KR102071199B1 - Control method, device and system of knowledge sharing platform for 2d / 3d design result management - Google Patents

Abstract

Disclosed is a knowledge sharing platform capable of providing environment that various pieces of knowledge content may be traded. A method for controlling the knowledge sharing platform according to the present invention comprises the steps of: generating data for at least one of coding education content, a character, an APK file, a package, and design content by a seller terminal; transmitting the generated data to a server by the seller terminal; determining a price of the received data by the server; registering the received data into the platform by the server; and transmitting the data to a purchaser terminal when a user instruction for purchasing the data is received from the purchaser terminal by the server.

Description

CONTROL METHOD, DEVICE AND SYSTEM OF KNOWLEDGE SHARING PLATFORM FOR 2D / 3D DESIGN RESULT MANAGEMENT}

The present invention relates to a control method, apparatus and system of a knowledge sharing platform for D / 3D design management.

As IT technology advances, the need to share diverse knowledge is increasing. For example, there is an increasing need to build a platform that can share content for coding education, written programming files, design content, and the like.

Many conventional content trading platforms have only a feature of simply registering and selling content. However, due to the nature of knowledge content, there is a need to protect not only the content transaction but also the content, and furthermore, the method of evaluating the value of the knowledge content is important.

In addition, with the development of technology, technologies for building a virtual space providing a variety of experiences to users are being developed. Virtual Reality is a technology that allows people to experience the real world in a virtual world made of computers, and augmented reality, a technology that adds virtual information to VR and the real world. The technology that mixes AR: augmented reality is called mixed reality (MR). VR, AR, and MR are all common in that they are technologies that realize reality that does not exist so that people can recognize it. AR differs from the fact that virtual information is added to real reality and VR is presented in a fictional situation. MR provides a mixture of AR and VR to provide a mixture of real and virtual information on a realistic background, requiring a technology that can process large amounts of data.

At this time, the processing of a large amount of data includes a technique for transmitting a large amount of data. Conventionally, there has been a problem in transmitting large data rapidly, but recently, the use of ultra high-band frequency of 28 GHz and the maximum download speed of 20 Gbps (5G Networks) began to spread, and to build a virtual space. It complements the technical problems that arise.

In this situation, it is time to implement a knowledge sharing platform to effectively protect various knowledge contents.

Korean Patent Publication No. 10-1783476, published October 10, 2017

An object of the present invention is to provide a method, apparatus, and system for controlling a knowledge sharing platform for managing characters, programming files, and programming instructions.

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

According to an aspect of the present invention, there is provided a method of controlling a knowledge sharing platform, including: generating, by a seller terminal, data of at least one of coding education contents, characters, APK files, packages, and design contents; The seller terminal transmitting the generated at least one data to the server; Determining, by the server, a price of the received at least one data; Registering, by the server, the received at least one data with the platform; And when the server receives a user command for purchasing the at least one data from a buyer terminal, transmitting the at least one data to the buyer terminal. It includes.

In this case, when the at least one data is data for a character, the generating of the at least one data may include: generating, by the server, a character by receiving information about a character to be generated from the seller terminal; Transmitting, by the server, category information for learning the character to the seller terminal; Receiving category information on at least one of the transmitted category information from the seller terminal; And learning a character based on the received category information. It may include.

In this case, when the at least one data is data related to an APK file, the control method may include extracting at least one package data from data related to the APK file; The extracting of the at least one package data may include: receiving a condition for at least one of size information, function information, class information, and function information about a package from the seller terminal; And extracting at least one package data from the APK file based on the received condition.

In this case, the training may include receiving learning data corresponding to the received category information and learning data received from the seller terminal; And learning by inputting the received learning data into an artificial intelligence model. The method may further include: generating summary information about the character based on the learning data input to the artificial intelligence model, when the at least one data is data related to the learned character; And registering the summary information with the character. It may include.

In this case, when the at least one data is data for at least one of an APK file and a package, the control method may determine whether there is data matching a predetermined ratio or more with the APK file or the package among the plurality of registered data. Determining; If the matching data exists, transmitting a message indicating that the APK file or the package is in violation of copyright to the seller terminal; And if the matching data does not exist, transmitting a message for instructing copyright registration of the APK file or the package to the seller terminal; It may include.

In this case, the determining of the price of the received at least one data may include: clustering the registered plurality of data to obtain a plurality of clusters; Acquiring a first cluster including the received at least one data; Determining a price of the received at least one data based on a price and a number of sales of a plurality of data included in the first cluster; It may include.

The control method may include determining a platform fee based on the number of the plurality of data included in the first cluster and the price of the determined at least one data; And if the purchaser terminal purchases the at least one data, paying the platform fee determined for the at least one data to the platform.

In this case, the obtaining of the summary information may include deleting learning data related to user personal information of the seller terminal, from the learning data input to the artificial intelligence model; It may further include.

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

According to various embodiments of the present invention described above, the platform may provide an environment in which various knowledge contents may be traded.

Effects of the present invention are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a block diagram illustrating a knowledge sharing platform according to an embodiment of the present invention.
2 is a block diagram illustrating a configuration of a server according to an embodiment of the present invention.
3 is a flowchart illustrating an operation of a knowledge sharing platform according to an embodiment of the present invention.
4 is a flowchart illustrating a case in which data traded in a knowledge sharing platform according to an embodiment of the present invention is character data.
5 is a flowchart illustrating a case in which data traded in a knowledge sharing platform according to an embodiment of the present invention is a programming file.
6 is a flowchart illustrating a method of generating summary information of data traded in a knowledge sharing platform according to an embodiment of the present invention.
7 is a flowchart illustrating a method of determining whether data registered in a knowledge sharing platform is in violation of copyright according to an embodiment of the present invention.
8 is a flowchart illustrating a method of determining a price of data traded in a knowledge sharing platform according to an embodiment of the present invention.
9 is a flowchart illustrating a method of determining a fee paid to a platform when a data transaction is performed in a knowledge sharing platform according to an embodiment of the present invention.
10 is a block diagram of an apparatus according to an embodiment of the present invention.

Advantages and features of the present invention, and methods for achieving them will be apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below but may be embodied in various different forms, only the present embodiments are intended to complete the disclosure of the present invention, and those skilled in the art It is provided to fully inform the skilled person of the scope of the invention, which is defined only by the scope of the claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and / or “comprising” does not exclude the presence or addition of one or more other components in addition to the mentioned components. Like reference numerals refer to like elements throughout, and "and / or" includes each and all combinations of one or more of the mentioned components. Although "first", "second", etc. are used to describe various components, these components are of course not limited by these terms. These terms are only used to distinguish one component from another. Therefore, of course, the first component mentioned below may be the second component within the technical spirit of the present invention.

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

The term "part" or "module" as used herein refers to a hardware component such as software, FPGA or ASIC, and the "part" or "module" plays 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 play one or more processors. Thus, as an example, a "part" or "module" may include components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. Functions provided within components and "parts" or "modules" may be combined into smaller numbers of components and "parts" or "modules" or into additional components and "parts" or "modules". Can be further separated.

The spatially relative terms " below ", " beneath ", " lower ", " above ", " upper " It can be used to easily describe a component and its correlation with other components. Spatially relative terms are to be understood as including terms in different directions of components in use or operation in addition to the directions shown in the figures. For example, when flipping a component shown in the drawing, a component described as "below" or "beneath" of another component may be placed "above" the other component. Can be. Thus, the exemplary term "below" can encompass both an orientation of above and below. Components may be oriented in other directions as well, so spatially relative terms may be interpreted according to orientation.

In the present specification, a computer refers to any kind of hardware device including at least one processor, and according to an embodiment, it may be understood as a meaning encompassing a software configuration that operates on the hardware device. For example, a computer may be understood as including, but not limited to, a smartphone, a tablet PC, a desktop, a notebook, and a user client and an application running on each device.

Hereinafter, with reference to the accompanying drawings will be described an embodiment of the present invention;

Each step 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 block diagram illustrating a knowledge sharing platform according to an embodiment of the present invention.

The knowledge sharing platform may be composed of a plurality of seller terminals 10, a server 20, and a plurality of buyer terminals 30.

The plurality of seller terminals 10 and the plurality of buyer terminals 30 according to the present invention can be implemented as a smart phone, but this is only one embodiment, a smartphone, a tablet personal computer, Mobile phones, video phones, e-book readers, desktop PCs, laptop PCs, netbook computers, workstations, servers, PDAs It may include at least one of a personal digital assistant, a portable multimedia player (PMP), an MP3 player, a mobile medical device, a camera, and a wearable device.

In another embodiment, the seller terminal 10 or the purchaser terminal 30 may be a home appliance. Home appliances are, for example, televisions, digital video disk players, audio, refrigerators, air conditioners, vacuum cleaners, ovens, microwave ovens, washing machines, air cleaners, set-top boxes, home automation Home automation control panel, security control panel, TV box (e.g. Samsung HomeSync ?, Apple TV ?, or Google TV?), Game console (e.g. Xbox ?, PlayStation?), Electronics It may include at least one of a dictionary, an electronic key, a camcorder, or an electronic picture frame.

The plurality of seller terminals 10 may include a first seller terminal 11, a second seller terminal 12, and a third seller terminal 13, and the plurality of buyer terminals 30 may include a first buyer terminal ( 31), but may include the second buyer terminal 32 and the third buyer terminal 33, but is not limited thereto, and may include a variety of seller terminal and buyer terminal as necessary.

In the present invention, unless otherwise specified, for convenience of explanation, the first seller terminal 11, the second seller terminal 12 and the third seller terminal 13 as the seller terminal 10, The first buyer terminal 31, the second buyer terminal 32 and the third buyer terminal 33 are represented by the buyer terminal 30.

The seller terminal 10 and the purchaser terminal 30 merely distinguish between a terminal of a user who sells various knowledge contents and a terminal of a user who purchases various contents, and each of the seller terminal 10 and the purchaser terminal 30 freely select various contents. Of course, it can be generated, of course, the seller terminal 10 and the buyer terminal 30 may be changed with each other.

The server 20 is a component for transmitting and receiving data on knowledge content from the seller terminal 10 and the purchaser terminal 30. The server 20 may perform various functions described below for the transaction of the knowledge content.

2 is a block diagram illustrating a configuration of a server according to an embodiment of the present invention.

According to various embodiments of the present disclosure, the server 20 may include a coding education game management module 21, a character management module 22, a programming file management module 23, a programming instruction management module 24, and 2D / 3D. The design management module 25 may be included.

Coding education game management module 21 is a module for controlling to receive the content for the coding education game from the seller terminal 10 to transmit to the buyer terminal (30).

In one embodiment, the coding education game may be a program including an augmented reality self-directed coding education learning system using a virtual robot. Coding education game according to the present invention, may be performed by the seller terminal 10 or the buyer terminal 30 including a camera.

In one embodiment, the coding education game traded in the present invention, the first step of obtaining the coding training content, the second step of selecting one or more points included in the coding training content, obtains the image taken from the camera A third step of recognizing the photographed image and matching one or more objects and one or more points included in the photographed image, and a step of displaying the photographed image and the matched one or more points Step 5, a sixth step of displaying a virtual robot on the captured image, a seventh step of displaying one or more command blocks each including one or more commands for controlling the movement of the virtual robot, the one or more displayed An eighth stage for placing the one or more instruction blocks at the one or more points based on user input to the instruction block; Controlling the virtual robot according to the order in which the one or more command blocks are arranged, wherein the virtual robot moves along the one or more points and performs a command corresponding to the command block disposed at each point, A ninth step and a tenth step of displaying the movement of the virtual robot according to the control result.

In this case, the first step may include an eleventh step of obtaining a mission included in the coding education content and a twelfth step of displaying the mission, and the ninth step may include: achieving the mission according to the control result A thirteenth step of determining whether or not, and a fourteenth step of displaying feedback based on whether the mission is achieved.

In this case, the thirteenth step includes a fifteenth step of determining an interaction between the one or more objects and the virtual robot and a sixteenth step of determining whether the mission is achieved at each of the one or more points based on the interaction. The fourteenth step may include a seventeenth step of displaying feedback based on whether the mission is achieved at each of the one or more points.

In this case, the fifteenth step may include an eighteenth step of determining one or more contact points between the one or more objects and the virtual robot, and determining the one or more objects corresponding to each of the one or more contact points and a part of the virtual robot. A nineteenth step and a twentieth step of determining the interaction based on the position, the movement of the one or more contacts and the portion corresponding to each of the one or more contacts.

In this case, the twenty-first step may include a twenty-first step of dividing a first contact included in the one or more contacts into a second contact point and a third contact point, and a twenty-second step of tracking movements of the second contact point and the third contact point. When the distance between the second contact point and the third contact point is greater than a predetermined reference value, determining a portion of the at least one object and the virtual robot corresponding to each of the second contact point and the third contact point; And a twenty-fourth step of determining the interaction based on the position, the movement of the second contact point and the third contact point, and a portion corresponding to the second contact point and the third contact point, respectively.

In this case, the eleventh step may include: twenty-fifth step of determining the type of the one or more objects; and generating a movement path of the virtual robot based on the location and type of the one or more objects included in the captured image. In step 26, obtaining a first mission for allowing the virtual robot to move along the movement path; and performing a second mission for allowing the virtual robot to perform a predetermined interaction with at least one of the one or more objects. It may include the 28 th step of obtaining.

In this case, the eleventh step may include: twenty-ninth step of generating and displaying one or more virtual objects on the movement path; thirty thirty step of determining the type of the one or more virtual objects; And a thirty-one step of updating the movement route based on a type, a thirty-second step of acquiring a third mission for causing the virtual robot to move along the updated movement route, and the virtual robot is configured to perform the at least one virtual operation. The method may include a thirty-third step of obtaining a fourth mission for performing a predetermined interaction with at least one of the objects.

In this case, the eighth step may further include a thirty-fourth step of disposing at least some of the one or more instruction blocks on the virtual robot, and the ninth step may correspond to the instruction block disposed on the virtual robot. A step 35 of performing a command; a step 36 of performing a command corresponding to the command block disposed at the at least one point; and a command corresponding to the command block disposed at the virtual robot; The method may further include a thirty-seventh step of stopping operation and displaying feedback when a command corresponding to the instruction block disposed at the arrived point collides.

The character management module 22 is a component for controlling the transmission of the character generated in the seller terminal 10 to the purchaser terminal 30. Furthermore, the character management module 22 may perform an operation for generating a character to be sold by the seller terminal 10. That is, the seller terminal 10 may generate and learn a character using the character management module 22, and provide the learned character to the purchaser terminal 30.

In one embodiment, the character may be generated and learned by the operation of the seller terminal 10 and the server 20. The server 20 may generate a character by receiving a character generation request from the seller terminal 10. The generated character may be used by the seller terminal 10 or the purchaser terminal 30. In this case, the character may perform voice recognition and output a result corresponding to the user's voice as voice. To this end, the seller terminal 10 or the buyer terminal 30 may include a microphone and a speaker, the seller terminal 10 or the buyer terminal 30 receives the user's voice and delivers to the character, generated from the character The answer can be output through the speaker.

The programming file management module 23 is a configuration for managing completed executable files such as APK files. The seller terminal 10 may register the programming file with the server 20 and transmit the programming file to the purchaser terminal 30. For convenience of description, the following description of the present invention is limited to the case where the programming file is an APK file, but the technical idea of the present invention may be applied to various executable programming files.

The programming instruction module 24 is a module for managing programming instructions close to natural language, and is a module for managing program compression instructions for producing a program. For convenience of description, in the present invention, but limited to programming instructions described in the package, the technical spirit of the present invention can be applied to a variety of instructions, of course. In this case, the package is a configuration for a related class, function or interface set, and may be a module for performing one or a plurality of operations. By using the package, the purchaser terminal 30 can purchase several packages and use them to make complex games, SNSs, and the like.

The 2D / 3D design management module 25 is a module for managing various design contents such as characters, UX / UI icons, emoticons, and fonts necessary for program production.

By the above-described various modules, the server 20 may be linked with the seller terminal 10 or the purchaser terminal 30 to help produce various contents, and may create a platform environment for trading the produced contents.

Hereinafter, specific embodiments of the present invention will be described with reference to FIGS. 3 to 9.

3 is a flowchart illustrating an operation of a knowledge sharing platform according to an embodiment of the present invention.

In operation S110, the seller terminal 10 may generate data of at least one of coding education contents, characters, APK files, packages, and design contents. As described above, at least one data generated by the seller terminal 10 may be generated in association with the server 20.

In operation S120, the seller terminal 10 may transmit at least one data of coding education content, a character, an APK file, a package, and design content to the server 20.

In operation S130, the server 20 may determine a price of at least one data received from the seller terminal 10.

In operation S140, the server 20 may register at least one data received from the seller terminal 10 with the platform.

In operation S150, upon receiving a user command to purchase at least one registered data from the purchaser terminal 30, the server 20 may transmit at least one data to the purchaser terminal 30.

In detail, the purchaser terminal 30 may transmit a user command for purchasing at least one registered data and a payment point corresponding to the at least one data to the server 20. When the received payment point is the same as the price of at least one data, the server 20 may transmit at least one data to the purchaser terminal 30.

Meanwhile, according to various embodiments of the present disclosure, the data sale may be made using a QR code. QR Code is an abbreviation of Quick Response Code. It is a kind of two-dimensional barcode that displays information in black and white lattice pattern. It was developed by Denso Wave Co. of Japan and released in 1994.It is faster than other 2D barcodes. It has the advantage of being able to save. When at least one data is registered on the platform, the server 20 may generate and register a QR code corresponding to the registered data. By using the QR code, when the purchaser terminal 30 wants to purchase data, the purchaser terminal 30 may purchase data through the QR code.

4 is a flowchart illustrating a case in which data traded in a knowledge sharing platform according to an embodiment of the present invention is character data.

In operation S210, when the at least one data is data on the character, the server 20 may generate the character by receiving information on the character to be generated from the seller terminal 10.

The generated character is a character that includes only basic functions, and can learn new functions according to a user command.

In operation S220, the server 20 may transmit category information for learning the character to the seller terminal 10. That is, when the user command for learning the character is input, the server 20 may learn the character based on the user command.

In operation S230, the server 20 may receive at least one category information from the seller terminal 10.

In operation S240, the server 20 may train the character based on the received category information.

In this case, the category information may be information on various categories for learning the character. In one embodiment, the category information may be information on various categories such as language category, sports category, shopping category, and education category. The category information may further include detailed category information obtained by subdividing the category information. For example, the category may further include a first category, a second category subdividing the first category, a third category subdividing the second category, and the like.

In one embodiment, the category information may be category information related to a growth-type character targeting children. In this case, the category information related to the growth type character may include an education learning category, an emotion learning category, and the like as the first category. The second category of the education learning category may be a category related to a subject such as a math category, an English category, a social category, a science category, and the emotion learning category may be a category related to various emotions such as a joy category, a sad category, a happiness category, and an anxiety category. Can be.

The server 20 may learn the character by receiving the training data on the selected category information. For example, when the selected category is a social category, the server 20 may receive an input of learning data from a user and train an artificial intelligence model for learning a social subject.

In this case, the learning data input from the user may be data related to knowledge that the child has acquired at school or academy. For example, the learning data may be data about handwritten notes that are taken at school or academy. The server 20 may receive an image file for the handwritten note, and extract the text from the image through the OCR technique. If necessary, when the handwritten note image includes non-text such as a picture, a chart, a graph, the server 20 may generate training data by imaging the non-text data. However, the learning data is not limited thereto, and may be various data such as user voice, website information collected by the user, blog information, and information of a read book.

When a user command for using the learned character is input, the server 20 may output a result of the user command through the character. In this case, the user command may be a voice signal including a user question.

The result of the user command may be determined based on the learning data input from the user. For example, when the learning data input by the user is learning data related to the first grade social studies curriculum, and the user generates a user command to query the contents of the social subject, the server 20 inputs the entered first grade social studies. You can generate answers within the scope of the course. The server 20 may not output a response to the learning data that is not input by the user even in a range of social studies in the first grade.

Therefore, the plurality of characters generated by the plurality of users may be differently learned according to the learning data input by each of the plurality of users. A character who inputs a lot of learning data can grow into a smarter character than a character who inputs less learning data. Therefore, the user may be interested in growing his character to make a better character than other characters, and more efforts may be made for this.

On the other hand, the server 20 may delete the character if the learning data is not input to the character for a predetermined time, or if the character is not queried. That is, the server 20 may induce a user to steadily manage his or her character not to be deleted by deleting the unused character.

In another embodiment, the category information may be an emotional learning category. The server 20 may receive various interactions from the seller terminal 10 and determine the emotion according to the user interaction. That is, the server 20 may train the character using learning data related to user emotion. The character may build up the character's own personality according to the learning data related to the emotion of the user input.

In one embodiment, after the character outputs a response to the user data, the character's personality may be determined according to the user interaction with respect to the output character's response. For example, when the user interaction with the response output of the character is a "praise" interaction, the character's personality may be a confident personality. Alternatively, when the user interaction with the response output of the character is a "scrolling" interaction, the character's personality may be timid personality. In another embodiment, the personality of the character may be a personality similar to the personality of the user obtained by analyzing the user data.

That is, the character's personality may be changed, expanded or reduced as the user interacts with the user. Depending on the character of the generated character, the response that the character outputs in the same situation may be different.

Therefore, the plurality of characters generated by the plurality of users may be differently learned according to the learning data input by each of the plurality of users. A character who inputs a lot of learning data can grow into a character that is more emotional than a character who inputs less learning data.

However, the present invention is not limited thereto, and the category information may be language category information for translation. At this time, the sub-category of the language category is the first sub-category constituting the kind of language, such as Korean, Japanese, Chinese, and English, and the second sub-category representing the acts on the language such as translation, writing, reading, speaking, and listening. And a third subcategory that determines the degree of learning of the category.

In this case, the third subcategory is, for example, a category related to the degree of learning about the English translation category, and may be a subcategory for determining a translation level such as a beginner level translation, an intermediate level translation, and an advanced level translation. In general, a character with a high level of learning requires more resources (data amount, cost, etc.) than a character with a low level of learning. Therefore, categories can be classified according to the amount of available resources, and the characters can be trained according to user selection.

The user may select at least one of the category information provided from the server 20 to train the character. In this case, the process of learning the character may be configured by transmitting a pre-stored learning module. However, the process of learning a character may be implemented through an artificial intelligence model.

On the other hand, the server 20 can continuously learn the learned character. Specifically, when data related to the category information learned in the character is input, the server 20 may update the character. In this case, the data for updating may be provided from the seller terminal 10. However, the present invention is not limited thereto, and the server 20 may select the data to be updated to train the character. For example, when data corresponding to the learned category information is received through various methods, the server 20 may update the character based on the received data.

As an example, it is assumed that a character is in a state of learning a beginner level translation of English (which is also the same for various languages such as Chinese and Japanese). Seller terminal 10 provides a variety of English (and its translation data) for the English translation learning to the server 20, the server 20 may learn the character using the provided data. Characters that were capable of translating English at the beginner level can improve the level of English translation through continuous learning.

Furthermore, since learning is performed using the data provided by the terminal, a character personalized to the user of the terminal may be obtained. That is, the data provided from the terminal may be data related to a word or a sentence that the user of the terminal is interested in or frequently used. Thus, the learned character data can translate the user's field of interest better and there is an effect that a personalized character can be generated. For example, if the user of the terminal has learned the character by continuously providing data on the sports field of his or her field of interest, the character will have a higher quality of translation in the sports field than the quality of the translation for other fields. .

Furthermore, when comparing the native English level translation with the character who has learned from the beginning, there is an effect that the user can reduce the amount of data for learning and the capacity of the character by improving only the translation ability for the required field.

Meanwhile, in the above-described embodiment, the seller terminal 10 transmits the category information so that the server 20 learns the character, but the present invention is not limited thereto. In an embodiment according to the present disclosure, the server 20 may receive user information of the terminal from the seller terminal 10, and may learn a character based on the received user information. In this case, the user information includes biometric information of the user collected through the terminal and usage history records of the terminal collected through the terminal (the usage history record includes schedule information, types of applications used, usage time, website address, and access time). , At least one of all user commands through the terminal, such as an alarm setting time, a terminal use time, and a distribution.)

In other words, the character may be learned and used as a personalized character according to the personal usage of each terminal.

In this case, the seller terminal 10 may pay the server 20 according to the degree of learning the character according to the character generation and the selected category information. In detail, the seller terminal 10 may pay the server 20 for a character generation cost, a cost for learning data according to a selected category, and a cost for learning a character with the selected learning data.

That is, the character data, the category information provided, the learning data for learning the character according to the selected category using the resources of the server 20, the seller terminal 10 generates a character by paying points to the server 20 and You can learn. However, when the seller terminal 10 directly provides the learning data, the seller terminal 10 does not pay the cost for the learning data to the server 20, but may provide only the cost of learning the character with the provided learning data. Of course it can.

Therefore, the seller terminal 10 may learn the character within the range available using its resources.

Meanwhile, the learned character may be stored in the form of an APK file. In this case, when the APK file is executed, the seller terminal 10 or the purchaser terminal 30 may display a character corresponding to the executed APK file on the screen.

Meanwhile, the above-described various learning may be implemented through an artificial intelligence system. Artificial Intelligence (AI) system is a computer system that implements human-level intelligence, and unlike conventional rule-based smart systems, machines learn, judge, and become smart. As the AI system is used, the recognition rate is improved and the user's taste can be understood more accurately, and the existing Rule-based smart system is gradually replaced by the deep learning-based AI system.

AI technology is composed of elementary technology that utilizes machine learning (deep learning) and machine learning.

Machine learning is an algorithm technology that classifies / learns characteristics of input data by itself, and element technology is a technology that simulates the functions of human brain cognition and judgment by using machine learning algorithms such as deep learning. It consists of technical fields such as understanding, reasoning / prediction, knowledge representation and motion control.

Specifically, deep learning is a machine learning algorithm that attempts a high level of abstraction (summarizing key content or functions in large amounts of data or complex data) through a combination of several nonlinear transformations. It is defined as a set of. Deep learning can be seen as a field of machine learning that teaches computers how people think in a large framework.

When there is any data, it is represented in a form that can be understood by a computer (for example, in the case of an image, the pixel information is represented by a column vector), and a lot of research is performed to apply it to learning. How to create expression techniques and how to build models to learn them). As a result of these efforts, various deep learning techniques have been developed. Deep learning techniques include Deep Neural Networks (DNN), Convolutional Deep Neural Networks (CNN), Reccurent Neural Networks (RNN), and Deep Belief Networks (DBN). have.

Deep Neural Networks (DNNs) are artificial neural networks (ANNs) made up of a plurality of hidden layers between an input layer and an output layer.

At this time, the structure of the deep neural network may be composed of a perceptron (perceptron). A perceptron consists of several inputs, one processor, and one output. The processor multiplies a plurality of input values by each weight, and then adds the weighted input values together. The processor then substitutes the sum of the values into the activation function and outputs one output value. If a specific value is desired as an output value of the activation function, the weight multiplied by each input value may be modified, and the output value may be recalculated using the modified weight. At this time, each perceptron may use a different activation function. Each perceptron also accepts the output from the previous layer as input, then uses the activation function to get the output. The obtained output is passed to the input of the next layer. Through the above process, several output values can be finally obtained.

According to an exemplary embodiment, the server 20 may collect various user information of the user from the seller terminal 10 and use the weight to be used for deep learning. In this case, the user information is a user's biometric information collected through the terminal, the usage history record of the terminal collected through the terminal (use history record is the type of the application used, the usage time, the website address, the access time, the alarm It may be at least one of all user commands through the terminal, such as a set time, a terminal use time, and a distribution. Can be collected and the respective weights can be reset based thereon.

Returning to the deep learning technique, Convolutional deep Neural Networks (CNN) is a type of multilayer perceptrons designed to use minimal preprocessing. A convolutional neural network consists of one or several convolutional layers and a general artificial neural network layer on top of them, and further utilizes weights and pooling layers. This structure allows the multiplicative neural network to fully utilize the input data of the two-dimensional structure. In addition, convolutional neural networks can be trained through standard backtransmission. The multiplicative neural network is more easily trained than other feedforward neural network techniques and has the advantage of using fewer parameters. The convolutional neural network extracts features from the input image by performing alternating convolutional and subsampling on the input image.

The convolutional neural network includes a plurality of convolution layers, a plurality of subsampling layers, a lacal pooling layer, a max-pooling layer, and a fully connected layer. The convolution product layer is a layer that performs a convolution product on an input image. The subsampling layer is a layer that locally extracts the maximum value of the input image and maps it to the 2D image. The subsampling layer enlarges the local region and performs subsampling.

In the product multiplication layer, information such as kernel size, the number of kernels to be used (that is, the number of maps to be generated), and a weight table to be applied in the product calculation are required. For example, assume that the size of the input image is 32 × 32, the size of the kernel is 5 × 5, and the number of kernels to be used is 20. In this case, if a 5 × 5 kernel is applied to an input image having a size of 32 × 32, it is impossible to apply the kernel to two pixels at the top, bottom, left, and right sides of the input image. When the kernel is placed on the input image and then the result is composite, the resulting value of '-8' is determined as the pixel value corresponding to the center element of the kernel among the pixels of the input image included in the kernel. to be. Therefore, when a composite product is performed by applying a 5 × 5 kernel to a 32 × 32 input image, a 28 × 28 map is generated. Since it is assumed that the total number of kernels to be used is 20 in total, 20 maps of 28 × 28 sizes are generated in the first convolutional layer.

In the subsampling layer, information on the size of the kernel to be subsampled and information on whether to select the maximum value or the minimum value among the values in the kernel region are needed.

Returning to the deep learning technique, a cyclic neural network (RNN) refers to a neural network in which connections between units forming an artificial neural network form a directed cycle. The cyclic neural network, unlike the front neural network, can utilize the memory inside the neural network to process arbitrary input.

Deep Belief Networks (DBN) is a generic graphical model used in machine learning. In deep learning, deep Belief Networks (DBN) is a deep neural network composed of multiple layers of latent variables. There is a connection between layers, but there is no connection between units within a layer.

Deep trust neural networks can be used for preliminary learning due to the nature of the generation model, and after the initial weights are learned through prior learning, the weights can be fine-tuned through back propagation or other discrimination algorithms. This characteristic is very useful when the training data is small, because the smaller the training data, the greater the influence of the initial value of the weight on the resulting model. The pre-learned weight initial value is closer to the optimal weight than the arbitrarily set weight initial value, which enables the performance and speed improvement of the fine tuning step.

Details of the above-described artificial intelligence and its learning method are described for the purpose of illustration, and the artificial intelligence used in the embodiments described below and its learning method are not limited. For example, all kinds of artificial intelligence techniques and learning methods that can be applied by those skilled in the art to solve the same problem may be utilized to implement a system according to the disclosed embodiments.

5 is a flowchart illustrating a case in which data traded in a knowledge sharing platform according to an embodiment of the present invention is a programming file.

In operation S310, when the at least one data is data related to the APK file, the server 20 may extract at least one package data from data related to the APK file.

That is, the seller terminal 10 may register and trade only the APK file itself in the server 20, but the APK file may include a plurality of packages. Therefore, the server 20 may of course extract the package from the registered APK file and provide it to the seller terminal 10.

In this case, the server 20 may divide the APK file according to a predetermined criterion, and then examine the completeness of the divided file. In one embodiment, when the APK file is written in Java, the server 20 may divide the file based on the 'import' command among the codes included in the APK file. As another example, the server 20 may split a file based on a 'package' command. The server 20 may determine whether an error occurs by compiling the divided packages. If an error occurs, the server 20 may reconfigure the package to further include additional code by changing the method of partitioning, or reconfigure the package by deleting a part of the code of the partitioned package. The server 20 may extract the package from the APK file by applying the same method until there is no error in compilation.

In operation S320, the server 20 may receive a condition for at least one of size information, function information, class information, and function information about the package from the seller terminal 10.

That is, the server 20 may obtain basic information of the package to be extracted from the user and extract the package to match the basic information.

In operation S330, the server 20 may extract at least one package data from the APK file based on the received information.

6 is a flowchart illustrating a method of generating summary information of character data traded in a knowledge sharing platform according to an embodiment of the present invention.

In operation S410, the server 20 may receive training data corresponding to the category information received from the seller terminal 10 and training data received from the seller terminal 10.

In operation S420, the server 20 may learn the character by inputting the received training data into the artificial intelligence model.

As described above, the character may learn the character based on the learning data corresponding to the category information stored in the server 20 and the learning data provided from the seller terminal 10.

Characters learned through the various methods described above may be implemented in various forms according to the learning method and the learning content. Therefore, the character learned by a particular user may exist only as a specific user. Therefore, the server 20 may provide a platform for purchasing or selling a plurality of characters corresponding to the plurality of terminals.

In operation S430, the server 20 may obtain summary information about the character based on the training data input to the artificial intelligence model.

That is, the server 20 may generate summary information on the character for sale by the seller terminal 10 and transmit the generated summary information to the purchaser terminal 30. In this case, the summary information about the character may be generated based on the learning data input for learning the character.

In one embodiment, the server 20 may obtain the learning data input for learning the character. The server 20 may cluster the acquired learning data to obtain a plurality of clusters in which similar learning data are clustered. The server 20 may determine the categories of the obtained plurality of clusters. The server 20 may obtain summary information that can best represent the character based on the amount of learning data included in the obtained plurality of clusters and the determined category. For example, if the category of the first cluster is an English translation category, the category of the second cluster is a Japanese translation, and the amount of learning data in the first cluster is larger than the amount of learning data in the second cluster, the server 20 may be a character. Features that are characters for English translation can be obtained as summary information. That is, the server 20 may obtain a cluster having the largest amount of learning data as the representative cluster of the character, and obtain summary information about the obtained cluster. However, the present invention is not limited thereto, and the server 20 generates summary information about all clusters, and determines the order of each summary information generated for each of the plurality of clusters according to the amount of learning data, and according to the determined order. Of course, it is possible to generate a summary information about the character.

In operation S440, the server 20 may register the generated summary information with the character on the platform.

According to the embodiment described above, the server 20 may be used to generate a summary information for describing the character while at the same time learning data can be input to the artificial intelligence model to learn the character.

On the other hand, the character to be sold is personalized to a specific user, and thus may include personal information of the user. Therefore, the server 20 may delete the user's personal information stored in the character to be sold and proceed with the selling procedure.

In an embodiment, the server 20 may delete not only the personal information of the seller, but also delete the learning contents related to the personal information of the seller.

Furthermore, in performing clustering for generating summary information, the server 20 may delete the contents related to the personal information of the user from the contents of the learning data and perform clustering.

7 is a flowchart illustrating a method of determining whether data registered in a knowledge sharing platform is in violation of copyright according to an embodiment of the present invention.

In operation S510, when the at least one data is data for at least one of the APK file and the package, the server 20 may have data matching the APK file or the package among the plurality of data registered in the server 20 by a predetermined ratio or more. You can determine if it exists.

In operation S520, the server 20 may perform the method of step S530 when there is a matched data (S520 -Y) or the method of step S540 when there is no matched data (S520 -N).

In operation S530, the server 20 may transmit a message indicating that the APK file or package violates copyright to the seller terminal 10.

However, the server 20 may determine whether the registrant of the data matching the predetermined ratio or more and the user of the seller terminal 10 are the same person. If the registrant of the pre-registered data and the user of the seller terminal 10 are the same person, the server 20 may transmit a message guiding that the work is similar to the pre-registered work, instead of a message informing that it is in violation of copyright.

In operation S540, the server 20 may transmit a message for instructing copyright registration of the APK file or package to the seller terminal 10.

8 is a flowchart illustrating a method of determining a price of data traded in a knowledge sharing platform according to an embodiment of the present invention.

In operation S610, the server 20 may cluster a plurality of registered data to obtain a plurality of clusters.

At this time, the server 20 is provided in each of the coding education game management module 21, character management module 22, programming file management module 23, programming instruction management module 24 and 2D / 3D design management module 25. Cluster multiple data for That is, the server 20 clusters the data included in the coding education game management module 21, clusters the data included in the character management module 22, and clusters the data included in the programming file management module 23. And cluster the data included in the programming instruction management module 24 and the data included in the 2D / 3D design management module 25. In other words, the server 20 may independently cluster data managed by each module.

However, the present invention is not limited thereto, and the server 20 may include a coding education game management module 21, a character management module 22, a programming file management module 23, a programming instruction management module 24, and a 2D / 3D design management. Of course, it is also possible to create a cluster by clustering all data managed by the module 25.

In operation S620, the server 20 may determine a first cluster including at least one received data.

In operation S630, the server 20 may determine the price of at least one data received based on the price and the number of sales of the plurality of data included in the first cluster.

That is, the server 20 may generate a cluster to classify similar data, and determine the price of the data according to whether there is data similar to the data to be registered by the seller terminal 10. For example, if the number of similar data is large, the price of the data can be lowered.

However, according to various embodiments of the present disclosure, the price of the data may be determined by considering various factors along with the number of data included in the first cluster.

In an embodiment, the price of the data may be determined based on the number of data included in the first cluster, the price of each data, and the sales amount of each data. In detail, the price of the data may include obtaining a first weight calculated based on the number of data included in the first cluster and the number of each data included in each of the remaining clusters, and the data included in the first cluster. The method may include obtaining a first price index determined based on the sales amount of the data and the price of the data, and determining a price of the data based on the first weight and the first price index.

In this case, the step of obtaining the first weight calculated based on the number of data included in the first cluster and the number of data included in each of the remaining clusters may be determined based on the number of data of each cluster. For example, if the data of the first cluster is 100, the data of the second cluster is 50, and the data of the third cluster is 10, the weight of the first cluster is 0.01, the weight of the second cluster is 0.02, The weight for the third cluster may be 0.1. That is, as the number of data included in the cluster increases, the server 20 may set a lower value of the weight.

Meanwhile, the obtaining of the first price index determined based on the sales amount of each data included in the first cluster and the price of each data may be determined in various ways.

In one embodiment, the server 20 obtains an average value and a variance value for the data price based on the sale price for each data, and obtains an average value and the variance value for the data sale amount based on the sales amount for each data. Can be.

When the variance value of the price for each data is less than or equal to the preset value, the server 20 may calculate the first price index of the data transmitted by the seller terminal 10 based on the average value of the price for each data. have. That is, when the price difference of the data included in the first cluster is not large, the server 20 may obtain the first price index based on all the data.

However, when the variance value for the price for each data is greater than the preset value, the server 20 determines the data to exclude in the first price index calculation based on the variance value, and excludes the price for the determined data. The first price indicator may be calculated. For example, the server 20 may exclude the data having the price of the upper n% and the lower n% based on the variance value, and based on the average value of the prices of the remaining data, The first price indicator may be calculated. That is, when the price difference of the data included in the first cluster is large, the server 20 may obtain the first price index by excluding data having a large price difference.

Furthermore, the server 20 may further determine the first price index by further considering the information on the sales amount. In an embodiment, when the variance of the sales amount for each data is less than or equal to the preset value, the server 20 may use the first price index calculated for each data as it is.

However, when the variance value of the sales amount for each data is equal to or greater than the preset value, the server 20 determines the data to be used for calculating the first price index based on the variance value, and based on the determined data, Can be calculated. For example, the server 20 determines the data having the sales amount of the upper n% based on the variance value as data necessary for calculating the first price index, and the seller terminal 10 based on the average value of the prices of the determined data. The first price index of the transmitted data may be calculated. That is, the server 20 may obtain the first price index based on data with a large amount of sales among data included in the first cluster.

In another embodiment, the server 20 may determine the first price index by applying different weights according to the sales volume of the data.

9 is a flowchart illustrating a method of determining a fee paid to a platform when a data transaction is performed in a knowledge sharing platform according to an embodiment of the present invention.

In operation S710, the server 20 may determine the platform fee based on the number of the plurality of data included in the first cluster and the price of the determined at least one data. Furthermore, of course, the server 20 may determine the platform fee based on the sales volume of the data of the first cluster.

In an embodiment, the server 20 may determine the platform fee in proportion to the number of data included in the first cluster and the sales amount of the data included in the first cluster. That is, when the number and sales of the data included in the cluster are large, it means that the transaction of the data for the corresponding field is active, so that the server 20 may set a high platform fee. However, when determining the platform fee in proportion to the number of data and the sales volume of the data, there may be a problem that the platform fee may be set high according to the malicious data registration behavior of the concerned person. Therefore, in another embodiment according to the present invention, the server 20 may determine the platform fee according to the relationship between the number of data and the sales amount of the data. For example, the server 20 may determine the platform fee based on the number of data and the sales amount of the data, but only the data sold more than a predetermined number of times may be determined as the number of data. That is, the server 20 may ignore the data sold less than the predetermined number of times and determine the platform fee.

In operation S720, when the purchaser terminal 30 purchases the at least one data, the platform fee determined for the at least one data may be paid to the platform. At this time, the platform fee may be a ratio of the data price, of course.

Meanwhile, according to various embodiments of the present disclosure, the plurality of data may be settled using cash, settled using points used in the platform, or settled using blockchain-based virtual currency. Of course, the points used on the platform can be linked to the e-commerce market.

Meanwhile, the coding education game management module 21, the character management module 22, the programming file management module 23, the programming instruction management module 24, and the 2D / 3D design management module 25 are mutually in accordance with one embodiment. You can use different types of points. At this time, the points managed in each module may be exchanged in conjunction with each other. As described above, the exchange rate of each module may be determined based on the number of data managed by each module or the sales amount, but is not limited thereto. The exchange rate may be determined based on the time and cost of data generation. Of course.

On the other hand, the platform according to the present invention can be implemented in a virtual space. When the platform is implemented on the virtual space, the seller terminal 10 and the purchaser terminal 30 may have a built-in processing device for processing and playing the virtual space, or is connected to an external processing device and received from the processing device. Information can also be output using the display. The external processing device may be a computing device connected to the server 20. According to an exemplary embodiment, the virtual reality image may be output based on information received from the seller terminal 10 and the purchaser terminal 30 and the server 20.

The server 20 may be connected to the seller terminal 10 and the purchaser terminal 30, and may transmit content to each of the plurality of virtual reality devices. Each of the seller terminal 10 and the purchaser terminal 30 may include a server ( The virtual space may be output based on the content received from the 20). In this case, the seller terminal 10, the server 20 and the buyer terminal 30 may communicate using 5G mobile communication (5G Network). By using fifth generation mobile communication, a smooth virtual space can be provided.

In one embodiment, characters may be traded on a platform implemented in a virtual space. The server 20 may arrange a character registered in the virtual space, and the purchaser terminal 30 may try to use the function of the character disposed in the virtual space. By directly using a character implemented in a virtual space other than text or video, there is an effect of further increasing the user's purchase intention of the purchaser terminal 30.

10 is a block diagram of an apparatus according to an embodiment of the present invention.

The processor 102 may include a connection passage (eg, a bus, etc.) that transmits and receives signals with one or more cores (not shown) and graphics processing unit (not shown) and / or other components. .

The processor 102 according to one embodiment executes one or more instructions stored in the memory 104 to perform the method described in connection with FIGS. 1-9.

For example, the processor 102 obtains new training data by executing one or more instructions stored in a memory, performs a test on the acquired new training data using a trained model, and performs the test result and labeling. Extracting the first learning data for which the obtained information is obtained with an accuracy equal to or greater than a predetermined first reference value, deleting the extracted first learning data from the new learning data, and extracting the new learning data from which the extracted learning data is deleted. Can be used to retrain the learned model.

Meanwhile, the processor 102 may include random access memory (RAM) and read-only memory (ROM) for temporarily and / or permanently storing a signal (or data) processed in the processor 102. , Not shown) may be further included. In addition, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processor, a RAM, and a ROM.

The memory 104 may store programs (one or more instructions) for processing and controlling the processor 102. Programs stored in the memory 104 may be divided into a plurality of modules according to functions.

The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, in a software module executed by hardware, or by a combination thereof. Software modules may 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 in any form of computer readable recording medium well known in the art.

The components of the present invention may be embodied as a program (or an application) and stored in a medium for execution in combination with a computer which is hardware. The components of the present invention may be implemented in software programming or software elements, and similarly, embodiments include C, C ++, including various algorithms implemented in combinations of data structures, processes, routines, or other programming constructs. It may be implemented in a programming or scripting language such as Java, assembler, or the like. Functional aspects may be implemented in algorithms running on one or more processors.

In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those skilled in the art to which the present invention pertains may realize the present invention in other specific forms without changing the technical spirit or essential features thereof. I can understand that. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive.

10: seller terminal
20: server
30: Buyer terminal

Claims (1)

In the control method of the knowledge sharing platform performed by the server,
Generating, by the seller terminal, data of at least one of coding education contents, characters, APK files, packages, and design contents;
The seller terminal transmitting the generated at least one data to the server;
Determining, by the server, a price of the received at least one data;
Registering, by the server, the received at least one data with the platform; And
When the server receives a user command to purchase the at least one data from a purchaser terminal, transmitting the at least one data to the purchaser terminal; Including,
When the at least one data is data for a character, generating the at least one data may include:
Generating, by the server, a character by receiving information on a character to be generated from the seller terminal;
Transmitting, by the server, category information for learning the character to the seller terminal;
Receiving category information on at least one of the transmitted category information from the seller terminal; And
Learning a character based on the received category information; Including,
The learning step,
Receiving learning data corresponding to the received category information and learning data received from the seller terminal; And
Inputting the received training data into an artificial intelligence model to learn the training data; More,
Receiving the learning data received from the seller terminal,
Acquiring data about a handwritten note that the seller took in school or academy;
Extracting text based on the data on the handwritten note when the data on the handwritten note is an image file; And
Receiving the extracted text data as learning data; Including,
The registering step,
Generating summary information about the character based on the learning data input to the artificial intelligence model, when the at least one data is data related to the learned character; And
Registering the summary information with the character; Including,
Generating the summary information,
Acquiring learning data committed to learning the character;
Clustering the acquired learning data to obtain a plurality of clusters, and determining a category for each of the plurality of clusters;
Determining a representative cluster of the plurality of clusters based on the amount of learning data included in the plurality of clusters and the determined category;
Generating summary information for the representative cluster; And
Deleting training data related to user personal information of the seller terminal, from the training data input to the artificial intelligence model; More,
If the at least one data is data related to the APK file,
The control method,
Extracting at least one package data from data associated with the APK file; Including,
Extracting the at least one package data,
Receiving a condition for at least one of size information, function information, class information, and function information about a package from the seller terminal; And
Extracting at least one package data from the APK file based on the received condition;
The determining of the price of the received at least one data,
Clustering the registered plurality of data to obtain a plurality of clusters;
Acquiring a first cluster including the received at least one data; And
Determining a price of the received at least one data based on a price and a number of sales of a plurality of data included in the first cluster; Including,
The determining of the price of the received at least one data,
Obtaining the number of data included in the first cluster, each data price, and each data sale amount;
Obtain a first weight calculated based on the number of data included in the first cluster and the number of data included in each of the remaining clusters except the first cluster among the plurality of clusters, wherein the magnitude of the first weight is equal to the Inversely related to the number of data contained in the first cluster;
Acquiring a first price indicator determined based on a sales amount of each data included in the first cluster and a price of each of the data; And
Determining the received at least one price based on the first weight and the first price indicator; Including,
Acquiring the first price indicator,
Obtaining an average value and a variance value of the selling price for each of the data included in the first cluster;
Obtaining an average value and a variance value of the sales amount for each of the data included in the first cluster;
Calculating a first price index based on an average value of prices for each of the data when the variance value of the prices for each of the data is equal to or less than a preset value;
When the variance value of the price for each of the data is greater than the preset value, the data corresponding to the preset top n% price and the preset lower n% of the data are based on the variance value of the price for each of the data. Calculating a first price index based on an average value of prices of data excluding data corresponding to prices; And
When the variance value of the sales amount for each of the data is equal to or greater than a preset value, the data having a predetermined top n% sales amount is obtained based on the variance value of the sales amount for each of the data, and the preset top n% Obtaining a first price indicator based on data having a sales amount of? Including,
The design content includes at least one of design content associated with a character required for program production, design content associated with a UX / UI icon, design content associated with an emoticon, and design content associated with a font, and received from the seller terminal. Design content is transmitted to the purchaser terminal by the 2D / 3D design management module of the server.

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