect abnormal behavior of the driver performing valet parking. In addition, the vehicle may provide a notification to the owner of the vehicle after completion of valet parking. Also, the valet parking mode of the vehicle may be set to be released at any time by the owner of the vehicle.

Meanwhile, the artificial intelligence device 100 may provide a service of guiding a road to a destination by controlling lighting color and lighting on/off of at least one home device having a lighting unit.

14 and 15 are exemplary views of a guidance system 40 according to an embodiment of the present invention.

The user 1401 may input a desired destination 1403 into the artificial intelligence device 100 to receive directions.

The artificial intelligence device 100 may photograph the user 1401 through a camera, recognize and store the face of the user 1401 .

The artificial intelligence device 100 may continuously acquire the location of the user 1401 by acquiring images from a camera or CCTV installed in a building and tracking an object that matches the face of the user 1401 in the image.

The artificial intelligence apparatus 100 may guide the user 1401 on a path from the location of the user 1401 to the destination 1403 by controlling the lighting color and on/off of the lighting device 1402 .

For example, lighting of a lighting device 1402 installed on a road from the location of the user 1401 to the destination 1403 may be turned on. In addition, if the plurality of users 1401 input different destinations, the lighting device 1402 may be controlled so as to guide each user in a different color. The lighting device 1402 may include an LCD or LED panel. On the other hand, the lighting device 1402 may generate a sound, and when the user 1401 is approaching, it may guide the way with a sound.

In addition, the lighting device 1402 may be turned off on the path that the user 1401 has passed, and the path may be guided in a different color for other users.

On the other hand, the lighting device may include a beam projector installed on the ceiling of the building to emit light to the floor to guide the way.

Referring to FIG. 15 , the artificial intelligence device may control the beam projector to display a guide display 1404 in front of the user 1401 based on the location of the user 1401 .

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

Claims (22)

In the artificial intelligence device providing a connection between a plurality of home devices,
A communication unit for receiving a visitor image from a door locking device including a door camera for photographing the visitor; and
a processor for obtaining visitor information output from the visitor recognition model by inputting the visitor image into a visitor recognition model, and selecting a home device to receive the visitor image and the visitor information from among the plurality of home devices;
The communication department,
Transmitting the visitor image and the visitor information to the selected home device;
the processor,
determining a home device used by a user based on whether each of the plurality of home devices operates, and selecting a home device used by the user as a home device to receive the visitor image and visitor information;
selecting a home device to receive the visitor image and the visitor information based on a display size of each home device being used by the user when a plurality of home devices are being used by the user;
artificial intelligence device. delete delete According to claim 1,
The communication department,
Receiving a door control command for a door locking device from the selected home device;
the processor,
Controlling the door locking device based on the door control command,
artificial intelligence device. According to claim 1,
the processor,
Controlling the door locking device to output the voice data received from the selected home device through a door speaker included in the door locking device.
artificial intelligence device. According to claim 1,
the processor,
Controlling the locking device to enter a safe mode for a predetermined time when the visitor's face is not identified from the visitor image and visitor information cannot be obtained.
artificial intelligence device. According to claim 6,
the processor,
After entering the safe mode, to obtain a stay time of the visitor whose face is not identified based on the visitor image, and to output an emergency notification through a door speaker when the visitor whose face is not identified stays for a predetermined time or longer to control,
artificial intelligence device. According to claim 6,
The communication unit receives audio data acquired by the door microphone of the door locking device,
the processor,
After entering the safe mode, when a sound of a preset decibel or higher is detected in the audio data, controlling an emergency notification to be output through a door speaker,
artificial intelligence device. According to claim 1,
the processor,
Visit history information is acquired based on the visitor information, and if the visitor is not a pre-registered visitor and there is a history of repeated visits a preset number of times during a preset time, the locking device is set to safe mode for a preset time. to control entry into
artificial intelligence device. According to claim 1,
The communication department,
Receiving face information of a visitor whose visit is rejected from the first external door locking device;
the processor,
When face information obtained from the visitor image matches face information of the rejected visitor, controlling the locking device to enter a safe mode for a predetermined period of time;
The communication department,
Transmitting face information of the rejected visitor to a second external door locking device,
artificial intelligence device. According to claim 1,
The communication department,
Receive a connection request from the first home device to the second home device;
the processor,
A first communication protocol used by the first home device is acquired, a second communication protocol used by the second home device is acquired, and the first communication protocol is obtained based on the first communication protocol and the second communication protocol. Transmitting the data received from the device to the second home device through the communication unit,
artificial intelligence device. According to claim 1,
the processor,
Based on the visitor information, it is determined whether the visitor is a member whose access is allowed, and if the visitor is a member who is allowed to enter, the entry/exit history of the member is updated, and the updated entry/exit history and the operation of the plurality of home devices Obtaining activity information of the member based on the information,
artificial intelligence device. According to claim 12,
the processor,
Detecting abnormal activity based on the activity information of the member,
artificial intelligence device. Receiving a visitor image from a door locking device including a door camera for photographing the visitor;
acquiring visitor information output from the visitor recognition model by inputting the visitor image to a visitor recognition model;
selecting a home device to receive the visitor image and the visitor information from among a plurality of home devices; and
Transmitting the visitor image and the visitor information to the selected home device;
In the step of selecting the home device,
determining a home device used by a user based on whether each of the plurality of home devices operates, and selecting a home device used by the user as a home device to receive the visitor image and visitor information;
When a plurality of home devices are being used by the user, a home device to receive the visitor image and the visitor information is selected based on a display size of each home device being used by the user. How to provide connectivity. delete delete According to claim 14,
receiving a door control command for a door locking device from the selected home device; and
The method of providing a connection between a plurality of home devices, further comprising controlling the door locking device based on the door control command. According to claim 14,
If the visitor's face is not identified from the visitor image and visitor information cannot be obtained, controlling the locking device to enter a safe mode for a predetermined period of time, providing a connection between a plurality of home devices method. According to claim 18,
obtaining a stay time of a visitor whose face is not identified based on the visitor image after entering the safe mode; and
The method of providing a connection between a plurality of home devices, further comprising controlling an emergency notification to be output through a door speaker when a visitor whose face is not identified stays for a predetermined period of time or longer. According to claim 18,
receiving audio data acquired by the door microphone of the door locking device; and
The method of providing a connection between a plurality of home devices, further comprising controlling an emergency notification to be outputted through a door speaker when sound of a predetermined decibel or higher is detected in the audio data after entering the safe mode. According to claim 14,
obtaining visit history information based on the visitor information; and
If the visitor is not a pre-registered visitor and has a history of repeated visits a preset number of times during a preset time, controlling the locking device to enter a safe mode for a preset time, further comprising: How to provide connectivity between devices. According to claim 14,
Receiving face information of a visitor whose visit is rejected from a first external door locking device;
controlling the locking device to enter a safe mode for a predetermined period of time when face information obtained from the visitor image matches face information of the rejected visitor; and
The method of providing a connection between a plurality of home devices, further comprising transmitting facial information of the rejected visitor to a second external door locking device.

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