KR102637198B1 - Method, computing device and computer program for sharing, renting and selling artificial intelligence model through artificial intelligence model production platform - Google Patents

Abstract

Artificial intelligence model sharing, rental and sales methods, devices and computer programs are provided through the artificial intelligence model production platform. The method of sharing, renting, and selling an artificial intelligence model through an artificial intelligence model production platform according to various embodiments of the present invention is a method performed by a computing device, and includes information on an artificial intelligence model desired to be shared, rented, or purchased from a user. Obtaining one or more condition information, selecting an artificial intelligence model that satisfies the acquired one or more condition information among a plurality of artificial intelligence models pre-registered on the platform, and providing the selected artificial intelligence model to the user. Includes.

Description

How to share, rent and sell artificial intelligence models through an artificial intelligence model production platform, devices and computer programs {METHOD, COMPUTING DEVICE AND COMPUTER PROGRAM FOR SHARING, RENTING AND SELLING ARTIFICIAL INTELLIGENCE MODEL THROUGH ARTIFICIAL INTELLIGENCE MODEL PRODUCTION PLATFORM}

Various embodiments of the present invention relate to methods, devices, and computer programs for sharing, renting, and selling artificial intelligence models through an artificial intelligence model production platform.

AI and big data are the core technologies of the 4th Industrial Revolution and the crude oil of future industries. The Korean version of the Digital New Deal policy (selecting the digital field (data, AI, etc.) as a key factor in determining national competitiveness and setting a project cost of KRW 58.2 trillion by 2025) investment), innovation growth engine promotion plan (expanding the domestic data market to 30 trillion won by 2023), data and AI economy revitalization plan (to revitalize the entire value chain from data collection, distribution, and utilization over the next five years) Various domestic policies, including policy promotion, are being implemented and promoted.

The domestic artificial intelligence (AI) market is expected to grow at an average annual rate of 17.8% over the next five years to reach more than KRW 640 billion in 2023, and the domestic data analysis (big data) market is expected to reach KRW 1.6744 trillion in 2019 (10.9% increase compared to the previous year). The domestic data analysis and artificial intelligence market is expected to grow rapidly, with a forecast of KRW 2.5692 trillion (average annual growth rate of 11.2%) by 2023, and AI application and platform implementation projects for work process efficiency and business automation are increasing significantly. , Both the related services and software markets are expected to grow at an average annual growth rate of more than 30% over the next five years, and the artificial intelligence technology-applied services and software markets are expected to grow in earnest.

In line with this trend, AI technology is being developed to provide customized customer service. For example, AI and machine learning are technologies that all industries must keep an eye on, and are being used as technologies to implement customized customer services, and AI technology is being introduced across all areas of society, including national defense/security, medical and healthcare, and marketing.

AI deep learning technology is a field with active open source utilization and collaboration (co-creation), and because it is a field with very fast technology development, there are limitations to independent development, so it has been expanded to an open platform method to create an AI solution utilizing open source. This is universal. In other words, there are open sources that can be utilized, including the development stage, framework learning, evaluation stage, and resource management, and how to configure them is the key to creating current AI solutions.

As with all technologies, it is expected that in the near future, AI technology will reach a stage of ‘technology generalization-expansion-personalization’ at a level where even the general public can use it.

Meanwhile, AI technology has a problem of insufficient practical use compared to the rapid pace of technological development. For example, due to cost issues, the adoption rate among small and medium-sized enterprises is less than 10%, and the ‘lack of AI technology and solutions that meet corporate demands’ is becoming the biggest obstacle.

Therefore, in order to contribute to the spread of technology by improving the accessibility of AI technology to small and medium-sized businesses and the general public, we provide an environment where even non-experts can utilize AI technology by reducing the learning steps required to understand specialized technology, and provide automation to enable AI services at low cost. There is a need to convert to a system and build an environment where data can be directly used to create an AI learning model optimized for small to medium-sized or personal environments.

Korean Patent Publication No. 10-2022-0030680 (2022.03.11)

The problem that the present invention aims to solve is to solve the above-described conventional problems. By providing a service that allows the production of user-customized artificial intelligence models, users can directly create the artificial intelligence model they want to create through the service. Through an artificial intelligence model creation platform that provides an environment where even non-experts can utilize AI technology, enables AI services at a low cost, and creates AI learning models optimized for small to medium-sized or personal environments. It provides artificial intelligence model sharing, rental and sales methods, devices and computer programs.

Another problem that the present invention aims to solve is to provide users with artificial intelligence models produced by multiple users for the purpose of sharing, renting, and selling through an artificial intelligence model production platform that provides a user-customized artificial intelligence model production service. Not only can you create the artificial intelligence model you want, but you can also share, rent, and rent artificial intelligence models through an artificial intelligence model creation platform that allows you to share, rent, and purchase artificial intelligence models created by multiple users. It provides sales methods, devices, and computer programs.

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

The method of sharing, renting, and selling an artificial intelligence model through an artificial intelligence model production platform according to an embodiment of the present invention to solve the above-described problem involves sharing, renting, or purchasing from a user in a method performed by a computing device. Obtaining one or more condition information regarding the desired artificial intelligence model, selecting an artificial intelligence model that satisfies the obtained one or more condition information among a plurality of artificial intelligence models pre-registered on the platform, and providing the selected artificial intelligence model to the user. It may include providing an artificial intelligence model.

In various embodiments, a plurality of artificial intelligence models are created using a user-customized artificial intelligence model production service provided through the platform, and the user-customized artificial intelligence model production service is produced through a user interface (UI). A service that generates a user-customized artificial intelligence model by generating training data for the desired artificial intelligence model and training the desired artificial intelligence model using the generated learning data, steps and the generated plurality It may further include registering at least one artificial intelligence model to be rented or sold among the artificial intelligence models in the platform.

In various embodiments, the step of registering on the platform includes determining the validity of the at least one artificial intelligence model to be rented or sold, and based on the determined validity, the step of registering the at least one artificial intelligence model to be rented or sold. If the artificial intelligence model is determined to be valid, it may include registering at least one artificial intelligence model to be rented or sold on the platform.

In various embodiments, the step of determining the validity includes sharing at least one artificial intelligence model to be rented or sold for free for a predetermined period of time, and at least one artificial intelligence model to be rented or sold for a predetermined period of time. Based on the sharing history of the intelligence model, calculating a validity score for at least one artificial intelligence model to be rented or sold, and if the calculated effectiveness score is more than a threshold score, at least one artificial intelligence model to be rented or sold It may include a step of determining that the artificial intelligence model is valid.

In various embodiments, the step of selecting the artificial intelligence model may include, if there is no artificial intelligence model that satisfies the one or more condition information among the plurality of pre-registered artificial intelligence models, an artificial intelligence model that satisfies the one or more condition information. It may include providing a user-customized artificial intelligence model production service for producing the model.

In various embodiments, providing the selected artificial intelligence model includes, when obtaining a purchase request for the selected artificial intelligence model from the user, providing a payment process for purchasing the selected artificial intelligence model, and When the purchase cost for the selected artificial intelligence model is paid through the provided payment process, it may include generating a URL corresponding to the selected artificial intelligence model and providing the generated URL.

In various embodiments, the step of providing the generated URL includes, when the user purchases a specific artificial intelligence model, selecting an artificial intelligence model that has a positive correlation with the specific artificial intelligence model, and the selected artificial intelligence model. It includes the step of recommending the purchase of an intelligence model, and the step of selecting an artificial intelligence model that has a positive correlation with the specific artificial intelligence model includes two or more different types of artificial intelligence models among the plurality of pre-registered artificial intelligence models. Evaluating the performance of ensemble models including a model, defining correlations for the two or more different artificial intelligence models based on the evaluated performance, and generating a correlation map based on the defined correlations. It may include selecting an artificial intelligence model that has a positive correlation with the specific artificial intelligence model based on the step and the generated correlation map.

In various embodiments, the one or more condition information includes input data, and the step of providing the selected artificial intelligence model includes information about the selected at least one artificial intelligence model and the selected at least one artificial intelligence model. It may include providing result data output as the input data is input.

A computing device that performs a method of sharing, renting, and selling an artificial intelligence model through an artificial intelligence model production platform according to another embodiment of the present invention to solve the above-described problem includes a processor, a network interface, a memory, and a load on the memory. ), and includes a computer program executed by the processor, wherein the computer program includes instructions for obtaining one or more conditional information about the artificial intelligence model that the user wishes to share, rent, or purchase, based on the platform. It may include an instruction for selecting an artificial intelligence model that satisfies the acquired one or more condition information among a plurality of registered artificial intelligence models and an instruction for providing the selected artificial intelligence model to the user.

A computer program according to another embodiment of the present invention for solving the above-described problem is combined with a computing device, obtaining one or more condition information about an artificial intelligence model to be shared, rented, or purchased from a user, a platform. Through an artificial intelligence model production platform including the step of selecting an artificial intelligence model that satisfies the obtained one or more condition information among a plurality of artificial intelligence models already registered in the artificial intelligence model and providing the selected artificial intelligence model to the user. It can be stored in a recording medium that can be read by a computing device in order to execute the artificial intelligence model sharing, rental, and sales method.

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

According to various embodiments of the present invention, by providing a service that allows the creation of user-customized artificial intelligence models, the service allows users to directly create the artificial intelligence model they want to create, allowing even non-experts to utilize AI technology. It has the advantage of providing an environment where AI services can be provided at a low cost, and creating an AI learning model optimized for small to medium-sized or personal environments.

In addition, through an artificial intelligence model production platform that provides user-customized artificial intelligence model production services, artificial intelligence models created by multiple users are provided to users for the purpose of sharing, renting, and selling, allowing users to select the artificial intelligence model they want. There is an advantage in that not only can you create your own, but you can also share, rent, and purchase artificial intelligence models created by multiple users.

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 description below.

Figure 1 is a diagram illustrating an artificial intelligence model production platform operating system according to an embodiment of the present invention.
Figure 2 is a hardware configuration diagram of a computing device operating an artificial intelligence model production platform according to another embodiment of the present invention.
Figure 3 is a flowchart illustrating a method of creating and registering an artificial intelligence model performed through an artificial intelligence production platform in various embodiments.
4 to 6 are diagrams illustrating a user interface (UI) that provides a user-customized artificial intelligence model creation service provided by a computing device in various embodiments.
Figure 7 is a flowchart illustrating a method of sharing, renting, and selling an artificial intelligence model through an artificial intelligence model production platform according to another embodiment of the present invention.
Figures 8 to 10 are diagrams illustrating the process of sharing, renting, and selling artificial intelligence models through an artificial intelligence model production platform in various embodiments.
11 to 13 are diagrams illustrating user interfaces that provide artificial intelligence model sharing, rental, and sales services provided by a computing device, according to various embodiments.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the disclosure of the present invention is complete and to provide a general understanding of 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 embodiments and is not intended to limit the invention. As used herein, singular forms also include plural forms, unless specifically stated otherwise in the context. As used in the specification, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other elements in addition to the mentioned elements. Like reference numerals refer to like elements throughout the specification, and “and/or” includes each and every combination of one or more of the referenced elements. Although “first”, “second”, etc. are used to describe various components, these components are of course not limited by these terms. These terms are merely used to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may also be a second component within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless clearly specifically defined.

As used in the specification, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and the “unit” or “module” performs certain roles. However, “part” or “module” is not limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium and may be configured to run on one or more processors. Thus, as an example, a “part” or “module” refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within components and “parts” or “modules” can be combined into smaller components and “parts” or “modules” or into additional components and “parts” or “modules”. Could be further separated.

Spatially relative terms such as “below”, “beneath”, “lower”, “above”, “upper”, etc. are used as a single term as shown in the drawing. It can be used to easily describe the correlation between a component and other components. Spatially relative terms should be understood as terms that include different directions of components during use or operation in addition to the directions shown in the drawings. For example, if a component shown in a drawing is flipped over, a component described as “below” or “beneath” another component will be placed “above” the other component. You can. Accordingly, the illustrative term “down” may include both downward and upward directions. Components can also be oriented in other directions, so spatially relative terms can be interpreted according to orientation.

In this specification, a computer refers to all types of hardware devices including at least one processor, and depending on the embodiment, it may be understood as encompassing software configurations that operate on the hardware device. For example, a computer can be understood to include, but is not limited to, a smartphone, tablet PC, desktop, 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 attached 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 depending on the embodiment, at least part of each step may be performed in a different device.

Figure 1 is a diagram illustrating an artificial intelligence model production platform operating system according to an embodiment of the present invention.

Referring to FIG. 1, the artificial intelligence model production platform operating system according to an embodiment of the present invention may include a computing device 100, a user terminal 200, an external server 300, and a network 400.

Here, the artificial intelligence model production platform operating system shown in FIG. 1 is according to one embodiment, and its components are not limited to the embodiment shown in FIG. 1, and may be added, changed, or deleted as necessary. .

In one embodiment, the computing device 100 may operate an artificial intelligence model production platform that provides user-customized artificial intelligence model creation services and artificial intelligence model sharing, rental, and sales services.

First, the computing device 100 can provide a user-customized artificial intelligence model creation service through an artificial intelligence model production platform.

Here, the user-customized artificial intelligence model may refer to the artificial intelligence model that the user wishes to create, and the user-customized artificial intelligence model production service guides the production of the user-customized artificial intelligence model and customizes the user according to user input according to the guide. This may refer to a service that creates an artificial intelligence model, that is, a service that provides an environment in which users can more easily create the artificial intelligence model they want to create.

More specifically, when the computing device 100 selects an artificial intelligence model to be created through a UI (e.g., FIGS. 4 to 6) that provides a user-customized artificial intelligence model production service, a model corresponding to the selected artificial intelligence model is selected. By generating learning data and learning an artificial intelligence model using the learning data, a user-customized artificial intelligence model, that is, an artificial intelligence model that the user wants to create, can be created.

Here, an artificial intelligence model (e.g., a neural network) is composed of one or more network functions, and one or more network functions may be composed of a set of interconnected computational units, which can generally be referred to as ‘nodes.’ These ‘nodes’ may also be referred to as ‘neurons’. One or more network functions are composed of at least one or more nodes. Nodes (or neurons) that make up one or more network functions may be interconnected by one or more ‘links’.

Within an artificial intelligence model, one or more nodes connected through a link can form a relative input node and output node relationship. The concepts of input node and output node are relative, and any node in an output node relationship with one node may be in an input node relationship with another node, and vice versa. As described above, input node to output node relationships can be created around links. One or more output nodes can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, the nodes connecting the input node and the output node may have a weight. Weights may be variable and may be varied by the user or algorithm in order for the artificial intelligence model to perform the desired function. For example, when one or more input nodes are connected to one output node by respective links, the output node is set to the values input to the input nodes connected to the output node and the links corresponding to each input node. The output node value can be determined based on the weight.

As described above, in an artificial intelligence model, one or more nodes are interconnected through one or more links to form an input node and output node relationship within the artificial intelligence model. The characteristics of the artificial intelligence model can be determined according to the number of nodes and links within the artificial intelligence model, the correlation between nodes and links, and the weight value assigned to each link. For example, if there are two artificial intelligence models with the same number of nodes and links and different weight values between the links, the two artificial intelligence models may be recognized as different from each other.

Some of the nodes constituting the artificial intelligence model may form one layer based on the distances from the initial input node. For example, a set of nodes with a distance n from the initial input node may constitute n layers. The distance from the initial input node can be defined by the minimum number of links that must be passed to reach the node from the initial input node. However, this definition of the layer is arbitrary for explanation purposes, and the order of the layer within the artificial intelligence model may be defined in a different way than described above. For example, a layer of nodes may be defined by distance from the final output node.

The initial input node may refer to one or more nodes in the artificial intelligence model into which data is directly input without going through links in relationships with other nodes. Alternatively, in an artificial intelligence model network, in the relationship between nodes based on links, it may mean nodes that do not have other input nodes connected by links. Similarly, the final output node may refer to one or more nodes that do not have an output node in their relationship with other nodes among the nodes in the artificial intelligence model. Additionally, hidden nodes may refer to nodes that constitute an artificial intelligence model rather than the first input node and the last output node. The artificial intelligence model according to an embodiment of the present invention may have more nodes in the input layer than the nodes in the hidden layer close to the output layer, and is an artificial intelligence model in which the number of nodes decreases as it progresses from the input layer to the hidden layer. You can.

An artificial intelligence model may include one or more hidden layers. The hidden node of the hidden layer can take the output of the previous layer and the output of surrounding hidden nodes as input. The number of hidden nodes for each hidden layer may be the same or different. The number of nodes in the input layer may be determined based on the number of data fields of the input data and may be the same as or different from the number of hidden nodes. Input data input to the input layer can be operated by the hidden node of the hidden layer and output by the fully connected layer (FCL), which is the output layer.

In various embodiments, the artificial intelligence model may be a deep learning model.

A deep learning model (e.g., deep neural network (DNN)) may refer to an artificial intelligence model that includes multiple hidden layers in addition to the input layer and output layer. Using a deep neural network, data Latent structures can be identified, that is, the latent structures of photos, text, video, voice, and music (e.g., what objects are in the photo, what the content and emotion of the text are, and what the sound of the voice is like). content, emotions, etc.).

Deep neural networks include convolutional neural networks (CNN), recurrent neural networks (RNN), auto encoders, generative adversarial networks (GAN), and restricted Boltzmann machines (RBMs). Boltzmann machine), deep belief network (DBN), Q network, U network, Siamese network, etc., but are not limited to these.

Next, the computing device 100 can provide artificial intelligence model sharing, rental, and sales services through an artificial intelligence model production platform.

Here, the artificial intelligence model sharing, rental, and sales service may mean a service that shares, rents, and sells a plurality of artificial intelligence models already registered in the artificial intelligence model production platform according to the user's request.

More specifically, when the computing device 100 obtains condition information regarding an artificial intelligence model that the user wishes to share, rent, or purchase, the computing device 100 satisfies the condition information obtained from the user among a plurality of artificial intelligence models pre-registered on the platform. You can select an artificial intelligence model to share, rent, or sell to users.

In one embodiment, the user terminal 200 may be connected to the computing device 100 through the network 400 and generate a user-customized artificial intelligence model provided through an artificial intelligence model production platform provided by the computing device 100. As you run the service, you can create a user-customized artificial intelligence model, or you can share, rent, or purchase an artificial intelligence model that suits your needs by running an artificial intelligence model sharing, rental, and sales service.

Here, the user terminal 200 may refer to any type of entity(s) in the system that has a mechanism for communication with the computing device 100. For example, the user terminal 200 includes a personal computer (PC), a note book, a mobile terminal, a smart phone, a tablet PC, and a wearable device. etc., and may include all types of terminals that can access wired/wireless networks. Additionally, the user terminal 200 may include any computing device implemented by at least one of an agent, an application programming interface (API), and a plug-in. Additionally, the user terminal 200 may include an application source and/or a client application.

Also, here, the network 400 may mean a connection structure that allows information exchange between nodes, such as a plurality of terminals and servers. For example, the network 400 includes a local area network (LAN), a wide area network (WAN), the Internet (WWW: World Wide Web), a wired and wireless data communication network, a telephone network, a wired and wireless television network, and CAN ( Controller Area Network) and Ethernet.

Wireless data communication networks include 3G, 4G, 5G, 3GPP (3rd Generation Partnership Project), 5GPP (5th Generation Partnership Project), LTE (Long Term Evolution), WIMAX (World Interoperability for Microwave Access), Wi-Fi, and Internet. (Internet), LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), RF (Radio Frequency), Bluetooth (Bluetooth) network, NFC (Near- Field Communication) network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, etc., but is not limited thereto.

In one embodiment, the external server 300 may be connected to the computing device 100 through the network 400, and the computing device 100 may provide a user-customized artificial intelligence model production service method and an artificial intelligence model production platform. A method of storing and managing various information and data necessary to perform artificial intelligence model sharing, rental, and selling methods, or a method of providing a user-customized artificial intelligence model production service by the computing device 100 and an artificial intelligence model through an artificial intelligence model production platform. Various information and data derived from sharing, renting, and selling methods can be collected, stored, and managed. For example, the external server 300 may be a storage server separately provided outside the computing device 100, but is not limited thereto. Hereinafter, with reference to FIG. 2, the hardware configuration of the computing device 100 that performs a method of providing a user-customized artificial intelligence model production service and a method of sharing, renting, and selling artificial intelligence models through an artificial intelligence model production platform will be described. .

Figure 2 is a hardware configuration diagram of a computing device operating an artificial intelligence model production platform according to another embodiment of the present invention.

Referring to FIG. 2, in various embodiments, the computing device 100 includes one or more processors 110, a memory 120 that loads a computer program 151 performed by the processor 110, and a bus ( 130), a communication interface 140, and a storage 150 for storing a computer program 151. Here, only components related to the embodiment of the present invention are shown in Figure 2. Accordingly, anyone skilled in the art to which the present invention pertains can see that other general-purpose components other than those shown in FIG. 2 may be further included.

The processor 110 controls the overall operation of each component of the computing device 100. The processor 110 includes a Central Processing Unit (CPU), Micro Processor Unit (MPU), Micro Controller Unit (MCU), Graphic Processing Unit (GPU), or any other type of processor well known in the art of the present invention. It can be.

Additionally, the processor 110 may perform operations on at least one application or program for executing methods according to embodiments of the present invention, and the computing device 100 may include one or more processors.

In various embodiments, the processor 110 includes random access memory (RAM) (not shown) and read memory (ROM) that temporarily and/or permanently store signals (or data) processed within the processor 110. -Only Memory, not shown) may be further included. Additionally, the processor 110 may be implemented in the form of a system on chip (SoC) that includes at least one of a graphics processing unit, RAM, and ROM.

Memory 120 stores various data, commands and/or information. Memory 120 may load a computer program 151 from storage 150 to execute methods/operations according to various embodiments of the present invention. When the computer program 151 is loaded into the memory 120, the processor 110 can perform the method/operation by executing one or more instructions constituting the computer program 151. The memory 120 may be implemented as a volatile memory such as RAM, but the technical scope of the present disclosure is not limited thereto.

Bus 130 provides communication functionality between components of computing device 100. The bus 130 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The communication interface 140 supports wired and wireless Internet communication of the computing device 100. Additionally, the communication interface 140 may support various communication methods other than Internet communication. To this end, the communication interface 140 may be configured to include a communication module well known in the technical field of the present invention. In some embodiments, communication interface 140 may be omitted.

Storage 150 may store the computer program 151 non-temporarily. When performing the artificial intelligence model sharing, rental, and sales process through the artificial intelligence model production platform through the computing device 100, the storage 150 performs the artificial intelligence model sharing, rental, and sales process through the artificial intelligence model production platform. Various information needed to provide information can be stored.

The storage 150 is a non-volatile memory such as Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), flash memory, a hard disk, a removable disk, or a device well known in the technical field to which the present invention pertains. It may be configured to include any known type of computer-readable recording medium.

The computer program 151, when loaded into the memory 120, may include one or more instructions that cause the processor 110 to perform methods/operations according to various embodiments of the present invention. That is, the processor 110 can perform the method/operation according to various embodiments of the present invention by executing the one or more instructions.

In one embodiment, the computer program 151 includes the steps of obtaining one or more condition information regarding an artificial intelligence model that the user wishes to share, rent, or purchase, and one or more conditions obtained from a plurality of artificial intelligence models pre-registered on the platform. One or more instructions for performing a method of sharing, renting, and selling an artificial intelligence model through an artificial intelligence model creation platform, including selecting an artificial intelligence model that satisfies condition information and providing the selected artificial intelligence model to the user. It can be included.

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

The components of the present invention may be implemented as a program (or application) and stored in a medium in order to be executed in conjunction with a hardware computer. Components of the invention may be implemented as software programming or software elements, and similarly, embodiments may include various algorithms implemented as combinations of data structures, processes, routines or other programming constructs, such as C, C++, , may be implemented in a programming or scripting language such as Java, assembler, etc. Functional aspects may be implemented as algorithms running on one or more processors.

In various embodiments, computing device 100 may provide artificial intelligence model sharing, rental, and sales services. For example, the computing device 100 may select an artificial intelligence model that the user wishes to share, rent, or purchase from among a plurality of artificial intelligence models already registered in the artificial intelligence model production platform and provide the selected artificial intelligence model to the user. To this end, the computing device 100 builds an environment in advance to provide artificial intelligence model sharing, rental, and sales services by prioritizing the operation of creating and registering a plurality of artificial intelligence models through an artificial intelligence model production platform. You can. Hereinafter, with reference to FIGS. 3 to 6, the operation of creating and registering a plurality of artificial intelligence models through the artificial intelligence model production platform performed by the computing device 100 will be described in more detail.

Figure 3 is a flowchart illustrating a method of creating and registering an artificial intelligence model performed through an artificial intelligence production platform in various embodiments.

Referring to FIG. 3, in step S110, the computing device 100 may provide a UI (eg, FIGS. 4 to 6) for providing a user-customized artificial intelligence model production service. For example, the computing device 100 may provide a UI for providing a user-customized artificial intelligence model creation service to the user terminal 200 in response to obtaining a request for creating a user-customized artificial intelligence model from the user.

Here, the request for creating a user-customized artificial intelligence model is obtained through the initial screen displayed when accessing a website that provides a user-customized artificial intelligence model production service or running a computer program or application. However, it is not limited to this, and the request to create a user-customized artificial intelligence model may be the act of accessing a website or running a computer program or application. In other words, the computing device 100 provides a UI for providing a user-customized artificial intelligence model production service to the user terminal 200 in response to the user accessing a website or running a computer program and application even if a separate request is not made. can be provided.

In step S120, the computing device 100 may select an artificial intelligence model to be created from the user through the UI provided through step S120 (e.g., FIG. 4).

In various embodiments, the computing device 100 may provide a plurality of artificial intelligence model templates through a UI, and may select one of the plurality of artificial intelligence model templates provided through the UI.

Here, the plurality of artificial intelligence model templates may be templates that can produce different types of artificial intelligence models.

For example, multiple artificial intelligence model templates are templates capable of producing artificial intelligence models based on supervised learning (e.g., classification models (e.g., kNN, Naive Bayes, Support Vector, Machine Decision, etc.) and regression models (e.g., Linear Regression, Locally Weighted Linear, Ridge, Lasso, etc.), templates capable of producing artificial intelligence models based on unsupervised learning (e.g. Clustering, K mean, Density Estimation, Exception Maximization, Pazen Window, DBSCAN, etc.) ), may include a template capable of producing an artificial intelligence model based on reinforcement learning, but is not limited to this.

In various embodiments, the computing device 100 may receive information about the selected artificial intelligence model from the user through a UI. For example, the computing device 100 may receive the model name of the selected artificial intelligence model and a keyword for describing the artificial intelligence model (e.g., a hashtag corresponding to the artificial intelligence model) through the UI, but is not limited to this. Here, keywords (e.g. hashtags) to describe the artificial intelligence model may be set to search for the artificial intelligence model or filter it according to condition information when performing artificial intelligence model sharing, rental, and sales services described later. .

In various embodiments, the computing device 100 may obtain information about the purpose of use of the artificial intelligence model from the user, and based on the information about the purpose of use obtained from the user, type of artificial intelligence suitable for use by the user. Models can be recommended. For example, when a user wants to use an artificial intelligence model for a specific purpose, the computing device 100 can evaluate the performance of artificial intelligence models produced for a specific purpose among a plurality of artificial intelligence models that have already been created, and use the artificial intelligence model for a specific purpose. Among the produced artificial intelligence models, the type of artificial intelligence model with the highest evaluated performance may be recommended as the type of artificial intelligence model suitable for use by users, but is not limited to this.

In addition, when the user selects a specific artificial intelligence model as an artificial intelligence model that the user wishes to create, the computing device 100 is an artificial intelligence model that has good synergy with a specific artificial intelligence model among a plurality of artificial intelligence models, that is, a specific artificial intelligence model. In combination with , an artificial intelligence model that is good for ensemble learning can be recommended. To this end, the computing device 100 may define in advance artificial intelligence model relationship information that defines whether and the degree of synergy effect between each of the plurality of artificial intelligence models.

For example, artificial intelligence model relationship information may be defined in advance by a provider that provides a customized artificial intelligence model production service.

As another example, artificial intelligence model relationship information can evaluate the performance of ensemble models containing different types of artificial intelligence models based on the artificial intelligence model creation history of multiple users, and model the ensemble model according to the evaluated performance. Interrelationships (e.g. positive, negative) between different types of artificial intelligence models included in can be defined, and can be created according to the defined relationships.

In step S130, the computing device 100 may generate learning data for the selected artificial intelligence model through step S120.

In various embodiments, the computing device 100 may receive a plurality of labels for an artificial intelligence model through a UI, upload data corresponding to each of the plurality of labels, and upload data on the uploaded data. Learning data can be created by labeling each piece of data with a corresponding label. For example, as shown in FIG. 5, when a user wants to create a classification model that classifies 'cherry blossoms' and 'plum blossoms', the computing device 100 can set 'cherry blossoms' and 'plum blossoms' as labels, respectively. , By labeling images uploaded to the 'Cherry Blossoms' category with 'Cherry Blossoms' and images uploaded to the 'Plum Blossom' category with 'Plum Blossoms', a classification model is learned to classify 'Cherry Blossoms' and 'Plum Blossoms'. You can create learning data to do this.

Here, if the artificial intelligence model that the user wants to create is an unsupervised learning-based artificial intelligence model that does not require separate labeling, the computing device 100 can only upload data to be used as learning data from the user. .

In various embodiments, the computing device 100 generates learning data for training an artificial intelligence model, but when the generated learning data is less than a preset number, a plurality of recommendation data may be provided through the UI, and a plurality of recommendation data may be provided through the UI. At least one recommendation data selected by the user among the recommendation data may be generated as learning data.

For example, when the number of learning data generated for the artificial intelligence model is less than a preset number, the computing device 100 randomly converts and processes the learning data generated for the artificial intelligence model within a predetermined range to generate a plurality of learning data. Augmented data can be generated, and a plurality of the generated augmented data can be recommended through the UI. For example, when the learning data generated for the artificial intelligence model is image data, the computing device 100 randomly converts the color, size, and angle of the image data within a predetermined range to convert the converted image data into augmented data. can be created.

As another example, when the number of learning data generated for an artificial intelligence model is less than a preset number, the computing device 100 selects learning data with the same label as the generated learning data from among the plurality of pre-generated learning data as augmented data. As such, recommendations can be provided through the UI. For example, when the learning data generated for the artificial intelligence model is image data labeled with 'cherry blossoms', the computing device 100 provides recommendations through the UI for learning data labeled with 'cherry blossoms' among the plurality of previously generated learning data. can do. However, it is not limited to this.

Here, the plurality of pre-generated learning data is generated in the process of multiple users creating artificial intelligence models through a user-customized artificial intelligence model production service.

In various embodiments, the computing device 100 may collect (e.g., crawl) a plurality of data from the external server 300, and among the plurality of data collected from the external server 300, the artificial intelligence that the user wishes to create Data suitable for training a model can be selected, and learning data can be created using the selected data.

For example, when a plurality of labels are set by the user through the UI, the computing device 100 may select data corresponding to each of the plurality of labels among the plurality of data collected from the external server 300, and select the selected data. Learning data can be created using data. For example, when 'cherry blossoms' and 'plum blossoms' are set as labels to create a classification model that classifies 'cherry blossoms' and 'plum blossoms' through image data analysis, the computing device 100 connects the external server 300 Learning data can be generated by selecting image data related to 'cherry blossoms' and 'plum blossoms' among a plurality of image data crawled from and labeling the selected image data with 'cherry blossoms' or 'plum blossoms'.

In step S140, the computing device 100 may generate a user-customized artificial intelligence model by training the selected artificial intelligence model through step S120 using the training data generated through step S130 (e.g., FIG. 6). .

In various embodiments, the computing device 100 may train an artificial intelligence model using at least one of supervised learning, unsupervised learning, and semi-supervised learning.

Here, learning of the artificial intelligence model is aimed at minimizing output errors. In the learning of an artificial intelligence model, learning data is repeatedly input into the artificial intelligence model, the output of the artificial intelligence model and the error of the target for the learning data are calculated, and the error of the artificial intelligence model is converted into that of the artificial intelligence model in order to reduce errors. This is the process of updating the weight of each node of the artificial intelligence model by backpropagating from the output layer to the input layer.

In the case of supervised learning, learning data in which the correct answer is labeled for each learning data is used (i.e., labeled learning data), and in the case of unsupervised learning, the correct answer may not be labeled in each learning data. That is, for example, in the case of supervised learning on data classification, the training data may be data in which each training data is labeled with a category. Labeled training data is input to the artificial intelligence model, and error can be calculated by comparing the output (category) of the artificial intelligence model and the label of the training data. As another example, in the case of unsupervised learning on data classification, the error can be calculated by comparing the input learning data with the artificial intelligence model output. The calculated error is back-propagated in the artificial intelligence model in the reverse direction (i.e., from the output layer to the input layer), and the connection weight of each node in each layer of the artificial intelligence model can be updated according to back-propagation. The amount of change in the connection weight of each updated node may be determined according to the learning rate. The calculation of an artificial intelligence model on input data and backpropagation of errors can constitute a learning cycle (epoch). The learning rate can be applied differently depending on the number of repetitions of the learning cycle of the artificial intelligence model. For example, in the early stages of training an artificial intelligence model, a high learning rate can be used to ensure that the artificial intelligence model quickly achieves a certain level of performance to increase efficiency, and in the later stages of training, a low learning rate can be used to increase accuracy.

In the training of an artificial intelligence model, generally, the learning data can be a subset of actual data (i.e., the data to be processed using the learned artificial intelligence model), and therefore, errors for the learning data are reduced, but for the actual data, the There may be a learning cycle in which errors increase. Overfitting is a phenomenon in which errors in actual data increase due to excessive learning on training data. For example, a phenomenon in which an artificial intelligence model that learned a cat by showing a yellow cat fails to recognize that it is a cat when it sees a non-yellow cat may be a type of overfitting. Overfitting can cause errors in machine learning algorithms to increase. To prevent such overfitting, various optimization methods can be used. To prevent overfitting, methods such as increasing the learning data, regularization, or dropout, which omits some of the network nodes during the learning process, can be applied.

In various embodiments, the computing device 100 may train an artificial intelligence model using only valid learning data among a plurality of learning data generated for learning an artificial intelligence model.

More specifically, the computing device 100 may determine the validity of each of the plurality of learning data generated for the artificial intelligence model.

For example, the computing device 100 may calculate the similarity between a plurality of learning data generated for an artificial intelligence model, and determine that learning data whose similarity is greater than or equal to a threshold is valid learning data, and where the similarity is equal to or greater than the threshold. It may be determined that training data with less than .

As another example, the computing device 100 may use specific learning data and a plurality of previously generated learning data among a plurality of learning data generated for an artificial intelligence model (e.g., in response to other users' artificial intelligence model creation requests). Among the corresponding pre-generated learning data), the similarity between specific learning data and at least one learning data with the same label can be calculated, and if the similarity is greater than a threshold, the specific learning data can be determined to be valid learning data. And, if the similarity is less than the threshold, it can be determined that the specific learning data is invalid learning data. However, it is not limited to this.

Thereafter, the computing device 100 learns the artificial intelligence model using only the valid learning data, that is, the learning data determined to be valid, based on the validity determination result for each of the plurality of learning data generated for learning the artificial intelligence model. You can do it.

At this time, if the number of valid learning data is less than the preset number, the computing device 100 sorts the learning data determined to be invalid in descending order according to similarity, and sets the number of learning data to the extent that the number of learning data exceeds the preset number. can be selected sequentially according to the sorted order.

In various embodiments, the computing device 100 may perform a test on a learned artificial intelligence model using learning data, and determine the artificial intelligence model as a user-customized artificial intelligence model based on the results of the test. .

More specifically, first, the computing device 100 may perform a test on an artificial intelligence model using test data for the artificial intelligence model. Here, test data for the artificial intelligence model may mean data for verifying the performance of the artificial intelligence model.

For example, test data may be part of the learning data used to train an artificial intelligence model. For example, the computing device 100 may use 70% of the total learning data to learn an artificial intelligence model and use the remaining 30% of the learning data to test the artificial intelligence model.

As another example, test data may be data obtained from a user for testing an artificial intelligence model. For example, test data may be test data uploaded from a user through a UI or acquired in real time. However, it is not limited to this.

Thereafter, the computing device 100 may calculate an error between the label corresponding to the test data and the output of the artificial intelligence model, based on the results of testing the artificial intelligence model, and calculate the error of the artificial intelligence model according to the calculated error. The weights of the artificial intelligence model can be adjusted until the performance exceeds the predetermined performance. Here, the computing device 100 may determine the performance of the artificial intelligence model based on factors such as accuracy, precision, and recall, but is not limited to this.

Thereafter, the computing device 100 adjusts the weight of the artificial intelligence model, and when the performance of the artificial intelligence model exceeds the predetermined performance, the computing device 100 can finally adopt the artificial intelligence model with the adjusted weight as a user-customized artificial intelligence model. there is.

Meanwhile, if the performance of the artificial intelligence model does not reach the predetermined performance even if the weight of the artificial intelligence model is adjusted, the computing device 100 may request the user to upload additional data to be used as learning data through the UI. there is. However, it is not limited to this, and in cases where the performance of the artificial intelligence model does not reach the predetermined performance, a plurality of augmented data may be generated using previously generated learning data.

In various embodiments, the computing device 100 may provide a production guide for an artificial intelligence model to a user through a UI.

At this time, the computing device 100 may provide a production guide for the artificial intelligence model to the user based on the user's artificial intelligence model production ability and the difficulty level of the artificial intelligence model to be produced.

First, the computing device 100 may determine the user's ability to create an artificial intelligence model. For example, the computing device 100 may provide the user with a plurality of queries to evaluate the artificial intelligence model creation ability through the UI, and determine the user's artificial intelligence model creation ability based on the answers to the plurality of questions. You can. Here, the computing device 100 may determine the user's artificial intelligence model creation ability as high, medium, or low, but is not limited to this, and is not limited to any one of the highest grade, 1st grade, to the lowest grade, 10th grade. It can be classified by grade. However, it is not limited to this.

Thereafter, the computing device 100 may compare the user's artificial intelligence model production ability with the difficulty of the artificial intelligence model that the user wishes to produce, determine a guide level, and provide a production guide to the user based on the determined guide level. You can.

For example, if the user's ability to create an artificial intelligence model is higher than the difficulty of the artificial intelligence model that the user wants to create, the computing device 100 does not provide a separate production guide or, in some cases, produces only the parts requested by the user. A guide can be provided optionally.

Meanwhile, if the user's artificial intelligence model production ability is the same as the difficulty level of the artificial intelligence model that the user wants to produce, the computing device 100 provides the user with a guide in the form of text containing a method of producing the artificial intelligence model that the user wants to produce. can be provided to.

Meanwhile, if the user's artificial intelligence model production ability is lower than the difficulty level of the artificial intelligence model that the user wants to produce, the computing device 100 provides a guide in the form of a text containing a method of producing the artificial intelligence model that the user wants to produce. Depending on the method, guides in the form of videos and/or images of the process of creating an artificial intelligence model may be provided.

In various embodiments, the computing device 100 may provide a label setting guide that recommends a label to the user based on the type of artificial intelligence model the user wishes to create. Additionally, the computing device 100 may provide a learning data preparation guide on what kind of learning data should be prepared based on the label recommended to the user or the label set by the user.

In various embodiments, the computing device 100 obtains a request to store or delete the user-customized artificial intelligence model from the user with respect to the user-customized artificial intelligence model that is finally generated through the above process, thereby creating the user-customized artificial intelligence model. can be saved or deleted in response to the user's account.

In various embodiments, the computing device 100 may generate a URL for a user-customized artificial intelligence model stored in response to the user's account, and may generate a URL for a user-customized artificial intelligence model created through a user-customized artificial intelligence model production service. It can be exported externally.

Meanwhile, in some cases, the computing device 100 can register an externally created artificial intelligence model through the URL by obtaining the URL of the externally created artificial intelligence model, and manage and add the registered artificial intelligence model. Learning can be done.

In various embodiments, the computing device 100 may generate an application to which a user-customized artificial intelligence model is applied by obtaining a request for creating an application for a user-customized artificial intelligence model stored in response to the user's account from the user.

In step S150, the computing device 100 may register the artificial intelligence model (user-customized artificial intelligence model) generated through step S140 to the artificial intelligence model production platform.

Here, registering an artificial intelligence model on an artificial intelligence model production platform means a separate space provided by the artificial intelligence model production platform (e.g., artificial intelligence model sharing bulletin board, artificial intelligence model rental bulletin board, artificial intelligence model purchase/sale bulletin board, etc.) It may be registering an artificial intelligence model, but is not limited to this, and in some cases, it may be registering on a personal page corresponding to each user's account, as shown in FIG. 12.

In various embodiments, the computing device 100 generates a specific artificial intelligence model according to a request from a specific user to share, rent, and sell a specific artificial intelligence model created through a user-customized artificial intelligence model creation service. You can register on the model creation platform.

Here, sharing an artificial intelligence model may mean distributing the artificial intelligence model to all users (or a specific group of users) free of charge. Sharing of an artificial intelligence model means releasing the artificial intelligence model for free so that many users can use it without conditions. Sharing an artificial intelligence model not only gives the right to use the artificial intelligence model, but also converts and processes the artificial intelligence model. It may also provide the authority to do so.

Additionally, here, renting an artificial intelligence model may mean lending the artificial intelligence model to a user who has paid a predetermined fee for a fee for a period corresponding to the rental cost. Renting an artificial intelligence model means renting an artificial intelligence model for a fee and receiving it back when the rental period expires. Renting an artificial intelligence model may only provide the right to use the artificial intelligence model.

Also, here, selling an artificial intelligence model may mean providing the artificial intelligence model to a user who pays a certain fee. Selling an AI model may mean giving full ownership of the AI model.

In various embodiments, the computing device 100 may determine the validity of a specific artificial intelligence model when a specific user wishes to register a specific artificial intelligence model created by a specific artificial intelligence model in order to rent or sell it. Depending on the validity, it can be decided whether to register the artificial intelligence model on the artificial intelligence model production platform.

More specifically, first, the computing device 100 may determine the validity of a specific artificial intelligence model to be rented or sold.

For example, the computing device 100 may evaluate the performance of a specific artificial intelligence model to be rented or sold, and if the evaluated performance is greater than a threshold, the computing device 100 may determine that the specific artificial intelligence model is valid, and the evaluated performance may be determined to be valid. If it is below the threshold, the specific artificial intelligence model may be determined to be invalid.

As another example, the computing device 100 may share a specific artificial intelligence model that it wishes to rent or sell for free for a predetermined period of time, and may share the sharing history (e.g., number of shares, number of shared users) of the specific artificial intelligence model for a predetermined period of time. The validity score for a specific artificial intelligence model can be calculated based on the number of users who shared it, the ratings of shared users, etc.), and if the validity score is higher than the threshold score, the specific artificial intelligence model can be judged to be valid, and the validity score is the threshold score. If it is less than that, the specific artificial intelligence model may be judged to be invalid.

Here, if a specific user wishes to register on the artificial intelligence model creation platform to share a specific artificial intelligence model created by a specific user, the artificial intelligence model created by the user is disclosed free of charge, and the artificial intelligence model creation platform is used to share it. If you wish to register an artificial intelligence model, you may not perform the operation of determining whether to register it through the validity determination described above. However, if you unconditionally register all the artificial intelligence models you want to share on the artificial intelligence model production platform, problems such as registering an artificial intelligence model with poor performance or registering a large number of identical/similar artificial intelligence models may occur. For the artificial intelligence model you wish to share, only the artificial intelligence model that is judged to be valid after going through the validity determination process described above can be shared and registered on the artificial intelligence model production platform.

In various embodiments, when the computing device 100 wishes to register with an artificial intelligence model production platform to rent or sell a specific artificial intelligence model created by a specific user, the computing device 100 is pre-registered with a specific artificial intelligence model and the artificial intelligence model production platform. The similarity of each of the plurality of artificial intelligence models can be calculated, and a specific artificial intelligence model can be registered on the artificial intelligence model production platform only when the maximum value of the calculated similarity is less than the standard value.

In various embodiments, the computing device 100 may set a rental cost or sales cost for a specific artificial intelligence model when it is determined that the specific artificial intelligence model is valid. For example, the computing device 100 may set rental costs and sales costs for a specific artificial intelligence model by receiving cost information of a specific artificial intelligence model from a user who created the specific artificial intelligence model, but is not limited to this. In some cases, the computing device 100 is an artificial intelligence model that has the same properties (e.g., type, performance, keyword (hashtag), etc.) as a specific artificial intelligence model among a plurality of artificial intelligence models already registered in the artificial intelligence model production platform. Based on the cost information set in the field, the appropriate price for a specific artificial intelligence model can be calculated, the calculated appropriate price can be suggested to the user, or the cost of a specific artificial intelligence model can be automatically set based on the appropriate price. Hereinafter, with reference to FIGS. 7 to 10, a method of sharing, renting, and selling an artificial intelligence model through an artificial intelligence model production platform performed by the computing device 100 will be described.

Figure 7 is a flowchart illustrating a method of sharing, renting, and selling an artificial intelligence model through an artificial intelligence model production platform according to another embodiment of the present invention, and Figures 8 to 10 show artificial intelligence model production in various embodiments. This diagram illustrates the process of sharing, renting, and selling artificial intelligence models through the platform.

Referring to FIGS. 7 to 10, in step S210, the computing device 100 may provide a UI (eg, FIGS. 11 to 13) for providing artificial intelligence model sharing, rental, and sales services. For example, the computing device 100 may provide a UI for providing artificial intelligence model sharing, rental, and sales services to the user terminal 200 in response to obtaining a request for sharing, renting, or purchasing an artificial intelligence model from the user. You can.

In step S220, the computing device 100 may obtain one or more condition information regarding an artificial intelligence model that the user wishes to share, rent, or purchase through the UI provided through step S210.

Here, the condition information is information about the conditions for searching and filtering the artificial intelligence model that the user wants to share, rent, or purchase. For example, the condition information includes the type of artificial intelligence model to be searched and filtered, registration method (e.g. Share registration, rental registration, sale registration), rental or sale cost (scope), period (for rentals), performance (scope), and keywords (e.g. keywords to be included and/or excluded, etc.), Without being limited to this, various types of condition information available for artificial intelligence model search and filtering may be applied.

In step S230, the computing device 100 may select an artificial intelligence model that satisfies one or more condition information obtained through step S220 among a plurality of artificial intelligence models pre-registered in the artificial intelligence model production platform. For example, the computing device 100 selects only artificial intelligence models that satisfy the above condition information among a plurality of artificial intelligence models pre-registered in the artificial intelligence model production platform, or selects only artificial intelligence models that satisfy the above condition information, or By filtering one or more condition information among the intelligence models, an artificial intelligence model that satisfies one or more condition information can be selected.

In various embodiments, the computing device 100 may provide an artificial intelligence model that satisfies the specific condition information when there is no artificial intelligence model that satisfies the specific condition information obtained from a specific user among the plurality of artificial intelligence models pre-registered in the artificial intelligence model production platform. A user-customized artificial intelligence model production service for producing an artificial intelligence model (e.g., steps S110 to S140 in FIG. 3) may be provided to a specific user.

At this time, in response to providing a user-customized artificial intelligence model production service to a specific user, the computing device 100 generates an artificial intelligence model that satisfies specific condition information for the specific user through the user-customized artificial intelligence model production service, When registering the created artificial intelligence model on the artificial intelligence model production platform, a certain reward can be provided to a specific user. Here, the predetermined compensation may be a point to replace the cost paid to use the services provided through the artificial intelligence model production platform, but is not limited to this, and various forms of compensation such as discount coupons and gifticons may be applied. .

In various embodiments, when there is no artificial intelligence model that satisfies a plurality of condition information among the plurality of artificial intelligence models pre-registered in the artificial intelligence model production platform, the computing device 100 may use multiple artificial intelligence models pre-registered in the artificial intelligence model production platform. Among the artificial intelligence models, N artificial intelligence models can be selected sequentially, starting with the artificial intelligence model that satisfies the largest number of condition information.

In various embodiments, when there is no artificial intelligence model that satisfies a plurality of condition information among the plurality of artificial intelligence models pre-registered in the artificial intelligence model production platform, the computing device 100 uses the plurality of condition information to create an artificial intelligence model. Scores are calculated for each of the multiple artificial intelligence models already registered in the production platform, and N artificial intelligence models can be selected sequentially, starting from the artificial intelligence model with the highest calculated score. For example, the computing device 100 may be assigned an importance-based score to each of a plurality of condition information in advance, and each of the plurality of artificial intelligence models pre-registered in the artificial intelligence model production platform corresponds to each of the plurality of condition information. A score assigned to each of a plurality of condition information can be assigned depending on whether the condition information is available, and N artificial intelligence models can be sequentially selected, starting from the artificial intelligence model with the highest total score as described above. However, it is not limited to this.

In step S240, the computing device 100 may provide the user with the artificial intelligence model selected through step S230.

In various embodiments, as shown in FIG. 8, the computing device 100 may provide an advertisement to the user when a request to share an artificial intelligence model including one or more condition information is obtained from the user, and the user may select the advertisement. Only if you have watched , an artificial intelligence model that satisfies one or more condition information can be shared with the user. At this time, when the computing device 100 shares an artificial intelligence model that satisfies one or more condition information with a user, it may pay a certain amount of compensation to the creator of the artificial intelligence model that satisfies one or more condition information. At this time, the predetermined compensation may be at least a portion of the revenue (e.g., advertising revenue) generated due to the advertisement viewed by the user, but is not limited to this.

In various embodiments, the computing device 100 may provide a payment process to the user when an artificial intelligence model rental request including one or more condition information is obtained from the user, as shown in FIG. 9, and the user may make the payment. If the rental fee is paid through the process, an artificial intelligence model that satisfies one or more condition information can be rented to the user, and at least the rental fee paid by the user to the creator of the artificial intelligence model that satisfies one or more condition information can be rented to the user. A portion may be paid. for example

In various embodiments, the computing device 100 may provide a payment process to the user when an artificial intelligence model purchase request including one or more condition information is obtained from the user, as shown in FIG. 10, and the user may make the payment. When paying a purchase fee through the process, an artificial intelligence model that satisfies one or more condition information can be provided to the user, and at least the purchase cost paid by the user can be provided to the creator of an artificial intelligence model that satisfies one or more condition information. A portion may be paid.

In various embodiments, the computing device 100 may provide a URL generated in response to a specific artificial intelligence model to the user when the purchase fee for a specific artificial intelligence model is paid by the user through a payment process, and the user may By registering a URL through your account, you can use the artificial intelligence model you purchased.

In various embodiments, when the computing device 100 obtains input data together with one or more condition information from a user, at least one that satisfies one or more condition information among a plurality of artificial intelligence models pre-registered through an artificial intelligence model production platform. Not only does it provide information about one artificial intelligence model to the user, but it also provides result data output as input data obtained from the user is entered into at least one artificial intelligence model that satisfies one or more condition information, thereby helping the user You can share, rent, and purchase exactly the artificial intelligence model you want.

In various embodiments, when a user purchases a specific artificial intelligence model, the computing device 100 may select an artificial intelligence model that has a positive correlation with the specific artificial intelligence model and recommend the purchase of the selected artificial intelligence model. there is.

More specifically, first, the computing device 100 selects an ensemble model including two or more artificial intelligence models of different types among a plurality of artificial intelligence models already registered through an artificial intelligence model production platform, and selects the selected ensemble model. You can evaluate their performance.

Afterwards, the computing device 100 defines the intercorrelation between the artificial intelligence models included in each of the ensemble models based on the performance evaluation results for the ensemble models (e.g., strong negative, negative, weak negative, weak positive) , affirmation, strong affirmation, etc.). For example, if the performance of the first ensemble model including the first artificial intelligence model and the second artificial intelligence model is better than the performance of the second ensemble model including the first artificial intelligence model and the third artificial intelligence model, the first artificial intelligence model The correlation between the intelligence model and the second artificial intelligence model can be defined as more positive than the correlation between the first artificial intelligence model and the third artificial intelligence model.

Thereafter, the computing device 100 may generate a correlation map based on the correlation between different artificial intelligence models. For example, a correlation map is a map in which nodes corresponding to each of a plurality of artificial intelligence models are displayed and intercorrelated artificial intelligence models are connected by lines, where the properties of the lines (e.g. type, thickness, color, etc.) can be determined depending on the type and degree of correlation.

Thereafter, when the user purchases a specific artificial intelligence model, the computing device 100 can select an artificial intelligence model that has a positive correlation with the specific artificial intelligence model based on the correlation map, and the selected artificial intelligence model purchase can be recommended.

The method of sharing, renting, and selling artificial intelligence models through the aforementioned artificial intelligence model production platform was explained with reference to the flow chart shown in the drawing. For simple explanation, the method of sharing, renting, and selling an artificial intelligence model through an artificial intelligence model production platform is illustrated as a series of blocks. However, the present invention is not limited to the order of the blocks, and some blocks are shown herein. and may be performed in a different order than that in which the procedure was performed, or may be performed simultaneously. Additionally, new blocks not described in this specification and drawings may be added, or some blocks may be deleted or changed.

Above, embodiments of the present invention have been described with reference to the attached drawings, but those skilled in the art will understand that the present invention can be implemented in other specific forms without changing its technical idea or essential features. You will be able to understand it. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

100: computing device
200: user terminal
300: external server
400: Network

Claims (10)

In a method performed by a computing device,
Obtaining, from a user, one or more conditional information regarding an artificial intelligence model to be shared, rented, or purchased;
Selecting an artificial intelligence model that satisfies the obtained one or more condition information among a plurality of artificial intelligence models already registered on the platform;
providing the selected artificial intelligence model to the user;
Creating a plurality of artificial intelligence models using a user-customized artificial intelligence model production service provided through the platform; and
Comprising the step of registering at least one artificial intelligence model to be rented or sold among the plurality of created artificial intelligence models on the platform,
The steps to register on the platform are:
determining the validity of at least one artificial intelligence model to be rented or sold; and
Based on the determined validity, when it is determined that the at least one artificial intelligence model to be rented or sold is valid, registering the at least one artificial intelligence model to be rented or sold to the platform,
The step of determining the validity is,
sharing at least one artificial intelligence model to be rented or sold for free for a predetermined period of time;
Based on the sharing history of at least one artificial intelligence model to be rented or sold during the predetermined period - the sharing history includes the number of sharing times, the number of sharing users, and the ratings of sharing users - the rental or Calculating a validity score for at least one artificial intelligence model to be sold; and
When the calculated validity score is greater than or equal to a threshold score, determining that at least one artificial intelligence model to be rented or sold is valid,
How to share, rent, and sell artificial intelligence models through an artificial intelligence model production platform. According to paragraph 1,
The user-customized artificial intelligence model production service generates learning data for the artificial intelligence model to be produced through a user interface (UI), and uses the generated learning data to learn the artificial intelligence model to be produced. A service that creates a customized artificial intelligence model according to the user's request,
How to share, rent, and sell artificial intelligence models through an artificial intelligence model production platform. delete delete According to paragraph 1,
The step of selecting the artificial intelligence model is,
If there is no artificial intelligence model that satisfies the one or more condition information among the plurality of pre-registered artificial intelligence models, a user-customized artificial intelligence model production service is provided for the production of an artificial intelligence model that satisfies the one or more condition information. comprising steps,
How to share, rent, and sell artificial intelligence models through an artificial intelligence model production platform. According to paragraph 1,
The step of providing the selected artificial intelligence model is,
When obtaining a purchase request for the selected artificial intelligence model from the user, providing a payment process for purchasing the selected artificial intelligence model; and
When the purchase cost for the selected artificial intelligence model is paid through the provided payment process, generating a URL corresponding to the selected artificial intelligence model, and providing the generated URL,
How to share, rent, and sell artificial intelligence models through an artificial intelligence model production platform. According to clause 6,
The step of providing the generated URL is,
When the user purchases a specific artificial intelligence model, selecting an artificial intelligence model that has a positive correlation with the specific artificial intelligence model; and
It includes a step of recommending the purchase of the selected artificial intelligence model,
The step of selecting an artificial intelligence model that has a positive correlation with the specific artificial intelligence model is,
Evaluating the performance of ensemble models including two or more different types of artificial intelligence models among the plurality of pre-registered artificial intelligence models;
Defining a correlation between the two or more different artificial intelligence models based on the evaluated performance and generating a correlation map based on the defined correlation;
Based on the generated correlation map, selecting an artificial intelligence model that has a positive correlation with the specific artificial intelligence model,
How to share, rent, and sell artificial intelligence models through an artificial intelligence model production platform. According to paragraph 1,
The one or more condition information is,
Contains input data,
The step of providing the selected artificial intelligence model is,
Comprising the step of providing information about the selected at least one artificial intelligence model and result data output as the input data is input to the selected at least one artificial intelligence model,
How to share, rent, and sell artificial intelligence models through an artificial intelligence model production platform. processor;
network interface;
Memory; and
Includes a computer program loaded into the memory and executed by the processor,
The computer program is,
Instructions for obtaining one or more conditional information from the user regarding the artificial intelligence model to be shared, rented, or purchased;
Instructions for selecting an artificial intelligence model that satisfies the obtained one or more condition information among a plurality of artificial intelligence models already registered on the platform;
Instructions for providing the selected artificial intelligence model to the user;
Instructions for creating a plurality of artificial intelligence models using a user-customized artificial intelligence model production service provided through the platform; and
Includes instructions for registering at least one artificial intelligence model to be rented or sold among the plurality of created artificial intelligence models on the platform,
Instructions registered on the platform are:
Instructions for determining the validity of at least one artificial intelligence model to be rented or sold; and
Based on the determined validity, if the at least one artificial intelligence model to be rented or sold is determined to be valid, instructions for registering the at least one artificial intelligence model to be rented or sold to the platform,
The instruction for determining the validity is,
Instructions for sharing at least one artificial intelligence model to be rented or sold for free for a predetermined period of time;
Based on the sharing history of at least one artificial intelligence model to be rented or sold during the predetermined period - the sharing history includes the number of sharing times, the number of sharing users, and the ratings of sharing users - the rental or Instructions for calculating a validity score for at least one artificial intelligence model to be sold; and
Including instructions for determining that at least one artificial intelligence model to be rented or sold is valid when the calculated validity score is greater than or equal to a threshold score,
A computing device that performs methods of sharing, renting, and selling artificial intelligence models through an artificial intelligence model creation platform. Combined with a computing device,
Obtaining, from a user, one or more conditional information regarding an artificial intelligence model to be shared, rented, or purchased;
Selecting an artificial intelligence model that satisfies the obtained one or more condition information among a plurality of artificial intelligence models already registered on the platform;
providing the selected artificial intelligence model to the user;
Creating a plurality of artificial intelligence models using a user-customized artificial intelligence model production service provided through the platform; and
Comprising the step of registering at least one artificial intelligence model to be rented or sold among the plurality of created artificial intelligence models on the platform,
The steps to register on the platform are:
determining the validity of at least one artificial intelligence model to be rented or sold; and
Based on the determined validity, when it is determined that the at least one artificial intelligence model to be rented or sold is valid, registering the at least one artificial intelligence model to be rented or sold to the platform,
The step of determining the validity is,
sharing at least one artificial intelligence model to be rented or sold for free for a predetermined period of time;
Based on the sharing history of at least one artificial intelligence model to be rented or sold during the predetermined period - the sharing history includes the number of sharing times, the number of sharing users, and the ratings of sharing users - the rental or Calculating a validity score for at least one artificial intelligence model to be sold; and
If the calculated validity score is higher than the threshold score, executing a method of sharing, renting and selling an artificial intelligence model through an artificial intelligence model production platform, including determining that at least one artificial intelligence model to be rented or sold is valid. A computer program stored on a recording medium that can be read by a computing device.