KR102142524B1 - Method, apparatus and computer program for forecasting cryptocurrency cost variability using artificial intelligence - Google Patents

Abstract

Provided are a method, device, and computer program for forecasting price variability of cryptocurrency using artificial intelligence. According to various embodiments of the present invention, the method may comprise the steps of: determining use of each of the plurality of cryptocurrency account by using information on cryptocurrency transaction occurred between the plurality of cryptocurrency accounts; collecting information related to the cryptocurrency for each of the plurality of cryptocurrency accounts that the use is determined; and forecasting price variability of the cryptocurrency by analyzing the collected information related to the cryptocurrency.

Description

Method, device, and computer program for predicting cryptocurrency price volatility using artificial intelligence {METHOD, APPARATUS AND COMPUTER PROGRAM FOR FORECASTING CRYPTOCURRENCY COST VARIABILITY USING ARTIFICIAL INTELLIGENCE}

Various embodiments of the present invention relate to a method, apparatus, and computer program for predicting cryptocurrency price volatility using artificial intelligence.

As the cryptocurrency (e.g., bitcoin), which used to be common among a small number of users, gradually gained attention from the media and the general public, the number of users began to increase exponentially, and as a result, the price and transaction volume of the cryptocurrency rapidly increased. A bubble phenomenon occurred and had a profound effect on society.

Various industries using the cryptocurrency system are developing, but the evaluation of the impact this will have on society and the prospects for future development are greatly mixed, and the responses of each country are also showing a large difference. Countries that negatively evaluate the impact of cryptocurrency are trying to mitigate the social impact of the introduction of new technologies by illegally regulating the entire use of cryptocurrency or restricting cryptocurrency use by financial institutions.

Analyzing the price and volume of cryptocurrency, trading volume decreased due to a sharp price drop in early 2018, but trading volume recovered rapidly in 2019, indicating that the global cryptocurrency market has matured after the bubble.

In addition, the value of cryptocurrency has been confirmed as an alternative investment asset in the financial market, and the cryptocurrency trading market is expected to continue to grow in the future, considering that cryptocurrency is actively used in place of legal currency in Brazil, Argentina and Venezuela. do.

As various types of cryptocurrency emerged and trading started through exchanges, many users are directly involved or interested in cryptocurrency trading. As cryptocurrencies are traded in this way, the prospects of various cryptocurrencies are emerging.

However, the prospects for cryptocurrency are simply news, press releases, or predictions, and users are only able to refer to them and it is difficult to give more value.

Korean Patent Registration No. 10-1862000 (2018.05.23)

The problem to be solved by the present invention is a method for predicting cryptocurrency price volatility using artificial intelligence capable of predicting the price volatility of cryptocurrency by using information related to cryptocurrency collected in a plurality of cryptocurrency accounts and cryptocurrency accounts. , To provide devices and computer programs.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

A method for predicting cryptocurrency price volatility using artificial intelligence according to an embodiment of the present invention for solving the above-described problem is a method performed by a computing device, in which cryptocurrency transaction information generated between a plurality of cryptocurrency accounts is Determining a usage for each of the plurality of cryptocurrency accounts by using, collecting information related to cryptocurrency for each of the plurality of cryptocurrency accounts for which the use is determined, and analyzing information related to the collected cryptocurrency It may include predicting the price volatility of the cryptocurrency.

In various embodiments, the step of collecting the information related to the cryptocurrency may include the amount of cryptocurrency holdings of the cryptocurrency account determined as a miners account and Comprising the step of collecting information related to the mining pattern, and predicting the price volatility of the cryptocurrency, predicting the price volatility of the cryptocurrency by analyzing based on the amount of cryptocurrency holdings in the miner's account and the mining pattern It may include the step of.

In various embodiments, the collecting of information related to the cryptocurrency includes collecting information on the total amount of cryptocurrency introduced from the cryptocurrency derivatives exchange, and predicting the price volatility of the cryptocurrency. Includes determining the futures trading volume of the cryptocurrency based on the total amount of cryptocurrency introduced from the cryptocurrency derivatives exchange, and predicting the price volatility of the cryptocurrency using the futures trading volume of the cryptocurrency. I can.

In various embodiments, the step of predicting the price volatility of the cryptocurrency using the futures trading volume of the cryptocurrency may include an increase in the difference between the withdrawal amount and the deposit amount of the cryptocurrency by analyzing the futures trading volume of the cryptocurrency. It may include predicting the magnitude of the price volatility of the cryptocurrency in proportion.

In various embodiments, the step of predicting the magnitude of the price volatility of the cryptocurrency in proportion to an increase in the difference between the withdrawal amount and the deposit amount of the cryptocurrency, predicting that the magnitude of the price volatility of the cryptocurrency is greater than or equal to a reference value. When the short selling amount of the cryptocurrency is greater than the short selling amount at one point in time, the step of predicting that the price of the cryptocurrency will decrease, and the short selling of the cryptocurrency at the time when it is predicted that the amount of price volatility of the cryptocurrency is more than a reference value If the amount is greater than the short purchase amount, predicting that the price of the cryptocurrency will increase.

In various embodiments, the step of predicting the price volatility of the cryptocurrency comprises analyzing the collected cryptocurrency-related information, and the cryptocurrency transaction target according to the amount of cryptocurrency holdings of each of the plurality of cryptocurrency accounts and the cryptocurrency transaction information. It may include predicting the price volatility of the cryptocurrency based on.

In various embodiments, determining the use of each of the plurality of cryptocurrency accounts comprises visualizing cryptocurrency transaction information generated between the plurality of cryptocurrency accounts, analyzing the visualized cryptocurrency transaction information, and It may include determining the usage for each of the cryptocurrency accounts.

In various embodiments, determining the use of each of the plurality of cryptocurrency accounts includes, when the number of the plurality of cryptocurrency accounts included in the visualized cryptocurrency transaction information is less than or equal to a first reference value, the plurality of Analyzing the transaction pattern for each cryptocurrency account to determine the use of each of the plurality of cryptocurrency accounts, and when the number of transactions according to the cryptocurrency transaction information is less than or equal to the second reference value, based on the owner of the cryptocurrency account A pattern for the grouped at least one cryptocurrency account by grouping at least one cryptocurrency account among the plurality of cryptocurrency accounts, and using the cryptocurrency transaction information generated in the at least one grouped cryptocurrency account It may include the step of determining the usage for each of the plurality of cryptocurrency accounts by analyzing.

An apparatus for predicting cryptocurrency price volatility using artificial intelligence according to another embodiment of the present invention for solving the above-described problem includes a memory storing one or more instructions and a processor executing the one or more instructions stored in the memory, , By executing the one or more instructions, the processor may perform a cryptocurrency price volatility prediction method using artificial intelligence according to an embodiment of the present invention.

A computer program according to another embodiment of the present invention for solving the above-described problems is combined with a computer that is hardware, so that a method for predicting cryptocurrency price volatility using artificial intelligence according to an embodiment of the present invention can be performed. It may be stored in a computer-readable recording medium.

Other specific matters of the present invention are included in the detailed description and drawings.

According to various embodiments of the present invention, there is an advantage in that it is possible to accurately predict the price volatility of a cryptocurrency by using the use of each of the plurality of cryptocurrency accounts and information related to cryptocurrency collected in the cryptocurrency account.

The effects of the present invention 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 following description.

1 is a diagram showing a cryptocurrency price volatility prediction system using artificial intelligence according to an embodiment of the present invention.
2 is a hardware configuration diagram of a cryptocurrency price volatility prediction apparatus using artificial intelligence according to another embodiment of the present invention.
3 is a flow chart of a method for predicting cryptocurrency price volatility using artificial intelligence according to another embodiment of the present invention.
4 is a flowchart of a method of classifying a cryptocurrency account performed by an apparatus for predicting volatility of cryptocurrency prices in various embodiments.
5 is a diagram illustrating visualized cryptocurrency transaction information in various embodiments.
6 is a diagram illustrating a configuration for determining a usage of a cryptocurrency account using a graph-based ring map learning model in various embodiments.
FIG. 7 is a diagram illustrating a configuration for determining a usage of a cryptocurrency account using an account address-based supervised learning model in various embodiments.
8 is a diagram illustrating a configuration for determining a usage of a cryptocurrency account using an owner-based ring map learning model in various embodiments.
9 is a diagram illustrating a configuration for determining a usage of a cryptocurrency account by using cryptographic account information and keywords in the form of images obtained from a website in various embodiments.
10 is a table showing the amount of deposits and withdrawals of cryptocurrencies by time in various embodiments.
11 is a graph showing a price change amount of a cryptocurrency according to an amount of deposit and withdrawal of cryptocurrency in various embodiments.

Advantages and features of the present invention, and methods for achieving them will be clarified with reference to embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, only the present embodiments are intended to complete the disclosure of the present invention, It is provided to fully inform the technician of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for describing the embodiments and is not intended to limit the present invention. In the present specification, the singular form also includes the plural form unless otherwise specified in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components other than the components mentioned. Throughout the specification, the same reference numerals refer to the same components, and “and/or” includes each and every combination of one or more of the components mentioned. Although "first", "second", etc. are used to describe various components, it goes without saying that these components are not limited by these terms. These terms are only used to distinguish one component from another component. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used in a sense that can be commonly understood by those skilled in the art to which the present invention pertains. In addition, terms that are defined in a commonly used dictionary are not ideally or excessively interpreted unless specifically defined.

The term "part" or "module" as used in the specification means a hardware component such as software, FPGA or ASIC, and "part" or "module" performs certain roles. However, "part" or "module" is not meant to be limited to software or hardware. The "unit" or "module" may be configured to be in an addressable storage medium, or may be configured to reproduce one or more processors. Thus, as an example, “part” or “module” means components such as software components, object-oriented software components, class components and task components, processes, functions, attributes, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, database, data structures, tables, arrays and variables. Components and functions provided within "sub" or "module" may be combined into a smaller number of components and "sub" or "module" or by additional components and "sub" or "module" Can be further separated.

The spatially relative terms “below”, “beneath”, “lower”, “above”, “upper”, etc., are as shown in the figure. It can be used to easily describe a correlation between a component and other components. The spatially relative terms should be understood as terms including different directions of components in use or operation in addition to the directions shown in the drawings. For example, if a component shown in the drawing is turned over, a component described as "below" or "beneath" of another component will be placed "above" of the other component. Can. Thus, the exemplary term “below” can include both the directions below and above. Components may be oriented in other directions, and thus spatially relative terms may be interpreted according to orientation.

In the present specification, the computer means all kinds of hardware devices including at least one processor, and may be understood as a meaning encompassing a software configuration operating in a corresponding hardware device according to an embodiment. For example, a computer may be understood as meaning including, but not limited to, a smart phone, a tablet PC, a desktop, a notebook, and a user client and application running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each of the steps described herein is described as being performed by a computer, but the subject of each step is not limited thereto, and at least some of the steps may be performed in different devices according to embodiments.

1 is a diagram showing a cryptocurrency price volatility prediction system using artificial intelligence according to an embodiment of the present invention.

The cryptocurrency price volatility prediction system using artificial intelligence according to an embodiment of the present invention shown in FIG. 1 may include a cryptocurrency price volatility prediction apparatus 100, a user terminal 200, and an external server 300. .

Here, the cryptocurrency price volatility prediction system using artificial intelligence shown in FIG. 1 is according to an embodiment, and its components are not limited to the embodiment shown in FIG. 1, and are added, changed, or deleted as necessary. Can be.

In one embodiment, the cryptocurrency price volatility prediction apparatus 100 collects cryptocurrency transaction information (eg, transaction) generated between a plurality of cryptocurrency accounts, and uses the collected cryptocurrency transaction information to You can determine the purpose of each cryptocurrency account (e.g. account type, role).

In various embodiments, the cryptocurrency price volatility prediction apparatus 100 collects cryptocurrency transaction information generated between a plurality of cryptocurrency accounts from an external server 300 (eg, a cryptocurrency transaction intermediary server) through the network 400 It can, and can visualize the collected cryptocurrency transaction information (for example, Figs. 4 and 5), and analyze the visualized cryptocurrency transaction information to determine the use of each of the plurality of cryptocurrency accounts. However, it is not limited thereto.

In various embodiments, the cryptocurrency price volatility prediction apparatus 100 analyzes the visualized cryptocurrency transaction information to determine the use of each of the plurality of cryptocurrency accounts, but according to the analysis result, an individual account, exchanges Account, Miners account, Mixers account, Merchant account, Services account, Darkweb account, and Hedge funds account. However, it is not limited thereto.

In one embodiment, the cryptocurrency price volatility prediction apparatus 100 includes information related to cryptocurrency (eg, cryptocurrency account) for each of a plurality of cryptocurrency accounts whose use is determined by using a predetermined purpose for each of the plurality of cryptocurrency accounts. It is possible to predict the price volatility of cryptocurrency by collecting the balance information and transaction history information) and analyzing the information related to the collected cryptocurrency. For example, the cryptocurrency price volatility prediction apparatus 100 may analyze the collected cryptocurrency-related information to predict that the price will rise or fall. In addition, the cryptocurrency price volatility prediction apparatus 100 may predict the price of the cryptocurrency (eg, an increase in price or a decrease in price). However, it is not limited thereto.

In one embodiment, the user terminal 200 may be connected to the cryptocurrency price volatility prediction apparatus 100 through the network 400, and various information related to the cryptocurrency tax evasion from the cryptocurrency price volatility prediction apparatus 100 ( Example: Information on predicting price volatility of cryptocurrency) can be provided. For example, the user terminal 200 may include at least one of a smartphone, a tablet PC, a laptop, and a desktop having a display in at least a portion of the user terminal 200, and predicts cryptocurrency price volatility through the display. The cryptocurrency price volatility prediction information provided by the cryptocurrency price volatility prediction apparatus 100 may be outputted through a user interface (UI) provided by the device 100. However, it is not limited thereto.

In one embodiment, the external server 300 may be connected to the cryptocurrency price volatility prediction apparatus 100 through the network 400, and the cryptocurrency price volatility prediction apparatus 100 predicts the price volatility of the cryptocurrency. Various necessary information (eg, cryptocurrency transaction information, information related to the use of each of a plurality of cryptocurrency accounts) can be provided. For example, the external server 300 may be a cryptocurrency transaction brokerage server that provides a service that brokers cryptocurrency transactions, and is generated by conducting a transaction between cryptocurrency accounts through a service that brokers cryptocurrency transactions. The cryptocurrency transaction information may be stored and the stored cryptocurrency transaction information may be provided to the cryptocurrency price volatility prediction apparatus 100.

In various embodiments, the external server 300 is provided with various data (eg, information on a purpose determined for each of a plurality of cryptocurrency accounts, information on predicting price volatility of cryptocurrencies) from the cryptocurrency price volatility prediction apparatus 100. Can be saved. For example, the external server 300 may be a storage device separately provided outside the apparatus 100 for predicting volatility of cryptocurrency prices. However, it is not limited thereto.

In various embodiments, the cryptocurrency price volatility prediction apparatus 100 may itself include a storage device (not shown) that stores various types of data generated by the cryptocurrency price volatility prediction apparatus 100. For example, the cryptocurrency price volatility prediction apparatus 100 may systematically store and manage tens of billions of account information and transaction information using a storage device. For example, the cryptocurrency price volatility prediction apparatus 100 may store visualized cryptocurrency transaction information, and may have a graph DB optimized for tracking cryptocurrency funds, and use this to To predict price volatility in real time, the DB's analysis engine can be tuned to a high-performance engine dedicated to graph information. Hereinafter, a hardware configuration of the apparatus 100 for predicting cryptocurrency price volatility will be described with reference to FIG. 2.

2 is a hardware configuration diagram of a cryptocurrency price volatility prediction apparatus using artificial intelligence according to another embodiment of the present invention.

Referring to FIG. 2, a cryptocurrency price volatility prediction apparatus 100 (hereinafter, “computing apparatus 100”) according to another embodiment of the present invention may include a processor 110 and a memory 120. In various embodiments, the computing device 100 may further include a network interface (or communication interface) (not shown), a storage (not shown), and a bus (not shown).

In one embodiment, the processor 110 may control the overall operation of each component of the computing device 100. The processor 110 may be configured to include a CPU (Central Processing Unit), MPU (Micro Processor Unit), MCU (Micro Controller Unit), or any type of processor well known in the art of the present invention.

In various embodiments, the processor 110 may perform an operation on at least one application or program for executing the method according to the embodiments of the present invention. In various embodiments, the processor 110 includes one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus) for transmitting and receiving signals with other components. can do.

In various embodiments, the processor 110 temporarily and/or permanently stores a signal (or data) processed inside the processor 110, and a RAM (Random Access Memory, not shown) and a ROM (ROM). -Only Memory, not shown) may be further included. In addition, the processor 110 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, RAM, and ROM.

In one embodiment, the processor 110 executes one or more instructions stored in the memory 120, so that a method to be described with reference to FIGS. 3 to 11 (eg, a method for predicting cryptocurrency price volatility using artificial intelligence) Can be done. For example, by executing one or more instructions stored in the memory 120, the processor 110 determines a usage for each of the plurality of cryptocurrency accounts using cryptocurrency transaction information generated between a plurality of cryptocurrency accounts. , Cryptocurrency using artificial intelligence comprising the step of collecting information related to cryptocurrency for each of the plurality of cryptocurrency accounts whose purpose is determined, and predicting the price volatility of cryptocurrency by analyzing the information related to the collected cryptocurrency. You can perform the method of predicting price volatility.

In one embodiment, the memory 120 may store various types of data, commands, and/or information. The memory 120 may store programs (one or more instructions) for processing and controlling the processor 110. Programs stored in the memory 120 may be divided into a plurality of modules according to functions.

In various embodiments, steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. Software modules include Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a hardware computer, and stored in a medium. The components of the present invention may be implemented in software programming or software components, and similarly, embodiments include C, C++, including various algorithms implemented in a combination of data structures, processes, routines or other programming components. , Can be implemented in programming or scripting languages such as Java, assembler, etc. Functional aspects can be implemented with algorithms running on one or more processors. Hereinafter, a method for predicting cryptocurrency price volatility using artificial intelligence performed by the computing device 100 will be described with reference to FIG. 3.

3 is a flow chart of a method for predicting cryptocurrency price volatility using artificial intelligence according to another embodiment of the present invention.

Referring to FIG. 3, in step S110, the computing device 100 may determine a usage for each of a plurality of cryptocurrency accounts by using cryptocurrency transaction information generated between a plurality of cryptocurrency accounts.

In various embodiments, the computing device 100 may collect cryptocurrency transaction information generated between a plurality of cryptocurrency accounts, visualize the collected cryptocurrency transaction information, and analyze the visualized cryptocurrency transaction information. You can decide the use of each of the multiple cryptocurrency accounts. Hereinafter, a method for determining the usage of a cryptocurrency account performed by the computing device 100 will be described with reference to FIGS. 4 to 9.

4 is a flowchart of a method of classifying a cryptocurrency account performed by an apparatus for predicting volatility of cryptocurrency prices in various embodiments.

Referring to FIG. 4, in step S210, the computing device 100 may collect cryptocurrency transaction information generated between a plurality of cryptocurrency accounts. For example, the computing device 100 includes cryptocurrency transaction information (eg, transaction) generated between a plurality of cryptocurrency accounts from an external server 300 (eg, a server that mediates cryptocurrency transactions between a plurality of cryptocurrency accounts). Can be provided. However, it is not limited thereto.

In step S220, the computing device 100 may visualize cryptocurrency transaction information generated between a plurality of cryptocurrencies collected in step S110.

In various embodiments, the computing device 100 may visualize cryptocurrency transaction information generated between a plurality of cryptocurrencies in a graph form. However, the present invention is not limited thereto, and the computing device 100 may visualize cryptocurrency transaction information in various forms. Hereinafter, a configuration in which the computing device 100 visualizes cryptocurrency transaction information will be described with reference to FIG. 5.

5 is a diagram illustrating visualized cryptocurrency transaction information in various embodiments.

Referring to FIG. 5, in an embodiment, the computing device 100 may generate a plurality of nodes 10 corresponding to each of a plurality of cryptocurrency accounts. For example, the computing device 100 may create one node corresponding to one cryptocurrency account.

In various embodiments, the computing device 100 may generate a node in a square shape having a predetermined size, but is not limited thereto, and various types (for example, based on various information about each of a plurality of cryptocurrency accounts) Round, oval, rectangular).

In various embodiments, the computing device 100 generates a node corresponding to each of a plurality of cryptocurrency accounts, but a node corresponding to each of the plurality of cryptocurrency accounts based on the usage of the cryptocurrency account determined through step S240 to be described later. The properties of can be determined. For example, the computing device 100 may configure a node 11 corresponding to a cryptocurrency account (eg, a black market account, a fraudulent account, and a gambling account) used for crime and fraud based on the use of each of the plurality of cryptocurrency accounts. The first color (eg, red) may be set, and the node 12 corresponding to the cryptocurrency account associated with the criminal and fraudulent account may be set to a second color (eg, yellow). In addition, the computing device 100 may set the node 13 corresponding to the normally used cryptocurrency account (eg, exchange account, mining account, and commerce account) to a third color (eg, white). Through this, a user who receives and monitors visualized cryptocurrency transaction information can more easily track and monitor cryptocurrency accounts used for crime and fraud. However, it is not limited thereto.

In an embodiment, the computing device 100 may connect a cryptocurrency account of a sender and a cryptocurrency account of a receiver among nodes corresponding to a plurality of cryptocurrency accounts in an edge based on cryptocurrency transaction information. For example, when it is determined that remittance has been made from a first cryptocurrency account to a second cryptocurrency account among a plurality of cryptocurrency accounts based on cryptocurrency transaction information, the computing device 100 corresponds to the first cryptocurrency account. The node 11 and the node 12 corresponding to the second cryptocurrency account may be connected by a line.

In various embodiments, the computing device 100 connects a line (for example, a line in the form of an arrow) from the node of the sender's cryptocurrency account to the node of the receiver's cryptocurrency account, and connects the cryptocurrency at a position adjacent to the connected line. Transaction quantity, number of transactions, and transaction time information according to the transaction information can be output together. However, it is not limited thereto.

According to an embodiment, the computing device 100 is an edge connecting the node of the sender's cryptocurrency account and the receiver's cryptocurrency account according to the transaction quantity, the number of transactions, and the transaction time information according to the cryptocurrency transaction information. You can change the shape of For example, the thickness of the line may change depending on the amount of cryptocurrency transaction (or transaction amount), the shape of the line (e.g., broken line, double line, etc.) may change depending on the number of cryptocurrency transactions. The length of the line may change depending on the transaction frequency, but is not limited thereto.

The shape of the line connecting the nodes may be variously set to visualize and read at least a part of the transaction quantity, the number of transactions, and the transaction time information according to the cryptocurrency transaction information according to a preset rule.

The computing device 100 may extract various feature values based on the connection relationship between nodes, the characteristics of each node, and the shape of a line connecting each node by using a pre-learned artificial intelligence model. You can determine the properties of the corresponding account.

In an embodiment, the computing device 100 may determine a property of a transaction corresponding to each line by using a pre-learned artificial intelligence model. For example, when remittance is made between nodes, it may be remittance for cryptocurrency transactions for investment purposes, remittances to pay for products or services, asset management, tax evasion, or illegal transfer of funds. It can also be a remittance for

In order to determine this, the computing device 100 may utilize not only information on the quantity, frequency, frequency, and time of each transaction, but also cryptocurrency price information at a corresponding transaction time. Unlike traditional currency, cryptocurrency has the characteristic that its value can change greatly from moment to moment. Therefore, by using the price information at the time of the transaction of the cryptocurrency together, the value of the amount actually transferred can be determined, and accordingly, the attribute of the transaction can be determined.

For example, if there are multiple remittances between different nodes, and the value of the remittance calculated by reflecting the market price of the cryptocurrency is constant, it can be determined that the price for a product or service having a specific value has been paid. . On the other hand, if a certain amount of cryptocurrency is being remitted despite changes in the cryptocurrency market price, it can be determined that the remittance has occurred regardless of the actual value and has other purposes. However, the purpose of remittance may be evaluated differently according to specific situational information, and the criteria for determination are not limited to the above examples.

In addition, if a number of transactions occur in a situation where the cryptocurrency market price changes rapidly, it may be judged that this is an investment purpose, and transactions that occur regardless of changes in cryptocurrency market prices are far from investment (e.g. Transactions between accounts, asset management, tax evasion, etc.)

Depending on the embodiment, as described above, the line connecting each node may be visualized according to a preset rule, and the computing device 100 uses a pre-learned artificial intelligence model to determine the transaction attribute represented by each line. Can be judged. For example, the computing device 100 may extract various features from the shape of each visualized line, and estimate a transaction attribute corresponding thereto.

In the disclosed embodiment, the artificial intelligence model may be learned based on a deep learning model and training data including information on a visualized image and a solution corresponding to each image (eg, account property, transaction property, etc.) However, it is not limited thereto.

Referring to FIG. 4 again, in step S230, the computing device 100 may analyze the cryptocurrency transaction information visualized in step S220, and in step S240, the computing device 100 is visualized derived through step S230. The use of each of the plurality of cryptocurrency accounts can be determined based on the analysis result of cryptocurrency transaction information.

In various embodiments, the computing device 100 automatically extracts a feature value from the cryptocurrency transaction information visualized in a graph form in step S220 using a previously learned artificial intelligence model (eg, a deep learning model). And use of the extracted feature values to determine the purpose of each of the plurality of cryptocurrency accounts. For example, the feature value may be in the overall form of a graph, and the computing device 100 determines which cryptocurrency account is located on the graph according to the overall shape of the cryptocurrency transaction information visualized in the graph form. It can be determined whether the account is playing a role. However, it is not limited thereto.

In one embodiment, the pre-trained artificial intelligence model is based on the learning data labeled with the attributes of the cryptocurrency account corresponding to the cryptocurrency transaction information visualized in a graph form and one or more nodes included in the visualized cryptocurrency transaction information. Can be learned. As another example, a pre-trained artificial intelligence model clusters the attributes of accounts corresponding to one or more nodes from the cryptocurrency transaction information visualized in the form of a graph, and receives the attribute information of each clustered account or automatically determines the cryptocurrency account. You can also perform classification on the properties of.

In various embodiments, the computing device 100 uses a graph-based ring map learning model based on the status of the visualized cryptocurrency transaction information (eg, the number of nodes, the number of lines connecting nodes, etc.). The use of the cryptocurrency account can be determined using any one of the decision method, the method of determining the use of the cryptocurrency account using the account address-based supervised learning model, and the method of determining the use of the cryptocurrency account using the owner-based ring map learning model. have.

In various embodiments, the computing device 100 uses any one of a graph-based ring map learning model, an account address-based supervised learning model, and an owner-based ring map learning model based on the type of input data. You can determine the use of. Hereinafter, it will be described with reference to FIGS. 6 to 8.

6 is a diagram illustrating a configuration for determining a usage of a cryptocurrency account using a graph-based ring map learning model in various embodiments.

6, in various embodiments, when the number of interconnected nodes in the visualized cryptocurrency transaction information is not large, the computing device 100 determines the use of a cryptocurrency account using a graph-based ring map learning model. I can.

The graph-based ring map learning model is a model that can process tens of billions of cryptocurrency transaction information stored in a graph format into meaningful information, and analyzes the characteristics of the cryptocurrency transaction information converted to a graph and the graph automatically extracted from it (e.g. : Deep learning analysis) to determine the purpose of the cryptocurrency account. For example, a graph-based ring map learning model provides in-depth analysis (e.g. clustering, category classification) of the node corresponding to the cryptocurrency account and the characteristics of the line representing cryptocurrency transaction information. It is possible to derive the necessary information in determining the use. In addition, the graph-based semi-supervised learning model may be a model to which semi-supervised learning is applied to have higher accuracy by using a small amount of correct answer data. However, it is not limited thereto.

In various embodiments, when it is determined that the number of nodes included in the visualized cryptocurrency transaction information exceeds a preset first reference value, the computing device 100 uses a graph-based ring map learning model (eg : Using the visualized cryptocurrency transaction information as an input value), it is possible to determine the form in which multiple nodes are connected by lines (e.g., the overall form of the graph), and based on the form in which multiple nodes are connected by lines, the cryptocurrency account You can determine the use of. For example, when the overall form of the graph has a graph pattern of an exchange, the computing device 100 may determine the use of the corresponding cryptocurrency account as an exchange account. However, it is not limited thereto.

FIG. 7 is a diagram illustrating a configuration for determining a usage of a cryptocurrency account using an account address-based supervised learning model in various embodiments.

Referring to FIG. 7, in the case where the number of interconnected nodes is not large in the visualized cryptocurrency transaction information, the computing device 100 may determine the purpose of the cryptocurrency account using an account address-based supervised learning model.

The account address-based supervised learning model may be a model that identifies the purpose of each cryptocurrency account by analyzing transaction patterns based on individual cryptocurrency accounts. The account address-based supervised learning model may be a model in which a cryptocurrency transaction pattern is learned using only a cryptocurrency account and a cryptocurrency account that is 100% reliable through manual operation in advance for which the use is determined through the above method.

In various embodiments, when the number of nodes connected by lines is less than or equal to a first reference value, and the number of lines for each of the plurality of nodes exceeds a second reference value, the computing device 100 learns account address-based guidance. Using the model, a transaction pattern for each cryptocurrency account may be analyzed according to the transaction history of a plurality of cryptocurrency accounts, and the use of each of the plurality of cryptocurrency accounts may be determined based on the analyzed transaction pattern. For example, when the transaction pattern for a cryptocurrency account shows a wave shape, the computing device 100 may determine the use of the cryptocurrency account as an ICO account, and the transaction pattern for a cryptocurrency account frequently occurs. If visible, the use of the cryptocurrency account can be determined as an exchange hot willet account, and if the transaction pattern for the cryptocurrency account shows a staircase shape, the use of the cryptocurrency account can be determined as a personal account and an exchange cold willet account, If the amount of deposit and withdrawal is the same in the transaction pattern for a cryptocurrency account, the purpose of the cryptocurrency account can be determined as the exchange user's Willet account. However, it is not limited thereto.

8 is a diagram illustrating a configuration for determining a usage of a cryptocurrency account using an owner-based ring map learning model in various embodiments.

Referring to FIG. 8, in various embodiments, when the number of transactions between cryptocurrency accounts for determining a transaction pattern is a small number (eg, 1), the computing device 100 uses an owner-based ring map learning model to perform encryption. You can determine the purpose of the monetary account.

The owner-based ring map learning model is a technology that processes the reclassified cryptocurrency transaction information based on the owner of the cryptocurrency account into meaningful information, and identifies cases where the owners of multiple cryptocurrency accounts are the same and reclassifies them as one owner. It may be a model that learns the transaction pattern of the owner identified through Entity Heuristic Clustering. The owner-based ring map learning model may be a model in which various data are learned by ring map learning with high accuracy with only a small amount of correct answer data, and the ring map deep learning analysis can be performed to determine the purpose of the cryptocurrency account.

In various embodiments, when the number of nodes connected by lines is less than or equal to the first reference value, and the number of lines for each of the plurality of nodes is less than or equal to the second reference value, the computing device 100 uses an owner-based ring degree learning model. At least one cryptocurrency account is grouped based on the owner of the cryptocurrency account using at least one cryptocurrency account, and at least one cryptocurrency grouped using cryptocurrency transaction information generated in at least one grouped cryptocurrency account The pattern for the currency account may be analyzed, and a usage for each of the grouped at least one cryptocurrency account may be determined based on the pattern for the grouped at least one cryptocurrency account (eg, FIG. 8 ). However, it is not limited thereto.

In various embodiments, the computing device 100 may collect cryptocurrency account information in the form of images and one or more keywords from an external web site, and determine the purpose of the cryptocurrency account by using them. Hereinafter, it will be described with reference to FIG. 9.

9 is a diagram illustrating a configuration for determining a usage of a cryptocurrency account by using cryptographic account information and keywords in the form of images obtained from a website in various embodiments.

Referring to FIG. 9, the computing device 100 may collect image-type cryptocurrency account information and one or more keywords from an external web site, and analyze cryptocurrency account information in the form of an image to obtain a text-type cryptocurrency account. Information may be extracted, and the purpose of the cryptocurrency account corresponding to the extracted textual cryptographic account information may be determined by analyzing one or more keywords.

In general, since transactions using cryptocurrency values anonymity, the address of the cryptocurrency account is often stored in the form of an image. At this time, the computing device 100 may search for cryptocurrency account information in the form of an image, and extract a cryptocurrency account in a text form from the searched image form cryptocurrency account information (eg, using OCR character reading technology). .

In addition, the computing device 100 may extract one or more major keywords from the web site on which the cryptocurrency account address is posted, and may determine the purpose of the cryptocurrency account extracted in text form according to the extracted keywords. For example, when the keywords extracted from the web site on which the cryptocurrency account address is posted are Drugs and Weeds, the computing device 100 may determine the use of the cryptocurrency account extracted in text form as a black market account.

In various embodiments, the computing device 100 may collect information related to cryptocurrency from an illegal dark web site, and use this to determine the purpose of the cryptocurrency account.

In general, a dark web site refers to a web that requires a specific program to be accessed, and is used for crime in cyberspace because the visitor or server cannot be identified in a general way. In particular, various illegal crimes using cryptocurrency as a payment method are prevalent on the dark web. In order to monitor the cryptocurrency account that performs such illegal transactions, the computing device 100 uses a previously stored Darkweb access software (using Tor browser, software accessing a domain ending in *.onion) from an external device. Access to the dark website, automatically collect cryptocurrency-related information from the dark website at preset cycles, and determine the use of each of the plurality of cryptocurrency accounts based on the information related to cryptocurrency collected from the dark website. I can. For example, the computing device 100 may set a cryptocurrency account corresponding to a cryptocurrency account address obtained from a dark web site among a plurality of cryptocurrency accounts as a dark web account. However, it is not limited thereto.

Again, referring to FIG. 3, in step S120, the computing device 100 may collect information related to cryptocurrency for each of the plurality of cryptocurrency accounts for which the purpose obtained in step S110 is determined. For example, the computing device 100 may obtain information on the use of each of the plurality of cryptocurrency accounts, the amount of cryptocurrency held in the cryptocurrency account, and cryptocurrency transaction history information. However, it is not limited thereto.

In various embodiments, the computing device 100 may collect information related to a cryptocurrency holding amount of the cryptocurrency account and a mining pattern according to the cryptocurrency holding amount in relation to the cryptocurrency account determined as the miner account in step S110. For example, the computing device 100 may collect information related to the amount of cryptocurrency held in the miner's account and whether cryptocurrency mining continues according to the amount of cryptocurrency held.

In an embodiment, the computing device 100 may monitor the amount of cryptocurrency entering or leaving an exchange account (ie, depositing and withdrawing) using attribute information of each account. In addition, the computing device 100 may monitor the number of users of the exchange (eg, the number of personal accounts transacting with the exchange account).

The computing device 100 may predict the volatility of the cryptocurrency based on the amount of deposits and withdrawals of the monitored exchange and the number of users.

Unlike other existing financial products such as stocks, cryptocurrency is characterized by simultaneous execution, clearing, and settlement. Accordingly, it is possible to obtain settlement information of cryptocurrency in real time, and by analyzing this, it is possible to predict the volatility information of cryptocurrency.

In particular, if a large amount of deposit occurs from the exchange account to the miner's account, it can be predicted that the miner will perform some operation or strategy against the exchange. In the disclosed embodiment, the computing device 100 collects the volatility information of cryptocurrency from settlement information acquired in real time using an artificial intelligence model learned (machine learning) using training data including various fund flows and settlement information. It is predictable.

In various embodiments, the computing device 100 may collect information on the total amount of cryptocurrencies introduced from a cryptocurrency derivatives exchange by using transaction information of a plurality of cryptocurrency accounts.

In step S130, the computing device 100 may predict the price volatility of the cryptocurrency by analyzing information related to the cryptocurrency collected in step S120. For example, the computing device 100 may analyze the information related to the cryptocurrency and predict the price volatility of the cryptocurrency based on the amount of cryptocurrency held in each of the plurality of cryptocurrency accounts and the target of cryptocurrency transaction according to cryptocurrency transaction information. have.

In various embodiments, the computing device 100 may predict the price volatility of the cryptocurrency by analyzing based on the amount of cryptocurrency holdings and mining patterns in the miner's account. For example, the computing device 100 may determine whether or not the miner continues to mine the cryptocurrency by analyzing the cryptocurrency holding amount of the miner's cryptocurrency account and the mining pattern according to the cryptocurrency holding amount. In addition, the computing device 100 may predict the price volatility of the cryptocurrency according to whether the miner continues to mine the cryptocurrency. For example, the computing device 100 predicts that the price of the cryptocurrency will fall in response to the miner's determination to continue cryptocurrency mining, and the cryptocurrency in response to the miner's determination to stop cryptocurrency mining. It can be predicted that the price of However, it is not limited thereto.

In various embodiments, the computing device 100 determines the futures trading volume of cryptocurrency based on the total amount of cryptocurrency flowing from the cryptocurrency derivatives exchange, and predicts the price volatility of cryptocurrency using the futures trading volume of cryptocurrency. I can. For example, the computing device 100 may analyze the futures transaction volume of the cryptocurrency and predict the magnitude of the price volatility of the cryptocurrency in proportion to an increase in the difference between the withdrawal amount and the deposit amount of the cryptocurrency. Hereinafter, it will be described with reference to FIGS. 10 and 11.

10 is a table showing the amount of deposit and withdrawal of cryptocurrency by time in various embodiments, and FIG. 11 is a graph showing the amount of change in the price of cryptocurrency according to the amount of deposit and withdrawal of cryptocurrency in various embodiments.

Referring to Figs. 10 and 11, it can be seen that a large-scale cryptocurrency withdrawal occurred before a rapid fluctuation in cryptocurrency price occurred. In the case of cryptocurrencies (e.g., bitcoin), unlike general stocks, there is a policy to withdraw collectively only at 13:00 UTC, so if a large amount of bitcoins are withdrawn at the same time, the internal exchange Bitcoin flow tends to decrease sharply.

In addition, since all futures transactions are made in bitcoin, a large amount of bitcoin is deposited and withdrawn on the exchange per day. At this time, the deposit of bitcoin is always possible, but in the case of withdrawal, it is made at once at a fixed time, so if a large amount of bitcoin is withdrawn at one time, the amount of bitcoin withdrawn will overwhelm the amount of bitcoin deposited. Can occur. In this case, the amount of bitcoin flow on the exchange temporarily decreases, and as the difference between the withdrawal amount and the deposit amount of cryptocurrency increases, a rapid price change may occur.

In addition, if the amount of bitcoin flow in the exchange decreases (e.g., the difference between the amount of withdrawal and deposit of cryptocurrency increases), the spread increases and the price difference between the buyer and the seller widens. Changes are bound to happen. Here, in futures trading, since contracts are liquidated when price fluctuations of more than a certain percentage occur, more rapid price fluctuations occur as futures trading is liquidated, and this price change causes a chain reaction to liquidate large-scale transactions again.

In various embodiments, the computing device 100 determines the futures trading volume of the cryptocurrency based on the total amount of cryptocurrency flowing from the cryptocurrency derivatives exchange, and predicts the price volatility of the cryptocurrency using the futures trading volume of the cryptocurrency. However, the larger the difference between the withdrawal amount and the deposit amount of the cryptocurrency, the larger the price volatility of the cryptocurrency is predicted, and the smaller the difference between the withdrawal amount and the deposit amount of the cryptocurrency, the smaller the cryptocurrency price volatility. However, it is not limited thereto.

In various embodiments, the computing device 100 may predict price volatility of cryptocurrency based on the short buying amount and short selling amount of the cryptocurrency.

In general, large-scale withdrawals of Bitcoin have a great influence on price volatility, but it may be difficult to predict perfect price volatility simply by large-scale withdrawals. Therefore, for a more complete prediction of price volatility, the computing device 100 improves the accuracy of price volatility prediction by using the ratio of the cryptocurrency short purchase amount (eg, Long) and short purchase amount (eg, Short). I can make it.

In various embodiments, when the magnitude of the price volatility of the cryptocurrency is predicted to be greater than or equal to the reference value, when the short purchase amount of the cryptocurrency is greater than the short sale amount, the price of the cryptocurrency may be predicted to decrease. In addition, when it is predicted that the price volatility of the cryptocurrency is greater than or equal to the reference value, the computing device 100 may predict that the price of the cryptocurrency will increase when the short selling amount of the cryptocurrency is greater than the short buying amount.

The above-described method for predicting cryptocurrency price volatility using artificial intelligence has been described with reference to the flowchart shown in the drawings. For the sake of simplicity, the method of predicting cryptocurrency price volatility using artificial intelligence has been described by showing a series of blocks, but the present invention is not limited to the order of the blocks, and some blocks are in a different order from those shown and described herein. May be performed as or simultaneously. In addition, new blocks not described in the specification and drawings may be added, or some blocks may be deleted or changed.

In various embodiments, the computing device 100 tracks the money flow of the cryptocurrency based on the cryptocurrency account attribute corresponding to each block obtained based on the above-described method, and accordingly, the circulation or money laundering of illegal money. Information, etc. can be obtained.

For example, money laundering technology in the cryptocurrency field is gradually developing, and accordingly, the transaction is divided into thousands or tens of thousands of transactions per transfer, sending some to various exchanges in each country, and opening a cannon passbook at the exchange to pass through some transactions. A method of mixing via an exchange through an exchange method and a coin join method in which hundreds of recipients and senders are entered in multiple times are used.

Accordingly, it is practically impossible to view all transaction volumes with a manual data collection method, and there are difficulties in preventing and tracking crime. In the disclosed embodiment, a mixer account may be found based on the above-described method for determining account attributes, and the flow of funds including the mixer account may be determined to be an illegal flow of funds and tracked.

In addition, the computing device 100 according to the disclosed embodiment performs machine learning learning to determine its properties (eg, normal flow and illegal money flow) based on the flow information of cryptocurrency funds, and the learned artificial intelligence model The attribute corresponding to the flow information of cryptocurrency funds can also be determined using.

For example, the flow information of cryptocurrency funds may be expressed by nodes and lines based on the above-described visualization method, but is not limited thereto. In addition, the flow information of the cryptocurrency funds may include attribute information of the cryptocurrency account corresponding to each node, but is not limited thereto. As another example, attribute information for at least some or all nodes may not be provided.

The computing device 100 may determine a property of the money flow according to the flow information of the money by using the previously learned artificial intelligence model. More specifically, the property of the money flow may include the property of illegal money flow, such as money laundering or tax evasion, payment for illegal goods or services.

In the method for predicting cryptocurrency price volatility according to the disclosed embodiment, attribute information of each transaction or money flow obtained as described above may be used to predict cryptocurrency price volatility.

As a non-limiting example, if it is determined that the purpose of a specific transaction being carried out is an investment, if the transaction volume increases by more than a certain level and the price of the cryptocurrency rises or falls above a preset range, the transaction is stopped and the transaction is resumed at a later time. However, if it is determined that the purpose of a specific transaction being carried out is money laundering or illegal transaction, a fixed amount of transactions may be performed regardless of the fluctuation of cryptocurrency.

Accordingly, the computing device 100 may predict a transaction pattern according to a transaction attribute based on a previously learned artificial intelligence model or a previously stored database, and utilize this for predicting the volatility of cryptocurrency.

The embodiments of the present invention have been described above with reference to the accompanying drawings, but those skilled in the art to which the present invention pertains may be implemented in other specific forms without changing the technical spirit or essential features of the present invention. You will understand. Therefore, the embodiments described above are illustrative in all respects and should be understood as non-limiting.

100: cryptocurrency price volatility prediction device (computing device)
200: user terminal
300: external server
400: network

Claims (10)

In the method performed by the computing device,
Determining a usage for each of the plurality of cryptocurrency accounts by using cryptocurrency transaction information generated between a plurality of cryptocurrency accounts;
Collecting information related to cryptocurrency for each of the plurality of cryptocurrency accounts for which the use is determined; And
Analyzing the collected cryptocurrency-related information to predict price volatility of the cryptocurrency,
The step of determining the usage for each of the plurality of cryptocurrency accounts,
Visualizing the cryptocurrency transaction information generated between the plurality of cryptocurrency accounts, analyzing the visualized cryptocurrency transaction information, and determining a usage for each of the plurality of cryptocurrency accounts,
The step of analyzing the visualized cryptocurrency transaction information to determine a usage for each of the plurality of cryptocurrency accounts,
When the number of the plurality of cryptocurrency accounts included in the visualized cryptocurrency transaction information is less than a first reference value and the number of transactions according to the cryptocurrency transaction information exceeds a second reference value, the plurality of cryptocurrencies Analyzing a transaction pattern for each account to determine a usage for each of the plurality of cryptocurrency accounts; And
When the number of the plurality of cryptocurrency accounts included in the visualized cryptocurrency transaction information is less than the first reference value, and the number of transactions according to the cryptocurrency transaction information is less than the second reference value, the owner of the cryptocurrency account Grouping at least one cryptocurrency account among the plurality of cryptocurrency accounts based on, and using the cryptocurrency transaction information generated in the at least one grouped cryptocurrency account, the grouped at least one cryptocurrency account Including the step of determining a usage for each of the plurality of cryptocurrency accounts by analyzing the pattern for,
A method of predicting cryptocurrency price volatility using artificial intelligence. The method of claim 1,
Collecting the information related to the cryptocurrency,
Comprising the step of collecting information related to a mining pattern according to the amount of cryptocurrency holdings of the cryptocurrency account for which a predetermined purpose of the plurality of cryptocurrency accounts is determined as a miners account, and the amount of cryptocurrency holdings,
Predicting the price volatility of the cryptocurrency,
Comprising the step of predicting the price volatility of the cryptocurrency by analyzing based on the amount of cryptocurrency held in the miner's account and the mining pattern,
A method of predicting cryptocurrency price volatility using artificial intelligence. The method of claim 1,
Collecting the information related to the cryptocurrency,
Including the step of collecting information on the total amount of cryptocurrencies flowing from the cryptocurrency derivatives exchange,
Predicting the price volatility of the cryptocurrency,
Comprising the step of determining the futures trading volume of the cryptocurrency based on the total amount of cryptocurrency introduced from the cryptocurrency derivatives exchange, and predicting the price volatility of the cryptocurrency using the futures trading volume of the cryptocurrency,
A method of predicting cryptocurrency price volatility using artificial intelligence. The method of claim 3,
Predicting the price volatility of the cryptocurrency using the futures transaction volume of the cryptocurrency,
Analyzing the futures transaction volume of the cryptocurrency and predicting the magnitude of the price volatility of the cryptocurrency in proportion to an increase in the difference between the withdrawal amount and the deposit amount of the cryptocurrency,
A method of predicting cryptocurrency price volatility using artificial intelligence. According to claim 4,
Predicting the magnitude of the price volatility of the cryptocurrency in proportion to the increase in the difference between the withdrawal amount and the deposit amount of the cryptocurrency,
Predicting that the price of the cryptocurrency will decrease when the short purchase amount of the cryptocurrency is greater than the short sale amount at a time when it is predicted that the price volatility of the cryptocurrency is greater than or equal to the reference value; And
Comprising the step of predicting that the price of the cryptocurrency will increase when the short selling amount of the cryptocurrency is larger than the short buying amount at a time when it is predicted that the size of the price volatility of the cryptocurrency is greater than or equal to the reference value,
A method of predicting cryptocurrency price volatility using artificial intelligence. The method of claim 1,
Predicting the price volatility of the cryptocurrency,
Analyzing the information related to the collected cryptocurrency and predicting the price volatility of the cryptocurrency based on the amount of cryptocurrency holdings of each of the plurality of cryptocurrency accounts and the target of cryptocurrency transaction according to the cryptocurrency transaction information,
A method of predicting cryptocurrency price volatility using artificial intelligence. delete delete A memory that stores one or more instructions; And
And a processor executing the one or more instructions stored in the memory.
The processor executes the one or more instructions,
An apparatus for performing the method of claim 1. A computer program stored in a recording medium readable by a computer to perform the method of claim 1 in combination with a computer that is hardware.

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