KR20230012648A - Method and apparatus for minting non fungible token - Google Patents

Abstract

Embodiments provide NFT issuance technology. According to one embodiment, the NFT token issuing method, by the data collection unit, the operation of obtaining a user's face image taken at a plurality of points of view; analyzing, by the face analyzer, each face image captured at the plurality of viewpoints using a neural network model; generating, by the avatar generator, a 3D avatar of the user on a metaverse based on a result of analyzing the face image; an operation of determining, by the NFT issuing unit, whether an NFT token issuing condition is satisfied based on the user's 3D avatar; When the issuance condition is satisfied, an operation of transmitting, by the communication unit, a transaction indicating that the generation of the NFT token is valid to one of the nodes of the blockchain, and on the basis of the transaction on the blockchain All nodes can be updated.

Description

NFT issuance method and apparatus using neural network {METHOD AND APPARATUS FOR MINTING NON FUNGIBLE TOKEN}

Embodiments of the present invention relate to technology for issuing NFTs, and to technologies for issuing NFTs for objects created on the metaverse.

NFT (Non Fungible Token) is a term that refers to a cryptocurrency in which it is impossible to replace one token with another. It is a technology that proves and protects the ownership of digital assets by utilizing blockchain technology that cannot be manipulated in the middle.

As a higher concept of virtual reality and augmented reality, metaverse is a system that expands reality into a digital-based virtual world to enable all activities in virtual space. Specifically, it is widely used in the sense of a life-type and game-type virtual world in which reality and unreality coexist in overall aspects of politics, economy, society, and culture.

NFT token issuing method according to an embodiment, by the data collection unit, the operation of obtaining a user's face image taken at a plurality of viewpoints; analyzing, by the face analyzer, each face image captured at the plurality of viewpoints using a neural network model; generating, by the avatar generator, a 3D avatar of the user on a metaverse based on a result of analyzing the face image; an operation of determining, by the NFT issuing unit, whether an NFT token issuance condition is satisfied based on the user's 3D avatar; When the issuance condition is satisfied, an operation of transmitting, by the communication unit, a transaction indicating that the generation of the NFT token is valid to one of the nodes of the blockchain, and on the basis of the transaction on the blockchain All nodes can be updated.

The neural network model includes a first input layer, a first hidden layer, and a first output layer, and the first hidden layer includes a first convolutional layer and a first pooling layer connected to the first convolutional layer. The first output layer is composed of a first fully connected layer, and in the operation of analyzing the face image, the first input layer converts the face image into flattening information of a predetermined dimension, and the first convolution The layer performs a convolution operation on a first kernel composed of a plurality of weights and the flattening information using a first stride, applies a first bias, performs padding, and outputs first nonlinear information, and the first pooling layer converts the first nonlinear information into a first feature map in which facial features are emphasized, and the first fully connected layer extracts face shape information from the first feature map through a function for extracting face shape information of a predetermined dimension. and output an analysis result of the face image including the face shape information.

The generating of the user's 3D avatar may include selecting a 3D face model matching the user's face from among a plurality of 3D face models stored in a database based on the face shape information; and personalizing the selected 3D face model based on the face shape information.

Determining whether the issuing condition is satisfied may include generating a 2D avatar image corresponding to the 3D avatar of the user based on a preset shooting condition; transmitting the 2D avatar image and a confirmation request signal to determine whether the 2D avatar image is recognized as the user to terminals of the N number of contacts stored in the terminal of the user; receiving a response signal from each terminal of the N number of contacts in response to the confirmation request signal; and determining whether the issuance condition is satisfied based on response signals received from each of the N contact terminals.

The photographing condition includes a photographing time point, lighting angle, illuminance, and a simplification processing index of a metaverse, and the operation of determining whether the issuing condition is satisfied based on the response signal includes the photographing time point, the lighting angle, the Calculating a recognition degree based on illuminance and a simplification processing index of the metaverse; comparing a result obtained by multiplying the recognition degree by N with the number of response signals indicating that the 2D avatar image is recognized as the user among the response signals; and determining whether the issue condition is satisfied based on the comparison result.

The operation of calculating the recognition degree includes an operation of calculating a simplification processing index of the metaverse based on the number of polygons representing the 3D avatar on the metaverse, the number of adjustable face regions, and the degree of freedom of the shape of the face region. can do.

NFT issuing apparatus according to an embodiment, the data collection unit; face analysis unit; Avatar creation unit; NFT issuance department; and a communication unit, wherein the data collection unit acquires facial images of a user captured at a plurality of viewpoints, the face analysis unit analyzes each facial image captured at the plurality of viewpoints using a neural network model, and The avatar creation unit generates the user's 3D avatar on the metaverse based on the analysis result of the face image, and the NFT issuing unit determines whether the issuance condition of the NFT token is satisfied based on the user's 3D avatar, When the issuance condition is satisfied, the communication unit transmits a transaction indicating that the generation of the NFT token is valid to one of the nodes of the blockchain, and all nodes on the blockchain can be updated based on the transaction. there is.

A computer program according to an embodiment may be combined with hardware and stored in a computer readable recording medium to execute the method of claim 1 .

A computer readable recording medium storing a computer program according to an embodiment may be combined with hardware to execute the method of claim 1.

BRIEF DESCRIPTION OF THE DRAWINGS Included as part of the detailed description to aid understanding of the embodiments, the accompanying drawings provide various embodiments and, together with the detailed description, describe technical features of the various embodiments.
1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.
2 is a diagram showing the configuration of a program according to an embodiment.
3 is a diagram showing the overall configuration of an NFT token issuing system according to an embodiment.
4 is a diagram illustrating the operation of an NFT token issuing method according to an embodiment.
5 is a diagram illustrating the structure of a neural network model according to an embodiment.
6 is a diagram illustrating an example of a user's avatar implemented on the metaverse according to an embodiment.
7 is a diagram showing the configuration of an NFT token issuing device according to an embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form not combined with other components or features. In addition, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may be changed. Some components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components or features of another embodiment.

In the description of the drawings, procedures or steps that may obscure the gist of various embodiments are not described, and procedures or steps that can be understood by those skilled in the art are not described. did

Throughout the specification, when a part is said to "comprising" or "including" a certain element, it means that it may further include other elements, not excluding other elements, unless otherwise stated. do. In addition, terms such as “… unit”, “… unit”, and “module” described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, “a or an”, “one”, “the” and like terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, both the singular and the plural can be used.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description set forth below in conjunction with the accompanying drawings is intended to describe exemplary embodiments of various embodiments, and is not intended to represent a single embodiment.

In addition, specific terms used in various embodiments are provided to help understanding of various embodiments, and the use of these specific terms may be changed into other forms without departing from the technical spirit of various embodiments. .

1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.

1 is a block diagram of an electronic device 101 within a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100, an electronic device 101 communicates with an electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or through a second network 199. It may communicate with at least one of the electronic device 104 or the server 108 through (eg, a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or the antenna module 197 may be included. In some embodiments, in the electronic device 101, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added. In some embodiments, some of these components (eg, sensor module 176, camera module 180, or antenna module 197) are integrated into a single component (eg, display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (eg, the program 140) to cause at least one other component (eg, hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or calculations. According to one embodiment, as at least part of data processing or operation, the processor 120 transfers instructions or data received from other components (e.g., sensor module 176 or communication module 190) to volatile memory 132. , processing commands or data stored in the volatile memory 132 , and storing resultant data in the non-volatile memory 134 . According to one embodiment, the processor 120 may include a main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit ( NPU: neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may use less power than the main processor 121 or be set to be specialized for a designated function. can The secondary processor 123 may be implemented separately from or as part of the main processor 121 .

The secondary processor 123 may, for example, take the place of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, running an application). ) state, together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to one embodiment, the auxiliary processor 123 (eg, image signal processor or communication processor) may be implemented as part of other functionally related components (eg, camera module 180 or communication module 190). there is. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning or reinforcement learning, but in the above example Not limited. The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the foregoing, but is not limited to the foregoing examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101 . The data may include, for example, input data or output data for software (eg, program 140) and commands related thereto. The memory 130 may include volatile memory 132 or non-volatile memory 134 .

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142 , middleware 144 , or an application 146 .

The input module 150 may receive a command or data to be used by a component (eg, the processor 120) of the electronic device 101 from the outside of the electronic device 101 (eg, a user). The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101 . The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback. A receiver may be used to receive an incoming call. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 may visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor set to detect a touch or a pressure sensor set to measure the intensity of force generated by the touch.

The audio module 170 may convert sound into an electrical signal or vice versa. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device connected directly or wirelessly to the electronic device 101 (eg: Sound may be output through the electronic device 102 (eg, a speaker or a headphone).

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a bio sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device 101 to an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 may be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 may convert electrical signals into mechanical stimuli (eg, vibration or motion) or electrical stimuli that a user may perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied to the electronic device 101 . According to one embodiment, the power management module 188 may be implemented as at least part of a power management integrated circuit (PMIC), for example.

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). Establishment and communication through the established communication channel may be supported. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : a local area network (LAN) communication module or a power line communication module). Among these communication modules, a corresponding communication module is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a telecommunications network such as a computer network (eg, a LAN or a WAN). These various types of communication modules may be integrated as one component (eg, a single chip) or implemented as a plurality of separate components (eg, multiple chips). The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 may be identified or authenticated.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, NR access technology (new radio access technology). NR access technologies include high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and access of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low latency (URLLC)). -latency communications)) can be supported. The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data rate, for example. The wireless communication module 192 uses various technologies for securing performance in a high frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. Technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna may be supported. The wireless communication module 192 may support various requirements defined for the electronic device 101, an external electronic device (eg, the electronic device 104), or a network system (eg, the second network 199). According to one embodiment, the wireless communication module 192 is a peak data rate for eMBB realization (eg, 20 Gbps or more), a loss coverage for mMTC realization (eg, 164 dB or less), or a U-plane latency for URLLC realization (eg, Example: downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) may be supported.

The antenna module 197 may transmit or receive signals or power to the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is selected from the plurality of antennas by the communication module 190, for example. can be chosen A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module 197 in addition to the radiator.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module includes a printed circuit board, an RFIC disposed on or adjacent to a first surface (eg, a lower surface) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, array antennas) disposed on or adjacent to a second surface (eg, a top surface or a side surface) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( e.g. commands or data) can be exchanged with each other.

According to an embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or part of operations executed in the electronic device 101 may be executed in one or more external electronic devices among the external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 instead of executing the function or service by itself. Alternatively or additionally, one or more external electronic devices may be requested to perform the function or at least part of the service. One or more external electronic devices receiving the request may execute at least a part of the requested function or service or an additional function or service related to the request, and deliver the execution result to the electronic device 101 . The electronic device 101 may provide the result as at least part of a response to the request as it is or additionally processed. To this end, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an internet of things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199 . The electronic device 101 may be applied to intelligent services (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide a service to the connected electronic device 101 . In addition, the server 108 may proceed with a membership sign-up procedure, store and manage various types of information of users subscribed as members, and provide various purchase and payment functions related to services. In addition, the server 108 may share execution data of service applications executed in each of the plurality of electronic devices 101 in real time so that the service can be shared among users. This server 108 may have the same configuration as a conventional web server or WAP server in terms of hardware. However, in terms of software, it may include a program module that is implemented through any language such as C, C++, Java, Visual Basic, or Visual C and performs various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives requests from clients or other servers to perform tasks and derives and provides work results. It means a computer system and the computer software (server program) installed for it. In addition, the server 108, in addition to the above-described server program, a series of application programs operating on the server 108 and various databases (DB: Database, hereinafter referred to as It should be understood as a broad concept including DB"). Accordingly, the server 108 classifies member registration information and various information and data about games, stores them in a DB, and manages them. This DB may be implemented inside or outside the server 108 . In addition, the server 108 uses a server program provided in various ways according to operating systems such as DOS, Windows, Linux, UNIX, and Macintosh in general server hardware. As a representative example, a website used in a Windows environment, Internet Information Server (IIS), and CERN, NCSA, APPACH, etc. used in a Unix environment can be used. In addition, the server 108 may interoperate with an authentication system and a payment system for user authentication of services or payment for purchases related to services.

The first network 198 and the second network 199 are a connection structure capable of exchanging information between nodes such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. means (Network). The first network 198 and the second network 199 include Internet, Local Area Network (LAN), Wireless Local Area Network (Wireless Local Area Network), Wide Area Network (WAN), Personal Area Network (PAN), and 3G , 4G, LTE, 5G, Wi-Fi, etc., but are not limited thereto. The first network 198 and the second network 199 may be closed first networks 198 and second networks 199 such as LAN and WAN, but are preferably open such as the Internet. The Internet is based on the TCP/IP protocol and several services that exist on its upper layer, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP ( Simple Network Management Protocol), Network File Service (NFS), and Network Information Service (NIS).

The database may have a general data structure implemented in a storage space (hard disk or memory) of a computer system using a database management program (DBMS). The database may have a data storage form in which data can be freely searched for (extracted), deleted, edited, added, and the like. Databases are relational database management systems (RDBMS) such as Oracle, Informix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and XML Native Databases such as Excelon, Tamino, and Sekaiju, and its functions may have appropriate fields or elements to achieve.

2 is a diagram showing the configuration of a program according to an embodiment.

2 is a block diagram 200 illustrating a program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable in the operating system 142 for controlling one or more resources of the electronic device 101. can include The operating system 142 may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs 140 are, for example, preloaded in the electronic device 101 at the time of manufacture, or when used by a user, an external electronic device (eg, the electronic device 102 or 104), or a server ( 108)) can be downloaded or updated. All or part of program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or reclamation) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively include other hardware devices of electronic device 101 , such as input module 150 , sound output module 155 , display module 160 , audio module 170 . , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that the function or information provided from one or more resources of the electronic device 101 may be used by the application 146 . The middleware 144 includes, for example, the application manager 201, the window manager 203, the multimedia manager 205, the resource manager 207, the power manager 209, the database manager 211, and the package manager 213. ), connectivity manager 215, notification manager 217, location manager 219, graphics manager 221, security manager 223, call manager 225, or voice recognition manager 227. can

The application manager 201 may manage the life cycle of the application 146 , for example. The window manager 203 may manage one or more GUI resources used in a screen, for example. The multimedia manager 205 identifies, for example, one or more formats necessary for reproducing media files, and encodes or decodes a corresponding media file among the media files using a codec suitable for the selected format. can be done The resource manager 207 may manage a source code of the application 146 or a memory space of the memory 130 . The power manager 209 may manage, for example, the capacity, temperature, or power of the battery 189, and determine or provide related information necessary for the operation of the electronic device 101 by using corresponding information among them. . According to an embodiment, the power manager 209 may interoperate with a basic input/output system (BIOS) (not shown) of the electronic device 101 .

The database manager 211 may create, search, or change a database to be used by the application 146, for example. The package manager 213 may manage installation or update of applications distributed in the form of package files, for example. The connectivity manager 215 may manage, for example, a wireless connection or a direct connection between the electronic device 101 and an external electronic device. The notification manager 217 may provide a function for notifying a user of occurrence of a designated event (eg, an incoming call, message, or alarm), for example. The location manager 219 may manage location information of the electronic device 101, for example. The graphic manager 221 may manage, for example, one or more graphic effects to be provided to a user or a user interface related thereto.

Security manager 223 may provide system security or user authentication, for example. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101 . The voice recognition manager 227 transmits, for example, the user's voice data to the server 108, and at least partially based on the voice data, a command corresponding to a function to be performed in the electronic device 101; Alternatively, text data converted at least partially based on the voice data may be received from the server 108 . According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least part of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142 .

The application 146 includes, for example, a home 251, a dialer 253, an SMS/MMS 255, an instant message (IM) 257, a browser 259, a camera 261, and an alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Albums (275), Watch (277), Health (279) (e.g. exercise or blood sugar) measurement of biometric information) or environmental information 281 (eg, measurement of atmospheric pressure, humidity, or temperature information). According to an embodiment, the application 146 may further include an information exchange application (not shown) capable of supporting information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit designated information (eg, a call, message, or alarm) to an external electronic device, or a device management application configured to manage an external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a designated event (eg, mail reception) generated in another application (eg, the email application 269) of the electronic device 101 to an external electronic device. can Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide the notification information to the user of the electronic device 101 .

The device management application is, for example, a power source (eg, turn-on or turn-off) of an external electronic device that communicates with the electronic device 101 or some component thereof (eg, a display module or a camera module of the external electronic device). ) or functions (eg brightness, resolution, or focus). The device management application may additionally or alternatively support installation, deletion, or update of an application operating in an external electronic device.

Throughout this specification, a neural network, a neural network, and a network function may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may be generally referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network includes at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

In a neural network, two or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

3 is a diagram showing the overall configuration of an NFT token issuing system according to an embodiment.

According to one embodiment, the NFT issuing system may include a server 100, a terminal 200, an external server 300, a database 400, a network 500, and a blockchain 600. The NFT issuing system may further include a metaverse 700 implemented by the server 100.

According to one embodiment, the NFT issuing system may generate a 3D avatar implemented on the metaverse 700 by analyzing a user's face image. The NFT issuance system checks whether certain conditions are satisfied when creating a 3D avatar, and when the conditions are met, NFT tokens can be issued using a blockchain.

The NFT issuing system can secure the identity of the 3D avatar and the user by receiving the identity of the user's 3D avatar from the user's acquaintances. The NFT issuance system may generate a 3D avatar having a unique value identified by the user's acquaintances.

In this way, the NFT issuance system can create unique value by issuing NFT tokens based on the user's unique identity on a specific metaverse. The NFT issuance system can maximize the profitability of the NFT issuance system by charging fees for NFT token transactions.

According to one embodiment, referring to FIG. 7 , the server 100 includes a data collection unit 801, a face analysis unit 803, an avatar creation unit 805, an NFT issuing unit 807 and a communication unit 809. can include

The data collection unit 801 may acquire user's face images captured from a plurality of viewpoints. For example, when the user's terminal 200 is a smart phone, the user may photograph his/her face from various angles using the terminal 200 and transmit the photographed images to the cosmetics recommendation device.

The face analyzer 803 may analyze each face image captured from a plurality of viewpoints using a neural network model.

The neural network model may include a first input layer, a first hidden layer, and a first output layer. The first hidden layer may include a first convolutional layer and a first pooling layer connected to the first convolutional layer. The first output layer may consist of a first fully connected layer.

The first input layer converts the face image into flattening information of a predetermined dimension, and the first convolution layer performs a convolution operation on a first kernel composed of a plurality of weights and the flattening information using a first stride, and a first bias is applied and padding is performed to output first nonlinear information, the first pooling layer converts the first nonlinear information into a first feature map in which facial features are emphasized, and the first fully connected layer converts face shape information of a predetermined dimension. Face shape information may be extracted from the first feature map through a function for extraction, and an analysis result of a face image including the face shape information may be output. Here, the face shape information means final information extracted from the first feature map in which shape features are emphasized.

The avatar generator 805 may generate a 3D avatar of the user on the metaverse based on the analysis result of the face image. The avatar generator 805 may select a 3D face model that matches the user's face from among a plurality of 3D face models stored in the database based on the face shape information. The database 400 may store various types of 3D face models. The 3D face models stored in the database 400 may include face models in which face types of a plurality of people of various races, ages, genders, and ages are classified by type.

The avatar generator 805 may personalize the selected 3D face model based on face shape information. The avatar generator 805 may transform the selected 3D face model based on the user's face shape information. In this way, the NFT issuing system can effectively personalize the 3D face model with small resources by selecting a basic template model and reflecting the user's characteristics.

The NFT issuance system is operated based on a blockchain 600 that can transmit and store data in a P2P network. Blockchain is a distributed ledger technology that allows any participant to transparently check and verify transaction details.

The NFT issuing unit 807 may determine whether a condition for issuing (minting) an NFT token is satisfied based on the user's 3D avatar. The issuance condition may include user identification. For example, the NFT issuing unit 807 may determine whether the user has completed identity verification through a public certificate. The issuance condition may further include a condition of receiving an identification signal indicating identification from friends of the user. The NFT issuing system can secure the identity of the 3D avatar and the user by receiving the identity of the user's 3D avatar from the user's acquaintances.

For example, the server 100 may request user identification from the terminal 200 . The user may request identity verification to the external server 300 through the public certificate stored in the terminal 200 . For example, the external server 300 may include a server operated by an institution that verifies a user's identity. The external server 300 may transmit a signal for authenticating the user's identity to the server 100 . However, the process of verifying the user's identity through the above-described public certificate is exemplary, and various methods may be applied to an object for identity verification or a transmission sequence of signals between objects. For example, the server 100 may request the external server 300 to verify the user's identity, and the external server 300 may request the user's terminal 200 to verify the user's identity. After verifying the user's identity through the public certificate, identity verification by the user's acquaintances may be performed. Hereinafter, the identification process by the user's acquaintances will be described.

First, the NFT issuing unit 807 may generate a 2D avatar image corresponding to the user's 3D avatar based on preset shooting conditions. The photographing condition means a variable that degrades the 2D avatar image compared to the 3D avatar image generated from the user's image. For example, the photographing condition may include a photographing point of view, an illumination angle, an illuminance, and a simplification processing index of the meta bus. The simplification processing index of the meta bus means an index indicating how simple a 3D avatar expressed on the meta bus is compared to a real person.

The NFT issuing unit 807 may calculate the simplification processing index of the metaverse based on the number of polygons representing the 3D avatar on the metaverse, the number of adjustable face regions, and the degree of freedom of the face region shape. For example, the NFT issuing unit 807 may calculate the simplification processing index of the meta bus using Equation 1.

[Equation 1]

은 메타버스 상의 단순화 처리 지수를 나타내고, 예를 들어, m은 3인 경우에,

은 수학식 1을 따르며, D는 폴리곤 수, E는 조정 가능한 얼굴 영역의 수, F는 얼굴 영역의 형상의 자유도를 나타낸다.At this time,

Represents the simplification processing index on the metaverse, for example, when m is 3,

follows Equation 1, where D is the number of polygons, E is the number of adjustable face regions, and F is the degree of freedom of the shape of the face region.

The NFT issuing unit 807 may calculate the recognition based on the shooting time point, lighting angle, illuminance, and simplification processing index of the meta bus. For example, the NFT issuing unit 807 may calculate the recognition level using Equation 2.

[Equation 2]

은 인식도를 나타내고,

은 메타 버스 상의 단순화 처리 지수를 나타내고, m은 2 이상의 정수로서, 단순화 처리 지수의 변수의 종류의 수를 나타낸다. 또한

은 인식도에 상응하는 표준 편차를 나타낼 수 있으며,

는 서버에 의해 산출되었던 인식도들의 값 및/또는 서버에 저장된 다른 사용자들에 대한 인식도들에 기반하여 통계적으로 결정(및/또는 산출, 설정)될 수 있다. h는 촬영 시점을 나타내며, 구체적으로 사용자의 얼굴의 정면을 기준으로 하는 각도를 나타내고,

는 h에 대한 조정 지수로서 실험적으로 결정되며, 예를 들어, 0.1301

일 수 있다. S는 조명 각도를 나타내며, 구체적으로, 사용자의 얼굴의 정면을 기준으로 하는 각도를 나타내고,

는 s에 대한 조정 지수로서 실험적으로 결정되며, 예를 들어, 0.0036

일 수 있다.

는 조도를 나타낸다. At this time,

represents the degree of recognition,

represents the simplification processing index on the meta bus, and m is an integer of 2 or more, representing the number of types of variables of the simplification processing index. also

can represent the standard deviation corresponding to the degree of recognition,

may be statistically determined (and/or calculated, set) based on the values of the recognition degrees calculated by the server and/or the recognition degrees of other users stored in the server. h represents the shooting point, and specifically represents the angle based on the front of the user's face,

is experimentally determined as an adjustment exponent for h, for example, 0.1301

can be S represents the lighting angle, specifically, represents the angle relative to the front of the user's face,

is experimentally determined as the adjusted exponent for s, for example, 0.0036

can be

represents the roughness.

In this way, the NFT issuing system can assign a unique value of identity to the 3D avatar by calculating the degree of deterioration and expected recognition of the 2D avatar image and actually comparing it with the responses of acquaintances.

The communication unit 809 may transmit a 2D avatar image and a confirmation request signal to determine whether the 2D avatar image is recognized as a user to terminals of N contacts stored in the terminal of the user. The communication unit 809 may receive a response signal from each terminal of the N number of contacts in response to the confirmation request signal.

The NFT issuing unit 807 may determine whether the issuing condition is satisfied based on the response signals received from each of the terminals of the N number of contacts. For example, the NFT issuing unit 807 may compare the number of response signals indicating that the 2D avatar image is recognized as a user among the result obtained by multiplying the recognition degree by N and the response signal.

The NFT issuing unit 807 may determine whether the issuing condition is satisfied based on the comparison result. For example, the NFT issuing unit 807 may set the result of multiplying recognition by N as the threshold value. The NFT issuing unit 807 may determine that the issuing condition is satisfied when the number of response signals indicating that the 2D avatar image is recognized as a user is greater than a threshold value.

When the issuance condition is satisfied, the communication unit 809 may transmit a transaction indicating that generation of the NFT token is valid to one of the nodes of the blockchain. All nodes on the blockchain can be updated based on the transaction being sent.

In the blockchain 600, there is a smart contract, which is a program code that is automatically executed when issuance conditions are met. Through this smart contract, the function of the token is defined and issued. Participants in the NFT system consist of creators and buyers. Creators can mint analog or digital goods as NFTs through the NFT system. Whenever an NFT is issued or sold, a smart contract is executed and a new transaction is sent.

After the transaction is confirmed on the blockchain, the NFT's metadata and owner information are added to the new block. When a buyer purchases an NFT, metadata information such as the owner of the 3D avatar, purchase price, ownership change, and acquisition price is stored in the blockchain. The purchaser can take ownership of the 3D avatar, but cannot receive the actual 3D avatar of the creator. Creators are rewarded whenever NFTs are sold and owners change. Here, the creator may be a user who is the original model of the 3D avatar.

4 is a diagram illustrating the operation of an NFT token issuing method according to an embodiment.

According to an embodiment, in operation S401, the data collection unit may acquire user's face images taken from a plurality of viewpoints.

According to an embodiment, in operation S403, the face analyzer may analyze each face image captured from a plurality of viewpoints using a neural network model.

According to one embodiment, in operation S405, the avatar generator may generate a 3D avatar of the user on the metaverse based on the analysis result of the face image.

According to one embodiment, in operation S407, the NFT issuing unit may determine whether the issuance condition of the NFT token is satisfied based on the user's 3D avatar.

According to one embodiment, in operation S409, when the issuance condition is satisfied, the communication unit may transmit a transaction indicating that generation of the NFT token is valid to one of the nodes of the blockchain. Based on the transaction, all nodes on the blockchain can be updated.

5 is a diagram illustrating the structure of a neural network model according to an embodiment.

The neural network model may include a first input layer, a first hidden layer, and a first output layer. The first hidden layer may include a first convolutional layer and a first pooling layer connected to the first convolutional layer. The first output layer may consist of a first fully connected layer.

The first input layer converts the face image into flattening information of a predetermined dimension, and the first convolution layer performs a convolution operation on a first kernel composed of a plurality of weights and the flattening information using a first stride, and a first bias is applied and padding is performed to output first nonlinear information, the first pooling layer converts the first nonlinear information into a first feature map in which facial features are emphasized, and the first fully connected layer converts face shape information of a predetermined dimension. Face shape information may be extracted from the first feature map through a function for extraction, and an analysis result of a face image including the face shape information may be output. Here, the face shape information means final information extracted from the first feature map in which shape features are emphasized.

The neural network model described above can be learned in advance through a large amount of training data. Each training data composed of facial images or skin condition raw information captured at multiple viewpoints is input to the neural network model input layer, passes through one or more hidden layers and output layers, and outputs an output vector, and the output vector is output to the output layer. It is input to the connected loss function layer, and the loss function layer outputs a loss value using a loss function that compares the output vector with the correct answer vector for each learning data, and the parameters of the neural network model are in the direction of decreasing the loss value. can be learned

[Equation 1]

The loss function may follow Equation 1 above. In Equation 1, n is the number of training data per class, y and j are identifiers representing classes, C is a constant value, M is the number of classes, x_y is the probability that the training data belongs to class y, x_j is the training data A probability value belonging to class j, L may mean a loss value.

Since Equation 1 reflects the number of learning data per class, a class with a small number of training data may have a small effect on learning, and a class with a large number of training data may have a large effect on learning.

6 is a diagram illustrating an example of a user's avatar implemented on the metaverse according to an embodiment.

Referring to FIG. 6 , information on the shape of a user's face is displayed on the terminal 200 . The user's 3D avatar 701 on the metaverse 700 may reflect the result of transforming the 3D face model based on the user's face shape information. In addition, since the 3D avatar 701 is generated when a condition for receiving an identification signal indicating identification from acquaintances of the user is satisfied, identity between the 3D avatar and the user can be secured.

The NFT issuance system can create unique value by issuing NFT tokens based on the user's unique identity on a specific metaverse. The NFT issuance system can maximize the profitability of the NFT issuance system by charging fees for NFT token transactions.

7 is a diagram showing the configuration of an NFT token issuing device according to an embodiment.

According to one embodiment, the NFT token issuing device includes a data collection unit 801, a face analysis unit 803, an avatar creation unit 805, an NFT issuing unit 807, and a communication unit 809.

The data collection unit 801 may acquire user's face images captured from a plurality of viewpoints.

The face analyzer 803 may analyze each face image captured from a plurality of viewpoints using a neural network model.

The avatar generator 805 may generate a 3D avatar of the user on the metaverse based on the analysis result of the face image.

The NFT issuing unit 807 may determine whether the issuing condition of the NFT token is satisfied based on the user's 3D avatar.

When the issuance condition is satisfied, the communication unit 809 may transmit a transaction indicating that generation of the NFT token is valid to one of the nodes of the blockchain. Based on the transaction, all nodes on the blockchain can be updated.

The embodiments described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. You can command the device. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program commands recorded on the medium may be specially designed and configured for the embodiment or may be known and usable to those skilled in computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floptical disks. - includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, and ROM, RAM, flash memory, and the like. Examples of program instructions include high-level language codes that can be executed by a computer using an interpreter, as well as machine language codes such as those produced by a compiler. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with limited drawings, those skilled in the art can apply various technical modifications and variations based on the above. For example, the described techniques may be performed in an order different from the method described, and/or components of the described system, structure, device, circuit, etc. may be combined or combined in a different form than the method described, or other components may be used. Or even if it is replaced or substituted by equivalents, appropriate results can be achieved.

Therefore, other implementations, other embodiments, and equivalents of the claims are within the scope of the following claims.

Claims (9)

obtaining, by the data collection unit, user's face images captured at a plurality of viewpoints;
analyzing, by the face analyzer, each face image captured at the plurality of viewpoints using a neural network model;
generating, by the avatar generator, a 3D avatar of the user on a metaverse based on a result of analyzing the face image;
an operation of determining, by the NFT issuing unit, whether an NFT token issuance condition is satisfied based on the user's 3D avatar;
When the issuance condition is satisfied, the communication unit transmits a transaction indicating that the generation of the NFT token is valid to one of the nodes of the blockchain,
Based on the transaction, all nodes on the blockchain are updated.
How to issue NFT tokens. According to claim 1,
The neural network model includes a first input layer, a first hidden layer, and a first output layer, and the first hidden layer includes a first convolutional layer and a first pooling layer connected to the first convolutional layer. And, the first output layer is composed of a first fully connected layer,
In the operation of analyzing the face image,
The first input layer converts the face image into flattening information of a predetermined dimension, and the first convolution layer performs a convolution operation on the flattening information and a first kernel composed of a plurality of weights using a first stride. and applies a first bias and performs padding to output first nonlinear information, the first pooling layer converts the first nonlinear information into a first feature map in which facial features are emphasized, and the first fully connected layer Extracting face shape information from the first feature map through a function for extracting face shape information of a predetermined dimension, and outputting an analysis result of the face image including the face shape information,
How to issue NFT tokens. According to claim 1,
The operation of generating the user's 3D avatar,
selecting a 3D face model matching the face of the user from among a plurality of 3D face models stored in a database based on the face shape information; and
Personalizing the selected 3D face model based on the face shape information,
How to issue NFT tokens. According to claim 1,
The operation of determining whether the issuance condition is satisfied,
generating a 2D avatar image corresponding to the 3D avatar of the user based on preset shooting conditions;
transmitting the 2D avatar image and a confirmation request signal to determine whether the 2D avatar image is recognized as the user to terminals of the N number of contacts stored in the terminal of the user;
receiving a response signal from each terminal of the N number of contacts in response to the confirmation request signal; and
An operation of determining whether the issuance condition is satisfied based on response signals received from each of the N contact terminals.
Including, NFT token issuing method. According to claim 4,
The shooting condition includes a shooting time point, lighting angle, illumination intensity, and a simplification processing index of the metaverse,
The operation of determining whether the issue condition is satisfied based on the response signal,
calculating a recognition degree based on the photographing time point, the illumination angle, the illuminance, and a simplification processing index of the metaverse;
comparing a result obtained by multiplying the recognition degree by N with the number of response signals indicating that the 2D avatar image is recognized as the user among the response signals; and
Comprising the operation of determining whether the issuance condition is satisfied based on the comparison result, NFT token issuing method. According to claim 5,
The operation of calculating the recognition degree,
Calculating a simplification processing index of the metaverse based on the number of polygons representing the 3D avatar on the metaverse, the number of adjustable face regions, and the degree of freedom of the shape of the face region,
How to issue NFT tokens. data collection unit;
face analysis unit;
Avatar creation unit;
NFT issuance department; and
Including the communication department,
The data collection unit acquires a user's face image taken at a plurality of viewpoints,
The face analyzer analyzes each face image captured at the plurality of viewpoints using a neural network model;
The avatar generator generates a 3D avatar of the user on the metaverse based on the analysis result of the face image,
The NFT issuing unit determines whether the issuing condition of the NFT token is satisfied based on the user's 3D avatar,
When the issuance condition is satisfied, the communication unit transmits a transaction indicating that the generation of the NFT token is valid to one of the nodes of the blockchain,
Based on the transaction, all nodes on the blockchain are updated.
NFT token issuance device. A computer program stored in a computer readable recording medium in order to execute the method of claim 1 in combination with hardware. A computer readable recording medium storing a computer program combined with hardware to execute the method of claim 1.

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