KR102552915B1 - Method and system for providing advertisement service using virtual model - Google Patents

Abstract

According to various embodiments of the present disclosure, in a virtual model-based advertising platform server for marketing through a virtual model, the advertisement request receiving unit receiving a client's advertisement request;
a virtual model generating unit generating virtual models used in advertisements; a virtual model-based advertisement performer matching at least one of the virtual models with the client and performing an advertisement through the at least one virtual model; A metaverse service provider for generating a metaverse space based on advertisement content received from a client terminal; And it may include a chat service providing unit for outputting a web page providing a chat service to the client terminal.

Description

Method and system for providing advertising service using virtual model {METHOD AND SYSTEM FOR PROVIDING ADVERTISEMENT SERVICE USING VIRTUAL MODEL}

The present invention relates to a method and system for providing an advertisement service using a virtual model, and more particularly, to a method and apparatus for providing an advertisement service using a virtual model according to the preferences of clients or advertisement targets.

As the Internet era begins, technology for forming and providing posts through text information, image information, video information, and multimedia information is rapidly developing. Recently, video platforms such as Youtube, Internet broadcasting platforms such as Afreeca TV, Twitch TV, and TV Daumpot, portal site platforms such as Naver, Google, Daum, and Yahoo, social media platforms such as facebook, Instagram, twitter, and cacao story, The number of users who check and use various information through blog platforms such as Daum Blog, Naver Blog, Tistory Blog, and Blog Spot is explosively increasing.

As the media diversified, marketing channels also diversified. Therefore, if in the past, publicity was effective only in a few mass media, now there are various publicity channels such as search channels, blogs, social media channels such as Facebook and Instagram. With the explosive increase in the number of users, influencers began to appear among users using various platforms.

Influencer means 'person who exerts influence'. In social network service (SNS), a real influencer who has a large number of followers (users who subscribe to the contents uploaded by the influencer) refers to

As the number of users of social network services increases due to the recent spread of Internet terminals such as smartphones, advertisers who want to utilize these influencers for marketing are increasing. SNS marketing through influencers is known to be more efficient than TV marketing through traditional advertising models such as movie stars or sports stars. It is common to appoint a celebrity with a high level of awareness that can influence most people because advertisements using TV or radio must be delivered to an unspecified number of people, but influencers are those who share specific interests in a specific field. Since the product has a particularly high awareness of the followers, it may be easy to intensively promote a product that the followers may be interested in.

However, advertising marketing using influencers is accompanied by some risks. For example, if negative news about an influencer, an advertising model, breaks out, the company that advertised through the influencer as a result. The internal client had to endure the risk of image damage. Therefore, it is necessary to prepare a marketing plan using virtual influencers that look the same as real people rather than actual influencers.

Unless otherwise indicated herein, material described in this section is not prior art to the claims in this application, and inclusion in this section is not an admission that it is prior art.

An object of the present invention to solve the above problems is to provide customized advertisements to clients by utilizing virtual influencers.

Another object of the present invention for solving the above problems may further provide objects directly or indirectly identified through this document.

According to various embodiments of the present disclosure, in a virtual model-based advertising platform server for marketing through a virtual model, the advertisement request receiving unit receiving a client's advertisement request;

a virtual model generating unit generating virtual models used in advertisements; a virtual model-based advertisement performer matching at least one of the virtual models with the client and performing an advertisement through the at least one virtual model; A metaverse service provider for generating a metaverse space based on advertisement content received from a client terminal; And it may include a chat service providing unit for outputting a web page providing a chat service to the client terminal.

According to various embodiments, the virtual model generating unit may analyze taste information based on SNS activity information of the client and generate a virtual model to perform advertisement of the client based on the analyzed taste information of the client. there is.

According to various embodiments of the present disclosure, the virtual model-based advertisement implementation unit may create a posting including the virtual model and upload the created posting to an activity platform of the client.

According to various embodiments of the present disclosure, the virtual model-based advertisement performing unit compares a degree of interest in each of the client's first post not including the virtual model and the client's second post including the virtual model, and the comparison It is possible to calculate the contribution of the virtual model based on.

According to various embodiments, the chatting service provider may determine a chatting field for each virtual model and learn an artificial intelligence model corresponding to the virtual model according to the determined chatting field.

According to various embodiments, a method of providing an online marketing platform for marketing through a virtual model, comprising: receiving a request for an advertisement from a client; generating virtual models used in advertisements; matching at least one virtual model among the virtual models with the client; performing an advertisement through the at least one virtual model; Generating a metaverse space based on the advertisement content received from the client terminal; and outputting a web page providing a chatting service to the client terminal.

According to various embodiments, a non-transitory recording medium on which a program for executing the method is recorded and which can be read by a computer may be disclosed.

According to various embodiments, a computer program recorded on a non-transitory recording medium may be started in the virtual model-based advertising platform server to execute the method.

According to various embodiments, a system for providing an online marketing platform based on a virtual model, comprising: a client terminal of a client requesting an advertisement; a user terminal of a user using SNS where the advertisement is uploaded; and a virtual model-based advertising platform server marketing through the virtual model. The virtual model-based advertisement platform server may include an advertisement request receiving unit receiving an advertisement request from the client; a virtual model generating unit generating virtual models used in the advertisement; a virtual model-based advertisement performer matching at least one of the virtual models with the client and performing an advertisement through the at least one virtual model; A metaverse service provider for generating a metaverse space based on advertisement content received from a client terminal; And it may include a chat service providing unit for outputting a web page providing a chat service to the client terminal.

According to various embodiments disclosed in this document, it is possible not to suffer damage when a scandal breaks out while using an actual influencer as an advertising model.

Also, according to various embodiments, since customized advertisements of advertisers are performed through customized virtual models, efficient advertisements are possible.

In addition to this, various effects identified directly or indirectly through this document may be provided.

1 is a diagram illustrating an advertising platform system based on a virtual model according to an embodiment.
2 is a diagram showing the main components of a virtual model-based advertising platform server.
3 is a diagram illustrating operations of a server using a virtual model according to a request of a client according to an embodiment.
4 is a diagram illustrating an SNS advertisement post using a virtual model according to an embodiment.
5 is a diagram illustrating a chat service using a virtual model according to an embodiment.
FIG. 6 is a diagram showing the hardware configuration of the virtual model-based advertising platform server according to FIG. 1 .
7 is a diagram illustrating a wireless communication system that can be applied in a communication process according to an embodiment of the present invention.
8 is a diagram illustrating a base station in the wireless communication system according to FIG. 7 .
9 is a diagram illustrating a terminal in the wireless communication system according to FIG. 7 .
10 is a diagram illustrating a communication interface in the wireless communication system according to FIG. 7 .

Since the present invention can make various changes and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. Like reference numerals have been used for like elements throughout the description of each figure.

Terms such as first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention. The terms and/or include any combination of a plurality of related recited items or any of a plurality of related recited items.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the present application, they should not be interpreted in an ideal or excessively formal meaning. don't

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

1 is a diagram illustrating a virtual model-based advertising platform system 10 according to an embodiment. Referring to FIG. 1 , the virtual model-based advertising platform system 10 may include a virtual model-based advertising platform server 100 , a client terminal 200 , a user terminal 300 , and the like. Operations described below may be performed or implemented through a platform (eg, a web page and/or application) controlled by the server 100 . In other words, the server 100 is a website through which a user accesses the server 100 through a network using the user terminal 300 or the client terminal 200 to input, register, and output various information. It is possible to provide, and by being installed and executed in the user terminal 300 or the client terminal 200, it is possible to provide an application capable of inputting, registering, and outputting various information.

The virtual model-based advertising platform server 100 may receive an advertisement request from the client terminal 200 and match a virtual model to be used for advertisement of the client terminal 200 based on the contents of the advertisement request. The virtual model-based advertising platform server 100 may promote products or services of a client through advertisements using virtual models.

The virtual model-based advertising platform server 100 provides marketing services using virtual models, but may also provide marketing services to clients who want social media advertisements. In other words, the virtual model-based advertising platform server 100 generates a marketing post using a virtual model matched with a client on platforms such as Instagram, YouTube, Facebook, Twitter, Naver Blog, and Tistory, and the generated marketing post. It is possible to publicize the contents of the client's advertisement request.

The virtual model-based advertising platform server 100 not only simply provides social media advertising services, but also provides advertising and promotion services for metaverse space or NFT products by utilizing the generated virtual models. In this specification, virtual models may be understood or referred to as virtual influencers, virtual influencers, virtual humans, metahumans, and the like. In other words, a metaverse advertisement service through the virtual model can be provided by matching a virtual model to clients who want advertisements in the metaverse space.

The virtual model-based advertising platform server 100 may provide a chat service capable of chatting with clients or users by utilizing a virtual model. The virtual model-based advertising platform server 100 may determine a virtual model for each field. For example, a first virtual model is set to enable chatting for a first field, and a second virtual model is set to enable chatting for a second field. It may be set to allow chatting about. The virtual model-based advertising platform server 100 may provide a text-based chatting service or a video-based chatting service, and use a keyboard input or microphone of the user terminal 300 or client terminal 200 to allow users or clients A question signal of can be received, and an answer signal of the virtual model can be transmitted using a display or speaker of the user terminal 300 or the client terminal 200 .

The client terminal 200 provides information on the product and/or service (ie, the product and/or service that the advertiser intends to promote) that is the target of the advertisement to be displayed and advertisement period information on when and until when the advertisement is to be displayed. etc. may be transmitted to the virtual model-based advertising platform server 100 through a network. The user terminal 300 may be understood as a terminal of a user using a service on a platform (eg, SNS, metaverse) on which a client advertises. The user terminal or client terminal is expressed as a 'terminal' for convenience, but does not necessarily mean only a single hardware component, and may be implemented in the form of a server. For example, the client terminal 200 is a desktop computer capable of communication. (desktop computer), laptop computer, notebook, smart phone, tablet PC, mobile phone, smart watch, smart glass ), e-book reader, PMP (portable multimedia player), portable game machine, navigation device, digital camera, DMB (digital multimedia broadcasting) player, digital audio recorder, digital voice player (digital audio player), digital video recorder, digital video player, and personal digital assistant (PDA).

The server 100, the client terminal 200, and the user terminal 300 are connected to a communication network, respectively, and can transmit and receive data to and from each other through the communication network. For example, communication networks include Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), High Speed Downlink Packet Access (HSDPA), and W-CDMA. (Wideband Code Division Multiple Access), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), Bluetooth, Zigbee, Wi-Fi, Voice over Internet Protocol (VoIP) , LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and MBWA (IEEE Various types of wired or wireless networks may be used, such as 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), World Interoperability for Microwave Access (Wi-MAX), and 5G.

2 is a diagram showing the main components of the virtual model-based advertising platform server 100 . 3 is a diagram illustrating operations of a server using a virtual model according to a request of a client according to an embodiment. 4 is a diagram illustrating an SNS advertisement post using a virtual model according to an embodiment. 5 is a diagram illustrating a chat service using a virtual model according to an embodiment.

The data management unit 101 may obtain data from the client terminal 200 and the user terminal 300 . For example, the data management unit 101 receives a marketing request from the client terminal 200, and provides marketing information including a marketing period corresponding to the requested marketing, a marketing method, a marketing medium, a marketing request fee, a marketing link, and the like. can be obtained

The data management unit 101 may crawl posts posted by a plurality of influencers for each platform based on big data, and obtain activity information of the plurality of influencers. For example, the data management unit 101 may crawl posts uploaded by a plurality of influencers for each platform, such as Instagram, YouTube, Facebook, Twitter, Naver Blog, and Tistory. For example, the activity information may include the number of posts uploaded by the influencer by platform, amount of activity by platform, post category, post content, post format, activity period, and feedback. The data management unit 101 may obtain information about upload tendencies of the plurality of influencers. This is to apply a marketing or advertising method by a virtual model (virtual influencer) by grasping information on upload propensity.

The advertisement request receiver 102 may receive an advertisement request request signal from the client terminal 200 . The advertisement request receiving unit 102 may receive information about an advertisement period, an advertisement object, an advertisement concept, and the like together with an advertisement request request signal from the client terminal 200 .

The virtual model generating unit 103 may generate a virtual model, but may generate a virtual model in advance before requesting an advertisement or may create a customized virtual model corresponding to the contents requested by a client together with requesting an advertisement.

The virtual model generator 103 may create a plurality of virtual models having preference conditions for each age group. For example, the virtual model generation unit 103 may include a virtual model preferred by teenagers with conditions preferred by teenagers, a virtual model preferred by 20s with conditions preferred by 20s, and a virtual model preferred by 30s with conditions preferred by 30s. , virtual models preferred by those in their 40s with conditions preferred by those in their 40s, virtual models preferred by those in their 50s with conditions preferred by those in their 50s, and virtual models preferred by those in their 60s with conditions preferred by those in their 60s can be created.

The virtual model generation unit 103 may generate a plurality of virtual models having preference conditions for each field. For example, the virtual model generation unit 103 may include a fashion preference virtual model having preference conditions for people interested in fashion, a popular music preference virtual model having preference conditions for people interested in pop music, and a classical music field. A classical music preference virtual model with preference conditions for interested people, a tennis preference virtual model with preference conditions for people interested in tennis, and the like can be created.

It may be most efficient to determine the appearance of the virtual model based on the preferred appearance of people who may be interested in advertisements or contents to be promoted by the client and contents of advertisements. Accordingly, the virtual model generation unit 103 may generate a virtual model based on the preferred appearance of the people and the content of the advertisement. For example, in the case of a cosmetics advertisement, a virtual model corresponding to the above condition may be generated because the preferred appearance of people interested in cosmetics and a virtual model having clean and elegant skin may match.

The virtual model generation unit 103 is based on facial images included in posts (posts that users who follow or subscribe to the client's account (an account operated by the client) are interested in (likes, comments, or interest). By doing so, an ideal type image can be created. The virtual model generator 103 may designate a landmark of each of the face images, and obtain facial feature points for each of the face images based on the landmark. The virtual model generating unit 103 may generate an ideal type image having an average of facial feature points. The virtual model generator 103 may generate a face of a virtual model based on the generated ideal type image.

Alternatively, the virtual model generation unit 103 may generate a virtual model based on the preference of the client as well as a virtual model based on the preferred appearance of people who are interested in advertisements or contents to be promoted by the client. there is.

The virtual model generating unit 103 may crawl public accounts of people the user follows through an SNS server (eg, Instagram, Facebook). The virtual model generator may calculate the ideal type preference of each of the people the user follows based on the number of accesses made by the user during a certain period of time, the duration of the connection, the number of likes the user clicked, the number of comments the user wrote, and the like. The virtual model generation unit may crawl photos uploaded to accounts of people whose calculated ideal type preference is greater than or equal to a threshold value, and may generate average faces of people whose ideal type preference is equal to or greater than the threshold value through the crawled photos. The virtual model generation unit may recognize faces for each of the crawled photos through object detection and extract feature points of the recognized faces. The virtual model generating unit may calculate an average feature point based on the extracted feature points, and generate an average face of people having the threshold value or more based on the calculated average feature point. The virtual model generator may determine the generated average face as the user's ideal type face, and determine the determined ideal type face as the face of the virtual model. The virtual model generating unit 103 may store the generated virtual model in a virtual model DB.

The virtual model-based advertisement execution unit 104 may determine target customers to promote a product or service that is an advertisement target, and analyze the determined target customers.

The virtual model-based advertisement performing unit 104 may determine a customized first virtual model based on the client's preference and advertisement request contents, and match the determined first virtual model with the client. The virtual model-based advertisement performing unit 104 may create a post to be posted on the main activity platform or SNS of the client with the determined first virtual model. The posts may consist of text, photos and/or videos.

The virtual model-based advertisement performer 104 may calculate the contribution of the first virtual model to the advertisement. The virtual model-based advertisement performer 104 may calculate the contribution of the first virtual model based on the number of likes, interests, comments, shares, etc. of a post including the first virtual model. For example, the degree of contribution of the virtual model may be calculated by comparing the degree of interest of the client post after using the virtual model with the degree of interest of the client post before utilizing the virtual model.

The virtual model-based advertisement performing unit 104 may calculate the advertisement contribution of the virtual model and generate an advertisement contribution table according to the calculated advertisement contribution. The advertisement contribution may be an index indicating how much the virtual model contributes to advertisement performance. The virtual model-based advertising platform server 100 may provide the generated advertisement contribution table to the client terminal 200, and have the client refer to the advertisement contribution of each of the virtual models shown in the advertisement contribution table, so that the virtual models are We can help you manage it.

The virtual model-based advertisement execution unit 104 determines the daily average number of users flowing into the client account, the daily average number of users viewing the client account's posts, and interest in the client account's posts before the virtual model-based posts are uploaded. The daily average number of users who display (the sum of likes, interests, comments, etc.) and the rate of change of users who follow or subscribe to the client account can be calculated. The virtual model-based advertisement execution unit 104 determines the daily average number of users who flow into the client account after a post based on the virtual model is uploaded, the average daily number of users who view posts based on the virtual model, and posts based on the virtual model. The daily average number of users who show interest (the sum of likes, interests, comments, etc.) and the rate of change of users following or subscribing to the client account after the post based on the virtual model is uploaded can be calculated. The virtual model-based advertisement performing unit 104 may compare before and after uploading of posts based on the virtual model to calculate or determine an advertisement contribution of the virtual model.

The number of accesses to the client's server during a preset period of time through the unique link assigned to each account of the virtual models (ie, link information assigned to the virtual model) in the course of the client's advertisement, the number of times the unique link was shared The advertisement contribution may be calculated based on the number of times, the degree of change before and after using the virtual model, and the like. The virtual model-based advertisement performer 104 may calculate the advertisement contribution through Equation 1 below.

In Equation 1, P _g is the advertising contribution, m is the number of users who accessed the sales server of the client during a preset period through the unique link assigned to the account of the virtual model, s is the unique link shared by the user The number of times that the client is viewed, c may mean the number of people following or subscribing to the client, and z may mean the number of interest in the post of the virtual model. The number of interests may be the sum of the number of likes, interest marks, and comments for posts (posts, videos, photos, etc.). Even if the value of z+m+s is the same, the smaller the number of people following or subscribing to the client, the more efficient marketing can be understood. Therefore, the value for the number of people following or subscribing can be understood as the denominator.

The virtual model-based advertisement performer 104 may change a virtual model used for advertisement from the first virtual model to the second virtual model according to the contribution of the first virtual model to the advertisement. When the contribution of the first virtual model is equal to or less than the preset first contribution, the virtual model-based advertisement performer 104 may change the first virtual model to the second virtual model as a model for the advertisement of the client.

The metaverse service provider 105 may create a metaverse space based on advertisement contents or marketing contents acquired from the client terminal 200 . The metaverse service provider 105 may place a virtual model corresponding to the client in the created metaverse space, and place virtual objects for the target (eg, clothes, cars, etc.) that the client wants to advertise together. can

In order to increase the realism of the metaverse space, the metaverse service provider 105 provides information on the object based on object information obtained through a sensor (eg, camera, ToF sensor, infrared sensor, etc.) linked to the server 100. A corresponding virtual object can be created. The metaverse service provider 105 can efficiently promote advertisement contents or marketing contents through the created metaverse space. In other words, the metaverse service provider 105 identifies an object using object information acquired through a sensor linked to the server 100, and the metaverse service provider 105 identifies a virtual object corresponding to the identified object. Objects can be placed in the metaverse space. Object information may include other objects (eg, curtains, chairs, tables, wallpaper, etc.) necessary for publicity.

The metaverse service provider 105 may obtain real-time spatial information and real-time object information from sensors linked to the server 100 . Specifically, the sensor linked to the server 100 may include an image sensor, a radar sensor, and/or a distance detection sensor (eg, a ToF sensor, a LiDAR sensor), acquires image data through the image sensor, Radar data may be obtained through a radar sensor, distance data may be obtained through a distance detection sensor, and distance data may be obtained through a distance detection sensor. The sensor linked to the server 100 may transmit or provide acquired image data, radar data, and/or lidar data to the metaverse service provider 105. An image sensor may be included in a camera module. Image data may be referred to as image data.

The metaverse service provider 105 generates real-time spatial information based on real-time lidar data and image data obtained from sensors linked to the server 100, and real-time generated to improve the reality of the generated metaverse space. Spatial information and metaverse space can be compared. The metaverse service provider 105 maintains overlapping portions of the real-time spatial information and the metaverse space, and may modify or regenerate the metaverse space based on the real-time spatial information for different portions.

The metaverse service provider 105 identifies objects in the real space through the metaverse service provider 105 and creates virtual objects corresponding to the identified objects in order to create virtual objects to be placed in the metaverse space. It can be created and placed in the metaverse space.

The metaverse service provider 105 provides point coordinates of distance data obtained from a distance detection sensor mounted on a sensor linked to the server 100 and image data obtained from an image sensor mounted on a sensor linked to the server 100. An object can be identified based on the pixel value of The distance detection sensor may be a 360-degree rotatable lidar sensor and/or a 360-degree rotatable ToF sensor, and the distance detection sensor may include point cloud information corresponding to an object within a sensing radius (eg, 360 degrees) of the distance detection sensor. can be obtained. The point cloud information may be mapped corresponding to an azimuth angle of a sensing radius of the distance sensor. The point cloud information may include distance information from the distance sensor to the object. The point cloud information may be obtained through a distance detection sensor and/or an image sensor.

The point cloud information is quantized at a constant quantization interval at an azimuth between the sensing radii, and the point cloud information quantized based on the azimuth may be framed according to rotation of the distance sensor.

The metaverse service provider 105 targets two frames temporally adjacent to each other among frames based on the quantized point cloud information, and the distance between each of the points mapped to a specific azimuth in the first frame. A difference frame may be generated by difference between a first average value of first distance values, which is information, and a second average value of second distance values, which is distance information of each of points mapped to the specific azimuth in the second frame. The metaverse service provider 105 may determine the object as an object to be identified when the average value of the third distance values, which is distance information of each of the points mapped to a specific azimuth in the generated difference frame, is greater than or equal to a preset threshold value. there is.

The metaverse service provider 105 may identify the object based on image data of the object determined as the object to be identified. Image data of the object may be acquired by an image sensor mounted on a sensor linked to the server 100 .

The metaverse service provider 105 may input image data obtained through a sensor linked to the server 100 to the artificial neural network as input data. The metaverse service provider 105 may output the type name of the object through an artificial neural network (ANN). The artificial neural network may be implemented using a convolutional neural network (CNN)-based object identification algorithm such as single shot detector (SSD), region with CNN (R-CNN), or you only look once (YOLO).

In one embodiment, the metaverse service provider 105 may perform supervised learning on an object estimation model according to image data of an object, and the object estimation model may be implemented as an artificial neural network. An artificial neural network is a predictive model implemented in software or hardware that mimics the computational power of a biological system using a large number of artificial neurons (or nodes).

The object guessing model may be supervised and learned by the object guessing model learning unit using 'image data for an object' and 'labeled data for an object recognized in the image data'. At this time, supervised learning means learning to find an output value according to a given input value by using data with input values and corresponding output values as learning data, and means learning that takes place in a state where the correct answer is known. For example, the labeled data may be data labeling the type of object. The set of inputs and outputs given to supervised learning is called training data. That is, the above-described 'image data for an object' and 'labeled data' are input values and output values, respectively, and may be used as training data for supervised learning of an object estimation model.

In other words, the metaverse service provider 105 may determine the type of the object by using an object estimation model that has been supervised learning in advance. The metaverse service provider 105 may convert the image data into an input image of a preset size in the supervised object estimation model and input the image data to the object estimation model. The metaverse service provider 105 may determine the type of object included in the image data based on the output of the object estimation model.

The metaverse service provider 105 may create a virtual object corresponding to the object in the metaverse space according to the determined object type. For example, if the identified object type is a person, the metaverse service provider 105 may create a virtual avatar corresponding to the person. When the identified object type is a chair, the metaverse service provider 105 may create a virtual chair corresponding to the chair.

The virtual model-based advertisement execution unit 104 may generate contents to promote advertisements or marketing based on the created metaverse space, created avatars, and other objects, and may perform advertisements based on the created contents. .

The chat service providing unit 106 may provide web page or application information for providing a chat service to the client terminal 200 and the user terminal 300 . A client or user may access a page providing a chatting service through the client terminal 200 and the user terminal 300 .

The chat service providing unit 106 may learn an artificial intelligence model corresponding to the virtual model in order to provide a chat service to clients or users. The chat service provider 106 may set a chatting field for each virtual model and train an artificial intelligence model for the set chatting field. At this time, the client terminal 200 and the user terminal 300 provide their own call information, age, gender, text conversation information, etc. to the chat service provider 106, or give permission to access the information It can be provided to the chat service provider 106. Accordingly, the chat service provider 106 can learn a conversation pattern of a user through voice calls generated by the client terminal 200 and the user terminal 300, and learn a text transmission/reception pattern through text message transmission and reception. , it can be applied to the learning of artificial intelligence models.

The chat service provider 106 may extract a topic using a topic modeling algorithm with respect to conversation information of a conversation with a user using a natural language processing function, and may set topic classification through topic clustering (clustering). According to topic classification, augmented information based on conversation information with a user may be generated, and the artificial intelligence model may be re-learned using learning data including the generated augmented information.

At this time, the language/action perspective study is the most representative of the theoretical basis for conversational interaction, and the CfA (Conversation for Action) model can be applied to introduce the process of linguistic communication into information system design. may, but is not limited thereto. Based on the CfA model, language is the most fundamental factor in all human cooperative behaviors, and this language-action perspective plays a very important role in the development of all CSCW (Computer-Supported Cooperative Work) systems. The focus from the language-action perspective is the structure of dialogue, the form in which the meaning and use of language accomplishes real tasks. The basic elements of the dialogue structure are request and response. When one of the conversation participants makes a request to the other party, the other party anticipates a series of subsequent actions and responds in the form of sympathy, acceptance, rejection, or amendment proposal. will do As this process proceeds in a circular fashion, mutual understanding is formed among the participants in the conversation, and based on this, meaningful collaborative actions occur. To this end, an experiment for observing the characteristics of a conversation between a person and a device and deriving implications and input data of the experiment are required, which include those separately provided by the user, but the client terminal 200 and the user terminal 300 An input value may be provided by streaming a call received in ) or a conversation exchanged in an outgoing call to the virtual model-based advertising platform server 100 in a background mode, and the client terminal 200 and the user terminal 300 ) may provide a value that reacts to a value input by a user in at least one application or messenger installed in the .

The chat service providing unit 106 provides not only text-based chatting but also video-based chatting services in the client terminal 200 and the user terminal 300, and when the client or user selects the video chatting service, the virtual chatting service is provided. You can provide an interface to select real influencers to map the model's expression to. The chat service provider 106 may load live action modeling data, voice replication data, and facial expression change data of the selected real influencer. Here, the actual influencer's live-action modeling data may be separately filmed according to emotion classification, or image processing of one facial expression with a warping technique to transform the shape of the image (face shape) to create a facial expression. can be diversified. In the case of the former, a method of photographing a shooting object (actual influencer) according to emotion classification changes such as sympathy, worry, anger, sadness, joy, and expressionless, classifying it with meta data or tags, and then learning it is used. can In the latter case, a facial expression change may be given to pixel location and movement information through geometric processing, and images including the changed facial expression may be classified and stored in a database.

The chat service provider 106 can give a facial expression change that changes in real time according to the emotions of the actual influencer (real influencer) modeled using live action, and warping is performed when there is an input image and an output image. Positions to be corresponded between images are described with control lines, and then the geometric relationship between the control lines and pixels is calculated to finally move them. If this warping process is performed over several frames, the conversion process from the input video to the output video can be subdivided. The control line of the intermediate frame is interpolated as much as the desired scene, and the warping process can be repeated whenever each frame is converted. there is. In addition, if the chat service provider 106 describes the control line of the expression according to the emotion in advance, it can be used to change the expression of the actual influencer by changing the control line as much as the weight of the input emotion. In addition to this, the chat service provider 106 calculates realistic facial expression changes based on research related to facial expression recognition or anatomical human facial expression in which the facial expression of the actual influencer or the avatar is determined and changed according to the currently input facial expression. A generating method may be used, but it is not limited to the above-described methods, and any method may be used as long as it is modified to classify facial expressions in detail. In addition, an emotion-adaptive facial expression conversion model can be used. As in the general warping technique, if the input image has a predetermined control line and a control line of a facial expression suitable for each emotion, it is suitable for each emotion according to the emotional state of the actual influencer. A control line is selected. In addition, the frame interpolation process through this control line can be processed according to the weight of emotion, and the expression that changes by the weight according to the emotional state set in the actual influencer of each frame can be checked.

To this end, if the basic control line of the live-action actual influencer input is already set, and if the control line of the corresponding expressions is determined, the control line corresponding to the new emotional change of the actual influencer is selected in real time, and the weight must change Changes in facial expression muscles according to emotions are based on P.Ekman's facial psychology, designating the muscles used for human facial expression changes according to exercise (expression) psychology and basic anatomy, and organizing them into a database. According to the control line selected The change amount of the output control line may be adjusted by the emotion change weight.

To this end, all pixels in the output image of each frame are affected by all predetermined control lines, copy the corresponding pixel values of the input image, search all pixels in the image in the horizontal and vertical directions of the image, and Calculations to retrieve values can be processed in data parallelism.

When voice is input from the client terminal 200 and the user terminal 300, the chat service provider 106 analyzes the input voice through a natural language processing algorithm and then determines the response text and facial expression of the input voice. can When voice is input from the client terminal 200 and the user terminal 300, the chat service provider 106 analyzes the input voice through a natural language processing algorithm, and then converts the response text and facial expression of the input voice. When making a decision, emotions can be recognized from the input voice signal to extract features, and patterns can be recognized using the extracted features. At this time, the feature of emotion recognition is extracted as any one or a combination of at least one of pitch, energy, formant, and speed of speech, and emotion recognition is a statistical value of pitch, loudness, and number of sections among acoustic features. , IR (Increasing Rate), and CR (Crossing Rate), any one or a combination of at least one feature may be applied to the artificial neural network.

At this time, Natural Language Processing (NLP) is a natural language understanding that mechanically analyzes language phenomena spoken by humans and converts them into a form that computers can understand, or various technologies that express such forms in a language that humans can understand. As for natural language processing methods, there are traditionally rule-based and statistical-based approaches, hybrid methods that integrate the strengths of the two, and artificial neural networks, which have recently emerged. ) corresponds to the artificial neural network method. The method using deep learning is a method of finding the most suitable expression and translation result by putting an input sentence and an output sentence as a pair.

In one embodiment, the chat service provider 106 should not only process natural language, but also analyze emotions from natural language. For emotion analysis, an emotion vocabulary may be used, and the polarity of words (i.e., positive, negative) , Neutral), it is possible to use a vocabulary dictionary, a semi-supervised learning algorithm, and a vocabulary dictionary in which the polarity of words divided into positive, negative, and neutral values is assigned numerically through a semi-supervised learning algorithm and a random walking process.

When voice is input from the client terminal 200 and the user terminal 300, the chat service provider 106 analyzes the input voice through a natural language processing algorithm and then determines the response text and facial expression of the input voice. At this time, when receiving facial expression data of the client terminal 200 and the user terminal 300, emotions can be recognized from the facial expression data using multidimensional feature vector data that is the input facial expression data. At this time, the multi-dimensional feature vector data is analyzed by any one or a combination of at least one of optical flow analysis, holistic analysis, and local expression analysis to recognize emotions, and holistic analysis extracts features based on the PCA method and It may be a method of recognizing emotions using a classification method, but is not limited to the above method.

The chat service provider 106 may control the virtual model to utter the determined response text through facial expressions. When the chat service provider 106 controls the virtual model to utter the determined response text through facial expressions, a plurality of orthogonal 2-dimensional or 3-dimensional face images corresponding to the determined facial expressions are generated from the front and sides. The face of the virtual model may be transformed by extracting at least one feature point included in the extracted face image, and the position, size, facial expression, and rotation information of the virtual model having the deformed face may be reflected and synthesized. When the chat service provider 106 controls the virtual model to utter the determined response text through a facial expression, it searches for a virtual model having a facial expression corresponding to the determined facial expression as metadata, and the virtual model having the retrieved facial expression. It can synthesize the determined response text to utter it.

The chat service provider 106 may acquire user data as learning data. As the learning data, data collected or tested by the chat service provider 106 may be used. Alternatively, the learning data may include voice data generated from a natural language uttered by a user through a microphone according to the present disclosure. Alternatively, the learning data may include user motion data photographed through a camera. Alternatively, the learning data may include sensing data sensed through various sensors. For example, the learning data includes data on the presence or absence of a user detected through an infrared sensor, data related to physical contact with the user detected through a pressure sensor, or data related to a user's physical contact detected through a light sensor or humidity sensor. It may include the detected ambient light or humidity data.

The chat service providing unit 106 may further include a pre-processing unit and a learning data selection unit in order to improve the recognition result of the data recognition model or to save resources or time required for generating the data recognition model.

The pre-processing unit may pre-process data obtained from the data management unit 101 . For example, the pre-processing unit may process the acquired data into a predefined format so that the model learning unit can easily use the data for learning the data recognition model. For example, the pre-processing unit may process audio data acquired by the data management unit 101 into text data, and process video data into video data in a preset format. The preprocessed data may be provided to a model learning unit to be described later as training data.

In addition, the learning data selector may selectively select learning data necessary for learning from among the preprocessed data. The selected learning data may be provided to the model learning unit. The learning data selection unit may select learning data necessary for learning from preprocessed data according to a predetermined selection criterion. In addition, the learning data selection unit may select learning data necessary for learning according to a selection criterion set by learning by the model learning unit. As an embodiment of the present disclosure, the learning data selector may select only audio data uttered by a specific user from among input audio data, and may select only an area including a person excluding the background from image data.

The model learning unit may train a data recognition model used for clustering or determining a response of a virtual model using training data. For example, the model learning unit may train a data recognition model through supervised learning using at least some of the learning data as a criterion. Alternatively, the model learning unit may learn a data recognition model through, for example, unsupervised learning in which a criterion for determining a situation is discovered by self-learning using learning data without any guidance. .

In addition, the model learning unit may learn a selection criterion for which learning data to use for clustering or determining the response of the virtual model.

The data recognition model may be constructed in consideration of the field of application of the recognition model, the purpose of learning, or the performance of a device. The data recognition model may be, for example, a model based on a neural network. A data recognition model can be designed to simulate the structure of the human brain on a computer. The data recognition model may include a plurality of network nodes having weights that simulate neurons of a human neural network. A plurality of network nodes may each form a connection relationship to simulate a synaptic activity in which neurons exchange signals through synapses. The data recognition model may include, for example, a neural network model or a deep learning model developed from a neural network model. In the deep learning model, a plurality of network nodes may exchange data according to a convolution connection relationship while being located at different depths (or layers). For example, a model such as a deep neural network (DNN), a recurrent neural network (RNN), or a bidirectional recurrent deep neural network (BRDNN) may be used as a data recognition model, but may not be limited thereto.

FIG. 6 is a diagram showing a hardware configuration of the server 100 according to FIG. 1 .

Referring to FIG. 6, the server 100 includes at least one processor 110 and a memory for storing instructions instructing the at least one processor 110 to perform at least one operation ( memory) may be included.

The at least one operation may include at least some of the above-described operations or functions of the server 100 and may be implemented in the form of instructions and performed by the processor 110 .

Here, the at least one processor 110 may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor for performing methods according to embodiments of the present invention. can Each of the memory 120 and the storage device 160 may include at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory 120 may be one of a read only memory (ROM) and a random access memory (RAM), and the storage device 160 may be a flash-memory. , a hard disk drive (HDD), a solid state drive (SSD), or various memory cards (eg, a micro SD card).

In addition, the server 100 may include a transceiver 130 that performs communication through a wireless network. In addition, the server 100 may further include an input interface device 140, an output interface device 150, a storage device 160, and the like. Each component included in the server 100 may be connected by a bus 170 to communicate with each other. In FIG. 6, the server 100 has been described as an example, but is not limited thereto. For example, a plurality of client terminals may include a component according to FIG. 6 .

7 is a diagram illustrating a wireless communication system that can be applied in a communication process according to an embodiment of the present invention. 8 is a diagram illustrating a base station in the wireless communication system according to FIG. 7 . 9 is a diagram illustrating a terminal in the wireless communication system according to FIG. 7 . 10 is a diagram illustrating a communication interface in the wireless communication system according to FIG. 7 .

Hereinafter, an example of a wireless communication network system supporting communication between the virtual model-based advertising platform server 100 and the terminals 201, 202, 203, and 204 according to the present invention will be described in detail as an example. In the following description, a first node (device) may be an anchor/donor node or a centralized unit (CU) of an anchor/donor node, and a second node (device) may be an anchor/donor node or a distributed unit (DU) of a relay node can be

A base station (BS), terminal, server, etc. may be included as a part of a node using a radio channel in a wireless communication system.

A base station is a terminal and a network infrastructure that provides wireless access to terminals. A base station has a coverage defined as a predetermined geographic area according to a distance over which a signal can be transmitted.

A base station, like a "base station," is referred to as "access point (AP)", "enodeb (eNB)", "5th generation (5G) node", "wireless point", " It may be referred to as a transmission/reception point (TRP).

The base station, the terminal, and the terminal may transmit and receive wireless signals in a millimeter wave (mmWave) band (eg, 28 GHz, 30 GHz, 38 GHz, and 60 GHz). At this time, the base station, the terminal, and the terminal may perform beamforming to improve the channel gain. Beamforming may include transmit beamforming and receive beamforming. That is, the base station, the terminal, and the terminal can give directivity to the transmitted signal and the received signal. To this end, the base station, the terminal, and the terminal may select a serving beam through a beam search procedure or a beam management procedure. After that, communication may be performed using a resource carrying a serving beam and a resource having a quasi co-located relationship.

A first antenna port and a second antenna port are considered quasi-colocated if the large-scale properties of the channel through which the symbol of the first antenna port carries can be inferred from the channel through which the symbol of the second antenna port carries. The large-scale properties may include one or more of delay spread, Doppler spread, Doppler shift, average gain, average delay, and spatial Rx parameters.

Hereinafter, a base station in the wireless communication system described above is exemplified. The terms "-module", "-unit" or "-er" used below may mean a unit that processes at least one function or operation, and may include hardware, software, or both hardware and software. It can be implemented as a combination of

The base station may include a wireless communication interface, a backhaul communication interface, a storage unit and a controller.

The wireless communication interface performs a function of transmitting and receiving signals through a wireless channel. For example, the wireless communication interface may perform a conversion function between a baseband signal and a bit stream according to a physical layer standard of the system. For example, in data transmission, a radio communication interface encodes and modulates a transmitted bit stream to generate composite symbols. Also, upon receiving data, the wireless communication interface demodulates and decodes the baseband signal to reconstruct the received bit stream.

The wireless communication interface performs a function of transmitting and receiving signals through a wireless channel. For example, the wireless communication interface may perform a conversion function between a baseband signal and a bit stream according to a system physical layer standard. For example, in data transmission, a radio communication interface encodes and modulates a transmitted bit stream to generate composite symbols. Also, upon receiving data, the wireless communication interface demodulates and decodes the baseband signal to reconstruct the received bit stream.

In addition, the wireless communication interface up-converts a baseband signal into a radio frequency (RF) band signal, transmits the converted signal through an antenna, and down-converts the RF band signal received through the antenna into a baseband signal. To this end, the wireless communication interface includes a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, a digital-to-analog converter (DAC), An analog-to-digital converter (ADC) and the like may be included. Also, the wireless communication interface may include a plurality of transmit/receive paths. Additionally, the wireless communication interface may include at least one antenna array comprising a plurality of antenna elements.

In terms of hardware, the wireless communication interface may include a digital unit and an analog unit, and the analog unit may include a plurality of sub-units according to operating power, operating frequency, and the like. A digital unit may be implemented with at least one processor (eg, a digital signal processor (DSP)).

The wireless communication interface transmits and receives signals as described above. Accordingly, a wireless communication interface may be referred to as a “transmitter”, “receiver” or “transceiver”. In addition, in the following description, transmission and reception performed through a wireless channel may be used as a meaning including processing performed in a wireless communication interface as described above.

The backhaul communication interface provides an interface for communicating with other nodes in the network. That is, the backhaul communication interface converts the bit stream transmitted to other nodes, for example, other access nodes, other base stations, upper nodes or core networks from base stations into physical signals, and the physical signals received from other nodes into bits. convert to stream

The storage unit stores data such as basic programs, applications, and setting information for operation of the base station. The storage unit may include volatile memory, non-volatile memory, or a combination of volatile and non-volatile memory.

The controller controls the overall operation of the base station. For example, the controller transmits and receives signals through a wireless communication interface or a backhaul communication interface. Also, the controller writes data to the storage unit and reads the recorded data. The controller can perform protocol stack functions required by communication standards. According to another implementation, a protocol stack may be included in a wireless communication interface. To this end, the controller may include at least one processor.

According to an embodiment, the controller may control the base station to perform an operation according to an embodiment of the present invention.

According to various embodiments, a donor node of a wireless communication system includes at least one processor, includes a transceiver operably coupled to the at least one processor, and includes a plurality of radio bearers for a terminal accessing the relay node. configured to transmit to a relay node a first message including first information related to the donor node about; receive a second message including second information related to the relay node regarding a plurality of radio bearers for the terminal from the relay node; Data for the terminal may be transmitted to the relay node. Data may be transmitted to the terminal through a plurality of radio bearers based on the first information and the second information.

According to various embodiments, a radio bearer among a plurality of radio bearers may integrate a plurality of radio bearers. the at least one processor is also configured to determine a radio bearer for a terminal accessing the relay node and multiple radio bearers aggregated by the radio bearer; Alternatively, a radio bearer for a UE accessing a relay node may be determined.

According to various embodiments, the first message may include one or more of: identification of a terminal accessing the relay node; display information indicating the type of terminal accessing the relay node; information about a radio bearer of a terminal accessing a relay node; information about a radio bearer transmitted by a terminal accessing a relay node; information about a tunnel established for a radio bearer between the donor node and the relay node; information about integrated multiple radio bearers; radio bearer mapping information; information about the address of the side of the donor node; information about the address of the relay node side; indication information corresponding to a radio bearer of a terminal accessing the relay node; indication information indicating the relay node to allocate a new address to a radio bearer for a terminal accessing the relay node; a list of address information that cannot be used by a relay node that transmits radio bearer data of a terminal accessing the relay node; and information related to security configuration.

According to various embodiments, the second message may include one or more of: identification of a terminal accessing the relay node; information about the radio bearer granted by the relay node; information about radio bearers not acknowledged by the relay node; information about radio bearers partially granted by the relay node; radio bearer mapping information; Configuration information of a terminal accessing the relay node created by the relay node; information about the address of the relay node side; and information related to security configuration.

According to various embodiments, the second message may further include information on integrated multiple radio bearers.

According to various embodiments, the donor node may include a central unit of the donor node, and the relay node may include a distribution unit of the donor node.

According to various embodiments, a relay node of a wireless communication system includes at least one processor, includes a transceiver operably coupled to the at least one processor, and provides a plurality of information from a donor node to a terminal accessing the relay node. configured to receive a first message including first information related to a donor node for a radio bearer of; transmit to the donor node a second message including second information related to the relay node for the plurality of radio bearers for the terminal; Data on the terminal may be received from the donor node. Data may be transmitted to the terminal through a plurality of radio bearers based on the first information and the second information.

According to various embodiments, a radio bearer among a plurality of radio bearers may integrate a plurality of radio bearers. the at least one processor is also configured to determine a radio bearer for a terminal accessing the relay node and multiple radio bearers aggregated by the radio bearer; Alternatively, multiple radio bearers integrated by radio bearer may be determined.

According to various embodiments, the first message may include one or more of: identification of a terminal accessing the relay node; display information indicating the type of terminal accessing the relay node; information about a radio bearer of a terminal accessing a relay node; information about a radio bearer transmitted by a terminal accessing a relay node; information about a tunnel established for a radio bearer between the donor node and the relay node; information about integrated multiple radio bearers; radio bearer mapping information; information about the address on the side of the donor node; information about the address of the relay node side; indication information corresponding to a radio bearer of a terminal accessing the relay node; indication information indicating the relay node to allocate a new address to a radio bearer for a terminal accessing the relay node; a list of address information that cannot be used by a relay node that transmits radio bearer data of a terminal accessing the relay node; and information related to security configuration.

According to various embodiments, the second message may include one or more of: identification of a terminal accessing the relay node; information about the radio bearer granted by the relay node; information about radio bearers not acknowledged by the relay node; information about radio bearers partially granted by the relay node; radio bearer mapping information; Configuration information of a terminal accessing the relay node created by the relay node; information about the address of the relay node side; and information related to security configuration.

According to various embodiments, the second message may further include information on integrated multiple radio bearers.

According to various embodiments, the donor node may include a central unit of the donor node, and the relay node may include a distribution unit of the donor node.

Hereinafter, components of a terminal in the wireless communication system described above are illustrated. Components of a terminal to be described below are components of a general-purpose terminal supported by a wireless communication system, and may be merged or integrated with components of a terminal according to the foregoing contents, and may overlap or conflict with the above drawings. It can be interpreted that the content described with reference takes precedence. The terms "-module", "-unit" or "-er" used below may mean a unit that processes at least one function.

The terminal includes a communication interface, a storage unit and a controller.

The communication interface performs a function of transmitting and receiving signals through a wireless channel. For example, the communication interface performs a conversion function between a baseband signal and a bit stream according to the physical layer standard of the system. For example, in data transmission, a communication interface encodes and modulates a transmission bit stream to generate composite symbols. Also, when receiving data, the communication interface demodulates and decodes the baseband signal to reconstruct the received bit stream. Further, the communication interface up-converts the baseband signal to an RF-band signal, transmits the converted signal through an antenna, and down-converts the RF-band signal received through the antenna into a baseband signal. For example, the communication interface includes a transmission filter, a reception filter, an amplifier, a mixer, an oscillator, and a digital-to-analog converter (DAC). , an analog-to-digital converter (ADC), and the like.

Also, the communication interface may include a plurality of transmit/receive paths. Additionally, the communication interface may include at least one antenna array comprising a plurality of antenna elements. On the hardware side, the wireless communication interface may include a digital circuit and an analog circuit (eg, a radio frequency integrated circuit (RFIC)). A digital circuit may be implemented with at least one processor (eg, DSP). A communication interface may include multiple RF chains. The communication interface may perform beamforming.

The communication interface transmits and receives signals as described above. Accordingly, a communication interface may be referred to as a “transmitter”, “receiver” or “transceiver”. In addition, in the following description, transmission and reception performed through a radio channel may be used as a meaning including processing performed in a communication interface as described above.

The storage unit stores data such as basic programs for operation of the terminal, applications, and setting information. The storage unit may include volatile memory, non-volatile memory, or a combination of volatile and non-volatile memory. In addition, the storage unit provides stored data according to the request of the controller.

The controller controls the overall operation of the terminal. For example, the controller sends and receives signals through a communication interface. Also, the controller writes data to the storage unit and reads the recorded data. The controller can perform protocol stack functions required by communication standards. According to another implementation, a protocol stack may be included in the communication interface. To this end, the controller may include at least one processor or microprocessor or reproduce parts of a processor. Also, a part of the communication interface or controller may be referred to as a communication processor (CP).

According to an embodiment of the present invention, a controller may control a terminal to perform an operation according to an embodiment of the present invention.

Hereinafter, a communication interface in a wireless communication system is illustrated.

The communication interface includes encoding and modulation circuitry, digital beamforming circuitry, a plurality of transmission paths, and analog beamforming circuitry.

Encoding and modulation circuitry performs channel encoding. At least one of a low-density parity check (LDPC) code, a convolution code, and a polar code may be used for channel encoding. An encoding and modulation circuit generates modulation symbols by performing constellation mapping.

A digital beamforming circuit performs beamforming on a digital signal (eg, a modulation symbol). To this end, a digital beamforming circuit multiplexes modulation symbols by beamforming weights. Beamforming weights can be used to change the size and phrase of a signal, and can be referred to as a "precoding matrix" or "precoder". The digital beamforming circuit outputs digital beamformed modulation symbols to a plurality of transmission paths. In this case, according to a multiple input multiple output (MIMO) transmission method, modulation symbols may be multiplexed or the same modulation symbol may be provided to a plurality of transmission paths.

The plurality of transmission paths convert digital beamformed digital signals into analog signals. To this end, each of the plurality of transmission paths may include an inverse fast fourier transform (IFFT) computation unit, a cyclic prefix (CP) insertion unit, a DAC, and an up conversion unit. The CP insertion unit is for an orthogonal frequency division multiplexing (OFDM) scheme and may be omitted when other physical layer schemes (eg, a filter bank multi-carrier: FBMC) are applied. That is, the plurality of transmission paths provide independent signal processing processes for a plurality of streams generated through digital beamforming. However, depending on the implementation, some elements of the plurality of transmission paths may be commonly used.

An analog beamforming circuit performs beamforming on an analog signal. To this end, the digital beamforming circuit multiplexes analog signals by beamforming weighting values. The beamformed weights are used to change the amplitude and phrase of the signal. More specifically, the analog beamforming circuit may be configured in various ways according to a connection structure between a plurality of transmission paths and an antenna. For example, each of a plurality of transmission paths may be connected to one antenna array. In another example, multiple transmission paths may be coupled to one antenna array. In another example, multiple transmission paths may be adaptively coupled to one antenna array or may be coupled to two or more antenna arrays.

The methods according to the present invention may be implemented in the form of program instructions that can be executed by various computer means and recorded on a computer readable medium. Computer readable media may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on a computer readable medium may be specially designed and configured for the present invention or may be known and usable to those skilled in computer software.

Examples of computer readable media may include hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions may include not only machine language codes generated by a compiler but also high-level language codes that can be executed by a computer using an interpreter and the like. The hardware device described above may be configured to operate with at least one software module to perform the operations of the present invention, and vice versa.

In addition, the above-described method or device may be implemented by combining all or some of its components or functions, or may be implemented separately.

Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art will variously modify and change the present invention within the scope not departing from the spirit and scope of the present invention described in the claims below. You will understand that it can be done.

100: virtual model-based advertising platform server 200: client terminal
300: user terminal

Claims (9)

In a virtual model-based advertising platform server marketing through a virtual model,
an advertisement request receiving unit receiving a client's advertisement request;
a virtual model generating unit generating virtual models used in advertisements;
a virtual model-based advertisement performer matching at least one of the virtual models with the client and performing an advertisement through the at least one virtual model;
A metaverse service provider for generating a metaverse space based on advertisement content received from a client terminal; and
Including a chat service provider for outputting a web page providing a chat service to the client terminal;
The virtual model-based advertisement execution unit,
creating a post containing the virtual model, and uploading the created post to an activity platform of the client;
Comparing the degree of interest in each of the client's first posts not including the virtual model and the client's second posts including the virtual model, and calculating the advertisement contribution of the virtual model based on the comparison,
The contribution of the virtual model is determined according to the following equation,

In the above equation, Pg is the contribution to the advertisement, m is the number of users accessing the sales server of the client during a preset period through the unique link assigned to the client's account, and s is the number of times the unique link is shared by the user , c is the number of people following or subscribing to the client, z is the number of interests in the first post or the second post, and the number of interests is the sum of the number of likes, interest, and comments, Advertising platform server. In claim 1,
The virtual model generator,
An advertising platform server that analyzes taste information based on SNS activity information of the client and generates a virtual model to perform advertisement of the client based on the analyzed taste information of the client. delete delete In claim 1,
The chat service provider,
An advertising platform server for determining a chatting field for each virtual model and learning an artificial intelligence model corresponding to the virtual model according to the determined chatting field. As a method of providing an online marketing platform marketing through a virtual model,
Receiving a client's advertisement request;
generating virtual models used in advertisements;
matching at least one virtual model among the virtual models with the client;
performing an advertisement through the at least one virtual model;
Generating a metaverse space based on the advertisement content received from the client terminal; and
And outputting a web page providing a chat service to the client terminal,
The step of performing an advertisement through the at least one virtual model,
generating a posting including the virtual model, and uploading the created posting to an activity platform of the client; and
comparing a level of interest in each of the client's first post not including the virtual model and the client's second post including the virtual model, and calculating an advertisement contribution of the virtual model based on the comparison; including,
The contribution of the virtual model is determined according to the following equation,

In the above equation, Pg is the contribution to the advertisement, m is the number of users accessing the sales server of the client during a preset period through the unique link assigned to the client's account, and s is the number of times the unique link is shared by the user , c is the number of people following or subscribing to the client, z is the number of interests in the first post or the second post, and the number of interests is the sum of the number of likes, interest, and comments, method. A non-transitory recording medium on which a program for executing the method according to claim 6 is recorded and can be read by a computer. A computer program recorded on a non-transitory recording medium to execute the method according to claim 6 in a virtual model-based advertising platform server. As a system that provides an online marketing platform based on a virtual model,
a client terminal of a client requesting an advertisement;
a user terminal of a user using SNS where the advertisement is uploaded; and
A virtual model-based advertising platform server marketing through the virtual model;
The virtual model-based advertising platform server,
an advertisement request receiving unit receiving an advertisement request from the client;
a virtual model generating unit generating virtual models used in the advertisement;
a virtual model-based advertisement performer matching at least one of the virtual models with the client and performing an advertisement through the at least one virtual model;
A metaverse service provider for generating a metaverse space based on advertisement content received from a client terminal; and
Including a chat service provider for outputting a web page providing a chat service to the client terminal;
The virtual model-based advertisement execution unit,
creating a post containing the virtual model, and uploading the created post to an activity platform of the client;
Comparing the degree of interest in each of the client's first posts not including the virtual model and the client's second posts including the virtual model, and calculating the advertisement contribution of the virtual model based on the comparison,
The contribution of the virtual model is determined according to the following equation,

In the above equation, Pg is the contribution to the advertisement, m is the number of users accessing the sales server of the client during a preset period through the unique link assigned to the client's account, and s is the number of times the unique link is shared by the user , c is the number of people following or subscribing to the client, z is the number of interests in the first post or the second post, and the number of interests is the sum of the number of likes, interest, and comments, system.

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