KR102314730B1 - Server providing product sales service and operation method thereof - Google Patents

Abstract

Embodiments of the present invention relate to a server for providing a product sales service and an operation method thereof for recommending a product sold in an online shopping mall and providing bundle purchase discount information to a user. Embodiments of the present invention relate to the server for providing the product sales service and the operation method thereof for using product information of an interested product selected by the user, analyzing attributes considered when the user selects the interested product, and, based on this, providing user-customized bundle purchase discount information.

Description

Server providing product sales service and its operation method {SERVER PROVIDING PRODUCT SALES SERVICE AND OPERATION METHOD THEREOF}

Embodiments of the present invention relate to a service providing server that recommends products sold in an online shopping mall to a user and provides bundle purchase discount information, and a method of operating the same.

Embodiments of the present invention relate to a service providing server that analyzes attributes considered in the process of a user selecting a product of interest using product information of a product of interest selected by a user and provides user-customized bundle discount information based thereon, and an operating method thereof will be.

An online shopping mall that sells products using the Internet and mobile-based e-commerce promotes products by exposing relevant information and images of the products provided by the shopping mall to online shopping mall visitors on a main page. A visitor of the online shopping mall may search for a product that the visitor wants to purchase through a search engine or the like, and may flow into the online shopping mall through a link provided by the search engine. There is a growing trend of product groups in which online-based purchases overtake offline sales, and this is being reflected in small home appliance products as well.

In the past, consumers visited a dealership and purchased the displayed home appliances through a clerk. However, due to price competitiveness and convenience on the Internet and mobile, transactions in online shopping malls are becoming more active. In particular, in the case of small home appliances, it is possible to compare and review products of various SMEs that are difficult to check at a single agency online, and many consumers use online shopping malls to purchase small home appliances.

On the other hand, as the offline social distance is getting longer, the time to stay at home is getting longer and the number of single-person households is increasing, so the interest and demand for small home appliances are also increasing. If you visit a dealership to purchase small home appliances offline, you can receive recommendations for products other than the specific small home appliances you were willing to purchase through the clerk. Accordingly. In an online shopping mall, an online shopping mall environment is required that recommends other small home appliances that can be purchased in the same online shopping mall to users who want to purchase a specific small home appliance and offers discount promotions for them.

(001) Korean Patent Publication No. 10-2003-0006854 (2003.01.23) (002) Korean Patent Publication No. 10-2000-0059026 (10.05.2000)

The technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention belongs from the following description. will be able

According to embodiments, there is provided an operating method of a server that provides an online product sales service for home appliances to a user, the method comprising: receiving user input information on any one of a plurality of products sold in an online shopping mall; determining a user's interest product based on user input information; calculating a specificity score for each of a design attribute, a size attribute, a price attribute, and a performance attribute of the product of interest based on the product information of the product of interest; generating a product selection type model of the user based on the specificity score for each of the design attribute, the size attribute, the price attribute, and the performance attribute of the product of interest; determining a product corresponding to the user's product selection type model from among a plurality of products excluding products having the same category as the product of interest as the product of interest and at least one recommended product for bundled sale; generating bundle discount information for inducing a bundle purchase of a product of interest and at least one recommended product based on the product information on the at least one recommended product; and transmitting output information including the generated bundle discount information to the user's terminal.

According to embodiments, the specificity score for each of the design attribute, the size attribute, the price attribute, and the performance attribute of the product of interest is a value of n (where n is an integer greater than 2) or less, and the product information of the product of interest is Including whether the product has won the design category, and calculating the specificity score may include setting the specificity score of the design attribute of the product of interest to n when the product of interest is a product that has won a design category award.

According to embodiments, the sum of the specificity scores of each of the price attribute and the performance attribute of the product of interest is n, and the calculating of the specificity score includes price ranking information of the product of interest and the product of interest among products of the same category as the product of interest. It may include calculating a specificity score of each of the price attribute and the performance attribute of the product of interest based on the performance ranking information.

According to embodiments, the calculating of the specificity score may include calculating the specificity score of the price attribute of the product of interest as n when the price ranking information of the product of interest indicates the lowest price, and calculating the specificity score of the performance attribute of the product of interest. may include calculating '0'.

According to embodiments, the determining of the at least one recommended product may include, based on the value of the following equation indicating the degree of suitability for the user's product selection type model from among a plurality of products having different categories from the product of interest, It may include determining a recommended product.

[Equation]

At this time, in the formula, A is the importance score of the design attribute of the user's product selection type model, B is the importance score of the size attribute of the user's product selection type model, C is the importance score of the user's product selection type model price attribute, , x is an identification number indicating a comparison target product that is one of a plurality of products having a different category from the product of interest, a(x) is the normalized specificity score of the design attribute of the comparison product, and b(x) is the comparison target The normalized specificity score of the size attribute of the product, c(x) is the normalized specificity score of the price attribute of the comparable product, d(x) is the normalized specificity score of the performance attribute of the comparable product, a(x) , b(x), c(x), and d(x) may each be a specificity score normalized according to the area of a product type model of a comparable product with axes (design-size) and (price-performance). .

According to embodiments, the method includes: when a user purchases at least one recommended product in combination with a product of interest, the specificity score for each of a design attribute, a size attribute, a price attribute, and a performance attribute of the at least one recommended product The method may further include updating the product selection type model of the user based on the method.

According to embodiments, a server providing an online product sales service for home appliances to a user includes: a memory for storing instructions for providing the service; a processor for generating output information provided to a user; and a communication unit for transmitting the output information to the user terminal, wherein the processor receives user input information about any one of a plurality of products sold in an online shopping mall according to an instruction stored in a memory, and based on the user input information to determine the product of interest of the user, calculate the specificity score for each of the design attribute, size attribute, price attribute, and performance attribute of the product of interest based on the product information of the product of interest, design attribute of the product of interest, size attribute, A product selection type model of the user is generated based on the specificity score for each of the price attribute and the performance attribute, and a product corresponding to the product selection type model of the user is interested in a product from among a plurality of products except for a product having the same category as the product of interest. Determining the product and at least one recommended product for a bundle purchase, and generating bundle discount information for inducing a bundle purchase of the product of interest and the at least one recommended product based on the product information on the at least one recommended product, and the generated Output information including bundle discount information may be transmitted to the user's terminal.

According to embodiments, the service providing server and method may generate a user's product selection type model to identify a key factor that the user considers when purchasing a product.

According to embodiments, the service providing server and method may recommend another product to the user in consideration of the specificity of the corresponding product even when the user selects one product.

Effects obtainable from the embodiments are not limited to the effects mentioned above, and other effects not mentioned are clearly derived and understood by those of ordinary skill in the art based on the following detailed description. can be

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as part of the detailed description to aid understanding of the embodiments, provide various embodiments and, together with the detailed description, explain technical features of the various embodiments.
1 is a schematic diagram illustrating an online product sales system that provides an online product sales service to a user according to an embodiment of the present invention.
2 is a flowchart illustrating a service providing method according to an embodiment.
3 is a diagram illustrating an example of a specificity score for each product calculated according to an embodiment of the present invention.
4 is a diagram illustrating an example of a user's product selection type model created according to an embodiment of the present invention.
5 is a diagram illustrating a user interface provided by a service providing server according to an embodiment of the present invention.
6 is a block diagram illustrating a configuration of a service providing server according to an embodiment.

The following embodiments combine elements and features of the embodiments in a predetermined form. Each component or feature may be considered optional unless explicitly stated otherwise. Each component or feature may be implemented in a form that is not combined with other components or features. In addition, various embodiments may be configured by combining some components and/or features. The order of operations described in various embodiments may be changed. Some features or features of one embodiment may be included in another embodiment, or may be replaced with corresponding features or features of another embodiment.

In the description of the drawings, procedures or steps that may obscure the gist of various embodiments are not described, and procedures or steps that can be understood at the level of those of ordinary skill in the art are also not described. did.

Throughout the specification, when a part is said to "comprising or including" a certain element, it means that other elements may be further included, rather than excluding other elements, unless otherwise stated. do. In addition, terms such as "... unit", "... group", and "module" described in the specification mean a unit that processes at least one function or operation, which is hardware or software or a combination of hardware and software. can be implemented as Also, "a or an", "one", "the" and like related terms are used herein in the context of describing various embodiments (especially in the context of the claims that follow). Unless otherwise indicated or clearly contradicted by context, it may be used in a sense including both the singular and the plural.

The present invention relates to a method of providing an online shopping service to a user. The service providing server according to various embodiments of the present invention may recommend a bundle purchase product to the user by using product information on the user's interest product. For example, the service providing server may predict a criterion for which a user selects a product of interest, and may recommend a product of a different category according to the predicted selection criterion. The service providing server according to various embodiments of the present disclosure may provide bundle discount information applied to the entire purchase price when the user bundles the recommended products. The service providing server according to various embodiments of the present disclosure may increase the probability that a user accessing the online shopping mall will purchase a plurality of products provided by the online shopping mall.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION The detailed description set forth below in conjunction with the appended drawings is intended to describe exemplary embodiments of various embodiments, and is not intended to represent the only embodiments.

In addition, specific terms used in various embodiments are provided to help the understanding of various embodiments, and the use of these specific terms may be changed to other forms without departing from the technical spirit of various embodiments. .

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1 is a schematic diagram illustrating an online product sales system 10 that provides an online product sales service to a user according to an embodiment of the present invention.

Referring to FIG. 1 , the online product sales system 10 may include a service providing server 100 , at least one user terminal 200 , and a product information providing server 300 .

According to embodiments of the present invention, the service providing server 100 may recommend the user's interest product and at least one product for bundle sale. For example, the service providing server 100 may determine at least one recommended product based on the user's product selection type model. In this case, the user's product selection type model may model the importance of attributes considered by the user in selecting the product of interest. The service providing server 100 may generate the user's product selection type model by using the product information of the user's interest product. The service providing server 100 may transmit information on the recommended product determined based on the generated user product selection type model to the at least one user terminal 200 .

The service providing server 100 according to embodiments of the present invention may be a server that provides a user with an online shopping mall selling specific products. For example, the service providing server 100 may provide a user with an online shopping mall selling a plurality of small home appliances. The service providing server 100 may provide a user interface for product sales. For example, the service providing server 100 may provide a user interface in which a plurality of products are displayed on at least one user terminal 200 . Also, the service providing server 100 may provide a user payment environment for product purchase through a user interface displayed on at least one user terminal 200 .

According to an embodiment, the service providing server 100 may obtain user input information for any one of a plurality of products from the at least one user terminal 200 . At least one user terminal 200 may obtain a user input for any one of a plurality of products. For example, the at least one user terminal 200 may include a user's touch input, touch and drag input, mouse click input, or click and drag input to the online shopping mall screen. drag) input can be obtained. At least one user terminal 200 may transmit user input information indicating the acquired user input to the service providing server 100 .

In an embodiment of the present invention, the at least one user terminal 200 may include all kinds of devices including a function of displaying an image screen and a user input interface. For example, the at least one user terminal 200 is a smart phone, a tablet PC, a PC, a smart TV, a mobile phone, a personal digital assistant (PDA), a laptop, a media player, a micro server, a global positioning system (GPS) device, an electronic device. book terminals, digital broadcast terminals, navigation devices, kiosks, MP3 players, digital cameras, home appliances, and other mobile or non-mobile computing devices. In addition, the at least one user terminal 200 may include a wearable device such as a watch, glasses, a hair band, and a ring having a display function and a user input interface.

According to an embodiment, the service providing server 100 may obtain product information of the product of interest from the product information providing server 300 . The product information providing server 300 may transmit information on products sold to the user through the service providing server 100 according to the request of the service providing server 100 . The service providing server 100 may receive product characteristic-related information and inventory information from the product information providing server 300 . The service providing server 100 may generate at least one of a user's product selection type model and bundle discount information by using the received information.

As shown in FIG. 1 , the components of the online product sales system 10 may be connected through a network 400 . According to an embodiment, a network refers to a connection structure capable of exchanging information between each node such as a plurality of terminals and servers, and an example of such a network includes RF, 3rd Generation Partnership Project (3GPP) network, LTE ( Long Term Evolution) network, 5th Generation Partnership Project (5GPP) network, World Interoperability for Microwave Access (WIMAX) network, Internet, Local Area Network (LAN), Wireless Local Area Network (Wireless LAN), Wide Area (WAN) network Network), PAN (Personal Area Network), Bluetooth (Bluetooth) network, NFC network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, and the like are included, but are not limited thereto.

Meanwhile, although FIG. 1 illustrates that the service providing server 100 obtains product related information from the product information providing server 300 , the present invention is not limited thereto. For example, the service providing server 100 may integrate and perform the functions of the product information providing server 300 described in FIG. 1 . Specifically, the service providing server 100 may generate the user's product selection type model by using pre-stored information related to the product. In addition, the service providing server 100 may manage inventory information for each product. In this case, the service providing server 100 may generate bundle discount information based on the inventory information managed by the service providing server 100 .

Hereinafter, a method for the service providing server according to an embodiment of the present invention to determine at least one recommended product and provide bundle discount information will be described.

2 is a flowchart illustrating a service providing method according to an embodiment.

Referring to FIG. 2 , in step S201, the service providing server may receive user input information for any one of a plurality of products. The user input information may be information indicating a user input for selecting any one of a plurality of products provided by the online shopping mall. For example, the user input information may represent a user's touch input, a touch and drag input, a mouse click input, or a click and drag input with respect to a screen in which a plurality of products sold in an online shopping mall are listed.

According to an embodiment, the user input information may be information indicating a user input obtained by the user terminal. The service providing server may obtain user input information about any one of a plurality of products provided by the online shopping mall from a user terminal accessing the online shopping mall. The user terminal may obtain a user input for a screen in which a plurality of products sold in the online shopping mall are listed, and transmit information indicating the obtained user input to the service providing server.

In step S202, the service providing server may determine the user's interest product based on the user input information obtained in step S201. For example, the service providing server may identify a product that the user wants to purchase based on user input information. Specifically, the service providing server may determine the product in the online shopping cart corresponding to the user's account information as the user's interest product. Alternatively, the service providing server may determine the user's product of interest based on user input information in which the user selects a specific product as the product of interest.

In step S203, the service providing server may calculate a specificity score for each of the design attribute, the size attribute, the price attribute, and the performance attribute of the product of interest based on the product information of the product of interest. The service providing server may extract information related to the design, size, price, and performance of the product of interest from the product information of the product of interest. The service providing server may calculate a specificity score for each of the design attribute, the size attribute, the price attribute, and the performance attribute by using the extracted information. The specificity score may be an index indicating the degree to which a specific product is distinguished from other products by attributes.

According to an embodiment, the information related to the design of the product may include information indicating at least one of a background in which the product was designed, the origin of the product design, the origin of the idea or idea, the merits of the design, and the award history in the design category. The information related to the size of the product may include at least one of an actual size of the product and a size ranking of the corresponding product among products of the same category sold through the service providing server.

The information related to the price of the product may include at least one of product line information and a price ranking of the corresponding product among products of the same category sold through the service providing server. In this case, the product line information indicates a marketing target of a product and may indicate either a low-priced line or a high-performance line. The information related to the performance of the product may include at least one of product line information and a performance ranking of the corresponding product among products of the same category sold through the service providing server.

According to an embodiment, the service providing server may acquire a specificity score by using a pre-trained learning model. The learning model may be a type of a pre-trained neural network. For example, when product information of a specific product is input, the learning model may be a neural network that outputs a specificity score of each of a design attribute, a size attribute, a price attribute, and a performance attribute of the corresponding product.

In step S204, the service providing server may generate the user's product selection type model based on the specificity score for each of the design attribute, the size attribute, the price attribute, and the performance attribute of the product of interest. The user's product selection type model may be a model indicating the degree (or level) that the user considers for each factor in order to purchase a product. For example, the user's product selection type model may include an importance score for each attribute indicating the degree to which the user considers each of a design attribute, a size attribute, a price attribute, and a performance attribute of the product in selecting a product to purchase. . According to an embodiment, the user's product selection type model may be a model representing the importance score considered by the user for each attribute with (design-size) and (price-performance) as axes.

The service providing server according to an embodiment of the present invention may generate a product selection type model of the user by using product information of the user's interest product to identify a key factor that the user considers when purchasing a product. Even when the user selects one product, the service providing server may recommend another product to the user in consideration of the specificity of the corresponding product.

In step S205, the service providing server may determine a product corresponding to the user's product selection type model from among a plurality of products excluding products having the same category as the product of interest as the product of interest and at least one recommended product for bundle purchase. In the present invention, the category of the product may indicate a sub-category to which the product belongs. For example, when a plurality of fans are sold in an online shopping mall, each category of the first fan and the second fan may be a fan. The service providing server may recommend that the user purchase a product having a different category from the product of interest. This is because the user is more likely to purchase the product when the service providing server recommends a product of a different category than when the service providing server recommends a product of the same category. According to a specific embodiment, when the user's interest product is the first fan, the service providing server may recommend any one of products of the dehumidifier category sold through the service providing server.

According to an embodiment, the service providing server may select at least one recommended product by comparing the user's product selection type model and the specificity score for each attribute of the product sold in the online shopping mall. For example, if the user's product of interest is the first fan, the service providing server may select at least one recommended product by comparing the specificity score for each attribute of a plurality of products except the fan category with the user's product selection type model. have. A method for the service providing server to select at least one recommended product using the user's product selection type model will be described in detail with reference to FIG. 4 to be described later.

In step S206, the service providing server may generate bundle discount information for inducing a bundle purchase of the product of interest and the at least one recommended product based on the product information on the at least one recommended product. The bundle discount information may include a discount rate applied to the entire purchase amount when purchasing by combining the product of interest and at least one recommended product. The service providing server may generate bundle discount information based on price information of at least one recommended product.

According to an embodiment, the service providing server may generate bundle discount information based on the stock quantity of at least one recommended product. For example, the discount rate under the bundle may increase as the inventory quantity of the at least one recommended product increases. Specifically, when the inventory quantity of at least one recommended product indicates '0', the bundle discount rate may be a preset minimum value.

According to an embodiment, the service providing server may provide a plurality of recommended products to the user. For example, the service providing server may generate bundle discount information for the user based on product information of each of the first recommended product and the second recommended product. In this case, the bundle discount information includes a first bundle discount rate when the user's product of interest and the first recommended product and the second recommended product are bundled, and a second bundle discount rate when the user's product of interest and the first recommended product are bundled. and a third bundle discount rate in the case of bundle purchase of the user's interest product and the second recommended product. In this case, the first bundle discount rate may be higher than the second bundle discount rate and the third bundle discount rate.

In step S207, the service providing server may transmit the output information including the bundle discount information generated in step S206 to the user terminal. According to an embodiment, the service providing server may output bundle discount information through the user's terminal. For example, the service providing server may transmit output information including bundle discount information to the user's terminal. In this case, the output information may include bundle discount information. The service providing server may transmit the output information to the user terminal so that the user terminal displays bundle discount information at a preset position on the online shopping mall providing screen. The user terminal may display bundle discount information on the online shopping mall providing screen based on the received output information.

Meanwhile, when the user bundles purchases of at least one recommended product together with a product of interest, the service providing server may update the user's product selection type model based on product information of the at least one recommended product.

According to an embodiment, when a user bundles purchases of at least one recommended product with a product of interest, the service providing server calculates the specificity score for each of the design attribute, size attribute, price attribute, and performance attribute of the at least one recommended product. Based on the user's product selection type model may be updated.

For example, the service providing server may update the user's product selection type model by using the average of the specificity scores for each attribute. The service providing server may calculate the average specificity score of the design attribute by using the specificity score of the design attribute of each of the product of interest and the recommended product. The service providing server may calculate the average specificity score of the size attribute by using the specificity score of the size attribute of each of the product of interest and the recommended product. The service providing server may calculate an average specificity score of the price attribute by using the specificity score of the price attribute of each of the product of interest and the recommended product. The service providing server may calculate the average specificity score of the performance attribute by using the specificity score of the performance attribute of each of the product of interest and the recommended product. The service providing server may generate the user's product selection type model based on the average specificity score for each attribute.

Hereinafter, a method for the service providing server to calculate a specificity score for each design, size, price, and performance attribute of a product will be described with reference to FIG. 3 .

3 is a diagram illustrating an example of a specificity score for each product calculated according to an embodiment of the present invention. Referring to FIG. 3 , the service providing server may create a product type model database 30 of products sold through the service providing server. The product type model database 30 may include an identification number 301 of a product, a score per attribute 302 , and a model area 303 .

The model area 303 may be a value for normalizing the specificity score for each attribute. For example, the service providing server may generate a product type model based on (design attribute-size attribute), and (price attribute-performance attribute). The service providing server may place a score for each attribute of the product (design attribute - size attribute), and (price attribute - performance attribute) on the axes. The model area may represent the area of a rectangle defined by using the specificity score of the design attribute, the specificity score of the size attribute, the specificity score of the price attribute, and the specificity score of the performance attribute as vertices.

According to an embodiment, the specificity score of each of the design attribute, the size attribute, the price attribute, and the performance attribute may be an integer within a preset range. For example, the specificity score of each of the design attribute, the size attribute, the price attribute, and the performance attribute may be a value of 0 or more and n or less. In this case, n is an integer greater than 1. Specifically, the service providing server may set n to 100. In the embodiment of FIG. 3 , n may be 50.

According to an embodiment, the service providing server may calculate a specificity score of the design attribute of the product based on the award history of the product in the design category. When the product of interest is a product that has won a design award, the service providing server may set the specificity score of the design attribute of the product to n. For example, 'Fan 1' sold through a service providing server may be a product that has won an award in the design category. In this case, the service providing server may calculate the specificity score of the design attribute of 'the fan 1' as n. Referring to FIG. 3 , 'Fan 1' is a product having an identification number '0001', and the specificity score of the design attribute may be '50'.

According to an embodiment, the sum of the specificity scores of each of the price attribute and the performance attribute of the product may be n. The price attribute and the performance attribute may have a high correlation between the attributes. The service providing server may calculate a specificity score for each of the price attribute and the performance attribute of the specific product based on the price ranking information of the corresponding product and the performance ranking information of the corresponding product among products of the same category as the specific product.

According to a specific embodiment, when the price ranking information of the product indicates the lowest price, the service providing server calculates 'n' as the specificity score of the price attribute of the product, and sets the specificity score of the performance attribute of the product to '0'. ' can be calculated. For example, the 'Fan 2' sold through the service providing server may be a product with the lowest price (ie, a product having the highest price ranking) among products of the fan category. In this case, the service providing server may calculate the specificity score of the price attribute of 'the fan 2' as n. Referring to FIG. 3 , 'Fan 2' is a product having an identification number '0002', and the specificity score of the price attribute may be '50' and the specificity score of the performance attribute may be '0'.

According to an embodiment, the service providing server may calculate a higher specificity score of the size attribute of the product as the size ranking of the product is smaller among products of the same category. For example, the size of the 'electric grill 1' sold through the service providing server may be smaller than the size of the 'electric grill 2'. In this case, 'electric grill 1' may be a product having the smallest size (ie, a product having the lowest size ranking) among products in the electric grill category. In this case, the service providing server may calculate the specificity score of the size attribute of 'electric grill 1' as n. Referring to FIG. 3 , 'electric grill 1' is a product with identification number '0003', a specificity score of size attribute is '50', and 'electric grill 2' is a product with identification number '0004', a size attribute of The specificity score may be '10'.

The service providing server may select at least one recommended product by using the product type model database 30 . For example, the service providing server may select at least one recommended product from among the plurality of products by comparing the product type model of each of the plurality of products with the user's product selection type model.

Hereinafter, a method for the service providing server to determine at least one recommended product using the user's product selection type model will be described with reference to FIG. 4 .

4 is a diagram illustrating an example of a user's product selection type model created according to an embodiment of the present invention. Referring to FIG. 4 , the user's product selection type model includes an importance score 401 for the design attribute, an importance score 402 for the size attribute, an importance score 403 for the price attribute, and an importance score for the performance attribute. 404 . According to an embodiment, the user's product selection type model may be a model in which an importance score for each attribute is indicated on the (design-size) axis 405 and the (price-performance) axis 406 .

According to an embodiment, the service providing server compares the user's product selection type model 407 with any one product type model 408 among a plurality of products sold through the service providing server to select at least one recommended product. can be selected In this case, the service providing server may normalize and compare the user's product selection type model 407 and the product type model of each of the plurality of products. In this case, the service providing server may use the area of the user's product selection type model 407 and the area of the product type model of each of the plurality of products.

For example, the service providing server may determine, as at least one recommended product, a product having a high degree of conformity with the user's product selection type model from among a plurality of products having different categories from the product of interest. Specifically, the service providing server may determine at least one recommended product based on the value of the following equation indicating the degree of fit with the user's product selection type model from among a plurality of products having different categories from the product of interest.

[Equation]

In the formula, A may represent the importance score of the design attribute of the user's product selection type model, B is the importance score of the size attribute of the user's product selection type model, and C may represent the importance score of the user's product selection type model price attribute .

In addition, in the formula, x is an identification number indicating a comparison target product that is any one of a plurality of products having a different category from the product of interest, a(x) is the normalized specificity score of the design attribute of the comparison target product, b(x) is the normalized specificity score of the size attribute of the comparison product, c(x) is the normalized specificity score of the price attribute of the comparison product, and d(x) is the normalized specificity score of the performance attribute of the comparison product. . In this case, a(x), b(x), c(x), and d(x) are normalized according to the area of the product type model of the comparison product with (design-size) and (price-performance) as axes, respectively. It may be a specified specificity score.

5 is a diagram illustrating a user interface 50 provided by a service providing server according to an embodiment of the present invention. As described above, the user interface 50 may be provided through a user's terminal. The service providing server may transmit output information representing the user interface 50 to the user's terminal.

Referring to FIG. 5 , the service providing server generates a user interface 50 including a first recommended product 52 and a second recommended product 53 selected based on product information of a user's product of interest 51 . can do. The first recommended product 52 and the second recommended product 53 may be products having a different category from the user's interest product 51 , respectively.

According to one embodiment, the service providing server is based on the product information of each of the first recommended product 52 and the second recommended product 53, the first bundle discount information 54, the second bundle discount information 55, and The third bundle discount information 56 may be generated.

For example, the stock quantity of the first recommended product 52 may be less than the stock quantity of the second recommended product 53 . In this case, the service providing server may set the third bundle discount rate when the second recommended product 53 is bundled to be higher than the second bundle discount rate when the first recommended product 52 is bundled. In addition, the service providing server may set the first bundle discount rate to be higher than the second bundle discount rate and the third bundle discount rate when purchasing the first recommended product 52 and the second recommended product 53 at the same time.

6 is a block diagram illustrating a configuration of a service providing server 600 according to an embodiment.

As shown in FIG. 6 , the service providing server 600 may include a processor 610 , a communication unit 620 , and a memory 630 . However, not all of the components shown in FIG. 6 are essential components of the service providing server 600 . The service providing server 600 may be implemented by more components than the components shown in FIG. 6 , and the service providing server 600 may be implemented by fewer components than the components shown in FIG. 6 . For example, the service providing server 600 according to some embodiments may further include a user input interface (not shown), an output unit (not shown), etc. in addition to the processor 610 , the communication unit 620 , and the memory 630 . may be

The processor 610 generally controls the overall operation of the service providing server 600 . The processor 610 may include one or more processors to control other components included in the service providing server 600 . For example, the processor 610 may control the communication unit 620 and the memory 630 in general by executing programs stored in the memory 630 . In addition, the processor 610 may perform the functions of the service providing server described in FIGS. 1 to 5 by executing programs stored in the memory 630 .

According to an embodiment, the processor 610 may receive user input information on any one of a plurality of products sold in the online shopping mall through the communication unit 620 . The processor 610 may receive user input information on any one of a plurality of products provided by the online shopping mall from a user terminal accessing the online shopping mall.

The processor 610 may determine the user's interest product based on the user input information. The processor 610 may identify a product that the user intends to purchase based on user input information. The processor 610 may calculate a specificity score for each of the design attribute, the size attribute, the price attribute, and the performance attribute of the product of interest based on the product information of the product of interest.

The processor 610 may extract information related to the design, size, price, and performance of the product of interest from the product information of the product of interest. The processor 610 may calculate a specificity score for each of the design attribute, the size attribute, the price attribute, and the performance attribute by using the extracted information. According to an embodiment, the processor 610 may calculate a higher specificity score of the size attribute of the product as the size ranking of the product is smaller among products of the same category.

According to an embodiment, the specificity score of each of the design attribute, the size attribute, the price attribute, and the performance attribute may be an integer within a preset range. For example, the specificity score of each of the design attribute, the size attribute, the price attribute, and the performance attribute may be a value of 0 or more and n or less. In this case, n is an integer greater than 1. According to an exemplary embodiment, the processor 610 may calculate a specificity score of the design attribute of the product based on the design category award history of the product. When the product of interest is a product that has won a design award, the processor 610 may set the specificity score of the design attribute of the product to n.

According to an embodiment, the sum of the specificity scores of each of the price attribute and the performance attribute of the product may be n. The processor 610 may calculate a price attribute and a specificity score of each of the performance attributes of the specific product based on the price ranking information of the corresponding product and the performance ranking information of the corresponding product among products of the same category as the specific product. When the price ranking information of the product indicates the lowest price, the processor 610 may calculate 'n' as the specificity score of the price attribute of the product, and calculate the specificity score of the performance attribute of the product as '0'. have.

The processor 610 may generate the user's product selection type model based on the specificity score for each of the design attribute, the size attribute, the price attribute, and the performance attribute of the product of interest. According to an embodiment, when the user bundles purchases of at least one recommended product together with a product of interest, the processor 610 may update the user's product selection type model based on product information of the at least one recommended product. .

For example, the processor 610 may update the user's product selection type model by using the average of the specificity scores for each attribute. The processor 610 may calculate an average specificity score of the design attribute by using the specificity score of the design attribute of each of the product of interest and the recommended product. The processor 610 may calculate an average specificity score of the size attribute by using the specificity score of the size attribute of each of the product of interest and the recommended product. The processor 610 may calculate an average specificity score of the price attribute by using the specificity score of the price attribute of each of the product of interest and the recommended product. The processor 610 may calculate an average specificity score of the performance attribute by using the specificity score of the performance attribute of each of the product of interest and the recommended product. The processor 610 may generate the user's product selection type model based on the average specificity score for each attribute.

The processor 610 may determine a product corresponding to the user's product selection type model among a plurality of products except for a product having the same category as the product of interest as the product of interest and at least one recommended product for bundle purchase. For example, the processor 610 may select at least one recommended product by comparing the user's product selection type model and the specificity score for each attribute of the product sold in the online shopping mall.

According to an embodiment, the processor 610 may determine, as at least one recommended product, a product having a high degree of suitability for the user's product selection type model from among a plurality of products having different categories from the product of interest. Specifically, the processor 610 may determine at least one recommended product by using the above-described equation with reference to FIG. 4 .

The processor 610 may generate bundle discount information for inducing a bundle purchase of the product of interest and the at least one recommended product based on the product information on the at least one recommended product. The processor 610 may generate bundle discount information based on the stock quantity of at least one recommended product. For example, the discount rate under the bundle may increase as the inventory quantity of the at least one recommended product increases. Specifically, when the inventory quantity of at least one recommended product indicates '0', the processor 610 may set the bundle discount rate to a preset minimum value.

According to an embodiment, the processor 610 may provide a plurality of recommended products to the user. For example, the processor 610 may generate bundle discount information for the user based on product information of each of the first recommended product and the second recommended product. In this case, the bundle discount information includes a first bundle discount rate when the user's product of interest and the first recommended product and the second recommended product are bundled, and a second bundle discount rate when the user's product of interest and the first recommended product are bundled. and a third bundle discount rate in the case of bundle purchase of the user's interest product and the second recommended product. In this case, the first bundle discount rate may be higher than the second bundle discount rate and the third bundle discount rate.

The processor 610 may transmit output information including the generated bundle discount information to the user's terminal through the communication unit 620 . In this case, the output information may include bundle discount information. The processor 610 may transmit the output information to the user terminal through the communication unit 620 so that the user terminal displays bundle discount information at a preset position on the online shopping mall providing screen.

The communication unit 620 may include one or more components that allow the service providing server 600 to communicate with another device (not shown) and a server (not shown). Another device (not shown) may be a computing device such as the service providing server 600 or a sensing device, but is not limited thereto. The communication unit 620 may receive a user input from another electronic device or data stored in an external device from an external device through a network.

The communication unit 620 may receive information necessary to provide bundle discount information to the user from the processor 610 . For example, the communication unit 620 may receive user input information on any one of a plurality of products sold in an online shopping mall from the user terminal. The communication unit 620 may receive product information of products sold through the service providing server 600 from an external server.

The communication unit 620 may transmit output information generated by the processor 610 . The communication unit 620 may transmit the user interface generated by the processor 610 to the user's terminal. For example, the communication unit 620 may transmit output information including bundle discount information to the user's terminal.

The memory 630 may store a program for processing and controlling the processor 610 . For example, the memory 630 may store an instruction for providing a service. Also, the memory 630 may store data generated by the processor 610 . For example, the memory 630 may store product information of a plurality of products sold through the processor 610 . The memory 630 may store information input to or output from the service providing server 600 . For example, the memory 630 may store at least one of a product type model of each of the plurality of products, a user's product selection type model, and information indicating a product of interest to the user.

The memory 630 may include a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg, SD or XD memory), and a RAM. (RAM, Random Access Memory) SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk , may include at least one type of storage medium among optical disks.

In addition, the service providing server 600 (and/or the processor 610) according to an embodiment of the present invention may further include the following features.

The online product sales system 10 according to an embodiment of the present invention includes at least one user terminal 200 and a service providing server 600 , and may further include at least one seller terminal in some cases. At this time, each component of the online product sales system 10 is generally connected through a network (network). For example, at least one user terminal 200 may be connected to the service providing server 600 through a network. In addition, the service providing server 600 may be connected to at least one user terminal 200 and at least one seller terminal through a network. In addition, at least one seller terminal may be connected to the service providing server 600 through a network.

In the following, the term at least one is defined as a term including the singular and the plural, and even if at least one term does not exist, each element may exist in the singular or plural, and may mean the singular or plural. it will be self-evident In addition, that each component is provided in singular or plural may be changed according to embodiments.

The at least one user terminal 200 mixes products using a package product sales service related web page, app page, program or application that determines a discount price based on the quantity of packaged products, which are products made by selecting several products together. It may be a terminal of a user who wants to bundle it and purchase it at a discounted price such as a single item discount. To this end, the at least one user terminal 200 may be a terminal that mixes products satisfying the quantity on the platform provided by the service providing server 600 and puts it in a shopping cart or order book, and satisfies the quantity that is a preset criterion. In this case, it may be a terminal of a user requesting delivery by paying an amount.

In addition, the at least one user terminal 200 may be a terminal that receives the delivery status shared from the service providing server 600 .

Here, the at least one user terminal 200 may be implemented as a computer capable of accessing a remote server or terminal through a network. Here, the computer may include, for example, a navigation device, a laptop computer equipped with a web browser, a desktop computer, and a laptop computer. In this case, the at least one user terminal 200 may be implemented as a terminal capable of accessing a remote server or terminal through a network.

The service providing server 600 may be a server providing a package product sales service web page, an app page, a program, or an application that determines a discount price based on the quantity of the packaged product. In addition, the service providing server 600 may be a server that receives each type of product, a unit price, a unit price per quantity, and a margin from at least one seller terminal and converts it into a database.

In addition, the service providing server 600 may be a server that, when payment is made in the user terminal 200 , transmits an order form to at least one seller terminal, receives tracking information and shares it with the user terminal 200 . In addition, the service providing server 600 may be a server that completes the order process when the user terminal 200 confirms receipt.

The at least one seller terminal may be a terminal of a seller using a web page, an app page, a program, or an application related to a package product sales service that determines a discount price based on the quantity of products to be sold. In addition, the at least one seller terminal may be a terminal that uploads at least one type of product and sale price, unit price per quantity, margin of unit price per quantity, etc. to the service providing server 600 , respectively. In addition, the at least one seller terminal may be a terminal that, when an order form is received from the service provision server 600 , outputs a waybill according to the order form and shares this information with the service provision server 600 .

Here, the at least one seller terminal may be implemented as a computer capable of accessing a remote server or terminal through a network. Here, the computer may include, for example, a navigation device, a laptop computer equipped with a web browser, a desktop computer, and a laptop computer. In this case, the at least one seller terminal may be implemented as a terminal capable of accessing a remote server or terminal through a network.

The service providing server 600 according to an embodiment of the present invention or another server (not shown) operating in conjunction with at least one user terminal 200 and at least one seller terminal determines a discount price based on the quantity of packaged goods. When transmitting the determined package product sales service application, program, app page, web page, etc., the at least one user terminal 200, and the at least one seller terminal, the package product for determining a discount price based on the quantity of the packaged product You can install or open sales service applications, programs, app pages, web pages, etc. In addition, a service program may be driven in at least one user terminal 200 and at least one seller terminal by using a script executed in a web browser. Here, the web browser is a program that enables the use of web (WWW: world wide web) services, and refers to a program that receives and displays hypertext written in HTML (hypertext mark-up language), for example, Netscape, Including Explorer, Chrome, etc. In addition, the application means an application on the terminal, for example, includes an app (app) executed in a mobile terminal (smartphone).

The processor 610 may collect information on at least one type of product from at least one seller terminal. In this case, the type of product is not limited to any one. In addition, the processor 610 may collect the unit price and margin for each quantity together when collecting product information.

As an example, when describing examples of products having different prices with reference to Table 1, it is assumed that a total of 65 units are ordered with 20 vacuum cleaners, 20 fans, and 25 humidifiers. In this case, when each product is ordered separately, since there are 20 vacuums, 900,000 won, which is the unit price for 20, should be applied, and a fan of 270,000 won and a humidifier of 180,000 won should be applied. However, in one embodiment of the present invention, as described above, when the combined quantity of different products satisfies the preset criterion, the unit price for the quantity is applied. The humidifier can be calculated as a unit price of 850,000 won, 250,000 won, and 150,000 won by applying a total unit price of 65 units to each product, and a table summarizing them is shown in Table 2 below.

vacuum cleaner fan humidifier Quantity unit price Quantity unit price Quantity unit price 1 to 10 1 million won 1 to 10 30 million won 1 to 10 20 million won 11-49 90 million won 11-49 27 million won 11-49 18 million won 50-99 85 million won 50-99 25 million won 50-99 15 million won 100 or more 70 million won 100 or more 23 million won 100 or more 10 million won

division Quantity amount per piece order price vacuum cleaner 20 85 million won 17 million won fan 20 25 million won 5 million won humidifier 25 15 million won 375 million won sum 65 25.7 million won

The processor 610 (and/or the communication unit 620 ) may map, store, and upload a discount price for each quantity of at least one type of product. In addition, the processor 610 (and/or the communication unit 620 ) may receive a selection of at least one type of package product from the user terminal 200 .

When the quantity of the selected package product satisfies the preset quantity, the processor 610 may calculate the payment amount by applying the stored discount price mapped to the preset quantity.

The processor 610 (and/or the communication unit 620 ) may transmit an ordering event to at least one seller terminal when the calculated payment amount is paid in the user terminal 200 . Accordingly, at least one seller terminal may prepare for delivery and register a waybill.

And, the processor 610 (and/or the communication unit 620), when the payment amount calculated by the processor 610 (and/or the communication unit 620) is paid in the user terminal 200, at least one seller After delivering the ordering event to the terminal, when at least one seller terminal uploads a waybill, delivery tracking information can be shared with the user terminal 200 , and when the user terminal 200 transmits a receipt event, the order process can be completed

In addition, an embodiment of the present invention may further use a method of predicting a product that can be bundled in advance through a price prediction model. For example, even in the case of a fan, the price varies depending on supply or demand. If you predict the price flow or trend, you need to increase the discount rate for which product to run out of stock, reduce spoilage, and prevent waste and environmental destruction. It can be used, etc. In particular, in the case of agricultural and fishery products or products with a set expiration date, more predictive data is needed than industrial products. At this time, multiple linear regression analysis and artificial neural network analysis can be used. Multiple linear regression analysis is _{a linear relationship between several independent variables (X 1} , X ₂ , ..., X _n ) and the dependent variable (Y). relationship) and based on the given learning data, the regression coefficient (β), which is the influence of each independent variable, is Y=β ₀ +β ₁ X ₁ +β ₂ X ₂ +... +β _n X _n +ε It is an estimation model using On the other hand, just as neurons, the basic structure of the human brain, are connected to process work, neurons as a mathematical model are interconnected to form a network, which is called a neural network. The basic functions of a neuron are information reception, arithmetic processing, and information output, and a number of neurons are combined to form a neural network.

Looking at the theory of artificially made neural networks, the output is formed by a predetermined non-linear function for a large number of inputs. All neurons in a neural network model are usually divided into an input layer, a hidden layer, and an output layer according to their functions, and each layer is functionally connected. The input layer connects the external input mode and is sent to the hidden layer unit according to the input unit. Here, the hidden layer is an internal processing unit layer of the neural network, whose main role is to switch the mode of the neural network, and the output layer is used to generate the calculation mode. At this time, the Back Propagation Algorithm is the most used as a method for learning an artificial neural network, which continuously adjusts the difference between the target result and the result calculated by the model through learning. It is a method of learning to minimize the error value and verifying the prediction target using the learning result. Using these two algorithms, a price prediction model can be created, and a 3-tier artificial neural network technique can be used as a prediction technique. Of course, it is not limited to those listed, and it will be apparent that various methods are available for reducing bundling and inventory.

In addition, according to an embodiment of the present invention, the price presentation of the bundled sale may be different from each other. For example, if the components and total price of a bundle are the same, the evaluation and selection of the entire bundle must be the same no matter how the discount is offered, but any of the components of the bundle are discounted even for the same discount amount. This is because the evaluation and choice of consumers about bundled sales differs depending on whether they are sold to the store. In general, when the prices of individual components are presented separately, compared to when the prices of package components are combined and presented as a single total price, consumers' evaluation of the bundle is higher, and even when presented as a split price, the price of any component is higher. Preference varies depending on whether the price is discounted or not.

Therefore, when a user bundles together a product that a customer wants and has a high preference using the history log, the item with the highest preference is extracted, and the percentage of discount on the extracted item or how much money is saved It is also possible to maximize the user's satisfaction by overlaying the

ⓐ The service providing server 600 collects information about at least one product from at least one seller terminal, ⓑ when an order is placed from the user terminal 200, confirms whether the amount is satisfied, and ⓒ if the amount is satisfied In this case, the payment amount is presented to the user terminal 200 by applying a pre-mapped discount rate or unit price to the amount, and ⓓ when the user completes the payment, the service providing server 600 transmits the order form to the seller terminal and sends a waybill When registered from the seller terminal, the tracking is shared with the user terminal 200 .

In addition, the package product sales service providing server 600 collects information on at least one type of product from at least one seller terminal.

In addition, the package product sales service providing server 600 maps, stores, and uploads a discount price for each quantity of the packaged product among at least one type of product.

In addition, the package product sales service providing server 600 receives a selection of at least one type of product from the user terminal, and when the quantity of the selected product satisfies the preset quantity, the discount price is mapped to the preset quantity and stored. The payment amount is calculated, and when the calculated payment amount is paid in the user terminal, an ordering event is transmitted to at least one seller terminal.

On the other hand, in the above-described package product sales service, the degree of fit (FitScore) with the user's product selection type model from among a plurality of products of different categories from the product of interest determined (and/or calculated) based on the above-described equation is a predetermined criterion. It may be provided to the user only when it satisfies .

For example, the service providing server 600 may compare the suitability with a predetermined reference value, and is provided to the user only when the suitability is lower than the predetermined reference value or provided by the online platform (and/or online page) of the present invention. can be This is because the suitability is lower than the predetermined threshold value indicates that the user is less likely to purchase, so a package product sales service that provides a discount to further increase the user's purchase probability is provided, but the suitability is higher than the predetermined threshold value, or This is in consideration of the fact that there is no need to provide a discount because the user is more likely to purchase the product even without a separate discount.

The various embodiments described above may be embodied in other specific forms without departing from the technical idea and essential characteristics thereof. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of the various embodiments should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of the various embodiments are included in the scope of the various embodiments. In addition, claims that are not explicitly cited in the claims may be combined to form an embodiment, or may be included as new claims by amendment after filing.

Claims (5)

A server that provides an online product sales service for home appliances to a user, the server comprising:
a memory for storing instructions for providing the service;
a processor for generating output information provided to a user; and
a communication unit for transmitting the output information to a user terminal; including,
The processor is:
Receive user input information for any one of a plurality of products sold in an online shopping mall,
Determine the product of interest of the user based on the user input information,
Calculating a specificity score for each of a design attribute, a size attribute, a price attribute, and a performance attribute of the product of interest based on product information including whether the product of interest has been awarded a design category, the design attribute and size attribute of the product of interest , the specificity score for each of the price attribute, and the performance attribute is a value less than or equal to n, where n is an integer greater than 2,
generating a product selection type model of the user based on a specificity score for each of a design attribute, a size attribute, a price attribute, and a performance attribute of the product of interest;
Determining a product corresponding to the user's product selection type model from among a plurality of products excluding products having the same category as the product of interest as at least one recommended product for bundle purchase with the product of interest,
generating bundle discount information for inducing a bundle purchase of the product of interest and the at least one recommended product based on product information on the at least one recommended product;
Transmitting output information including the generated bundle discount information to the user's terminal,
The processor is:
When the product of interest is a product with a history of winning a design category, the specificity score of the design attribute of the product of interest is set to n,
Calculate a specificity score of each of the price attribute and the performance attribute of the product of interest based on the price ranking information of the product of interest and the performance ranking information of the product of interest among products of the same category as the product of interest,
The processor is:
Determining the at least one recommended product based on the value of the following equation indicating a fit (FitScore) for the user's product selection type model from among a plurality of products having different categories from the product of interest,
[Equation]

In the above formula, A is the importance score of the design attribute of the user's product selection type model, B is the importance score of the size attribute of the user's product selection type model, C is the importance score of the user's product selection type model price attribute represents,
x is an identification number indicating a comparison target product that is any one of a plurality of products having a different category from the product of interest, a(x) is a normalized specificity score of the design attribute of the comparison target product, and b(x) is the The normalized specificity score of the size attribute of the comparison product, c(x) is the normalized specificity score of the price attribute of the comparison product, d(x) is the normalized specificity score of the performance attribute of the comparison product,
a(x), b(x), c(x) and d(x) are each normalized according to the area of the product type model of the compared product with axes (design-size) and (price-performance) A service providing server, characterized in that the specificity score. delete delete According to claim 1,
The processor is
When the price ranking information of the product of interest indicates the lowest price, the specificity score of the price attribute of the product of interest is calculated as n, and the specificity score of the performance attribute of the product of interest is calculated as '0'. , serving servers. 5. The method of claim 4,
The processor is
When the user purchases the at least one recommended product in combination with the product of interest,
and updating the product selection type model of the user based on a specificity score for each of a design attribute, a size attribute, a price attribute, and a performance attribute of the at least one recommended product.

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