KR20220075623A - Purchase product review curation and product purchase methods, devices and programs through live commerce - Google Patents

Abstract

A method of controlling a shopping platform tailored to consumers and sellers performed by an artificial intelligence model is provided. The control method may include: receiving, by the consumer terminal, at least one content from among a plurality of content uploaded by a plurality of other consumer terminals stored in a server; recommending, by the consumer terminal, at least one content among the plurality of content based on the user's personal information corresponding to the consumer terminal input to the artificial intelligence model; transmitting the product purchase request to the seller terminal when the consumer terminal always receives a product purchase request corresponding to at least one content; and transmitting, by the seller terminal, payment information corresponding to the product to the consumer terminal.

Description

{Purchase product review curation and product purchase methods, devices and programs through live commerce}

The present invention relates to a method, apparatus and program for purchasing a product through a product review curation and live commerce.

As the YOLO trend and war label-oriented culture are established, the population enjoying leisure life such as vacation/leisure/party is steadily increasing. Interest and consumption are continuously increasing.

As a result of the survey by the survey company, 96% of respondents answered that they separately purchase clothes for specific leisure activities such as travel/dates/party, but many consumers are experiencing considerable time and trouble in the process of finding and purchasing suitable clothes. However, many sellers are suffering from excessive burden of advertising costs and difficulties in creating online product sales pages.

For example, if a consumer wants to purchase clothes for a special event, he or she must visit various channels such as blogs and SNS to collect information about clothes every time to obtain clothes information. There is an inconvenience of having to understand purchase information.

In other words, consumers have to visit numerous shopping malls one by one to find a product that suits their taste or look at countless products in the same category on open market/shopping mall collection sites. There are difficulties in purchasing.

Furthermore, it takes a considerable amount of time to learn fashion trends by subscribing to various channels such as fashion magazines, blogs, and SNS, one by one, because it is difficult to acquire a sense of style suitable for one's body type and activity concept in a short time or to receive personal styling. Professional help is needed to complement or revive individual styles, but opportunities for personal styling are rare and costly.

Alternatively, the current situation is that it is difficult to intuitively configure clothes suitable for the concept of events and leisure activities without experience.

On the other hand, sellers also have to spend huge advertising expenses to secure the main sales page in a fiercely competitive market, and specifically, because the cost of advertising expenses such as top exposure and keyword advertising is quite large, some large shopping malls with the financial power Since it occupies the top and main sales pages, there is a problem that it is difficult for SMEs to expose the main page.

In addition, sellers spend a lot of time and effort in pre-work for online sales, photo retouching and page editing to create a product detail page, and a considerable amount of time and work are required, and it costs money when commissioning an expert. .

Accordingly, there is a need for a purchasing platform that can satisfy both consumers and sellers.

The problem to be solved by the present invention is to provide a product purchase method, apparatus, and program through purchase product review curation and live commerce.

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

In the method for controlling a shopping platform customized for consumers and sellers performed by an artificial intelligence model according to an aspect of the present invention for solving the above-described problems, a consumer terminal, a plurality of uploaded by a plurality of other consumer terminals stored in a server Receiving at least one content of the content; recommending, by the consumer terminal, at least one content among the plurality of content based on the user's personal information corresponding to the consumer terminal input to the artificial intelligence model; transmitting the product purchase request to the seller terminal when the consumer terminal always receives a product purchase request corresponding to at least one content; and transmitting, by the seller terminal, payment information corresponding to the product to the consumer terminal.

In this case, the server, based on the content information recommended to the plurality of consumer terminals, transmitting the target customer corresponding to the seller terminal to the seller terminal; transmitting, by the seller terminal, an advertisement corresponding to the target customer to the server; and transmitting, by the server, the advertisement to a consumer terminal corresponding to the target customer. may include

In this case, the content may be at least one of a text and an image related to a consumer's product purchase review.

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

According to various embodiments of the present invention described above, a platform can be provided that enables both consumers and sellers to efficiently purchase/sell products.

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

1 is a schematic diagram of a platform according to an embodiment of the present invention;
2 is a flowchart illustrating a method for controlling a platform according to an embodiment of the present invention.
3 is a block diagram of an apparatus according to an embodiment of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the present embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium or to reproduce one or more processors. Thus, by way of example, “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, circuitry, data, databases, data structures, tables, arrays, and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or as additional components and “parts” or “modules”. can be further separated.

Spatially relative terms "below", "beneath", "lower", "above", "upper", etc. It can be used to easily describe the correlation between a component and other components. Spatially relative terms should be understood as terms including different directions of components during use or operation in addition to the directions shown in the drawings. For example, when a component shown in the drawing is turned over, a component described as “beneath” or “beneath” of another component may be placed “above” of the other component. can Accordingly, the exemplary term “below” may include both directions below and above. Components may also be oriented in other orientations, and thus spatially relative terms may be interpreted according to orientation.

In this specification, a computer refers to all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

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

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

1 is a schematic diagram of a platform according to an embodiment of the present invention;

The platform according to the present invention is controlled by the server 100, and the server 100 may provide a curation service, a purchase connection service, and a streaming service to a user terminal or a seller terminal.

The curation service according to the present invention classifies the consumer's body type, skin tone, preferred coordinating concept, etc. into a specific type through a consumer preference analysis test based on a rule base, and a character for each type is set to recommend contents of users of the same/interested type can do.

As another embodiment, it is of course possible to upgrade the curation function through the analysis of consumers' purchasing patterns and purchasing decision factors through utilization of Amazon Personalize algorithm and secured customer DB analysis and advancement of AI technology.

On the other hand, the purchase connection function according to the present invention is a function of connecting to a merchant page linked through clicking on a tag of a product in a post and a payment support function, and can provide a service to a consumer terminal and a seller terminal through the corresponding function.

Meanwhile, the streaming service according to the present invention may be implemented as a seller's real-time live commerce broadcasting and payment interworking system. That is, as a consumer terminal corresponding to the recommended consumer as the seller terminal, the seller terminal may perform a real-time live commerce broadcast on the sold product.

In another embodiment according to the present invention, the server 100 performs a marketing and communication channel expansion function through SNS expansion, or provides platform sales promotion through traffic, effective potential customers, influencers, and sellers. can

Meanwhile, various embodiments according to the present invention may be implemented by an artificial intelligence model. In an embodiment, the server 100 may analyze a consumer's purchase pattern and purchase decision factors through the advancement of an AI curation algorithm. Furthermore, the server 100 may increase curation satisfaction through a taste analysis test and AI product recommendation algorithm and collect data thereon.

According to various embodiments of the present invention, the seller increases the quality and matching rate of matching products through direct management of the mall, closely observes customer needs and responds quickly, and compares with other companies through self-production and collaboration with affiliated wholesalers Products can be sourced at a low price, and consumers can purchase styling products planned according to various concepts in bulk, and can generate revenue through uploaded contents, making it easy to secure contents, and to perform character level-up and emotional events. Through this, shopping that satisfies the emotions of users is possible, and it is easy to collaborate with competent marketers and secure initial customers by using personal networks.

Meanwhile, various embodiments according to the present invention may be implemented through an artificial intelligence model. An artificial intelligence (AI) system is a computer system that implements human-level intelligence, and unlike the existing rule-based smart system, the machine learns, judges, and becomes smarter by itself. As artificial intelligence systems are used, the recognition rate improves and users can understand user preferences more accurately.

Artificial intelligence technology consists of machine learning (deep learning) and element technologies using machine learning.

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

Specifically, deep learning is a machine learning algorithm that attempts high-level abstractions (summarizing core contents or functions in large amounts of data or complex data) through a combination of several nonlinear transformation methods. is defined as a set of Deep learning can be viewed as a field of machine learning that teaches computers to think in a broad framework.

When there is some data, it is represented in a form that a computer can understand (for example, in the case of an image, pixel information is expressed as a column vector), and many studies (how to do better) to apply it to learning How to make representations and how to build models to learn them) is underway. As a result of these efforts, various deep learning techniques have been developed. Examples of deep learning techniques include Deep Neural Networks (DNN), Convolutional Deep Neural Networks (CNN), Reccurent Neural Network (RNN), and Deep Belief Networks (DBN). have.

A deep neural network (DNN) is an artificial neural network (ANN) composed of a plurality of hidden layers between an input layer and an output layer.

In this case, the structure of the deep neural network may be composed of a perceptron. A perceptron consists of several inputs, one processor, and one output value. The processor multiplies each input value by a weight, and then sums the input values multiplied by the weight. Then, the processor outputs one output value by substituting the summed value into the activation function. If a specific value is desired as the output value of the activation function, the weight multiplied by each input value may be corrected, and the output value may be recalculated using the modified weight. In this case, each perceptron may use a different activation function. In addition, each perceptron receives the outputs from the previous layer as input, and then uses the activation function to obtain the output. The obtained output is transferred to the input of the next layer. Through the process as described above, some output values can be finally obtained.

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

Returning to the description of deep learning techniques, convolutional deep neural networks (CNNs) are a type of multilayer perceptrons designed to use minimal preprocessing. A convolutional neural network consists of one or several convolutional layers and general artificial neural network layers on top of it, and additionally utilizes weights and pooling layers. Thanks to this structure, the convolutional neural network can fully utilize the input data of the two-dimensional structure. In addition, convolutional neural networks can be trained via standard backpropagation. Convolutional neural networks are easier to train than other feed-forward neural network techniques and have the advantage of using fewer parameters. A convolutional neural network extracts features from an input image by alternately performing convolution and subsampling on the input image.

A convolutional neural network includes several convolution layers, several subsampling layers (Subsampling layer, Lacal pooling layer, Max-Pooling layer), and a fully connected layer. The convolution layer is a layer that performs convolution on an input image. In addition, the subsampling layer is a layer that locally extracts the maximum value from the input image and maps it to a 2D image, and subsampling is performed by enlarging the local area.

In the convolution layer, information such as the kernel size, the number of kernels to be used (ie, the number of maps to be created), and a weight table to be applied during the convolution operation are required. For example, suppose that the size of the input image is 32x32, the size of the kernel is 5x5, and the number of kernels to be used is 20. In this case, if a 5×5 kernel is applied to a 32×32 input image, it is impossible to apply the kernel to two pixels above, below, left, and right of the input image. When a kernel is placed on top of an input image and convolution is performed, the result value of '-8' is determined as the value of a pixel corresponding to the center element of the kernel among the pixels of the input image included in the kernel. to be. Therefore, when convolution is performed by applying a 5×5 kernel to an input image of a size of 32×32, a map of size of 28×28 is generated. Previously, since it was assumed that the total number of kernels to be used is 20, in the first convolutional layer, a total of 20 maps with a size of 28×28 are generated.

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

Returning to the description of the deep learning technique, a Reccurent Neural Network (RNN) refers to a neural network in which connections between units constituting an artificial neural network constitute a directed cycle. Unlike forward neural networks, recurrent neural networks can utilize the memory inside the neural network to process arbitrary inputs.

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

The deep trust neural network can be used for prior learning due to the nature of the generative model, and after learning the initial weights through prior learning, the weights can be fine-tuned through backpropagation or other discrimination algorithms. This characteristic is very useful when the training data is small, because the smaller the training data, the stronger the effect of the initial value of the weight on the resulting model. The pre-learned initial weight value is closer to the optimal weight compared to the arbitrarily set initial weight value, which enables performance and speed improvement in the fine-tuning stage.

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

2 is a flowchart illustrating a method for controlling a platform according to an embodiment of the present invention.

In step S110, the consumer terminal may receive at least one content from among a plurality of content uploaded by a plurality of other consumer terminals stored in the server.

In step S120, the consumer terminal may recommend at least one content among the plurality of content based on the user's personal information corresponding to the consumer terminal input to the artificial intelligence model.

In step S130, when the consumer terminal receives the product purchase request corresponding to the at least one content, it may transmit the product purchase request to the seller terminal.

In step S140, the seller terminal may transmit payment information corresponding to the product to the consumer terminal.

In this case, the server, based on the content information recommended to the plurality of consumer terminals, transmitting the target customer corresponding to the seller terminal to the seller terminal; transmitting, by the seller terminal, an advertisement corresponding to the target customer to the server; and transmitting, by the server, the advertisement to a consumer terminal corresponding to the target customer. may include

In this case, the content may be at least one of a text and an image related to a consumer's product purchase review.

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

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

The processor 102 according to one embodiment performs the method described with respect to FIG. 2 by executing one or more instructions stored in the memory 104 .

For example, the processor 102 obtains new training data by executing one or more instructions stored in the memory, and performs a test on the acquired new training data using the learned model, and the test result, labeling Extracting the first learning data in which the obtained information is obtained with an accuracy greater than or equal to a predetermined first reference value, deleting the extracted first learning data from the new learning data, and removing the new learning data from which the extracted learning data is deleted It is possible to re-train the learned model using

On the other hand, the processor 102 is a RAM (Random Access Memory, not shown) and ROM (Read-Only Memory: ROM) for temporarily and / or permanently storing a signal (or, data) processed inside the processor 102. , not shown) may be further included. In addition, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

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

The steps of a method or algorithm described in relation to an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any 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) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , may be implemented in a programming or scripting language such as Java, assembler, or the like. Functional aspects may be implemented in an algorithm running on one or more processors.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains know that the present invention may be embodied in other specific forms without changing the technical spirit or essential features thereof. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (5)

In the control method of a shopping platform customized for consumers and sellers performed by an artificial intelligence model,
receiving, by the consumer terminal, at least one content from among a plurality of content uploaded by a plurality of other consumer terminals stored in a server;
recommending, by the consumer terminal, at least one content among the plurality of content based on the user's personal information corresponding to the consumer terminal input to the artificial intelligence model;
transmitting the product purchase request to the seller terminal when the consumer terminal always receives a product purchase request corresponding to at least one content; and
and transmitting, by the seller terminal, payment information corresponding to the product to the consumer terminal. According to claim 1,
transmitting, by the server, a target customer corresponding to the seller terminal to the seller terminal based on the content information recommended to the plurality of consumer terminals;
transmitting, by the seller terminal, an advertisement corresponding to the target customer to the server; and
transmitting, by the server, the advertisement to a consumer terminal corresponding to the target customer; A control method comprising a. According to claim 1,
The content is
A control method, characterized in that at least one of a text and an image related to a consumer's product purchase review. a memory storing one or more instructions; and
a processor executing the one or more instructions stored in the memory;
The processor by executing the one or more instructions,
An apparatus for performing the method of claim 1 . A computer program stored in a computer-readable recording medium in combination with a computer, which is hardware, to perform the method of claim 1 .

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