KR102625227B1 - Method, device and system for providing license distribution platform - Google Patents

Abstract

According to one embodiment, in the method of providing a license distribution platform performed by a device, when a sales request for a first license, which is a license of first content, is received from a first seller terminal, a license that can purchase the first license is provided. registering with; If it is confirmed that a purchase condition is not set in the first license, the first license is classified as an optional license, and if it is confirmed that a purchase condition is set in the first license, the first license is classified as a fixed license. Classifying step; If the first license is classified as an optional license, when a request for detailed information about the first license is received from the first purchaser terminal, the first page, which is a page for setting purchase conditions for the first license, is sent to the first license. Transmitting to the buyer terminal; If the purchase conditions including the usage period, purpose of use, and use goods of the first license are set through the first page, the purchase cost of the first license is calculated as the first cost based on the purchase conditions of the first license. calculating and controlling the first cost to be displayed on the first page; If the first license is classified as a fixed license, upon receiving a request to inquire detailed information about the first license from the first purchaser terminal, the usage period and purpose of use confirmed through the preset purchase conditions of the first license , transmitting a second page displaying used goods and a second cost to the first buyer terminal; When receiving a purchase request for the first license from the first buyer terminal, match the first seller and the first buyer to proceed with a contract for use of the first license between the first seller and the first buyer. creating contract space; and transmitting link information for accessing the contract space to the first seller terminal and the first buyer terminal, respectively. A method of providing a license distribution platform is provided.

Description

Method, device and system for providing a license distribution platform {METHOD, DEVICE AND SYSTEM FOR PROVIDING LICENSE DISTRIBUTION PLATFORM}

The embodiments below relate to technology for providing a license distribution platform.

IP (Intellectual Property) is an intellectual property right used in content such as dramas, games, and movies. It is something that can be of property value among everything created or discovered as a result of human creativity, and is worthy of protection by law. It can mean the right to receive legal protection against something.

License is a license to use design results developed using products/trademarks of dramas, movies, games, etc. A license contract is a license agreement in which an individual or organization holding a registered trademark rights the property rights in exchange for compensation from another person. It is a contract granting commercial rights for use, and may mean transferring a certain area of valuable commercial property rights, such as patents/company secrets/know-how/registered trademarks/knowledge held by the license holder, to the licensee during the contract period. .

License Merchandise is a product produced through intellectual property rights that enables various genres and additional industries based on content. IP stands for intellectual property rights, which protects the value of ideas, creations, inventions, etc. and protects the rights. Content refers to various forms of information or entertainment such as text, images, music, and video, and licensed products refer to various types of products or goods made based on these intellectual property rights, and contain the original It is created based on content and uses brands, characters, stories, etc. to develop and distribute various products.

The market size for licensed products continues to grow, but it is difficult to connect between sellers selling licenses and buyers purchasing licenses, and licensing contracts and purchasing procedures are complicated.

Therefore, there is a need for research and development on technologies that can increase the convenience of selling and purchasing licenses.

Korean Patent No. 10-2572690 Korean Patent No. 10-1858136 Korean Patent Publication No. 10-2014-0129666 Korean Patent Publication No. 10-2014-0046720

According to one embodiment, the purpose is to provide a method, device, and system for providing a license distribution platform.

The object of the present invention is not limited to the object mentioned above, and other objects not mentioned can be clearly understood from the description below.

According to one embodiment, in the method of providing a license distribution platform performed by a device, when a sales request for a first license, which is a license of first content, is received from a first seller terminal, a license that can purchase the first license is provided. registering with; If it is confirmed that a purchase condition is not set in the first license, the first license is classified as an optional license, and if it is confirmed that a purchase condition is set in the first license, the first license is classified as a fixed license. Classifying step; If the first license is classified as an optional license, when a request for detailed information about the first license is received from the first purchaser terminal, the first page, which is a page for setting purchase conditions for the first license, is sent to the first license. Transmitting to the buyer terminal; If the purchase conditions including the usage period, purpose of use, and use goods of the first license are set through the first page, the purchase cost of the first license is calculated as the first cost based on the purchase conditions of the first license. calculating and controlling the first cost to be displayed on the first page; If the first license is classified as a fixed license, upon receiving a request to inquire detailed information about the first license from the first purchaser terminal, the usage period and purpose of use confirmed through the preset purchase conditions of the first license , transmitting a second page displaying used goods and a second cost to the first buyer terminal; When receiving a purchase request for the first license from the first buyer terminal, match the first seller and the first buyer to proceed with a contract for use of the first license between the first seller and the first buyer. creating contract space; and transmitting link information for accessing the contract space to the first seller terminal and the first buyer terminal, respectively. A method of providing a license distribution platform is provided.

The method of providing a license distribution platform includes: receiving a license recommendation request from the first purchaser terminal before receiving a request to inquire detailed information about the first license from the first purchaser terminal; Obtaining profile information of the first buyer including purchase history, preferred content type, and areas of interest of the first buyer; generating a first input signal by encoding profile information of the first buyer; The first input signal is input to a first artificial intelligence model trained to recommend a license according to the buyer's profile, and when a recommended license is selected through the first input signal, the recommended license is received from the first artificial intelligence model. obtaining a first output signal representing; and, if the recommended license is confirmed to be the first license based on the first output signal, transmitting recommendation information about the first license to the first purchaser terminal.

When the first input signal is input, the first artificial intelligence model checks the purchase history, preferred content type, and field of interest of the first buyer based on the first input signal, and registers them as a purchaseable license. If the existing licenses are classified into a first group, and it is confirmed through the purchase history of the first purchaser that the first purchaser has purchased a second license, which is a license for the second content, each of the licenses classified into the first group For this, the higher the similarity between each content and the second content, the higher the first score is given, and the type of content preferred by the first buyer is assigned to the first category through the preferred content type of the first buyer. If it is confirmed that it is, for each license classified into the first group, the second score is given a higher value as the category of each content matches the first category, and the second score is assigned a higher value through the field of interest of the first purchaser. If it is confirmed that the field of interest of the first purchaser is the first topic, for each license classified into the first group, the third score is given a higher value as the topic of each content matches the first topic, For each license classified into the first group, the first score, the second score, and the third score are added to calculate a total score, and the total scores of each license classified into the first group are compared to obtain a total score. This highest license can be selected as the recommended license.

According to one embodiment, providing a license distribution platform has the effect of increasing the convenience of selling and purchasing licenses.

Meanwhile, the effects according to the embodiments are not limited to those mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the description below.

1 is a diagram schematically showing the configuration of a system according to an embodiment.
Figures 2 and 3 are diagrams illustrating a structure utilizing a system according to an embodiment.
Figure 4 is a diagram showing a profit structure using a system according to an embodiment.
Figure 5 is a diagram showing the main screen of an application according to one embodiment.
Figure 6 is a diagram showing a detailed information inquiry screen of an optional license according to an embodiment.
Figure 7 is a diagram showing a detailed information inquiry screen for a fixed license according to an embodiment.
Figure 8 is a diagram showing a license payment screen according to one embodiment.
Figure 9 is a flow chart to explain the process of providing a license distribution platform.
Figure 10 is a flowchart for explaining a process for providing a license recommendation function according to an embodiment.
Figure 11 is a flowchart for explaining the process of selecting a recommended license according to an embodiment.
FIG. 12 is a flowchart illustrating a process for providing a notification service for imminent contract expiration and contract renewal information according to the period remaining until the contract expiration date according to an embodiment.
Figure 13 is a flow chart to explain the process of registering a license scheduled to expire and registering a license available for purchase according to the period remaining until the contract expiration date according to an embodiment.
Figure 14 is a flow chart to explain the process of calculating the purchase cost of a license through setting purchase conditions according to an embodiment.
Figure 15 is an exemplary diagram of the configuration of a device according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the attached drawings. However, various changes can be made to the embodiments, so the scope of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents, or substitutes for the embodiments are included in the scope of rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Terms such as first or second may be used to describe various components, but these terms should be interpreted only for the purpose of distinguishing one component from another component. For example, a first component may be named a second component, and similarly, the second component may also be named a first component.

When a component is referred to as being “connected” to another component, it should be understood that it may be directly connected or connected to the other component, but that other components may exist in between.

The terms used in the examples are for descriptive purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the embodiments belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless explicitly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No.

In addition, when describing with reference to the accompanying drawings, identical components will be assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiments, if it is determined that detailed descriptions of related known technologies may unnecessarily obscure the gist of the embodiments, the detailed descriptions are omitted.

Embodiments may be implemented in various types of products such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices.

In an embodiment, an artificial intelligence (AI) system is a computer system that implements human-level intelligence, and unlike existing rule-based smart systems, it is a system in which machines learn and make decisions on their own. As artificial intelligence systems are used, recognition rates improve and sellers' tastes can be more accurately understood, and existing rule-based smart systems are gradually being replaced by deep learning-based artificial intelligence systems.

Artificial intelligence technology consists of machine learning and element technologies using machine learning. Machine learning is an algorithmic technology that classifies/learns the characteristics of input data on its own, and elemental technology is a technology that mimics the functions of the human brain such as cognition and judgment by utilizing machine learning algorithms such as deep learning, including linguistic understanding and visual It consists of technical areas such as understanding, reasoning/prediction, knowledge expression, and motion control.

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology that recognizes and applies/processes human language/characters and includes natural language processing, machine translation, conversation systems, question and answer, and voice recognition/synthesis. Visual understanding is a technology that recognizes and processes objects like human vision, and includes object recognition, object tracking, image search, person recognition, scene understanding, spatial understanding, and image improvement. Inferential prediction is a technology that judges information to make logical inferences and predictions, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, and recommendations. Knowledge expression is a technology that automatically processes human experience information into knowledge data, and includes knowledge construction (data creation/classification) and knowledge management (data utilization). Motion control is a technology that controls the autonomous driving of vehicles and the movement of robots, and includes motion control (navigation, collision, driving), operation control (behavior control), etc.

Generally, in order to apply machine learning algorithms to real life, learning is performed using a trial and error method due to the nature of the basic methodology of machine learning. In particular, deep learning requires hundreds of thousands of iterations. It is impossible to execute this in an actual physical external environment, so instead, the actual physical external environment is virtually implemented on a computer and learning is performed through simulation.

1 is a diagram schematically showing the configuration of a system according to an embodiment.

Referring to FIG. 1, a system according to an embodiment may include a plurality of seller terminals 100, a plurality of buyer terminals 200, and a device 300 that can communicate with each other through a communication network.

First, a communication network can be configured regardless of the communication mode, such as wired or wireless, and can be implemented in various forms to enable communication between servers and between servers and terminals.

Each of the plurality of seller terminals 100 and each of the plurality of buyer terminals 200 may be implemented as a computing device with a communication function, for example, a mobile phone, a desktop PC, a laptop PC, a tablet PC, a smartphone, etc. It may be implemented, but is not limited to this, and may be implemented as various types of communication devices that can be connected to an external server.

The plurality of seller terminals 100 are terminals used by sellers selling licenses and may include a first seller terminal 110 used by a first seller, a second seller terminal 120 used by a second seller, etc. The plurality of buyer terminals 200 are terminals used by buyers purchasing a license, such as the first buyer terminal 210 used by the first buyer, the second buyer terminal 220 used by the second buyer, etc. may include.

Below, for convenience of explanation, the description will focus on the operation of the first seller terminal 110 and the first buyer terminal 210, but the operation of the first seller terminal 110 in other seller terminals such as the second seller terminal 120 It goes without saying that the operation of the first buyer terminal 210 can be performed instead by another buyer terminal, such as the second buyer terminal 220.

Each of the plurality of seller terminals 100 and each of the plurality of buyer terminals 200 can connect to the device 300 through a web page, application, etc. provided by the device 300. To this end, an application that supports connection to the license distribution platform may be installed on each of the plurality of seller terminals 100 and each of the plurality of buyer terminals 200. At this time, an application that supports access to the license distribution platform may operate in conjunction with the device 300.

The device 300 may be its own server owned by a person or organization that provides services using the device 300, a cloud server, or a p2p (peer-to-peer) set of distributed nodes. It may be possible. The device 300 may be configured to perform all or part of the calculation function, storage/reference function, input/output function, and control function of a typical computer. The device 300 may be equipped with at least one artificial intelligence model that performs an inference function.

The device 300 may be configured to communicate wired or wirelessly with a plurality of seller terminals 100, control the operation of each of the plurality of seller terminals 100, and display certain information on the screen of each of the plurality of seller terminals 100. You can control whether to display it or not.

The device 300 may be configured to communicate wired or wirelessly with a plurality of buyer terminals 200, control the operation of each of the plurality of buyer terminals 200, and display certain information on the screen of each of the plurality of buyer terminals 200. You can control whether to display it or not.

The device 300 may be implemented as a server that provides a license distribution platform, thereby providing a platform for selling and purchasing licenses. A detailed description related to this will be described later with reference to FIGS. 2 and 3.

Meanwhile, for convenience of explanation, Figure 1 shows only the first seller terminal 110 and the second seller terminal 120 among the plurality of seller terminals 100, and the first buyer terminal ( 210) and the second purchaser terminal 220 are shown, but the number of terminals may vary depending on the embodiment. As long as the processing capacity of the device 300 allows, the number of terminals is not particularly limited.

According to one embodiment, the device 300 may select a recommended license according to the buyer's profile based on artificial intelligence and provide recommendation information about the license to the buyer.

In the present invention, artificial intelligence (AI) refers to technology that imitates human learning ability, reasoning ability, perception ability, etc. and implements this with a computer, and includes concepts such as machine learning and symbolic logic. may include. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the results of the analysis, and makes judgments or predictions based on the results of the learning. Additionally, technologies that mimic the functions of the human brain, such as cognition and judgment, using machine learning algorithms can also be understood as the category of artificial intelligence. For example, technical fields such as verbal understanding, visual understanding, reasoning/prediction, knowledge representation, and motion control may be included.

Machine learning can refer to the process of training a neural network model using experience processing data. Machine learning can mean that computer software improves its own data processing capabilities. A neural network model is built by modeling the correlation between data, and the correlation can be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data. Repeating this process to optimize the parameters of the neural network model can be called machine learning. For example, a neural network model can learn the mapping (correlation) between input and output for data given as input-output pairs. Alternatively, a neural network model may learn the relationships by deriving regularities between given data even when only input data is given.

An artificial intelligence learning model or neural network model may be designed to implement the human brain structure on a computer, and may include a plurality of network nodes with weights that simulate neurons of a human neural network. A plurality of network nodes may have a connection relationship with each other by simulating the synaptic activity of neurons in which neurons exchange signals through synapses. In an artificial intelligence learning model, multiple network nodes are located in layers of different depths and can exchange data according to convolutional connection relationships. The artificial intelligence learning model may be, for example, an artificial neural network model (Artificial Neural Network), a convolution neural network (CNN) model, etc. As an example, an artificial intelligence learning model may be machine-learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, and Ada-boost. , Perceptron, Genetic Programming, Clustering, etc. can be used.

Among them, CNN is a type of multilayer perceptrons designed to use minimal preprocessing. CNN consists of one or several convolution layers and general artificial neural network layers on top of them, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize input data with a two-dimensional structure. Compared to other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained via standard back propagation. CNNs have the advantage of being easier to train and using fewer parameters than other feedforward artificial neural network techniques.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increasing size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have led to significant improvements in many computer vision tasks. For extremely large data sets, such as those available for many tasks today, overfitting is not critical, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. For this purpose, distributed, scalable implementations of deep neural networks can be used.

Figures 2 and 3 are diagrams illustrating a structure utilizing a system according to an embodiment.

Referring to Figures 2 and 3, content and brand operators are license holders and sellers who sell licenses, and product and service operators are product developers and buyers who purchase licenses.

That is, in the present invention, the seller is a content intellectual property rights holder, which may mean a content creator or a company or production company holding content copyright, and the buyer is an IP commerce producer who plans, designs, and produces physical products linked to content. This can mean a business owner who wants to distribute.

The license distribution platform is a brokerage platform that generates revenue by brokering license transactions between license sellers and license buyers. License sellers have the advantage of introducing their content to an expanded market and generating additional profits, and license buyers You can easily find and purchase licenses for a variety of content, and through this, a business model can be provided that allows you to purchase licenses reasonably at a reasonable cost and according to the appropriate period and scope of use that suits your demand.

That is, the device 300 provides a license distribution platform, brokering online license transactions between a plurality of seller terminals 100 used by license sellers and a plurality of buyer terminals 200 used by license buyers, thereby Distribution can be supported.

Figure 4 is a diagram showing a profit structure using a system according to an embodiment.

Referring to FIG. 4, a company operating the device 300 can obtain a distribution fee through license sales from a seller, a purchase fee through license purchase from a purchaser, and in addition, an advertising fee from an advertiser. You can obtain it or obtain production brokerage profits from the product manufacturer.

In other words, the profit structure of the license distribution platform can be implemented by generating fees through IP sales and distribution, generating profits through the platform's own collaboration events using content, and generating profits through production brokerage with goods production companies.

According to one embodiment, device 300 may provide an application for a license distribution platform.

The main functions of the application may include a license purchase function through condition selection, a license purchase function through fixed conditions, and customer management/contract/matching functions through the application.

The license purchase function by selecting conditions allows the purchaser to directly adjust and select the conditions required to purchase and use the license, such as the period of use, license type and purpose of use, and type of product that can be produced, to the conditions desired by the purchaser, and the total cost calculated according to the conditions. This is a function that allows you to make a reasonable purchase by making purchases possible. In other words, the buyer can adjust the license fee according to the purchase conditions and choose whether or not to purchase.

The license purchase function through fixed conditions does not allow the purchaser to control the period of use, license type and purpose of use, types of products that can be produced, license costs, etc., and if the content seller registers with existing conditions specified, it can be selected among the licenses sold. This is a function that allows buyers to select and purchase a single sales license of their choice. In other words, the license fee is predetermined according to the seller's sales conditions, and the buyer can choose whether or not to purchase based on the predetermined license fee.

In the customer management/contract/matching function through the application, the customer management function can provide personalized services by storing and analyzing information such as purchase history, preferences, and interests, and sends notifications to users when the contract expiration date approaches. , A reminder service that guides contract renewal can be provided, and the contract function that manages contracts by automating the license sales and renewal process implements a digital contract management system to simplify contract conclusion and management, contract status, payment information, You can manage contracts smoothly by managing terms of use, copyright terms, etc., and the matching function that effectively matches license sellers and buyers encourages users to create their own profiles and enter their interests, preferred content types, etc. Information can be used for matching, implement a recommendation algorithm according to preferences based on the user's profile, connect buyers and licensed sellers with similar interests, provide useful recommendations based on previous purchase history, and provide users with useful recommendations. Customized recommendation results can be visualized and provided, including related images, descriptions, prices, etc.

Figure 5 is a diagram showing the main screen of an application according to one embodiment.

When the application is executed, the main screen may be displayed, as shown in FIG. 5. The main screen is divided into categories by type of license, so a menu for license sales categories by content can be displayed for easy IP purchase, and a menu for optional licenses with IP control and fixed licenses without IP control can be displayed. In addition, menus linking to production brokerage services, new content sales recommendations, advertising banners, event windows, etc. may be displayed.

Figure 6 is a diagram showing a detailed information inquiry screen of an optional license according to an embodiment.

After the optional license menu is selected on the main screen, when the license you want to purchase among the licenses classified as optional licenses is selected, a detailed information inquiry screen for the optional license may be displayed, as shown in FIG. 6. The detailed information inquiry screen of the optional license may include an interface for setting purchase conditions such as use period, purpose of use, and type of product produced, and the cost of the license may be calculated and displayed according to the settings of the purchase conditions. At this time, when the purpose of use, production product, and cost are selected on the detailed information inquiry screen of the optional license, the usage period may be automatically set according to the selected cost, and the license cost may be displayed as the selected cost.

Figure 7 is a diagram showing a detailed information inquiry screen for a fixed license according to an embodiment.

After the fixed license menu is selected on the main screen, when the license you want to purchase among the licenses classified as fixed licenses is selected, a detailed information inquiry screen for the fixed license may be displayed, as shown in FIG. 7. The detailed information inquiry screen for a fixed license may display fixed purchase conditions, fixed license costs, etc. At this time, fixed purchase conditions and fixed license fees may be set in advance by the seller.

Figure 8 is a diagram showing a license payment screen according to one embodiment.

After purchase conditions are set on the detailed information inquiry screen of the optional license shown in FIG. 6, payment for the license fee calculated according to the purchase conditions is requested, or displayed on the detailed information screen of the fixed license shown in FIG. 7. When payment for the license fee is requested, as shown in Figure 8, a payment screen for paying the license fee may be displayed, and when the license fee is paid on the payment screen, a license agreement between the seller and the buyer is concluded. can do.

According to one embodiment, the device 300 may provide AI functions through an application for a license distribution platform.

For example, an application for a license distribution platform may provide a license recommendation function, AI-based product development and simulation data provision function, market data analysis and provision function, etc.

The license recommendation function implements a recommendation algorithm based on the buyer's preferences, purchase history, areas of interest, etc., and can provide the function of recommending a similar type of license to content the user previously purchased or introducing related content. A detailed description regarding this will be provided later with reference to FIG. 10 .

The AI-based product development and simulation data provision function collects and analyzes content data from dramas, movies, etc. to derive new product development ideas, and based on these ideas, AI automatically creates product designs and performs simulations to predict profitability. It is possible to predict and present product development ideas that are expected to be highly profitable based on simulation results to sellers and buyers.

The market data analysis and provision function enables sellers to provide relevant market data and analysis to customers who have purchased a license, and provides information to sellers by analyzing the popularity of content such as the latest dramas and sales trends of related products. can be provided.

Figure 9 is a flow chart to explain the process of providing a license distribution platform.

Referring to FIG. 9 , first, in step S901, when the device 300 receives a sales request for the first license from the first seller terminal 110, it can register the first license as a purchaseable license. Here, the sales request for the first license is a request to sell the first license, and when the license for the first content is the first license, it may include detailed information on the first content.

When the sale of the first license is requested at the first seller terminal 110, the purchase conditions for the first license may or may not be set according to the request of the first seller, and the purchase condition for the first license may be set. If the condition is not set, step S902 may be performed, and if the purchase condition for the first license is set, step S903 may be performed.

In step S902, if it is confirmed that a purchase condition is not set for the first license, the device 300 may classify the first license as an optional license.

In step S903, when the first license is classified as an optional license, the device 300 receives a request to inquire detailed information about the first license from the first purchaser terminal 210, and sets the purchase conditions for the first license. By transmitting the first page, which is a page, to the first buyer terminal 210, the first page can be controlled to be displayed on the screen of the first buyer terminal 210. At this time, when the first license is selected among the licenses classified as optional licenses in the first purchaser terminal 210, the device 300 receives a request to query detailed information about the first license from the first purchaser terminal 210, , the first page can be transmitted to the first buyer terminal 210.

According to one embodiment, the first page is a page for setting the purchase conditions of the first license. As shown in FIG. 6, the first page sets the purchase conditions such as the use period, purpose of use, and goods to be used, of the first license. May include an interface for

In step S904, when the purchase conditions of the first license are set through the first page, the device 300 may calculate the purchase cost of the first license as the first cost based on the purchase conditions of the first license. Here, the purchase conditions of the first license may include the period of use of the first license, the purpose of use, and the goods to be used.

That is, when the purchase conditions including the use period, purpose of use, and goods used of the first license are set at the request of the first purchaser, the device 300 takes into account the use period, purpose of use, and goods used of the first license. , the purchase cost of the first license can be calculated as the first cost. A detailed description related to this will be described later with reference to FIG. 14.

In step S905, when the purchase cost of the first license is calculated as the first cost, the device 300 may control the first cost to be displayed on the first page.

Meanwhile, in step S906, if it is confirmed that a purchase condition is set in the first license, the device 300 may classify the first license as a fixed license.

In step S907, when the first license is classified as a fixed license, the device 300 receives a request to inquire detailed information about the first license from the first purchaser terminal 210 to confirm the purchase conditions of the first license. By transmitting the second page, which is a page, to the first buyer terminal 210, the second page can be controlled to be displayed on the screen of the first buyer terminal 210. At this time, when the first license is selected among the licenses classified as fixed licenses in the first purchaser terminal 210, the device 300 receives a request to query detailed information about the first license from the first purchaser terminal 210, , the second page can be transmitted to the first buyer terminal 210.

According to one embodiment, the second page is a page for confirming the purchase conditions of the first license. As shown in FIG. 7, the period of use, purpose of use, goods to be used, and A second cost may be displayed. Here, the period of use, purpose of use, goods used, and second cost of the first license may be set in advance at the request of the first seller.

After step S905 or step S907, in step S908, when the device 300 receives a purchase request for the first license from the first buyer terminal 210, the device 300 matches the first seller and the first buyer, and matches the first seller and the first buyer. A contract space can be created to proceed with a contract for the use of the first license between the first purchasers.

That is, as shown in FIG. 8, after the payment screen for paying the license fee is displayed and the payment for the license fee is completed at the first purchaser terminal 210, the device 300 is connected to the first purchaser terminal 210. A purchase request for the first license may be received from 210, and a contract space for a use agreement for the first license may be created. Here, the contract space is a cyberspace provided for a license use agreement between a seller and a buyer, and not only contract conclusion, but also overall procedures for a license contract, such as contract management and contract renewal, can be processed in the contract space.

In step S909, the device 300 may transmit link information for accessing the contract space to the first seller terminal 110 and the first buyer terminal 210, respectively.

When the first seller terminal 110 and the first buyer terminal 210 are connected to the contract space through link information for access to the contract space, a contract for use of the first license can be concluded in the contract space.

Figure 10 is a flowchart for explaining a process for providing a license recommendation function according to an embodiment.

According to one embodiment, each step shown in FIG. 10 may be performed before receiving a request to inquire detailed information about the first license from the first purchaser terminal 210.

Referring to FIG. 10, first, in step S1001, the device 300 may receive a license recommendation request from the first purchaser terminal 210.

In step S1002, the device 300 may obtain profile information of the first buyer. Here, the profile information of the first buyer may include the first buyer's purchase history, preferred content type, and areas of interest. Here, the purchase history is the history of purchasing a license and may include information indicating which license was purchased and when and how much it was used, and the preferred content type is the preferred content type, such as movies, dramas, games, YouTube, webtoons, etc. It may include at least one of the content types, and the field of interest may include at least one of topics such as music, exercise, beauty, fashion, cooking, and games.

Specifically, when receiving a license recommendation request from the first purchaser terminal 210, the device 300 may also receive the first purchaser's profile information and obtain the first purchaser's profile information.

Additionally, if the first buyer's profile information is registered in the first buyer's user information, the device 300 may query the user information stored in the database and obtain the first buyer's profile information from the database.

In step S1003, the device 300 may generate a first input signal by encoding profile information of the first buyer.

Specifically, in order to use the profile information of the first buyer as input to the first artificial intelligence model, the device 300 may perform normal information processing on the profile information and generate a first input signal.

In step S1004, the device 300 may input a first input signal to the first artificial intelligence model learned in advance. Here, the first artificial intelligence model may be trained to recommend a license according to the buyer's profile.

According to one embodiment, the first artificial intelligence model may be an algorithm that receives an input signal generated by encoding profile information, selects a recommended license through the input signal, and outputs an output signal representing the selected license.

In other words, the first artificial intelligence model can select a recommended license according to the profile, considering purchase history, preferred content type, and areas of interest confirmed through profile information, and output an output signal indicating the selected recommended license. A detailed description of the process of selecting a recommended license in the first artificial intelligence model will be described later with reference to FIG. 11.

In step S1005, when a recommended license is selected through a first input signal, the device 300 may obtain a first output signal indicating the recommended license from the first artificial intelligence model. At this time, the first output signal may include information indicating a recommended license.

For example, the device 300 inputs a first input signal to the first artificial intelligence model, and when the recommended license is selected as the first license through the first input signal, the device 300 indicates the first license from the first artificial intelligence model. A first output signal can be obtained, and if the recommended license is selected as the second license through the first input signal, a first output signal representing the second license can be obtained from the first artificial intelligence model.

That is, the first artificial intelligence model can select a recommended license through an input signal and output an output signal indicating the selected recommended license. To this end, the first artificial intelligence model may be pre-trained to analyze which license to recommend based on purchase history, preferred content types, and areas of interest.

The learning device in which learning of the first artificial intelligence model is performed may be the same device as the device 300 for selecting a recommended license using the learned first artificial intelligence model, or may be a separate device. Below, the process by which the first artificial intelligence model is learned is explained.

First, the learning device can generate input based on profile information.

Specifically, the learning device may perform a preprocessing process on profile information. The pre-processed profile information can be used as input to the first artificial intelligence model, or the input can be generated through normal processing to remove unnecessary information.

Next, the learning device can apply input to the first artificial intelligence model. The first artificial intelligence model may be an artificial neural network learned according to reinforcement learning. The first artificial intelligence model may be a Q-Network, DQN (Depp Q-Network), or relational network (RL) structure suitable for outputting abstract inference through reinforcement learning.

The first artificial intelligence model learned according to reinforcement learning can be updated and optimized by reflecting the evaluation of various rewards.

For example, the first reward can increase the reward value by selecting a license with content most similar to the content of the license confirmed through purchase history as a recommended license, and the second reward can be obtained within the category identified through preferred content type. Selecting a recommended license can increase the reward value, and the third reward can increase the reward value by selecting a recommended license within a topic identified through the field of interest.

Next, the learning device may obtain an output from the first artificial intelligence model. At this time, the output of the first artificial intelligence model may be information indicating a recommended license. In other words, the first artificial intelligence model can analyze which license to recommend according to the buyer's profile and output information about the analyzed recommended license.

Next, the learning device may evaluate the output of the first artificial intelligence model and pay a reward.

For example, if the learning device selects a license with content most similar to the content of the license confirmed through purchase history as a recommended license, it awards a large amount of the first reward and selects a recommended license within the category identified through preferred content type. If you do this, a lot of secondary rewards can be awarded, and if you select a recommended license within the topic identified through the field of interest, you can get a lot of tertiary rewards.

Next, the learning device may update the first artificial intelligence model based on the evaluation.

Specifically, the learning device takes actions to be taken in specific states so that the expected value of the sum of rewards is maximized in the environment where the first artificial intelligence model analyzes the recommended license. ) can be updated through the process of optimizing the policy that determines the first artificial intelligence model.

For example, if there is no problem with the result of the learning device analyzing the recommended license as the first license according to the profile of the first purchaser, the learning device may respond to the result of analyzing the recommended license as the first license according to the profile of the first purchaser. When generating first learning data indicating that there is no problem, applying the first learning data to the first artificial intelligence model, and analyzing the recommended license of a purchaser with a profile similar to that of the first purchaser, the recommended license is selected as the recommended license. The first artificial intelligence model can be updated through the process of training the first artificial intelligence model to select a license similar to license 1.

Meanwhile, the process of optimizing the policy can be accomplished through estimating the maximum expected value of the sum of rewards or the maximum value of the Q-function, or estimating the minimum value of the loss function of the Q-function. Estimation of the minimum value of the loss function can be done through stochastic gradient descent (SGD), etc. The process of optimizing the policy is not limited to this, and various optimization algorithms used in reinforcement learning can be used.

The learning device can gradually update the first artificial intelligence model by repeating the learning process of the first artificial intelligence model described above. Through this, the learning device can train a first artificial intelligence model that analyzes and outputs which license to recommend according to the profile.

That is, when the learning device analyzes the recommended license according to the buyer's profile, it can learn the first artificial intelligence model by reflecting reinforcement learning through the first reward to the third reward, etc., and adjusting the analysis standard.

In step S1006, if the recommended license is confirmed to be the first license based on the first output signal, the device 300 may transmit recommendation information about the first license to the first purchaser terminal 210. Here, the recommendation information for the first license is information recommending the first license, and when the license for the first content is the first license, it may include detailed information on the first content.

Figure 11 is a flowchart for explaining the process of selecting a recommended license according to an embodiment.

Referring to FIG. 11, first, in step S1101, when the first input signal is input, the first artificial intelligence model checks the purchase history, preferred content type, and area of interest of the first buyer based on the first input signal. You can.

In step S1102, the first artificial intelligence model may classify the licenses registered as purchasable licenses into the first group.

In other words, the first artificial intelligence model can classify the licenses currently registered as purchasable licenses into the first group.

In step S1103, the first artificial intelligence model can confirm that the first buyer has purchased a second license, which is a license for the second content, through the purchase history of the first buyer.

In step S1104, the first artificial intelligence model may assign a higher first score to each license classified into the first group as the similarity between each content and the second content increases. At this time, the similarity between contents can be set to a higher value as the type, theme, production company, performer, etc. of the contents match.

For example, if the license of the first content is the first license, and the first license is classified into the first group, the first artificial intelligence model compares the first content and the second content, and compares the first content and the second content. 2 The more the type, subject, production company, performer, etc. between the contents match, the higher the similarity between the first content and the second content can be calculated. If the similarity between the first content and the second content is calculated at 50%, the If the first score for 1 license is given as 50 points, and the similarity between the first content and the second content is calculated to be 70%, the first score for the first license may be given as 70 points.

In step S1105, the first artificial intelligence model can confirm that the type of content preferred by the first buyer is the first category through the preferred content type of the first buyer. At this time, types of content include movies, dramas, games, YouTube, webtoons, etc., and the first category may be any one of the types of content.

In step S1106, the first artificial intelligence model may assign a higher second score to each license classified into the first group as the category of each content matches the first category.

For example, if the license of the first content is the first license and the first license is classified into the first group, the first artificial intelligence model confirms that the category of the first content is a game and the first category is a movie. If so, the second score for the first license can be given as 30 points, and if it is confirmed that the category of the first content is drama and the first category is movie, the second score for the first license can be given as 70 points. If it is confirmed that the category of the first content is a movie and the first category is a movie, a second score of 100 points can be given for the first license,

In step S1107, the first artificial intelligence model can confirm that the field of interest of the first buyer is the first topic through the field of interest of the first buyer. At this time, areas of interest include music, exercise, beauty, fashion, cooking, games, etc., and the first topic may be any one of the areas of interest.

In step S1108, the first artificial intelligence model may assign a higher third score to each license classified into the first group as the subject of each content matches the first subject.

For example, if the license of the first content is the first license and the first license is classified into the first group, the first artificial intelligence model determines that the topic of the first content is exercise and the first topic is beauty. , the third score for the first license can be given as 30 points, and if the topic of the first content is fashion and it is confirmed that the first topic is beauty, the third score for the first license can be given as 70 points. and the subject of the first content is beauty, and if it is confirmed that the first subject is beauty, a third score of 100 points can be given for the first license,

The first artificial intelligence model is the minimum value that can be given to the first score, second score, and third score when assigning the first score, second score, and third score to each of the licenses classified into the first group. The and maximum values can be set to the same score, and the minimum and maximum values that can be given to the first score, second score, and third score can be set to different scores depending on importance.

In step S1109, the first artificial intelligence model may calculate a total score by adding up the first score, second score, and third score for each license classified into the first group.

In step S1110, the first artificial intelligence model may compare the total scores of each license classified into the first group and select the license with the highest total score as the recommended license.

That is, if the first artificial intelligence model determines that the license with the highest total score among the licenses classified into the first group is the first license, it can select the first license as the recommended license.

Thereafter, the first artificial intelligence model may generate a first output signal representing the selected recommended license.

FIG. 12 is a flowchart illustrating a process for providing a notification service for imminent contract expiration and contract renewal information according to the period remaining until the contract expiration date according to an embodiment.

Referring to FIG. 12, first, in step S1201, if a contract is concluded on the first date between the first seller and the first buyer to use the first license for the first period, the device 300 starts from the first date. The second date after the first period has passed can be set as the contract expiration date.

For example, if a contract was entered into on January 30th between the first seller and the first buyer to use the first license for 6 months, then July 30th is identified as the second date, and July 30th is identified as the second date. It can be set to the contract expiration date.

In step S1202, the device 300 may confirm the current date as the third date.

In step S1203, the device 300 may identify the remaining period from the third date to the second date as the second period.

In step S1204, the device 300 may check whether the second period is shorter than the first reference period. Here, the first reference period may be set differently depending on the embodiment.

If it is confirmed in step S1204 that the second period is not shorter than the first reference period, after a certain period of time has passed, the process returns to step S1202 and the process of confirming the current date as the third date can be performed again.

If it is determined in step S1204 that the second period is shorter than the first reference period, in step S1205, the device 300 sends a notification message to the first seller indicating that the period remaining until the contract expiration date of the first license is the second period. It can be transmitted to the terminal 110 and the first purchaser terminal 210, respectively.

After performing step S1205 and a certain period of time has elapsed, in step S1206, the device 300 can confirm the current date as the fourth date.

In step S1207, the device 300 may identify the remaining period from the fourth date to the second date as the third period.

In step S1208, the device 300 may check whether the third period is shorter than the second reference period. Here, the second reference period may be set to a shorter value than the first reference period.

If it is confirmed in step S1208 that the third period is not shorter than the second reference period, after a certain period of time has passed, the process returns to step S1206 and the process of confirming the current date as the fourth date can be performed again.

If it is confirmed in step S1208 that the third period is shorter than the second reference period, in step S1209, the device 300 sends a notification message indicating that the contract renewal of the first license is necessary to the first seller terminal 110 and the first seller terminal 110. Each can be transmitted to the purchaser terminal 210.

Figure 13 is a flow chart to explain the process of registering a license scheduled to expire and registering a license available for purchase according to the period remaining until the contract expiration date according to an embodiment.

Each step shown in FIG. 13 can be performed after each step shown in FIG. 12 is performed.

Referring to FIG. 13, after performing step S1209 and a certain period of time has passed, in step S1301, the device 300 can confirm the current date as the fifth date.

In step S1302, the device 300 may identify the remaining period from the fifth date to the second date as the fourth period.

In step S1303, the device 300 may check whether the fourth period is shorter than the third reference period. Here, the third reference period may be set to a shorter value than the second reference period.

If it is confirmed in step S1303 that the fourth period is not shorter than the third reference period, after a certain period of time has passed, the process returns to step S1301 and the process of confirming the current date as the fifth date can be performed again.

If it is determined in step S1303 that the fourth period is shorter than the third reference period, in step S1304, the device 300 may check whether the contract of the first license has been renewed.

If it is confirmed in step S1304 that the contract of the first license has not been renewed, in step S1305, the device 300 may register the first license as a license whose contract is scheduled to expire. At this time, the device 300 can collect licenses whose contracts are scheduled to expire and display them as a list in a separate posting space.

That is, if it is confirmed that the fourth period is shorter than the third reference period and the contract of the first license is not renewed, the device 300 may register the first license as a license whose contract is scheduled to expire. Through this, if the period remaining until the contract expiration date of the first license is shorter than the third reference period, but the contract of the first license has not yet been renewed, the device 300 registers the first license as a license whose contract is scheduled to expire. can do.

After performing step S1305 and a certain period of time has elapsed, in step S1306, the device 300 can confirm the current date as the sixth date.

In step S1307, the device 300 may identify the remaining period from the sixth date to the second date as the fifth period.

In step S1308, the device 300 may check whether the fifth period is shorter than the fourth reference period. Here, the fourth reference period may be set to a shorter value than the third reference period.

If it is confirmed in step S1308 that the fifth period is not shorter than the fourth reference period, after a certain period of time has passed, the process returns to step S1306 and the process of confirming the current date as the sixth date can be performed again.

If it is determined in step S1308 that the fifth period is shorter than the fourth reference period, in step S1309, the device 300 may check whether the contract of the first license has been renewed.

If it is confirmed that the contract of the first license has not been renewed in step S1309, the device 300 may register the first license as a purchasable license in step S1310. At this time, the device 300 can collect the licenses available for purchase and display them as a list in a separate posting space.

That is, if it is confirmed that the fifth period is shorter than the fourth reference period and the contract of the first license is not renewed, the device 300 may register the first license as a purchasable license. Through this, if the period remaining until the contract expiration date of the first license is shorter than the fourth reference period, but the contract of the first license has not yet been renewed, the device 300 may use the device 300 before the contract expiration date of the first license arrives. You can register the first license in advance as a purchasable license.

Figure 14 is a flow chart to explain the process of calculating the purchase cost of a license through setting purchase conditions according to an embodiment.

Referring to FIG. 14, first, in step S1401, when the type of first content is a movie, the device 300 sets the daily usage amount of the first license based on the number of viewers of the first content, and sets the daily usage amount of the first license for the first content. If the type of content is a game, the daily usage amount of the first license is set based on the daily average number of concurrent users of the first content, and if the type of first content is content uploaded on the Internet, the amount of the first license is set. Set the daily usage amount of the first license based on the number of views, and if the type of first content is content aired on TV, set the daily usage amount of the first license based on the viewership rate of the first content. And, when the type of first content is a product character, the daily usage amount of the first license can be set based on the sales volume of products produced using the first content.

For example, when the type of first content is a movie and the number of viewers of the first content is confirmed to be 1 million, the device 300 sets the daily usage amount of the first license to 10,000 won and sets the daily usage amount of the first license to 200. If the number of users is confirmed to be 10,000, the daily usage amount of the first license can be set to 20,000 won. At this time, in order to set the daily usage amount according to the number of moviegoers, a standard table indicating how much the daily usage amount will be for each section of the number of moviegoers may be stored in the database.

In addition, when the type of first content is a game, if the average daily number of concurrent users of the first content is confirmed to be 100,000, the device 300 sets the daily usage amount of the first license to 10,000 won, and sets the daily usage amount of the first content to 10,000 won. If the average number of concurrent users is confirmed to be 200,000, the daily usage amount of the first license can be set to 20,000 won. At this time, in order to set the daily usage amount according to the number of concurrent users of the game, a standard table indicating how much the daily usage amount will be for each section of the number of concurrent game users may be stored in the database.

In addition, when the type of first content is a video uploaded on the Internet (e.g., YouTube video, OTT video, etc.), the device 300 determines that the number of views of the first content is 2 million, If the daily usage amount is set to 10,000 won and the number of views of the first content is confirmed to be 4 million, the daily usage amount of the first license can be set to 20,000 won. At this time, in order to set the daily usage amount according to the number of video views, a standard table indicating how much the daily usage amount will be for each section of the number of video views may be stored in the database.

In addition, when the type of first content is a video (e.g., a TV program video) broadcast on TV, the device 300 determines the daily usage amount of the first license when the viewership rate of the first content is confirmed to be 1%. is set to 10,000 won, and if the viewership rate of the first content is confirmed to be 2%, the daily usage amount of the first license can be set to 20,000 won. At this time, in order to set the daily usage amount according to the viewership rating, a standard table indicating how much the daily usage amount will be for each viewership rating section may be stored in the database.

In addition, when the type of the first content is a product character, if the sales volume of the product produced using the first content is confirmed to be 100,000 units, the device 300 sets the daily usage amount of the first license to 10,000 won, If the sales volume of products produced using the first content is confirmed to be 200,000 units, the daily usage amount of the first license can be set to 20,000 won. At this time, in order to set the daily usage amount according to the sales volume of the product, a standard table indicating how much the daily usage amount will be for each section of the sales volume may be stored in the database.

In step S1402, the device 300 sets the use period of the first license as the first use period, the use purpose of the first license is set as the first purpose, and the use goods of the first license are set through the first page. It can be confirmed that it has been set as the first product.

In step S1403, the device 300 may check the first weight set for the first purpose and the second weight set for the first product. For this purpose, weights of different values are set according to importance for each purpose of use, and weights of different values are set according to importance for each article of use.

For example, if the first purpose is to sell products, the device 300 may check the first weight as 1.5, and if the first purpose is education, the device 300 may check the first weight as 1.3.

Additionally, the device 300 may determine the second weight as 1.5 when the first article is stationery, and may determine the second weight as 1.3 when the first article is food.

In step S1404, the device 300 may calculate the first cost as a value obtained by multiplying the daily usage amount of the first license, the first usage period, the first weight, and the second weight.

For example, if the daily usage amount of the first license is 10,000 won, the first usage period is 6 months, the first weight is 1.5, and the second weight is 1.3, the device 300 is (10000 The 3.51 million won calculated through X 1.3) can be calculated as the first cost.

Figure 15 is an exemplary diagram of the configuration of a device according to an embodiment.

Device 300 according to one embodiment includes a processor 310 and memory 320. The processor 310 may include at least one device described above with reference to FIGS. 1 to 14 or may perform at least one method described above with reference to FIGS. 1 to 14 . A person or organization using the device 300 may provide services related to some or all of the methods described above with reference to FIGS. 1 to 14 .

The memory 320 may store information related to the methods described above or store a program in which methods described later are implemented. Memory 320 may be volatile memory or non-volatile memory.

The processor 310 can execute programs and control the device 300. The code of the program executed by the processor 310 may be stored in the memory 320. The device 300 is connected to an external device (eg, a personal computer or a network) through an input/output device (not shown) and can exchange data through wired or wireless communication.

The device 300 may be used to learn an artificial intelligence model or use a learned artificial intelligence model. Memory 320 may include a learning or learned artificial intelligence model. The processor 310 may learn or execute the artificial intelligence model algorithm stored in the memory 320. The learning device that trains the artificial intelligence model and the device 300 that uses the learned artificial intelligence model may be the same or may be separate.

The embodiments described above may be implemented with hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, and a field programmable gate (FPGA). It may be implemented using one or more general-purpose or special-purpose computers, such as an array, programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. A processing device may execute an operating system (OS) and one or more software applications that run on the operating system. Additionally, a processing device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, a single processing device may be described as being used; however, those skilled in the art will understand that a processing device includes multiple processing elements and/or multiple types of processing elements. It can be seen that it may include. For example, a processing device may include a plurality of processors or one processor and one controller. Additionally, other processing configurations, such as parallel processors, are possible.

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

Software may include a computer program, code, instructions, or a combination of one or more of these, which may configure a processing unit to operate as desired, or may be processed independently or collectively. You can command the device. Software and/or data may be used on any type of machine, component, physical device, virtual equipment, computer storage medium or device to be interpreted by or to provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a transmitted signal wave. Software may be distributed over networked computer systems and thus stored or executed in a distributed manner. Software and data may be stored on one or more computer-readable recording media.

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

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

Claims (3)

In a method of providing a license distribution platform, performed by a device,
Upon receiving a sales request for a first license, which is a license for first content, from a first seller terminal, registering the first license as a purchasable license;
If it is confirmed that a purchase condition is not set in the first license, the first license is classified as an optional license, and if it is confirmed that a purchase condition is set in the first license, the first license is classified as a fixed license. Classifying step;
If the first license is classified as an optional license, when a request for detailed information about the first license is received from the first purchaser terminal, the first page, which is a page for setting purchase conditions for the first license, is sent to the first license. Transmitting to the buyer terminal;
If the purchase conditions including the usage period, purpose of use, and use goods of the first license are set through the first page, the purchase cost of the first license is calculated as the first cost based on the purchase conditions of the first license. calculating and controlling the first cost to be displayed on the first page;
If the first license is classified as a fixed license, upon receiving a request to inquire detailed information about the first license from the first purchaser terminal, the usage period and purpose of use confirmed through the preset purchase conditions of the first license , transmitting a second page displaying used goods and a second cost to the first buyer terminal;
When receiving a purchase request for the first license from the first buyer terminal, match the first seller and the first buyer to proceed with a contract for use of the first license between the first seller and the first buyer. creating contract space; and
Comprising the step of transmitting link information for accessing the contract space to the first seller terminal and the first buyer terminal, respectively,
Before receiving a request to inquire detailed information about the first license from the first purchaser terminal,
Receiving a license recommendation request from the first purchaser terminal;
Obtaining profile information of the first buyer including purchase history, preferred content type, and areas of interest of the first buyer;
generating a first input signal by encoding profile information of the first buyer;
The first input signal is input to a first artificial intelligence model trained to recommend a license according to the buyer's profile, and when a recommended license is selected through the first input signal, the recommended license is received from the first artificial intelligence model. obtaining a first output signal representing; and
Based on the first output signal, if the recommended license is confirmed to be the first license, transmitting recommendation information about the first license to the first purchaser terminal,
The first artificial intelligence model is,
When the first input signal is input, the purchase history, preferred content type, and area of interest of the first buyer are confirmed based on the first input signal, respectively,
Classify licenses registered as purchasable licenses into the first group,
If it is confirmed through the purchase history of the first purchaser that the first purchaser has purchased a second license, which is a license for the second content, for each license classified into the first group, each content and the second content The higher the similarity between the two, the higher the first score is given,
If it is confirmed that the type of content preferred by the first buyer is the first category through the preferred content type of the first buyer, for each license classified into the first group, the category of each content and the first category The more the category matches, the higher the second score is given,
If it is confirmed that the field of interest to the first buyer is the first topic through the field of interest of the first buyer, for each license classified into the first group, the more the topic of each content matches the first topic, The third score is given a high value,
For each license classified into the first group, calculate a total score by adding up the first score, the second score, and the third score,
Compare the total scores of each license classified into the first group, and select the license with the highest total score as the recommended license,
If a contract is entered into on the first date between the first seller and the first buyer to use the first license for a first period, the contract is entered into on a second date after the first period has elapsed from the first date. Setting an expiration date;
When the current date is confirmed as a third date, confirming the remaining period from the third date to the second date as a second period;
If the second period is confirmed to be shorter than the preset first reference period, a notification message indicating that the remaining period until the contract expiration date of the first license is the second period is sent to the first seller terminal and the first purchaser. Each step of transmitting to a terminal;
When the current date is confirmed as the fourth date, confirming the remaining period from the fourth date to the second date as a third period;
If it is confirmed that the third period is shorter than a preset second reference period, transmitting a notification message indicating that the contract of the first license needs to be renewed to the first seller terminal and the first purchaser terminal, respectively;
When the current date is confirmed as the fifth date, confirming the remaining period from the fifth date to the second date as a fourth period;
If it is confirmed that the fourth period is shorter than a preset third reference period and the contract of the first license is not renewed, registering the first license as a license whose contract is scheduled to expire;
When the current date is confirmed as the sixth date, confirming the remaining period from the sixth date to the second date as a fifth period; and
If it is confirmed that the fifth period is shorter than a preset fourth reference period and the contract of the first license is not renewed, further comprising registering the first license as a purchasable license,
The process of calculating the purchase cost of the first license as the first cost is,
If the type of the first content is a movie, setting a daily usage amount of the first license based on the number of viewers of the first content;
If the type of the first content is a game, setting a daily usage amount of the first license based on the daily average number of concurrent users of the first content;
When the type of the first content is a video uploaded on the Internet, setting a daily usage amount of the first license based on the number of views of the first content;
If the type of the first content is a video broadcast on TV, setting a daily usage amount of the first license based on a viewer rating of the first content;
When the type of the first content is a product character, setting a daily usage amount of the first license based on the sales volume of products produced using the first content;
Through the first page, the period of use of the first license is set as the first use period, the purpose of use of the first license is set as the first purpose, and the article used of the first license is set as the first article. Confirming that it is;
Confirming a first weight set to the first purpose and confirming a second weight set to the first product; and
Comprising calculating the first cost as a product of the daily usage amount of the first license, the first usage period, the first weight, and the second weight,
How to provide a license distribution platform. delete delete