KR102071197B1 - Control method, device and system of knowledge sharing platform for content management of coding education game - Google Patents

Abstract

Disclosed is a knowledge sharing platform for providing an environment where various knowledge content may be traded. According to the present invention, a method for controlling the knowledge sharing platform comprises the steps of: generating at least one piece of data 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 price of the received data by the server; registering the received data to the platform by the server; and transmitting the data to a purchaser terminal by the server if a user instruction for purchasing the data is received from the purchaser terminal.

Description

CONTROL METHOD, DEVICE AND SYSTEM OF KNOWLEDGE SHARING PLATFORM FOR CONTENT MANAGEMENT OF CODING EDUCATION GAME}

The present invention relates to a method, apparatus and system for controlling a knowledge sharing platform for coding education game content management.

With the development of IT technology, the need to share a variety of knowledge is increasing. For example, there is an increasing need for a platform for sharing content for coding education, written programming files, and design content.

Most of the conventional various content trading platforms have only the characteristics of simply registering and trading content. However, due to the nature of knowledge contents, there is also a need to protect contents as well as transaction of contents, and furthermore, it is an important situation to evaluate the value of knowledge contents.

Furthermore, with the development of technology, technologies for constructing a virtual space providing various experiences to users have been developed. Virtual reality is a technology that enables a person to experience life in a virtual world created by a computer, and augmented reality (VR) is a technology that adds virtual information to the real world. A technology that mixes AR: augmented reality is called mixed reality (MR). VR, AR, and MR are common in that they are technologies that enable people to recognize reality by realizing a reality that does not exist. However, AR is a method of adding virtual information to the real world, and VR is different in that a fictional situation is presented. MR mixes AR and VR to provide real and virtual information in a mixed background, and requires a technology capable of processing large amounts of data.

At this time, the processing of large-capacity data includes a technique for transmitting large-capacity data. In the past, there was a problem in transmitting large amounts of data quickly, but recently, the use of ultra-high frequency of 28 GHz and the dissemination of the 5th generation mobile communication (5G Networks), which reaches the maximum download speed of 20 Gbps, began 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.

Registered Patent Publication No. 10-1783476, October 10, 2017

The problem to be solved by the present invention is to provide a control method, apparatus and system for a knowledge sharing platform for coding education game content management.

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 control method of a knowledge sharing platform according to an aspect of the present invention for solving the above-described problems includes: a seller terminal generating at least one data among 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, the price of the received at least one data; The server registering 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: the server receiving information about a character to be generated from the seller terminal to generate a character; Transmitting, by the server, category information for learning the character to the seller terminal; Receiving category information for 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.

At this time, when the at least one data is data related to the APK file, the control method includes: extracting at least one package data from data related to the APK file; The step of extracting the at least one package data includes: receiving conditions for at least one of size information, function information, class information, and function information about the package from the seller terminal; And extracting at least one package data from the APK file based on the received condition.

At this time, the step of learning comprises: 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. Further comprising, the step of registering, when the at least one data is data related to the learned character, generating summary information about the character based on the learning data input to the artificial intelligence model; And registering the summary information together with the character. It may include.

At this time, if the at least one data is data for at least one of the APK file and the package, the control method may include whether there is data matching a predetermined ratio or more with the APK file or the package among the registered plurality of data. Judging; 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 when the matching data does not exist, transmitting a message informing the copyright registration of the APK file or the package to the seller terminal. It may include.

At this time, the step of determining the price of the received at least one data includes: obtaining a plurality of clusters by clustering the registered plurality of data; Obtaining a first cluster containing the received at least one data; And determining prices of the received at least one data based on price and number of sales of the plurality of data included in the first cluster. It may include.

At this time, 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 determined price of the at least one data; And when the purchaser terminal purchases the at least one data, paying the platform fee determined for the at least one data to the platform.

At this time, the step of acquiring the summary information includes: deleting learning data related to user personal information of the seller terminal from among learning data input to the artificial intelligence model; It may further include.

Other specific matters of the present 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 capable of trading various knowledge contents.

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 block diagram illustrating a knowledge sharing platform according to an embodiment of the present invention.
2 is a block diagram illustrating the configuration of a server according to an embodiment of the present invention.
3 is a flowchart illustrating the operation of the 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 for determining whether data registered in the knowledge sharing platform according to an embodiment of the present invention is in violation of copyright.
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 for explaining a method of determining a fee paid to a platform when trading data in a knowledge sharing platform according to an embodiment of the present invention.
10 is a configuration 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 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 various different forms, only the present embodiments allow the disclosure of the present invention to be complete, and are common in the technical field to which the present invention pertains. It is provided to fully inform the skilled person 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 this 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 mentioned components. 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 the present specification may be used as meanings commonly understood by those skilled in the art to which the present invention pertains. In addition, terms defined in the commonly used dictionary are not ideally or excessively interpreted unless explicitly 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. The functionality provided within components and "parts" or "modules" can be combined into a smaller number 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”, 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 flipped over, the component described as "below" or "beneath" the other component will be placed "above" the other component. You can. Thus, the exemplary term “below” can include both the directions below and above. Components can also be oriented in different directions, and thus spatially relative terms can 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 software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood as meaning including, but not limited to, a smartphone, a tablet PC, a desktop, a laptop, and user clients and applications running on each device.

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

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

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 may be implemented as a smart phone, but this is only an example, and includes a smart phone, a tablet personal computer (PC), Mobile phone, video phone, e-book reader, desktop PC, laptop PC, netbook computer, workstation, server, PDA (personal digital assistant), a portable multimedia player (PMP), an MP3 player, a mobile medical device, a camera, or a wearable device.

As another embodiment, the seller terminal 10 or the buyer terminal 30 may be a home appliance. Household appliances include, for example, televisions, DVD players (Digital Video Disk players), audio, refrigerators, air conditioners, vacuum cleaners, ovens, microwave ovens, washing machines, air cleaners, set-top boxes, and home automation. Home automation control panel, security control panel, TV box (eg Samsung HomeSync ?, Apple TV ?, or Google TV?), Game console (eg 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), may include a second buyer terminal 32 and a third buyer terminal 33, but is not limited thereto, and may include a variety of seller terminals and buyer terminals as needed.

On the other hand, in the present invention, unless specifically designated for convenience of description, the first seller terminal 11, the second seller terminal 12 and the third seller terminal 13 as the seller terminal 10, the The buyer terminal 31, the second buyer terminal 32, and the third buyer terminal 33 are represented as the buyer terminal 30.

The seller terminal 10 and the buyer terminal 30 are merely a terminal of a user who sells various knowledge contents and a user's terminal that are purchased, and each of the seller terminal 10 and the buyer terminal 30 is free to use various contents. Of course, the seller terminal 10 and the buyer terminal 30 may be changed from each other.

The server 20 is configured to transmit and receive data for knowledge content from the seller terminal 10 and the buyer terminal 30. For the transaction of knowledge content, the server 20 may perform various functions described below.

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

According to various embodiments of the present invention, the server 20 includes 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 Design management module 25 may be included.

The coding education game management module 21 is a module for controlling the reception of content for the coding education game from the seller terminal 10 and transmission 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. The coding education game according to the present invention may be performed by the seller terminal 10 or the buyer terminal 30 including the camera.

In one embodiment, the coding education game traded in the present invention comprises: a first step of acquiring coding education content, a second step of selecting one or more points included in the coding education content, and obtaining an image captured from the camera A third step of recognizing the captured image, and a fourth step of matching the one or more objects with the one or more points included in the captured image, and displaying the captured 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 Eighth stage to place the one or more instruction blocks at the one or more points based on user input to the instruction block , Controlling the virtual robot in the order in which the one or more command blocks are arranged, wherein the virtual robot moves along the one or more points, and executes 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 may be included.

In this case, the first step includes an eleventh step of acquiring a mission included in the coding education content and a twelfth step of displaying the mission, and the ninth step is achieved of the mission according to the control result. It may include a 13th step of judging whether or not and a 14th step of displaying feedback according to whether the mission is accomplished.

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 a mission is achieved at each of the one or more points based on the interaction. , The 14th step may include a 17th step of displaying feedback according to whether or not the mission is achieved at each of the one or more points.

In this case, the 15th step is a 18th step of determining one or more contact points between the one or more objects and the virtual robot, and determining a part of the one or more objects and the virtual robot corresponding to each of the one or more contact points. The 19th step and the 20th step of determining the interaction based on the location, movement of the one or more contact points, and a portion corresponding to each of the one or more contact points may be included.

In this case, the twentieth step is a twenty-first step of dividing the first contact included in the one or more contacts into second and third contacts, and a second step of tracking the movements of the second and third contacts. , When the distance between the second contact and the third contact is greater than a predetermined reference value, determining the part of the one or more objects corresponding to each of the second contact and the third contact and the virtual robot; And a 24th step of determining the interaction based on the position of the second contact point and the third contact point, the movement, and a portion corresponding to the second contact point and the third contact point, respectively.

At this time, the eleventh step is a step 25 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. Step 26, a step 27 of acquiring a first mission for the virtual robot to move along the movement path and a second mission of allowing the virtual robot to perform a predetermined interaction with at least one of the one or more objects It may include the 28th step of obtaining.

In this case, the eleventh step includes the step of generating and displaying one or more virtual objects on the moving path, the step of determining the type of the one or more virtual objects, and the position of the one or more virtual objects. And a 31st step of updating the movement path based on the type, a 32nd step of acquiring a third mission to allow the virtual robot to move along the updated movement path, and the virtual robot to perform the one or more virtual movements. It may include a step 33 of obtaining a fourth mission to perform a predetermined interaction with at least one of the objects.

In this case, the eighth step further includes a 34th step of placing at least a part of the one or more command blocks on the virtual robot, and the ninth step corresponds to a command block disposed on the virtual robot. A 35th step of executing a command, a 36th step of executing a command corresponding to a command block disposed at the arrival point when arriving at the one or more points, and a command corresponding to a command block disposed in the virtual robot; When the command corresponding to the command block disposed at the arrival point collides, the operation may be stopped and a 37th step of displaying feedback may be further included.

The character management module 22 is a configuration for controlling the transmission of the character generated in the seller terminal 10 to the buyer 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 buyer 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 creation request from the seller terminal 10. The generated character can be used by the seller terminal 10 or the buyer terminal 30. At this time, the character may perform voice recognition and output a result corresponding to the user's voice through voice. To this end, the seller terminal 10 or the buyer terminal 30 may include a microphone and a speaker, and the seller terminal 10 or the buyer terminal 30 receives the user voice and delivers it to the character, and is generated from the character The answer can be output through the speaker.

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

The programming instruction module 24 is a module for managing programming instructions that are 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, programming instructions are limited to packages, but the technical spirit of the present invention can be applied to various instructions. In this case, the package is a configuration for a set of related classes, functions, or interfaces, and may be a module for performing one or more operations. By using the package, the purchaser terminal 30 can purchase several packages and use them to make a complex game, SNS, or 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 required for program production.

By the various modules described above, the server 20 can be linked with the seller terminal 10 or the buyer terminal 30 to help the production of various contents, and can create a platform environment for trading the produced contents.

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

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

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

In step S120, the seller terminal 10 may transmit at least one data among coding education contents, characters, APK files, packages, and design contents to the server 20.

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

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

In step S150, upon receiving a user command for purchasing at least one data registered from the buyer terminal 30, the server 20 may transmit at least one data to the buyer terminal 30.

Specifically, the purchaser terminal 30 may transmit a user command for purchasing the registered at least one data and a payment point corresponding to the at least one data to the server 20. The server 20 may transmit at least one data to the purchaser terminal 30 when the received payment point is equal to the price of at least one data.

Meanwhile, according to various embodiments of the present invention, data sales may be performed using a QR code. QR code is an abbreviation of Quick Response Code. It is a kind of matrix-type two-dimensional barcode that displays information in a black and white grid pattern. It was developed and published in 1994 by Denso Wave Inc. of Japan. It has the advantage of being able to store. 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 a QR code, when the purchaser terminal 30 wants to purchase data, it can 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 step S210, when at least one data is data for a character, the server 20 may generate information by receiving information about the character to be generated from the seller terminal 10.

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

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

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

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

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

In one embodiment, the category information may be category information related to a growth-type character targeting children. At this time, 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 for the education learning category may be a category related to subjects such as a math category, an English category, a social category, a science category, and the emotion learning category is a category related to various emotions such as a joy category, a sadness category, a happiness category, an anxiety category, etc. Can be

The server 20 may receive the learning data for the selected category information to train the character. For example, when the selected category is a social category, the server 20 may receive learning data from a user and train an artificial intelligence model for learning a social subject.

In this case, the learning data received from the user may be data related to knowledge acquired by a child at a school or academy. For example, the learning data may be data for handwritten notes written at a school or academy. The server 20 may receive an image file for a handwritten note, and extract the text from the image through the OCR technique on the received image file. If necessary, if the handwritten note image includes non-texts such as pictures, diagrams, and graphs, the server 20 can generate learning data by imaging the non-text data. However, the learning data is not limited to this, and may be various data such as user voice, website information collected by the user, blog information, information of the book read.

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.

Meanwhile, a result of the user command may be determined based on learning data input from the user. For example, if the learning data input by the user is learning data related to the social studies of the first grader of the elementary school, and the user generates a user command to query the content of the social subject, the server 20 enters the first grader society of the elementary school Answers can be generated within the scope of the subject. Even if it is the scope of the social education subject of the first grade of the server 20, an answer to the learning data that the user has not input cannot be output.

Accordingly, a plurality of characters generated by a plurality of users may be learned differently according to learning data input by each of the plurality of users. A character that has entered a lot of learning data can grow into a smarter character than a character that has entered less learning data. Therefore, the user is interested in making a character better than other characters by growing his character, and can make more efforts to do this.

On the other hand, the server 20 may delete the character when learning data is not input to the character or a query is not made to the character for a predetermined time. That is, the server 20 may induce the user to steadily manage his / her character so that the character is not deleted by deleting the unused character.

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

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 the response of the output character. For example, when the user interaction with the character's reaction output is a 'praise' interaction, the character's personality may be a character full of confidence. Alternatively, when the user interaction with the response output of the character is an interaction with a 'crowd', the character's personality may be a timid personality. In another embodiment, the character's personality may be a personality similar to the user's personality obtained by analyzing user data.

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

Accordingly, a plurality of characters generated by a plurality of users may be learned differently according to learning data input by each of the plurality of users. A character that has entered a lot of learning data can grow into a character with more emotion than a character that has entered 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-categories of the language category include the first sub-category constituting the type of language such as Korean, Japanese, Chinese, and English, and the second sub-category representing translation, writing, reading, speaking, and listening to language, and each And a third detailed category for determining the degree of learning of the category.

In this case, the third detailed category is, for example, a category related to the degree of learning about the English translation category, and may be a detailed category 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 degree of learning requires more resources (amount of data, cost, etc.) than a character with a low degree of learning. Therefore, it is possible to classify categories according to the amount of available resources to train characters according to user selection.

The user can select at least one of the category information provided from the server 20 to train the character. At this time, 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.

Meanwhile, the server 20 may continuously learn the learned character. Specifically, when data related to the learned category information is input to the character, the server 20 may update the character. At this time, the data for the update 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, if data corresponding to the learned category information is received through various methods, the server 20 may update the character based on the received data.

In one embodiment, it is assumed that the character is in a state of learning a translation at the beginner level of English (which is also the same for various languages such as Chinese and Japanese). The seller terminal 10 provides various English (and its translation data) for learning English translation to the server 20, and the server 20 can learn a character using the provided data. Characters that were able to translate English at the beginner level at the beginning can improve the level of English translation through continuous learning.

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

Furthermore, when comparing English translations from the beginning with native-level English translations, the user has the effect of reducing the amount of data for learning and reducing the capacity of characters by improving only the translation ability for the required field.

Meanwhile, in the above-described embodiment, it has been described that the server 20 learns a character by transmitting the category information by the seller terminal 10, but is not limited thereto. In one embodiment according to the present disclosure, the server 20 may receive user information of the terminal from the seller terminal 10 and, of course, can learn a character based on the received user information. At this time, the user information is biometric information of the user collected through the terminal, and the usage history record of the terminal collected through the terminal (use history records are schedule information, type of application used, usage time, accessed website address, and access time) , It may be at least one of all user commands through the terminal, such as alarm setting time, terminal use time and distribution.)

That is, the character may be learned according to the personal use mode of each terminal and used as a personalized character.

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

That is, the character creation, the provision of category information, and the learning data for learning the character according to the selected category use resources of the server 20, and 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 server 20 for the learning data, but provides only the cost of learning the character with the provided learning data. Of course it can.

Therefore, the seller terminal 10 can learn a character within a range that can be utilized by using its own 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 buyer terminal 30 may display a character corresponding to the executed APK file on the screen.

Meanwhile, the various learnings described above may be implemented through an artificial intelligence system. The Artificial Intelligence (AI) system is a computer system that realizes human-level intelligence, and unlike the existing Rule-based smart system, the machine learns, judges, and becomes intelligent. 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 being replaced by the deep learning-based AI system.

Artificial intelligence technology is composed of machine learning (deep learning) and elemental technologies utilizing machine learning.

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

Specifically, deep learning is a machine learning algorithm that attempts to achieve a high level of abstraction (abstracts, summarizing the core content or functions in a large amount of data or complex data) through a combination of several nonlinear transformation methods. It is defined as a set of Deep learning can be seen as a field of machine learning that teaches a person's mindset to computers in a large framework.

When there is some data, it is represented in a form that can be understood by a computer (for example, in the case of an image, pixel information is expressed as a column vector), and a lot of research (how to do better) to apply it to learning (How to make expression techniques and how to make models to learn them) is going on. 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 Network (RNN) and Deep Belief Networks (DBN). have.

Deep Neural Networks (DNN) are artificial neural networks (ANNs) composed 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 perceptron. Perceptron is composed of several input values (input), one processor (prosessor), and one output value. The processor multiplies several input values by weights, and then sums the input values multiplied by the weights. Then, the processor substitutes the summed value into the activation function to output one output value. If a specific value is desired as the output value of the activation function, the weight multiplied by each input value may be corrected, and the output value may be recalculated using the modified weight. At this time, each perceptron may use a different activation function. In addition, each perceptron takes the outputs from the previous layer as inputs, and then uses the activation function to obtain the outputs. The obtained output is passed to the next layer of input. After the process as described above, several output values can be finally obtained.

According to the disclosed embodiment, the server 20 may collect various user information of the user from the seller terminal 10 and use it to set a weight used for deep learning. At this time, the user information is the biometric information of the user collected through the terminal, the usage history record of the terminal collected through the terminal (the usage history record is the type of application used, the usage time, the accessed website address, the access time, the alarm It may be at least one of all user commands through the terminal, such as set time, terminal use time, and distribution.) For example, an artificial intelligence model for learning a character provides information that complements a character based on various user information described above. It can be collected, and based on this, each weight can be reset.

Returning to the description of deep learning techniques, Convolutional Deep Neural Networks (CNNs) are a type of multilayer perceptrons designed to use minimal preprocessing. The convolutional neural network consists of one or several convolutional layers and a common artificial neural network layer on top of it, and additionally uses weights and pooling layers. Thanks to this structure, the convolutional neural network can fully utilize the input data of the two-dimensional structure. In addition, the convolutional neural network can be trained through standard reverse propagation. Convolutional neural networks tend to be easier to train than other feedforward artificial neural network techniques and have the advantage of using fewer parameters. The convolutional neural network extracts features from the input image by alternately performing the convergence product and subsampling on the input image.

The convolutional neural network includes a plurality of convolutional layers, a plurality of subsampling layers, a lacal pooling layer, and a Max-Pooling layer, and a fully connected layer. The composite product layer is a layer that performs a composite product on an input image. In addition, the subsampling layer is a layer that extracts a maximum value locally for an input image and maps it as a two-dimensional image, and enlarges a local area and performs subsampling.

In the convolution layer, information such as the size of the kernel (kernel size), the number of kernels to be used (ie, the number of maps to be generated), and a weight table to be applied when calculating the convolution is required. For example, suppose 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 of 32 × 32 size, it is impossible to apply a kernel to 2 pixels each from the top, bottom, left, and right of the input image. If a composite product is performed after the kernel is placed on the input image, the result value '-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 an input image of 32 × 32 size, a 28 × 28 size map is generated. Previously, since it is assumed that the number of kernels to be used is a total of 20, a total of 20 28 × 28 maps are generated in the first composite product layer.

In the subsampling layer, information about 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 area are required.

Returning to the description of the deep learning technique, the Reccurent Neural Network (RNN) refers to a neural network in which connections between units constituting an artificial neural network constitute a Directed Cycle. Cyclic neural networks, unlike pre-feeding neural networks, can utilize memory inside the neural network to process arbitrary input.

Deep Belief Networks (DBN) is a graphical graphical model used in machine learning, and deep learning refers to a deep neural network composed of multiple layers of latent variables. The feature is that there is a connection between layers, but there is no connection between units in the layer.

The deep trust neural network can be used for pre-learning due to the nature of the generation model, and after learning the initial weight through pre-learning, the weight can be fine-tuned through back propagation or other discrimination algorithm. This characteristic is very useful when there is little training data, because the smaller the training data, the stronger the initial value of the weight has on the resulting model. The pre-trained weight initial value is closer to the optimal weight than the arbitrarily set weight initial value, which enables performance and speed improvement in the unadjusted stage.

The contents of the above-described artificial intelligence and its learning method are described for illustrative purposes, and the artificial intelligence and its learning method used in the embodiments described below are not limited. For example, all kinds of artificial intelligence technologies and learning methods that can be applied by a person skilled in the art to solve the same problem may be utilized to implement a system according to the disclosed embodiment.

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 step 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 the data related to the APK file.

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

At this time, the server 20 may divide the APK file according to a preset criterion, and then review 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 an 'import' command among codes included in the APK file. As another example, the server 20 may divide the file based on the 'package' command. The server 20 may compile the partitioned package and determine whether an error occurs. When an error occurs, the server 20 may change the method of partitioning to reconstruct the package to further include additional code, or delete a portion of the code of the partitioned package to reconstruct the 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 step S320, the server 20 may receive a condition for at least one of size information, function information, class information, and function information for 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 step 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 step S410, the server 20 may receive learning data corresponding to category information received from the seller terminal 10 and learning data received from the seller terminal 10.

In step S420, the server 20 may input the received learning data into the artificial intelligence model to train the character.

As described above, the character may train 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 learning methods and learning contents. Therefore, a character learned by a specific user may exist only as a specific user and scarcity may exist. Accordingly, the server 20 may provide a platform for purchasing or selling a plurality of characters corresponding to a plurality of terminals.

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

That is, the server 20 may generate summary information about the character for the seller terminal 10 to sell and transmit it to the buyer terminal 30. At this time, summary information about the character may be generated based on learning data input for learning the character.

In one embodiment, the server 20 may acquire learning data input for learning a character. The server 20 may cluster the acquired learning data to obtain a plurality of clusters clustered with similar learning data. 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 and the determined category included in the obtained plurality of clusters. For example, if the category of the first cluster is the English translation category, the category of the second cluster is the Japanese translation, and the amount of learning data in the first cluster is greater than the amount of learning data in the second cluster, the server 20 has a character. Characters for English translation can be obtained as summary information. That is, the server 20 may acquire a cluster having the largest amount of learning data as a representative cluster of characters, and obtain summary information about the obtained cluster. However, the present invention is not limited thereto, and the server 20 generates summary information for all clusters, but 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 summary information about the character.

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

According to the above-described embodiment, the server 20 may input learning data into an artificial intelligence model to train a character, and may also be used to generate summary information for describing a character.

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

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

Further, the server 20 may perform clustering to delete the content related to the user's personal information among the contents of the learning data, even when performing clustering to generate summary information.

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

In step S510, when the at least one data is data for at least one of the APK file and the package, the server 20 has data matching the APK file or package with a predetermined ratio or more among a plurality of data registered in the server 20 You can judge if it exists.

In step S520, the server 20 may perform the method of step S530 when matching data exists (S520-Y), and the method of step S540 when matching data does not exist (S520-N).

In step S530, the server 20 may transmit a message indicating that the APK file or package is in violation of copyright to the seller terminal 10.

However, the server 20 may determine whether the user of the registrant and the seller terminal 10 of the data matching the preset ratio or more is the same person. When 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 the message notifying that the copyright is in violation.

In step S540, the server 20 may transmit a message instructing the 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 step S610, the server 20 may obtain a plurality of clusters by clustering a plurality of registered data.

At this time, the server 20 is a 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, respectively Multiple data can be clustered. That is, the server 20 clusters data included in the coding education game management module 21, clusters data included in the character management module 22, and clusters data included in the programming file management module 23. The data included in the programming command management module 24 may be clustered, and the data included in the 2D / 3D design management module 25 may be clustered. That is, the server 20 can independently cluster data managed by each module.

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

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

In step 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 create a cluster to classify similar data, and determine the price of the data according to whether there is any data similar to the data that the seller terminal 10 wants to register. For example, if the number of similar data is large, the price of the data may be lowered.

However, according to various embodiments of the present invention, it is needless to say that the price of data may be determined by considering various factors along with the number of data included in the first cluster.

In one 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. Specifically, the price of the data includes obtaining 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, and each data included in the first cluster It may include the step of obtaining a first price index determined based on the price of each of the sales volume and data, and determining the 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 each data included in each of the remaining clusters may be determined based on the number of data in 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 for the first cluster is 0.01, the weight for the second cluster is 0.02, The weight for the third cluster may be 0.1. That is, the larger the number of data included in the cluster, the lower the value of the weight can be set by the server 20.

Meanwhile, the step of acquiring 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 an average value and a variance value for the data sale amount based on the sales amount for each data. You can.

When the variance value for the price for each data is equal to or less than a 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 for the price for each data. have. That is, when the price difference between the data included in the first cluster is not large, the server 20 may obtain a first price index based on all data.

However, when the variance value for the price for each data exceeds a preset value, the server 20 determines data to be excluded in calculating the first price index based on the variance value, and excludes the price for the determined data The first price index can be calculated. For example, the server 20 excludes data having prices of the upper n% and the lower n% based on the variance value, and the data transmitted by the seller terminal 10 based on the average value of the prices of the remaining data. The first price index can be calculated. That is, when the price difference between the data included in the first cluster is large, the server 20 may obtain a first price index by excluding data having a large price difference.

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

However, when the variance value for the sales amount for each data is greater than or equal to a preset value, the server 20 determines data to be used for calculating the first price indicator based on the variance value, and based on the determined data, determines the first price indicator. Can be calculated. For example, the server 20 determines data having an upper n% sales amount 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 determined data prices. The first price index of the transmitted data can be calculated. That is, the server 20 may obtain a first price index based on data having 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 amount of data.

9 is a flowchart for explaining a method for determining a fee paid to a platform when trading data in a knowledge sharing platform according to an embodiment of the present invention.

In step 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 determined price of at least one data. Furthermore, it is needless to say that the server 20 can determine the platform fee based on the sales volume of the data of the first cluster.

In one 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 data included in the first cluster. That is, when the number and sales of data included in the cluster is large, it means that the transaction of data for the corresponding field is active, so the server 20 can set a high platform fee. However, if the platform fee is judged in proportion to the number of data and the amount of data sold, there may be a problem that the platform fee may be set high according to a malicious data registration act of the person concerned. Accordingly, according to another embodiment of 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 data. For example, the server 20 may determine a platform fee based on the number of data and the amount of data sold, but only the data sold for a predetermined number of times or more may be determined as the number of data. That is, the server 20 may ignore the data sold for a predetermined number of times or less, and determine the platform fee.

In step 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. In this case, of course, the platform fee may be a certain percentage of the data price.

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

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

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

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

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

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

The processor 102 may include one or more cores (not shown) and a connection passage (for example, a bus) for transmitting and receiving signals to and from a graphic processing unit (not shown) and / or other components. .

The processor 102 according to an embodiment performs the methods described with respect to FIGS. 1 to 9 by executing one or more instructions stored in the memory 104.

For example, the processor 102 obtains new training data by executing one or more instructions stored in memory, and uses the trained model to perform tests on the acquired new training data, and the test results and labeling Extracts the first learning data obtained by the obtained information with an accuracy equal to or greater than a predetermined first reference value, deletes the extracted first learning data from the new learning data, and extracts the new learning data from which the extracted learning data is deleted. By using it, the trained model can be trained again.

On the other hand, the processor 102 is a RAM (Random Access Memory, not shown) and a ROM (Read-Only Memory) that temporarily and / or permanently stores signals (or data) processed inside the processor 102. , Not shown). Further, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, RAM, and 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, a software module executed by hardware, or a combination thereof. The 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 on any type of computer readable recording medium well known in the art.

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 can 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.

The embodiments of the present invention have been described above with reference to the accompanying drawings, but a person skilled in the art to which the present invention pertains may implement the present invention in other specific forms without changing its technical spirit or essential features. You will understand. Therefore, it should be understood that the above-described embodiments are illustrative 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, at least one data among coding education content, character, APK file, package, and design content;
The seller terminal transmitting the generated at least one data to the server;
Determining, by the server, the price of the received at least one data;
The server registering 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; 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 for at least one of the transmitted category information from the seller terminal; And
Learning a character based on the received category information; Including,
The step of learning,
Receiving learning data corresponding to the received category information and learning data received from the seller terminal; And
Inputting and learning the received learning data into an artificial intelligence model; Further comprising,
The step of receiving the learning data received from the seller terminal,
Acquiring data on a handwritten note written by the seller at a school or academy;
Extracting text based on the data on the handwritten note when the obtained data on the handwritten note is an image file; And
Receiving the extracted text data as learning data; Including,
The step of registering,
If the at least one data is data related to a learned character, generating summary information about the character based on the learning data input to the artificial intelligence model; And
Registering the summary information together with the character; Including,
Generating the summary information,
Obtaining learning data input for 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 among 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 learning data related to user personal information of the seller terminal from the learning data input to the artificial intelligence model; Further comprising,
When 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 conditions for at least one of size information, function information, class information, and function information about the package from the seller terminal; And
And extracting at least one package data from the APK file based on the received condition.
Determining the price of the received at least one data,
Clustering the registered plurality of data to obtain a plurality of clusters;
Obtaining a first cluster containing the received at least one data; And
Determining prices of the received at least one data based on price and number of sales of a plurality of data included in the first cluster; Including,
Determining the price of the received at least one data,
Acquiring the number of data included in the first cluster, each data price, and each data sales amount;
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 clusters other than the first cluster among the plurality of clusters is obtained, and the size of the first weight is the A step inversely related to the number of data included in the first cluster;
Obtaining a first price index determined based on the sales amount of each data included in the first cluster and the 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,
The step of obtaining the first price index,
Obtaining an average value and a variance value for a sale price for each data included in the first cluster;
Obtaining an average value and a variance value for the sales amount for each data included in the first cluster;
Calculating a first price index based on an average value for a price for each of the data when the variance value for the price for each of the data is equal to or less than a preset value;
When the variance value for the price for each of the data exceeds a preset value, based on the variance value for the price for each of the data, the data corresponding to the price of the preset upper n% and the preset lower n% Calculating a first price index based on an average value of prices of data excluding data corresponding to prices; And
When the variance value for the sales amount for each of the data is greater than or equal to a preset value, data having a preset upper n% sales amount is acquired based on the variance value for each sales amount for each of the data, and the preset upper n% Obtaining a first price index based on data having a sales volume of; Including,
The coding education content includes content for the coding education game, and the content for the coding education game received from the seller terminal is transmitted to the buyer terminal by the coding education game management module of the server. Way.

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