KR20230097731A - APPARATUS AND METHOD for TRADING NFT token for golf content - Google Patents

Abstract

This specification provides a method for generating a Non-Fungible Token (NFT) token for golf content.
More specifically, the method includes digitizing, by a terminal, golf content to issue an NFT token; Transmitting, by a terminal, NFT token creation information including information about the digitized golf content, ownership information of the golf content, and detailed information of the golf content to a server; Generating, by the server, an NFT token corresponding to a unique identifier related to golf content and a code corresponding to the unique identifier based on the NFT token generation information; and transmitting, by the server, the generated NFT token to a distributed management system including a blockchain.

Description

Method for trading NFT coin for golf content and device supporting it {APPARATUS AND METHOD for TRADING NFT token for golf content}

This specification relates to a method for trading NFT tokens, and in particular, to a method for trading NFT tokens for golf content and an apparatus supporting the same.

Recently, asset values for various objects such as various contents or products have been created, and through this, the case of managing or investing in assets is increasing. In particular, objects such as ancient currency, artworks, and works of art created in the past are difficult to measure the actual asset value, and their asset value is rapidly increasing in the present situation compared to the past, attracting attention as an investment target.

In the case of content such as ancient currency, artwork, and works of art, there are various third-party organizations to determine the asset value of the content, and the asset value of the content is evaluated through the third-party organizations to determine the asset value of the content. customers are guided. In addition, a price corresponding to the asset value may be determined by proceeding with an auction procedure for owning the content beyond the asset value judged by third parties.

However, since the content has asset value, it can be subject to transaction with others, and in the process of trading the content, a method of clearly confirming ownership and then delivering it is required. To this end, a technology or method capable of verifying or transacting ownership information on a corresponding content using a blockchain-based NFT (Non-Fungible Token) has been proposed.

The purpose of this specification is to provide a method for trading NFT tokens for golf content using blockchain and NFT technology.

This specification provides a method for trading NFT coins for golf content.

More specifically, the method includes receiving, by the server, an NFT coin transaction request from the second terminal; Receiving, by the server, a response including acceptance of the NFT coin transaction request; Requesting, by the server, information related to NFT coin transactions to a first terminal; Receiving, by the server, information related to the NFT coin transaction from the first terminal; By the server, when the NFT transaction is terminated, transmitting transaction history information on the NFT token in which the transaction occurred to a distributed management system including a block chain; And by the distributed management system, characterized in that it comprises the step of recording the transaction history for the NFT token in the blockchain.

This specification has an effect of popularizing and activating golf services through a method of trading NFT tokens for golf content using blockchain and NFT technology.

On the other hand, the effects obtainable in this specification are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below. You will be able to.

1 is a diagram showing an example of a hash tree structure of a blockchain.
2 is a diagram showing an example of a conceptual diagram for performing NFT token issuance and NFT coin transactions proposed in this specification.
3 is a diagram showing an example of a perceptron structure to which the method proposed in this specification can be applied.
4 is a diagram showing an example of a multilayer perceptron structure to which the method proposed in this specification can be applied.
5 is a diagram showing an example of a deep neural network to which the method proposed in this specification can be applied.
6 is a diagram showing an example of a convolutional neural network to which the method proposed in this specification can be applied.
7 is a diagram illustrating an example of a filter operation in a convolutional neural network to which the method proposed in this specification can be applied.
8 is a diagram showing an example of a neural network structure in which a circular loop to which the method proposed in this specification can be applied exists.
9 is a diagram showing an example of an operating structure of a recurrent neural network to which the method proposed in this specification can be applied.
10 is an example of a DNN model to which the present invention can be applied.
11 is a block diagram of an AI device according to an embodiment of the present invention.
12 is a flowchart illustrating an example of a method of issuing an NFT token related to a golf service proposed in this specification.
13 is a flowchart illustrating an example of a method for trading NFT coins related to golf services proposed in this specification.
14 shows an example of an internal block diagram of a device to which the methods proposed in this specification can be applied.

The foregoing objects, features and advantages of this specification will become more apparent from the following detailed description taken in conjunction with the accompanying drawings. However, the present specification may apply various changes and may have various embodiments. Hereinafter, specific embodiments will be illustrated in the drawings and described in detail. Like reference numerals designate essentially like elements throughout the specification. In addition, if it is determined that a detailed description of a known function or configuration related to the present specification may unnecessarily obscure the gist of the present specification, the detailed description thereof will be omitted.

Hereinafter, the method and apparatus related to the present specification will be described in more detail with reference to the drawings. 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.

Blockchain and Non-Fungible Tokens (NFTs)

A brief look at blockchain technology.

A blockchain or network of blockchains, also known as a distributed ledger system (DLS) or consensus system, can enable participating entities to securely and immutably store data. Blockchains can include any DLS without reference to any particular use case, and can be used for public, private, and consortium blockchains. A public blockchain is open for any entity to use the system to participate in the consensus process. Private blockchains are provided to specific entities that centrally control read and write permissions. Consortium blockchains provide a group of selected entities that control the consensus process and include an access control layer.

A blockchain system maintains one or more blockchains. Blockchain is a data structure for storing data such as transactions, and can prevent malicious parties from tampering with data.

In normal recording of transaction data based on the blockchain system, the transaction data is provided synchronously and in real time to the blockchain system for recording. For example, as soon as a transaction data system obtains transaction data, it uploads the data to the blockchain system without checking the state of the blockchain system. As such, sometimes a blockchain system can become overloaded or even stall.

1 is a diagram showing an example of a hash tree structure of a blockchain.

1A is an example of a hash tree structure for one block, and FIG. 1B shows an example of a hash tree structure for a plurality of blocks.

A block chain combines multiple transaction details to form a block, connects this block to the end of the existing block chain, and distributes and stores it on multiple computers in a peer-to-peer (P2P) manner. In other words, the first thing to do to create a block chain is to combine multiple transaction records to form a single block. The minimum unit of a transaction is a transaction (abbreviated as 'Tx'), and a transaction refers to the minimum unit of business processing that cannot be further divided.

When a transaction occurs online, such as the Internet, one transaction details are configured for each transaction. This transaction is encrypted using a hash function. A hash function is an algorithm that maps data of variable length to data of fixed length. When each transaction is displayed as text, the length may be different, but when converted to a hash function, a hash value of a constant length always comes out. For example, when using the SHA-256 hash function, the hash value is always displayed as a value with a fixed length of 256 binary digits (i.e., 64 hexadecimal digits) no matter how short or long the transaction history is. Through this hash calculation process, a 1:1 structure is formed in which one transaction corresponds to one hash value.

Blockchain combines multiple transactions to form a single block. Regardless of whether there are many or few transactions, a new block is always formed once at a certain time. In the case of Bitcoin, a representative cryptocurrency applying block chain technology, a new block is formed about once every 10 minutes. One block has one root hash. The root hash is the final hash value obtained by converting all sub-hashes back to the hash function. To generate a root hash, first, a hash value corresponding to each transaction in a 1:1 ratio is generated, the two hashes are combined to create one higher hash, and the two upper hashes are combined to generate a higher hash again. generate If the number of hashes is odd, the last hash performs a hash operation with itself. If this process is repeated continuously, one root hash is finally created at the top of the tree. This hash value structure is called a hash tree or a Merkle tree. One block always has one root hash. If even one of the transaction details is forged, the corresponding hash value and all hash values above it are changed, resulting in a different root hash. Therefore, by comparing only the root hash of a block, it is possible to immediately check whether the data has been forged or tampered with without having to compare and inspect the hashes under the block one by one.

Newly constructed blocks are time stamped by the timestamp server and propagated throughout the network. A timestamp is a kind of electronic stamp that provides 'existence proof' that an electronic document existed at a specific point in time and 'content proof' that data has not been changed since then.

When a new block is formed, this new block must be connected to the end of the previous block chain, like a chain. To attach a new block to the end of an existing blockchain, it is necessary to find the hash value corresponding to the block's name. If a new hash value is successfully found, a new block is created and linked to the existing blockchain.

Finding the hash corresponding to the name of the newly formed block is a very laborious process that requires countless attempts. This is because the hash of the new block must satisfy the condition that it must be smaller than the target value pre-determined by the program. For example, assuming that the target hash value is 00ff32 and the hash value of a newly created block is 12fa3b, block generation fails because this value is greater than the target value. However, assuming that the newly found hash value is 00c3b1, this value is smaller than the target value, so the creation of a new block succeeds.

In general, the hash value of a block is created by combining the block's date and time, version, bits, root hash, previous block's hash, and a temporary value called a nonce, and converting it into a hash. The generation date, version, difficulty, root hash, and hash value of the previous block of the block are already fixed and have fixed values, but as the temporary value called the nonce changes, the block hash value generated as a result of hash operation will also vary. can For example, the hash value when the nonce is 1 and the hash value when the nonce is 2 are completely different. This nonce value is changed countless times and assigned one by one, and when the newly created hash value is smaller than a certain target value, a new block is successfully created. The act of creating a new block by finding a hash value with a size smaller than the target value for a specific block is called Proof of Work (PoW). Receiving a certain number of cryptocurrencies in exchange for proof-of-work is called mining or mining.

Blockchain technology can be used in various fields such as cryptocurrency, smart contract, logistics management, document management, medical information management, copyright management, social media management, game item management, electronic voting, and identification.

Next, "NFT" or "Non-Fungible Token" is a cryptographic asset, cryptographic token or digital ledger object that is recorded on a blockchain as discussed above. NFTs are indivisible and non-interchangeable. A given NFT has unique information, properties or characteristics, such as permanent and unalterable metadata that describe or define its authenticity. NFTs are transferable in terms of ownership. In contrast, other “fungible” cryptocurrencies, such as Bitcoin, are identical to each other and can be traded or exchanged in equal units and are generally subdivided infinitely. However, NFTs are unique and cannot be subdivided. It can function as a unique certificate of authenticity, making it suitable for commercializing or “tokenizing” digital assets such as copyrighted works.

NFT tokens correspond to NFT virtual assets including NFT coins. NFT tokens are issued and traded for a specific purpose or role, and NFT coins can be interpreted as being used as a payment and payment method for economic activities like currency. However, it can be understood that NFT tokens or NFT coins used in this specification are interpreted in the same meaning unless otherwise specified.

2 is a diagram showing an example of a conceptual diagram for performing NFT token issuance and NFT coin transactions proposed in this specification.

As shown in FIG. 2, the system proposed in this specification digitizes non-fungible assets such as sports, artworks, artworks, characters, and items through a terminal, and provides information about the assets and ownership of the assets. The NFT token is issued by sending it to the NFT token generating device, and the profits issued by trading through contracts such as transfer of ownership to the NFT coin buyer are paid to the original NFT token owner, and the NFT token creation and transaction history is recorded in a distributed system (100 ) is recorded and stored in the blockchain.

The NFT token issuance and NFT coin trading method proposed in this specification to be described later may be performed in combination with the AI of FIGS. 3 to 11.

That is, functions such as data analysis, search, and storage performed in an NFT token generating device, an NFT coin trading device, an NFT server, and a server may be combined with the AI function or AI device described in FIGS. 3 to 11. Can be performed.

Artificial Intelligence (AI)

In relation to the 4th industrial revolution, the most important and newly introduced technology for 5G and 6G systems is AI. AI was not involved in the 4G system. 5G systems will support partial or very limited AI. However, the 6G system will be AI-enabled for full automation. Advances in machine learning will create more intelligent networks for real-time communication in 6G. AI can use a plethora of analytics to determine how complex target tasks are performed. In other words, AI can increase efficiency and reduce processing delays.

AI can also play an important role in M2M, machine-to-human and human-to-machine communications. In addition, AI can be a rapid communication in BCI (Brain Computer Interface). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

Hereinafter, machine learning will be described in more detail.

Machine learning refers to a set of actions that train a machine to create a machine that can do tasks that humans can or cannot do. Machine learning requires data and a running model. In machine learning, data learning methods can be largely classified into three types: supervised learning, unsupervised learning, and reinforcement learning.

Neural network training is aimed at minimizing errors in the output. Neural network learning repeatedly inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and backpropagates the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error. ) to update the weight of each node in the neural network.

Supervised learning uses training data in which correct answers are labeled in the learning data, and unsupervised learning may not have correct answers labeled in the learning data. That is, for example, learning data in the case of supervised learning related to data classification may be data in which each learning data is labeled with a category. Labeled training data is input to the neural network, and an error may be calculated by comparing the output (category) of the neural network and the label of the training data. The calculated error is back-propagated in a reverse direction (ie, from the output layer to the input layer) in the neural network, and the connection weight of each node of each layer of the neural network may be updated according to the back-propagation. The amount of change in the connection weight of each updated node may be determined according to a learning rate. The neural network's computation of input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of iterations of the learning cycle of the neural network. For example, a high learning rate is used in the early stages of neural network learning to increase efficiency by allowing the neural network to quickly achieve a certain level of performance, and a low learning rate can be used in the late stage to increase accuracy.

The learning method may vary depending on the characteristics of the data. For example, in a case where the purpose of the receiver is to accurately predict data transmitted by the transmitter in a communication system, it is preferable to perform learning using supervised learning rather than unsupervised learning or reinforcement learning.

The learning model corresponds to the human brain, and the most basic linear model can be considered. ) is called

The neural network cord used as a learning method is largely divided into deep neural networks (DNN), convolutional deep neural networks (CNN), and recurrent Boltzmann Machine (RNN). there is.

An artificial neural network is an example of connecting several perceptrons.

3 is a diagram showing an example of a perceptron structure to which the method proposed in this specification can be applied.

Referring to FIG. 3, when the input vector x=(x1,x2,...,xd) is input, each component is multiplied by a weight (W1,W2,...,Wd), and after summing up the results, The entire process of applying the activation function σ(·) is called a perceptron. The huge artificial neural network structure may extend the simplified perceptron structure shown in FIG. 3 and apply input vectors to different multi-dimensional perceptrons. For convenience of description, an input value or an output value is referred to as a node.

Meanwhile, the perceptron structure shown in FIG. 3 can be described as being composed of a total of three layers based on input values and output values. An artificial neural network in which H number of (d + 1) dimensional perceptrons exist between the 1st layer and the 2nd layer and K number of (H + 1) dimensional perceptrons between the 2nd layer and the 3rd layer can be expressed as shown in FIG. 3 .

4 is a diagram showing an example of a multilayer perceptron structure to which the method proposed in this specification can be applied.

The layer where the input vector is located is called the input layer, the layer where the final output value is located is called the output layer, and all the layers located between the input layer and the output layer are called hidden layers. In the example of FIG. 4 , three layers are disclosed, but when counting the number of layers of an actual artificial neural network, since the count excludes the input layer, it can be regarded as a total of two layers. The artificial neural network is composed of two-dimensionally connected perceptrons of basic blocks.

The above-described input layer, hidden layer, and output layer can be jointly applied to various artificial neural network structures such as CNN and RNN, which will be described later, as well as multi-layer perceptrons. As the number of hidden layers increases, the artificial neural network becomes deeper, and a machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model is called deep learning. In addition, the artificial neural network used for deep learning is called a deep neural network (DNN).

5 is a diagram showing an example of a deep neural network to which the method proposed in this specification can be applied.

The deep neural network shown in FIG. 5 is a multi-layer perceptron composed of 8 hidden layers + 8 output layers. The multilayer perceptron structure is expressed as a fully-connected neural network. In a fully-connected neural network, there is no connection relationship between nodes located on the same layer, and a connection relationship exists only between nodes located on adjacent layers. DNN has a fully-connected neural network structure and is composed of a combination of multiple hidden layers and activation functions, so it can be usefully applied to identify the correlation characteristics between inputs and outputs. Here, the correlation characteristic may mean a joint probability of input and output.

On the other hand, depending on how a plurality of perceptrons are connected to each other, various artificial neural network structures different from the aforementioned DNN can be formed.

6 is a diagram showing an example of a convolutional neural network to which the method proposed in this specification can be applied.

In DNN, nodes located inside one layer are arranged in a one-dimensional vertical direction. However, in FIG. 6, it can be assumed that the nodes are two-dimensionally arranged with w nodes horizontally and h nodes vertically (convolutional neural network structure of FIG. 5). In this case, a weight is added for each connection in the connection process from one input node to the hidden layer, so a total of hХw weights must be considered. Since there are hХw nodes in the input layer, a total of h2w2 weights are required between two adjacent layers.

The convolutional neural network of FIG. 6 has a problem in that the number of weights increases exponentially according to the number of connections, so instead of considering all mode connections between adjacent layers, it is assumed that there is a filter with a small size. As shown in , weighted sum and activation function calculations are performed for overlapping filters.

7 is a diagram illustrating an example of a filter operation in a convolutional neural network to which the method proposed in this specification can be applied.

One filter has weights corresponding to the number of filters, and learning of weights can be performed so that a specific feature on an image can be extracted as a factor and output. In FIG. 6, a filter having a size of 3Х3 is applied to the 3Х3 area at the top left of the input layer, and an output value obtained by performing a weighted sum and an activation function operation for a corresponding node is stored in z22.

While scanning the input layer, the filter moves by a certain distance horizontally and vertically, performs weighted sum and activation function calculations, and places the output value at the position of the current filter. This operation method is similar to the convolution operation for images in the field of computer vision, so the deep neural network of this structure is called a convolutional neural network (CNN), and the hidden layer generated as a result of the convolution operation is called a convolutional layer. Also, a neural network having a plurality of convolutional layers is referred to as a deep convolutional neural network (DCNN).

In the convolution layer, the number of weights can be reduced by calculating a weighted sum by including only nodes located in a region covered by the filter from the node where the current filter is located. This allows one filter to be used to focus on features for a local area. Accordingly, CNN can be effectively applied to image data processing in which a physical distance in a 2D area is an important criterion. Meanwhile, in the CNN, a plurality of filters may be applied immediately before the convolution layer, and a plurality of output results may be generated through a convolution operation of each filter.

Meanwhile, there may be data whose sequence characteristics are important according to data attributes. Considering the length variability and precedence relationship of these sequence data, input each element on the data sequence one by one at each time step, and input the output vector (hidden vector) of the hidden layer output at a specific time point together with the next element on the sequence A structure in which this method is applied to an artificial neural network is called a recurrent neural network structure.

8 is a diagram showing an example of a neural network structure in which a circular loop to which the method proposed in this specification can be applied exists.

Referring to FIG. 8, a recurrent neural network (RNN) assigns an element (x1(t), x2(t), ,..., xd(t)) of a line t on a data sequence to a fully connected neural network. During the input process, the immediately preceding time point t-1 is a structure in which a weighted sum and an activation function are applied by inputting the hidden vectors (z1(t1), z2(t1), ..., zH(t1)) together. The reason why the hidden vector is transmitted to the next time point in this way is that information in the input vector at previous time points is regarded as being accumulated in the hidden vector of the current time point.

9 is a diagram showing an example of an operating structure of a recurrent neural network to which the method proposed in this specification can be applied.

Referring to FIG. 9 , the recurrent neural network operates in a sequence of predetermined views with respect to an input data sequence.

When the input vector (x1(t), x2(t), ,..., xd(t)) at time 　1 is input to the recurrent neural network, the hidden vector 　(z1(1),z2(1),.. .,zH(1)) is input together with the input vector of time 　2 (x1(2),x2(2),...,xd(2)), and the vector of the hidden layer 　 (z1( 2),z2(2) ,...,zH(2)). This process is repeatedly performed until time point 2, point 3, ,,, point T.

Meanwhile, when a plurality of hidden layers are arranged in a recurrent neural network, it is referred to as a deep recurrent neural network (DRNN). Recurrent neural networks are designed to be usefully applied to sequence data (eg, natural language processing).

As a neural network core used as a learning method, in addition to DNN, CNN, and RNN, Restricted Boltzmann Machine (RBM), deep belief networks (DBN), and Deep Q-Network It includes various deep learning techniques such as computer vision, voice recognition, natural language processing, and voice/signal processing.

Recently, there have been attempts to integrate AI with wireless communication systems, but these have been focused on the application layer and network layer, especially deep learning in the field of wireless resource management and allocation. However, these studies are gradually developing into the MAC layer and the physical layer, and in particular, attempts to combine deep learning with wireless transmission are appearing in the physical layer. AI-based physical layer transmission means applying a signal processing and communication mechanism based on an AI driver rather than a traditional communication framework in fundamental signal processing and communication mechanisms. For example, deep learning-based channel coding and decoding, deep learning-based signal estimation and detection, deep learning-based MIMO mechanism, AI-based resource scheduling and may include allocations, etc.

10 is an example of a DNN model to which the present invention can be applied.

A deep neural network (DNN) is an artificial neural network (ANN) composed of several hidden layers between an input layer and an output layer. Deep neural networks can model complex non-linear relationships, just like regular artificial neural networks.

For example, in a deep neural network structure for an object identification model, each object may be represented as a hierarchical composition of basic image elements. In this case, the additional layers may consolidate the characteristics of the gradually gathered lower layers. This feature of deep neural networks allows complex data to be modeled with fewer units (units, nodes) compared to similarly performed artificial neural networks.

As the number of hidden layers increases, the artificial neural network is called “deep.” The machine learning paradigm that uses a sufficiently deep artificial neural network as a learning model is called deep learning. In addition, a sufficiently deep artificial neural network used for such deep learning is collectively referred to as a deep neural network (DNN).

In the present invention, data required for training a POI data generation model may be input to the input layer of the DNN, and meaningful data usable by the user may be generated through the output layer while passing through hidden layers.

Although the artificial neural network used for this deep learning method is collectively referred to as DNN in the specification of the present invention, other deep learning methods can be applied as long as meaningful data can be output in a similar way.

AI device block diagram

11 is a block diagram of an AI device according to an embodiment of the present invention.

The AI device 20 may include an electronic device including an AI module capable of performing AI processing or a server including the AI module. In addition, the AI device 20 may be included in the configuration of at least a portion of the electronic device and may be provided to perform at least a portion of the AI processing together.

The AI device 20 may include an AI processor 21, a memory 25 and/or a communication unit 27.

The AI device 20 is a computing device capable of learning a neural network, and may be implemented in various electronic devices such as a server, a desktop PC, a notebook PC, and a tablet PC.

The AI processor 21 may learn a neural network using a program stored in the memory 25 . In particular, the AI processor 21 may learn a neural network for recognizing image-related data. Here, the neural network for recognizing image-related data may be designed to simulate the structure of the human brain on a computer, and may include a plurality of network nodes having weights that simulate neurons of the human neural network. A plurality of network modes may transmit and receive data according to a connection relationship, respectively, so as to simulate synaptic activity of neurons that transmit and receive signals through synapses. Here, the neural network may include a deep learning model developed from a neural network model. In the deep learning model, a plurality of network nodes may exchange data according to a convolution connection relationship while being located in different layers. Examples of neural network models are deep neural networks (DNN), convolutional deep neural networks (CNN), recurrent Boltzmann machines (RNNs), restricted Boltzmann machines (RBMs), deep trust It includes various deep learning techniques such as deep belief networks (DBN) and deep Q-network, and can be applied to fields such as computer vision, voice recognition, natural language processing, and voice/signal processing.

Meanwhile, the processor performing the functions described above may be a general-purpose processor (eg, CPU), or may be an AI-only processor (eg, GPU) for artificial intelligence learning.

The memory 25 may store various programs and data necessary for the operation of the AI device 20 . The memory 25 may be implemented as a non-volatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), or a solid state drive (SDD). The memory 25 is accessed by the AI processor 21, and reading/writing/modifying/deleting/updating of data by the AI processor 21 can be performed. In addition, the memory 25 may store a neural network model (eg, the deep learning model 26) generated through a learning algorithm for data classification/recognition according to an embodiment of the present invention.

Meanwhile, the AI processor 21 may include a data learning unit 22 that learns a neural network for data classification/recognition. The data learning unit 22 may learn criteria regarding which training data to use to determine data classification/recognition and how to classify and recognize data using the training data. The data learning unit 22 may acquire learning data to be used for learning and learn the deep learning model by applying the obtained learning data to the deep learning model.

The data learning unit 22 may be manufactured in the form of at least one hardware chip and mounted on the AI device 20 . For example, the data learning unit 22 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or manufactured as a part of a general-purpose processor (CPU) or a graphics-only processor (GPU) for the AI device 20. may be mounted. Also, the data learning unit 22 may be implemented as a software module. When implemented as a software module (or a program module including instructions), the software module may be stored in a computer-readable, non-transitory computer readable recording medium (non-transitory computer readable media). In this case, at least one software module may be provided by an Operating System (OS) or an application.

The data learning unit 22 may include a training data acquisition unit 23 and a model learning unit 24 .

The training data acquisition unit 23 may acquire training data required for a neural network model for classifying and recognizing data. For example, the learning data acquisition unit 23 may acquire vehicle data and/or sample data to be input to a neural network model as learning data.

The model learning unit 24 may learn to have a criterion for determining how to classify predetermined data by using the obtained training data. In this case, the model learning unit 24 may learn the neural network model through supervised learning using at least some of the learning data as a criterion. Alternatively, the model learning unit 24 may learn the neural network model through unsupervised learning in which a decision criterion is discovered by self-learning using learning data without guidance. In addition, the model learning unit 24 may learn the neural network model through reinforcement learning using feedback about whether the result of the situation judgment according to learning is correct. In addition, the model learning unit 24 may train the neural network model using a learning algorithm including error back-propagation or gradient decent.

When the neural network model is learned, the model learning unit 24 may store the learned neural network model in memory. The model learning unit 24 may store the learned neural network model in a memory of a server connected to the AI device 20 through a wired or wireless network.

The data learning unit 22 further includes a training data pre-processing unit (not shown) and a learning data selection unit (not shown) to improve the analysis result of the recognition model or save resources or time required for generating the recognition model. You may.

The learning data pre-processing unit may pre-process the acquired data so that the acquired data can be used for learning for situation determination. For example, the learning data pre-processing unit may process the acquired data into a preset format so that the model learning unit 24 can use the acquired learning data for learning for image recognition.

In addition, the learning data selector may select data necessary for learning from among the learning data acquired by the learning data acquisition unit 23 or the learning data preprocessed by the preprocessor. The selected learning data may be provided to the model learning unit 24 . For example, the learning data selection unit may select only data about an object included in a specific region as training data by detecting a specific region among acquired images.

In addition, the data learning unit 22 may further include a model evaluation unit (not shown) to improve the analysis result of the neural network model.

The model evaluation unit inputs evaluation data to the neural network model, and when an analysis result output from the evaluation data does not satisfy a predetermined criterion, it may cause the model learning unit 22 to learn again. In this case, the evaluation data may be predefined data for evaluating the recognition model. For example, the model evaluator may evaluate that the predetermined criterion is not satisfied when the number or ratio of the evaluation data for which the analysis result is inaccurate among the analysis results of the learned recognition model for the evaluation data exceeds a preset threshold. there is.

The communication unit 27 may transmit the AI processing result by the AI processor 21 to an external electronic device.

Here, the external electronic device is a device capable of wired/wireless communication with the AI device, and may include a broadcasting device and a viewer device to be described later. In this case, the AI device may be implemented in the manager device.

On the other hand, the AI device 20 shown in FIG. 11 has been functionally divided into an AI processor 21, a memory 25, a communication unit 27, etc., but the above-described components are integrated into one module and the AI module Note that it can also be called

Hereinafter, a method of issuing NFT tokens related to golf services proposed in this specification will be described.

12 is a flowchart illustrating an example of a method of issuing an NFT token related to a golf service proposed in this specification.

First, the terminal generates or converts golf content to issue NFT tokens into digital content (S1210).

Then, the terminal transmits NFT coin generation information including the generated digital content, the golf content information, ownership information of the golf content, and detailed information of the golf content to the NFT token generating device (S1220).

Here, the golf content may be a hole-in-one, an albatross, an eagle, a birdie, a club champion, a championship in a golf tournament, a round of golf companions, and the like, as described in each of the above examples.

Here, the ownership information refers to information about the owner who owns or will own golf-related content, and specifically, the owner's name, owner's contact information, owner's age, owner's gender, owner's address, blockchain-based It can include information about the account related to the ID of the owner used in the platform related to the network.

And, the NFT token generation device generates a unique ID and a code corresponding thereto for identifying the golf content based on the received NFT coin generation information (or assigns any one of predefined codes) , Generates an NFT token in which the generated code is recorded (S1230).

In addition, the NFT token generating device may transmit the generated NFT token to a distributed management system (S1240) and store information related to the generated NFT token in a DB.

And, the distributed management system records and stores information related to the NFT token in the blockchain (S1250).

Next, look at the method for trading NFT coins related to golf services proposed in this specification.

13 is a flowchart illustrating an example of a method for trading NFT coins related to golf services proposed in this specification.

First, the NFT coin trading device receives an NFT coin trading request from another terminal (S1310).

And, when the NFT coin trading device receives a response accepting the NFT coin transaction to the NFT coin transaction request from the terminal of the owner holding the NFT token (S1320), the NFT coin trading device is NFT coin trading from the terminal Information on is received (S1330).

Here, the information on the NFT coin transaction may include information on the selling price at the time of NFT coin transaction, royalty when ownership is transferred to a third party, complete or partial transfer of ownership to a third party, and the like.

In addition, when the NFT coin transaction is concluded or terminated, the NFT coin trading device transmits transaction history information on the NFT token in which the transaction occurred to the distributed management system (S1340).

And, the distributed ledger management system records and stores the NFT coin transaction history information in the block chain (S1350).

Here, the NFT coin trading device may be the same as the NFT token generating device described above, or may be implemented separately from the NFT token generating device, and may be included in the golf club server or implemented separately from the golf club server.

Here, the terminal may be referred to as a first terminal, and the other terminal may be referred to as a second terminal.

Hereinafter, a method of issuing an NFT token related to a golf service proposed in this specification and trading the issued NFT token will be looked at through various embodiments.

That is, various embodiments to be described later relate to methods of issuing NFT tokens and trading NFT coins according to golf content.

<First Embodiment>

The first embodiment relates to a method in which a specific person owns and trades golf content in the golf service proposed in this specification using the Salpin blockchain and NFT technology.

First, golf content that can be owned by a specific person in the golf service will be looked at.

First, you can look at hole-in-one, albatross, and eagle records recorded at golf courses or golf clubs (Golf CC) and issuing certificates to specific people.

A hole-in-one certificate for a hole-in-one, an albatross certificate for an albatross, and an eagle certificate for an eagle confer upon a person a hole-in-one, an albatross, or an eagle on a specified hole at a golf club, as an irreplaceable title owned by a specified person. can see.

Currently, the hole-in-one certificate, albatross certificate, and eagle certificate as above are given to a specific person as documents, but NFT technology can be applied by digitizing each certificate.

More specifically, the golf club determines to issue certificates for hole-in-one, albatross, and eagle only for a specific number of holes, and issues NFT tokens (or NFT coins) using NFT technology as many as the determined number of certificates. can

For example, a golf club called A may pre-set 10 hole-in-one certificates that can be issued for a specific period (eg, 1 year) when a hole-in-one is made at hole 3 corresponding to PAR 3. Then, the issued 10 hole-in-one certificates can be digitized and 10 NFT tokens can be issued through NFT technology.

Here, when a plurality of tokens with a specific number of NFT tokens are issued rather than one, a plurality of coins corresponding to the same type may be issued to distinguish the plurality of tokens by changing the size of the code.

That is, a plurality of tokens can be generated by setting the basic code size for a plurality of tokens and making the code size longer or shorter by a certain length for tokens to be issued sequentially, and according to the length of the plurality of tokens. In the future, it may be reflected in the token by changing the value of the token, that is, by applying a specific weight.

Assuming that only 10 hole-in-one certificates are given to hole-in-one certificates for 1 year in hole-in-one certificates for hole 3 corresponding to PAR 3 of golf club A, NFT technology determines the number of NFT tokens to be 10, corresponding to the number of hole-in-one certificates in hole 3. issue

Therefore, the first person to make a hole-in-one in hole 3 will have the first issued NFT token, and will own the NFT token composed of the smallest size. The next person to hole-in-one will have the second NFT token, You own an NFT token whose size is increased by a certain size from the size of the first NFT token.

In addition, since the hole-in-one certificate for a hole-in-one during a specific period, that is, the number of corresponding NFT tokens is fixed, if the number of hole-in-ones exceeding the number of NFT coins issued during a specific period is issued, the number of NFT tokens can be additionally increased. However, in order to increase the value of NFT tokens for hole-in-one, it may be desirable to allow owners who already own NFT tokens to trade their own NFT tokens without issuing more NFT tokens than a predetermined number.

In other words, an owner who already owns an NFT token sells all or part of his/her NFT token, or allows someone else to own the NFT token for a certain period of time and receives a certain amount of royalty for a certain period of time. You can do a token or coin transaction in the form of NFT token collateral that acquires this NFT token.

The specifics of the NFT token or coin trading method will be described later, and the trading price of the NFT coin may be determined by the owner of the NFT token. If there are multiple hole-in-one people who want to own the NFT token, The price of NFT coins to be sold can also be determined through an auction procedure.

Here, the royalty for the NFT token can be calculated as a certain percentage of the sales price received when the person who has transferred ownership transfers it to a third party.

Regarding the contents of salpin above, if it is explained that it can be implemented in the device, the NFT token generating device determines in advance the number of NFT tokens that can be issued for a hole-in-one, an albatross, and an eagle for a specific hole of a golf club. Here, when the number of issued NFT tokens is plural, the NFT token generating device changes the size of the tokens to be generated so that the plurality of NFT tokens can be distinguished.

Thereafter, when the NFT token generating device receives a request for generating an NFT token for any one of hole-in-one, albatross, or eagle from the terminal, the NFT token generating device uses the terminal to generate a hole-in-one, Receives golf content information, ownership information, and detailed information related to either Albatross or Eagle, and generates a unique ID and a corresponding NFT token based on the received information. And, the NFT token generating device records and stores information related to the generated NFT token in the blockchain of the distributed ledger management system.

The NFT token generating device may match the unique ID and the NFT token corresponding thereto, and convert and store them into a DB.

The NFT token generation device may be provided separately in the golf club or may be implemented by being included in the server of the golf club.

As another example, since the NFT token generated for the hole-in-one mentioned above can only occur in 4 holes of the golf club, the Albatross and Eagles that can occur in PAR 4 (10) and PAR 5 (4) For each, it is possible to issue NFT tokens corresponding to each of the Albatross certificate or Eagle certificate in the same way as the previous salver.

That is, in golf club A, 10 Albatross certificates and 20 Eagle certificates can be set at the 8th hole corresponding to PAR 5, and NFT tokens corresponding to each Albatross certificate and Eagle certificate can be set. can be issued.

Here, it is necessary to distinguish NFT tokens issued for hole-in-one, albatross, and eagle, and various methods can be used to distinguish each NFT token. As an example, the hole-in-one token is in the form of binary code, 's token can be issued in the form of a barcode, and Eagle's token can be issued in a circular code.

Additionally, although the golf club does not currently issue buddy certificates, NFT tokens may be issued similarly to the salpin method by issuing certificates to buddies for the activation and popularization of golf.

However, since the number of buddies may occur relatively more than the number of hole-in-ones, albatrosses, and eagles in a specific hole, the number of issued certificates and tokens can be relatively increased.

Next, a method of issuing golf contents such as hole-in-one, albatross, eagle, birdie, etc. in the salpin golf service as an NFT token using the corresponding NFT technology will be described in more detail.

That is, the golf content information, ownership information, and detailed golf content information required by the NFT token generation device to generate the NFT token are (in case of hole-in-one) the name of the golf course where the hole-in-one was made, the number of the hole where the hole-in-one was made, and Companion, caddy at the time of hole-in-one, handicap at hole-in-one, date of hole-in-one, name of person who made hole-in-one, brand of golf ball used for hole-in-one, type of club used for hole-in-one, distance of hole-in-one, video taken when hole-in-one was made information may be included.

Therefore, the NFT token generating device may receive the above hole-in-one information, digitize it, generate a unique ID and NFT code that can identify it, and issue it as an NFT token. It is recorded and stored in the blockchain of the distributed system.

In addition, the NFT token generating device can convert and manage the NFT tokens generated as above, and convert them into NFT coins so that they can be used in transactions.

Next, based on the above, a method of trading NFT coins when the number of NFT tokens is fixed and no more NFT tokens can be issued will be looked at in detail.

The NFT token generation device manages NFT tokens according to each golf content in a DB, and when the number of issued NFT tokens for a specific golf content is over, a request for NFT token issuance may be received from the terminal. In this case, the NFT token generating device may transmit a message indicating that the NFT token can be traded to the terminal along with a response that the issuance of the NFT token is terminated and the NFT token can no longer be owned by issuing the NFT token.

In this case, when receiving a message requesting an NFT coin transaction from the terminal, the NFT token generating device transmits the NFT coin transaction request to another terminal possessing the NFT token, and the NFT token from the other terminal. When a response approving the transaction request is received, the response is transmitted to the terminal.

Here, when the other terminal transmits a response indicating that the NFT coin is transacted, information required to transact the NFT coin may be transmitted to the NFT token generating device along with the response or separately.

Here, the information required to trade the NFT coin may include information on the sales type of the NFT coin.

That is, the information on the form of NFT coin sales may include information on whether to completely or partially transfer the ownership of the NFT token to a third party, or whether to have it owned by another person only for a certain period of time.

For example, when ownership of NFT tokens is completely transferred to a third party, information necessary for the transaction may include sales price information for transfer of ownership, and when allowing a third party to own NFT tokens only for a certain period of time , Information required for the transaction may include information on a certain period of time and information on monetary compensation (royalty, deposit, etc.) to be received by the original owner during the period to be used.

As discussed above, when transfer of ownership or transfer of ownership occurs during a certain period of time, the NFT token generation device includes information on the transaction history, etc., and transfers the transaction history, including information of the person who newly acquires ownership, to the distributed system. It is recorded and stored as a new transaction history in the blockchain.

In the case of the NFT coin transaction as above, it is also possible to trade with the NFT tokens that the traders have, and it is also possible to pay for the transaction with cash or other replaceable coins such as Bitcoin and Ethereum.

<Second Embodiment>

As a second embodiment, the aforementioned method may be performed by issuing an independent NFT token for each golf club, and all golf clubs issue the same NFT token according to golf content, but the number of golf clubs that perform the corresponding golf service , golf club size, golf holes operated by each club, etc., and certificates for hole-in-one, albatross, eagle or birdie that can be issued in PAR 3, PAR 4, and PAR 5 for all golf clubs and the corresponding certificates By adjusting the number of NFT tokens, it is possible to issue NFT tokens that can be operated as an integrated golf club.

At this time, the number of coins that can be issued in each hole may be differentially paid to the golf club in consideration of the number of members, the number of visitors, and the price of use of the golf club in the number of issued NFT tokens.

For example, it is assumed that the number of registered golf clubs is 3 (A club, B club, C club) to implement the method proposed in this specification.

Let's say Club A has 100 members, 1000 visitors per year, and the usage price is 200,000 won. Club B has 50 members, 500 visitors, and 100,000 won per year. The number of members of Club C is 25, the number of visitors for a year is 250, and the price is 50,000 won.

And, assuming that the number of NFT tokens that can issue NFT tokens corresponding to hole-in-one in PAR 3 hole of 3 golf clubs is 100, the number of members, number of visitors, and usage price of clubs A, B, and C are The number of NFT tokens that can be issued can be set at 6:3:1.

At this time, golf club A can issue 60 NFT tokens, golf club B can issue 30 NFT tokens, and golf club C can issue 10 NFT tokens.

Or, conversely, the ratio of the number of coins that can be issued by clubs A, B, and C can be set at 1:3:6, in which case golf club A can issue 10 NFT tokens, and golf club B can issue 10 NFT tokens. can issue 30 NFT tokens, and C golf club can issue 60 NFT tokens.

The number of NFT tokens to be issued may be advantageous depending on the value of the NFT coin, such as the price of the NFT coin and the degree of transaction.

That is, if benefits such as mortgage loans for golf courses and tax reductions for golf courses are given depending on the number of NFT tokens held, it may be advantageous for the golf club to possess a large number of NFT tokens.

The above salver can be implemented similarly to the above salver by the NFT token generating device, terminal, and distributed system.

<Third Embodiment>

As a third embodiment, a method of issuing NFT tokens using NFT technology for a certificate for a club champion of a golf club (golf course, golf CC) proposed in this specification will be looked at.

First, the golf content of the NFT token to be issued in the golf service proposed in this specification is a club champion that is being recorded by a golf club and providing a certificate.

That is, many golf clubs regularly hold competitions to determine club champions of golf clubs, and issue certificates or certificates to club champions of golf clubs that have won the competitions.

Therefore, the certificate issued by the golf club for the club champion can be digitized and issued as an NFT token through NFT technology.

That is, the golf club may limit the number of club champions to a specific number of people for a specific period of time and issue the number of NFT tokens issued to the limited club champions accordingly.

Therefore, the NFT token generating device generates NFT tokens by applying NFT technology through information on club champions determined in golf clubs, and records and stores information related to the generated NFT tokens in a blockchain of a distributed system.

Here, the information required for NFT token generation may be golf content, ownership information, and detailed information on golf content. For example, golf content may be a club champion, ownership information on a club champion certificate, and details on a club champion. Information includes the name of the golf club to which the club champion belongs, information about the club champion (champion's name, champion's age, champion's address, champion's old age, champion's gender, champion's occupation, champion's height/weight, etc.), champion It can include the date the champion became a champion, the number of champions, the number of champions, and the champion's award history.

In addition, when the number of NFT tokens that can be issued to club champions during a specific period is exhausted, and when a new club champion is created, a transaction may be performed for the NFT tokens owned by the existing club champion according to the selection of the new club champion. .

That is, an owner who owns an NFT token for an existing club champion can sell his/her NFT coin or offer it as collateral for a certain period of time, allowing others to own the NFT token as a new club champion.

The above salver can be implemented similarly to the above salver by the NFT token generating device, terminal, and distributed system.

<Fourth Embodiment>

As a fourth embodiment, NFT tokens for club champions may be separately issued for each golf club, or the number of NFT tokens issued may be determined by making the same number of NFT tokens issued to club champions for all golf clubs.

For example, if there are 100 golf clubs registered for the golf service proposed in this specification, only one club champion can have only one NFT token for a specific period of time for each golf club, so that one NFT token for each golf club can only have

In this case, since only one NFT token can be issued for each golf club, when a new club champion occurs, the NFT tokens held by the existing club champion can be converted into NFT coins according to the selection of the new club champion and/or the existing club champion. can trade

Here, in all golf clubs, if a separate NFT token is issued for the king of kings that can cover the champions of each golf club, the existing club champion acquires the NFT token for the king of kings as above for the new club champion Since the opportunity to do so is passed on together, the above circumstances may be considered when trading NFT coins for club champions, which may affect the NFT coin sales price.

As another embodiment, if all golf clubs are 100, the number of NFT tokens that can be issued for club champions may be different for each golf club according to the size of the golf club and the number of members of the golf club.

That is, when the number of all golf clubs is 100 and the number of NFT tokens that can be issued for club champions is set at 1,000, each golf club can equally own 10 NFT tokens, or the size of the golf club, golf The number of NFT tokens to be issued can be differentiated by considering the number of members of the club.

In the previous match between Salpin Golf Club champions, a certificate can be issued to King Wangjung, and one or a specific number of NFT tokens that can be owned by King Wangjung can be issued.

As seen above, when the predetermined number of NFT tokens are all issued and no new NFT tokens can be issued, the new King of Kings can trade NFT coins with the existing King of Kings by the selection of the existing King of Kings and/or the new King of Kings, When an NFT coin transaction is performed, the transaction history for the NFT coin transaction is recorded and stored as a new transaction history in the blockchain of the distributed system.

Here, the history of the NFT coin may include information on the NFT coin transaction price, the name of the new king, address, age, gender, belonging golf club, the date of the king king decision, the golf club where the king king was held, companion, accompanying caddy, etc. .

The above salver can be implemented similarly to the above salver by the NFT token generating device, terminal, and distributed system.

<Fifth Embodiment>

As a fifth embodiment, a method of issuing a certificate for a winner of a golf tournament as an NFT token using NFT technology will be described.

In golf competitions, there are numerous golf competitions such as an amateur golf competition, a professional golf competition, and a senior golf competition, and numerous winners are produced in the corresponding golf competition.

In addition, when a golf tournament is won, the association holding the tournament issues a certificate for the winner of the tournament. Accordingly, the certificate issued for the winner of the golf tournament can be issued as an NFT token using NFT technology, and the issued NFT token can be traded.

That is, the golf association holding the golf tournament may determine the number of winners of the golf tournament during a specific period and issue the number of NFT tokens corresponding to the determined number of winners.

Here, the NFT tokens to be issued may be issued for each specific competition, or may be issued as NFT tokens by combining all amateur golf tournaments, or by combining professional golf tournaments and issued as NFT tokens, or all golf tournaments such as amateur and professional tournaments. can be combined and issued as NFT tokens.

Limit the number of winners of the golf tournament to a certain number, limit the number of issuing NFT tokens to the number corresponding to the number of winners, and if NFT tokens cannot be issued when a new winner occurs, the NFT tokens held by the existing winners By enabling trades with new winners, new winners can own the NFT tokens.

Therefore, the NFT token generating device receives information about the winner of the golf tournament (golf content: golf tournament, ownership information: information about the winner, detailed content information), generates and generates a unique ID and a corresponding code based on this. The code, that is, the NFT token, can be recorded and stored in the blockchain of the distributed system.

Here, the information on the golf tournament winner is the date of winning, the winner's name, the winner's gender, the winner's age, the winner's address, the name of the winning golf club, the number of strokes won, the prize money won, the number of wins, the number of wins, Includes name of golf club affiliation, information about the winner's caddy, information about the winner's family, name of golf tournaments won, number of tournaments won, sponsors of tournaments won, past winners of tournaments won, etc. can do.

In addition, when all of the NFT tokens for the winner of the golf tournament are used, the NFT tokens of the previous winner may be traded to the new winner.

At this time, in order to activate the trading of NFT tokens between the existing winner and the new winner, the NFT owned by the existing winner is increased by increasing the revenue that can be obtained from the association hosting the golf tournament, such as the participation fee for participating in the golf tournament and the prize money for winning the golf tournament. In the case of trading coins, trading of NFT coins may be activated by providing a certain amount of compensation in addition to the amount resulting from the NFT coin transaction.

The above salver can be implemented similarly to the above salver by the NFT token generating device, terminal, and distributed system.

<Sixth Embodiment>

As a sixth embodiment, a method of issuing an NFT token related to a golf companion will be described.

Here, the golf content may be a golf companion.

In addition, the type and number of NFT tokens issued by golf companions may vary depending on golf professional players and celebrities (celebrities, talents, etc.).

First, we look at NFT tokens issued when a golf professional is included among golf companions.

The fact that there is a golf pro among golf companions is an opportunity to learn about golf for the other companions who are not golf pros, and they want to own the round of golf as golf content.

Accordingly, the NFT token generation device issues a specific number of NFT tokens for the golf round content accompanying the golf pro.

Specifically, the NFT token generating device receives information about golf round content, ownership information, and detailed information of content from a terminal for golf round content accompanied by a golf pro, and generates and generates an NFT token based on this. Records and stores the history of NFT tokens in the blockchain of the distributed system.

For example, the contents of a golf round accompanied by a golf pro include information about the golf pro, such as the name, gender, and career of the accompanying golf pro, photos with the companion, golf videos on each hole, and the golf pro's lesson on each hole. Comments, brands of golf pro's clothing, information on golf equipment used by golf pros (clubs, golf balls, rangefinders, etc.), conversations with golf pros, names of golf clubs accompanied by golf pros, golf dates accompanied by golf pros , companion information, etc.

The golf image in each hole may include information about the trajectory, distance, and terrain position of the golf ball shot by the golf pro, the speed of the ball putt by the golf pro, and the condition of the green.

And, if the golf pro is famous or the corresponding golf rounding content becomes famous, the ownership of the NFT coin for the golf rounding content can be sold or transferred as collateral for a specific period by trading the NFT coin for the corresponding golf rounding content. .

The above salver can be implemented similarly to the above salver by the NFT token generating device, terminal, and distributed system.

block diagram of device

14 shows an example of an internal block diagram of a device to which the methods proposed in this specification can be applied.

Referring to FIG. 14 , the system proposed in this specification includes a terminal 200 and a server 300 .

The server may include an NFT token generating device, an NFT coin trading device, or the like, or the server may be referred to as an NFT token generating device or an NFT coin trading device.

The terminal 200 includes a processor 211 , a memory 212 and a communication unit 213 .

The processor implements the functions, processes and/or methods proposed in FIGS. 1 to 13 above.

Layers of the air interface protocol may be implemented by a processor.

The memory is connected to the processor and stores various information for driving the processor.

The communication unit is connected to the processor and transmits and/or receives wired/wireless signals.

The server 300 includes a processor 321 , a memory 322 and a communication unit 323 .

The processor implements the functions, processes and/or methods proposed in FIGS. 1 to 13 above.

Layers of the air interface protocol may be implemented by a processor.

The memory is connected to the processor and stores various information for driving the processor.

The communication unit is connected to the processor and transmits and/or receives wired/wireless signals.

The memory may be internal or external to the processor, and may be connected to the processors 711 and 721 by various well-known means.

In addition, a terminal and/or a server may have a single antenna or multiple antennas.

As used herein, the term “unit” (eg, a controller) may refer to a unit including one or a combination of two or more of, for example, hardware, software, or firmware. “Unit” may be used interchangeably with terms such as, for example, unit, logic, logical block, component, or circuit. A "unit" may be a minimum unit of an integrally constituted part or a part thereof. A “unit” may be a minimal unit or part thereof that performs one or more functions. A “unit” may be implemented mechanically or electronically. For example, a "unit" may be any known or future developed application-specific integrated circuit (ASIC) chip, field-programmable gate arrays (FPGAs), or programmable-logic device that performs certain operations. may contain at least one.

At least some of the devices (eg, modules or functions thereof) or methods (eg, operations) according to various embodiments may be stored on computer-readable storage media in the form of, for example, program modules. It can be implemented as a command stored in . When the command is executed by a processor, the one or more processors may perform a function corresponding to the command. The computer-readable storage medium may be, for example, a memory.

Computer-readable storage media / computer-readable recording media include hard disks, floppy disks, magnetic media (eg magnetic tape), optical media (eg CD-ROM (compact) disc read only memory), digital versatile disc (DVD), magneto-optical media (e.g. floptical disk), hardware devices (e.g. read only memory (ROM), random access memory), or flash memory, etc. In addition, program commands may include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter, etc. The hardware devices described above may be configured to act as one or more software modules to perform the operations of various embodiments, and vice versa.

A module or program module according to various embodiments may include at least one or more of the above-described components, some may be omitted, or additional other components may be included. Operations performed by modules, program modules, or other components according to various embodiments may be executed in a sequential, parallel, repetitive, or heuristic manner. Also, some actions may be performed in a different order, omitted, or other actions may be added.

As used herein, the term "a" is defined as one or more than one. Also, the use of introductory phrases such as “at least one” and “one or more” in a claim means that the same claim includes introductory phrases such as “at least one” and “one or more” and ambiguous phrases such as “an”. If any, be construed to mean that the introduction of another claim element by the ambiguous phrase "an" limits any particular claim containing the so-introduced claim element to an invention containing only one such element. It shouldn't be.

Unless otherwise specified, terms such as "first" and "second" are used to arbitrarily distinguish the elements they describe. Thus, these terms are not necessarily intended to indicate a temporal or other order of priority of such elements, and the mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage. . Accordingly, these terms are not necessarily intended to indicate a temporal or other priority of such elements. The mere fact that certain measures are cited in different claims does not indicate that a combination of these measures cannot be useful.

Arrangements of components to achieve the same function are effectively "related" so that the desired function is achieved. Thus, any two components that are combined to achieve a particular functionality may be considered "related" to each other such that the desired function is achieved, regardless of structure or intervening components. Similarly, two components so associated can be considered "operably connected" or "operably coupled" to each other to achieve a desired function.

Further, those skilled in the art will recognize that the boundaries between the functionality of the foregoing operations are exemplary only. A plurality of actions may be combined into a single action, a single action may be distributed into additional actions, and the actions may be executed at least partially overlapping in time. Also, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be changed in various other embodiments. However, other modifications, variations and alternatives are also possible. Accordingly, the detailed description and drawings are to be regarded in an illustrative rather than a limiting sense.

The phrase “may be X” indicates that condition X can be met. This phrase also indicates that condition X may not be met. For example, a reference to a system that contains a specific component should also include a scenario where the system does not contain that specific component. For example, a reference to a method that includes a specific action must also include scenarios in which the method does not include that specific component. But to take another example, a reference to a system configured to perform a specific action should also include a scenario in which the system is not configured to perform the specific action.

The terms "comprising", "having", "consisting of", "consisting of" and "consisting essentially of" are used interchangeably. For example, any method may include at least the operations included in the drawings and/or specifications, and may include only the operations included in the drawings and/or specifications.

It should be understood that the boundaries between logical blocks are merely illustrative, and that alternative embodiments may merge logical blocks or circuit elements or impose alternative decompositions of functionality on various logical blocks or circuit elements. will recognize that there is Accordingly, it should be understood that the architecture shown herein is merely illustrative, and in fact many other architectures that achieve the same functionality may be implemented.

Also, for example, in one embodiment, the illustrated examples may be implemented on a single integrated circuit or as circuitry located within the same device. Alternatively, the above examples may be implemented as any number of discrete integrated circuits or discrete devices interconnected with each other in any suitable manner, and other alterations, modifications, variations and alternatives are also possible. Accordingly, the specification and drawings are to be regarded in an illustrative rather than restrictive sense.

Also, for example, any of the above examples or portions thereof may be implemented as a software or code representation of a physical circuit or a logical representation translatable to a physical circuit, such as any suitable type of hardware description language.

In addition, this specification is not limited to physical devices or units implemented with non-programmable hardware, but is generally referred to herein as a 'computer system' such as mainframes, mini computers, servers, workstations, personal computers, notepads, etc. Programmable devices capable of performing desired device functions by operating in accordance with appropriate program code, such as personal digital assistants (PDAs), electronic games, automobiles and other embedded systems, mobile phones and various other wireless devices. Or it can be applied to units as well.

A system, apparatus or device referred to in this specification includes at least one hardware component.

Connections as described herein may be any type of connection suitable for transmitting a signal from or to a respective node, unit or device via an intermediate device, for example. Thus, unless implicitly or otherwise stated, a connection may be, for example, a direct connection or an indirect connection. A connection may be described or described with reference to a single connection, multiple connections, unidirectional connections, or bidirectional connections. However, different embodiments may change the implementation of connectivity. For example, separate unidirectional connections can be used instead of bidirectional connections, and vice versa. In addition, multiple connections may be replaced by a single connection that transmits a plurality of signals sequentially or in a time multiplexed manner. Similarly, a single connection carrying multiple signals can be broken into multiple connections carrying subsets of these signals. Therefore, many options exist for transmitting signals.

It should be understood that the boundaries between logical blocks are merely illustrative, and that alternative embodiments may merge logical blocks or circuit elements or impose alternative decompositions of functionality on various logical blocks or circuit elements. will recognize that there is Accordingly, it should be understood that the architecture shown herein is merely illustrative, and in fact many other architectures that achieve the same functionality may be implemented.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word 'comprising' does not exclude the presence of any of the recited elements or acts in a claim.

In the above, a preferred embodiment of the technology of this specification has been described with reference to the accompanying drawings. Here, the terms or words used in this specification and claims should not be construed as being limited to ordinary or dictionary meanings, but should be interpreted as meanings and concepts consistent with the technical spirit of the present specification. The scope of the present specification is not limited to the embodiments disclosed herein, and the present specification may be modified, changed, or improved in various forms within the scope described in the spirit and claims of the present specification.

100: distributed system

Claims (1)

In the method of generating a non-fungible token (NFT) token for golf content,
Digitizing, by the terminal, golf content to issue NFT tokens;
Transmitting, by a terminal, NFT token creation information including information about the digitized golf content, ownership information of the golf content, and detailed information of the golf content to a server;
Generating, by the server, an NFT token corresponding to a unique identifier related to golf content and a code corresponding to the unique identifier based on the NFT token generation information; and
A method comprising transmitting, by the server, the generated NFT token to a distributed management system including a blockchain.

Images