KR20230021042A - Clothing and size recommendation method and server for performing the method - Google Patents

Abstract

The present invention relates to a method for recommending clothing and a size, which can recommend clothing suitable to the taste of a user, and to a server for performing same. The method of the present invention comprises the operations of: receiving user personal data of a user account; selecting a recommended clothing category; generating a first recommended clothing set; calculating a relationship score; receiving a selection signal and preferred clothing fitting information; recommending a recommended size of a selected clothing product; and transmitting the recommended size.

Description

Clothing and size recommendation method and server for performing the method}

The present invention relates to a method for recommending clothes and sizes and a server for performing the method, which recommends clothes by reflecting user tastes and regional trends in order to recommend clothes and sizes suitable for the user's taste, It's all about the art of providing the right size.

With the development of network technology such as the Internet, the frequency of using online clothing shopping malls for clothing shopping is increasing. A user can search for a product list in an online shopping menu of a company operating an online clothing shopping mall, select a desired product, and make payment.

A number of online shopping malls sell a variety of clothes, and users who perform online shopping have difficulty finding clothes that suit their taste.

On the other hand, an artificial intelligence (AI) system is a computer system that implements human-level intelligence based on machine learning (deep learning). Attempts are being made to provide user convenience during online clothing shopping by combining the development of the above-described network technology and the development of an artificial intelligence system.

An object of the present invention is to provide a method for recommending clothes and sizes suitable for a user's taste, and a server for performing the same.

In addition, based on the user's regional information, it is possible to provide recommended clothing reflecting regional trends.

In addition, it is possible to recommend a clothing size considering the user's body shape and preferred fit.

In addition, lawsuits or legal disputes that may occur in projects and meetings related to clothing design can be prevented in advance.

A clothing and size recommendation method according to an embodiment includes receiving, by a transceiver, user personal data of a user account from a user terminal; selecting a recommended clothing category based on clothing purchase information including a user's clothing purchase history by a clothing category selector; calculating, by a user taste degree calculation unit, a taste level of each piece of clothing in the recommended clothing category based on user information including the clothing purchase information and the user's personal information; generating a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category based on the degree of preference by a user taste reflecting unit; generating a second recommended clothing set including a plurality of second recommended clothing sets from the first recommended clothing set based on the regional information including the taste and the user's residence information by a regional trend reflector; calculating, by a relationship score calculation unit, a relationship score representing a degree of matching between the clothing previously purchased by the user and the second recommended clothing based on the clothing purchase history; The transceiver transmits the second recommended clothing set and the relationship score to a user terminal of the user, and from the user terminal, a selection signal for at least one of the plurality of second recommended clothing and a preferred clothing fit receiving information; and recommending, by a size recommendation unit, a recommended size of a selected clothing product included in the selection signal based on the user information and the preferred clothing fit information. and transmitting, by the transceiver, the recommended size to the user terminal; can include

The operation of calculating the degree of preference may include calculating, by the user taste reflector, a user matching color degree indicating a degree of contrast between colors of the first recommended clothes based on the skin color and hair color of the user information. ; calculating a brand similarity of each of the first recommended clothes based on the price and brand information of the clothes to be purchased in the clothes purchase information by the user taste reflecting unit; calculating a carryover preference of the user based on a release year and a purchase year of purchased clothing in the clothing purchase information by the user preference reflecting unit; and calculating, by the user taste reflecting unit, a taste level based on the user matching color degree, the brand similarity level, and the carryover preference. can include

The operation of generating the first recommended clothing set as the second recommended clothing set may include the plurality of clothing items included in the first recommended clothing set based on the taste and the residence information by the regional trend reflector. 1 calculating a regional tendency score for each of the recommended clothes; generating a second recommended clothing set excluding the first clothing from the first recommended clothing set based on the regional tendency score by the regional tendency reflecting unit; and generating a second recommended clothing set by adding a second clothing from the first recommended clothing set based on the regional tendency score by the regional tendency reflecting unit; The first clothing may correspond to clothing having a regional tendency score equal to or less than the threshold value, and the second clothing may correspond to clothing having the regional tendency score exceeding the threshold value.

The clothing purchase information includes information on a first color, a first material, and a first dress code of clothing purchased by the user within a predetermined period of time, and calculating the relationship score is performed by the relationship score calculator, calculating the relationship score based on the first color, first material, and first dress code information and the second color, second material, and second dress code information of the second recommended clothing; can include

The preferred clothing fit information includes tight fit, overfit, basic fit, and loose fit, and the operation of recommending the recommended size is based on the height and weight of the user included in the user information and the review information of the selected clothing product. calculating an appropriate size based on; and calculating a recommended size obtained by adjusting the appropriate size based on the preferred clothing fit information.

A server according to an embodiment includes a transceiver; apparel category selection department; a user preference degree calculation unit; user taste reflection unit; Local trend reflection unit; a relationship score calculator; and a size calculation unit, wherein the transceiver receives user personal data of a user account from a user terminal, and the clothing category selector selects a recommended clothing category based on clothing purchase information including a user's clothing purchase history; , The user preference degree calculation unit calculates a preference degree of each clothing of the recommended clothing category based on user information including the clothing purchase information and the user's personal information, and the user taste reflecting unit determines the preference level to generate a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category, and the regional trend reflector generates the clothing set based on the taste and regional information including the user's residence information. A first recommended clothing set is generated as a second recommended clothing set including a plurality of second recommended clothing sets, and the relationship score calculation unit includes clothing pre-purchased by the user and the second recommended clothing based on the clothing purchase history. calculates a relationship score representing a degree of matching suitability between the sets of clothes, the transceiver transmits the second recommended clothing set and a relationship score to a user terminal of the user, and the transceiver transmits at least one of the plurality of second recommended clothing sets to the user terminal. Receiving a clothing selection signal and preferred clothing fit information, the size recommendation unit recommends a recommended size of a selected clothing product included in the selection signal based on the user information and the preferred clothing fit information, and the transceiver , the recommended size may be transmitted to the user terminal.

A clothing and size recommendation method according to an embodiment includes transmitting, by a transceiver, user personal data of a user account stored in a memory to a server; receiving, by the transceiver, a second recommended clothing set and a relationship score recommended from the server; transmitting, by the transceiver, a selection signal for at least one garment among a plurality of second recommended garments and preferred garment fit information; receiving, by the transceiver, a recommended size from the server; and displaying the second recommended clothing set and the relationship score by means of a display, wherein the server selects a recommended clothing category based on clothing purchase information including a user's clothing purchase history; Based on user information including clothing purchase information and personal information of the user, a preference level of each item of clothing in the recommended clothing category is calculated, and a plurality of first recommended clothing belonging to the recommended clothing category is selected based on the preference level. A first recommended clothing set including a first recommended clothing set is generated, and the first recommended clothing set includes a plurality of second recommended clothing sets based on the taste level and regional information including the user's residence information. , wherein the relationship score indicating the degree of matching between the clothes pre-purchased by the user and the second recommended clothes is calculated based on the clothing purchase history, and the score is calculated based on the user information and the preferred clothing fit information. The recommended size of the selected clothing product included in the selection signal may be recommended.

A user terminal according to an embodiment includes a processor; transceiver; Memory; and

and a display, the transceiver transmits user personal data of a user account stored in a memory to a server, the transceiver receives a second recommended clothing set and a relationship score recommended from the server, the transceiver comprises a plurality of transmits a selection signal and preferred clothing fit information for at least one of second recommended clothing, the transceiver receives a recommended size from the server, and the display displays the second recommended clothing set and the relationship A score is displayed, a recommended clothing category is selected by the server based on clothing purchase information including a user's clothing purchase history, and the recommended clothing category is selected based on the clothing purchase information and user information including the user's personal information. The taste level of each piece of clothing in the recommended clothing category is calculated, a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category is created based on the taste level, and the taste level and the user's Based on regional information including residence information, the first recommended clothing set is generated as the second recommended clothing set including a plurality of second recommended clothing sets, and clothing pre-purchased by the user based on the clothing purchase history. The relationship score indicating the degree of matching between the and the second recommended clothes may be calculated, and the recommended size of the selected clothing product included in the selection signal may be recommended based on the user information and the preferred clothing fit information.

In addition, the clothing purchase information includes the user's interest product information and click product information, and the operation of selecting a recommended clothing category is performed by the clothing category selection unit to include clothing categories including tops, bottoms, shoes, and outerwear. calculating a category determination score for each item of clothing based on the clothing purchase history, the interest product information, and the click product information; and selecting, by the clothing category selection unit, a clothing category having the highest category determination score as the recommended clothing category. may further include.

The clothing category selection unit may calculate the category determination score by Equation 1 below.

[Equation 1]

In Equation 1, Category Decision Score represents the category decision score, C is an identifier for identifying purchased clothes of a specific category of the user, M_p represents the purchase price of clothes corresponding to C, and InterItems is the user's It may represent the number of items of interest for clothes corresponding to a specific category, and N_c may represent the number of times the user clicks to read clothes corresponding to a specific category.

A clothing and size recommendation system according to an embodiment includes a user terminal; and a server, wherein the user terminal transmits user personal data of a user account stored in a memory to the server, wherein the server selects a recommended clothing category based on clothing purchase information including a user's clothing purchase history; Based on user information including clothing purchase information and personal information of the user, a preference level of each item of clothing in the recommended clothing category is calculated, and a plurality of first recommended clothing items belonging to the recommended clothing category are selected based on the preference level. A first recommended clothing set is generated, and the first recommended clothing set is converted into a second recommended clothing set including a plurality of second recommended clothing based on the taste and regional information including the user's residence information. and calculates a relationship score indicating a degree of matching between the clothes already purchased by the user and the second recommended clothes based on the clothes purchase history, and calculates a relationship score between the second recommended clothes set and the user terminal of the user. and the user terminal transmits a selection signal for at least one of the plurality of second recommended clothes and preferred clothing fit information to the server, and the server transmits the user information and the preferred clothing fit information based on the preferred clothing fit information. Accordingly, a recommended size of the selected clothing product included in the selection signal may be recommended, and the recommended size may be transmitted to the user terminal.

Embodiments of the present invention have technical effects in terms of proposing an enhanced clothing and size recommendation method and a server for performing the method.

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

1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.
2 is a diagram showing the configuration of a program according to an embodiment.
3 is a schematic diagram illustrating a system for recommending clothes and sizes according to an exemplary embodiment.
4 is a flowchart illustrating an example of an operation of generating a first recommended clothing set according to an exemplary embodiment.
5 is a flowchart illustrating an example of an operation of generating a second recommended clothing set and a relationship score according to an embodiment.
6 is a flowchart illustrating an example of an operation of calculating a recommended size according to an embodiment.
7 is a flowchart illustrating an example of a specific operation of calculating a preference degree according to an exemplary embodiment.
8 is a diagram illustrating a recommended clothing set displayed on a display of a user terminal according to an exemplary embodiment.
9 is a diagram illustrating an example in which a user terminal outputs a recommended size of a selected clothing product according to an exemplary embodiment.
10 is a diagram showing the configuration of a 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 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 components or features of one embodiment may be included in another embodiment, or may be replaced with corresponding components 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 by those skilled in the art are not described. did

Throughout the specification, when a part is said to "comprising" or "including" a certain element, it means that it may further include other elements, not excluding other elements, unless otherwise stated. do. In addition, terms such as “… unit”, “… unit”, 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 terms are used herein in the context of describing various embodiments (particularly in the context of the claims below). Unless otherwise indicated or clearly contradicted by context, both the singular and the plural can be used.

Hereinafter, embodiments according to various embodiments will be described in detail with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The detailed description set forth below in conjunction with the accompanying drawings is intended to describe exemplary embodiments of various embodiments, and is not intended to represent a single embodiment.

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

1 is a diagram illustrating a configuration of an electronic device according to an exemplary embodiment.

1 is a block diagram of an electronic device 101 within a network environment 100, according to various embodiments. Referring to FIG. 1 , in a network environment 100, an electronic device 101 communicates with an electronic device 102 through a first network 198 (eg, a short-range wireless communication network) or through a second network 199. It may communicate with at least one of the electronic device 104 or the server 108 through (eg, a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108 . According to an embodiment, the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or the antenna module 197 may be included. In some embodiments, in the electronic device 101, at least one of these components (eg, the connection terminal 178) may be omitted or one or more other components may be added. In some embodiments, some of these components (eg, sensor module 176, camera module 180, or antenna module 197) are integrated into a single component (eg, display module 160). It can be. The electronic device 101 may also be referred to as a client, terminal, or peer.

The processor 120, for example, executes software (eg, the program 140) to cause at least one other component (eg, hardware or software component) of the electronic device 101 connected to the processor 120. It can control and perform various data processing or calculations. According to one embodiment, as at least part of data processing or operation, the processor 120 transfers instructions or data received from other components (e.g., sensor module 176 or communication module 190) to volatile memory 132. , processing commands or data stored in the volatile memory 132 , and storing resultant data in the non-volatile memory 134 . According to one embodiment, the processor 120 may include a main processor 121 (eg, a central processing unit or an application processor) or a secondary processor 123 (eg, a graphic processing unit, a neural network processing unit ( NPU: neural processing unit (NPU), image signal processor, sensor hub processor, or communication processor). For example, when the electronic device 101 includes the main processor 121 and the auxiliary processor 123, the auxiliary processor 123 may use less power than the main processor 121 or be set to be specialized for a designated function. can The secondary processor 123 may be implemented separately from or as part of the main processor 121 .

The secondary processor 123 may, for example, take the place of the main processor 121 while the main processor 121 is in an inactive (eg, sleep) state, or the main processor 121 is active (eg, running an application). ) state, together with the main processor 121, at least one of the components of the electronic device 101 (eg, the display module 160, the sensor module 176, or the communication module 190) It is possible to control at least some of the related functions or states. According to one embodiment, the auxiliary processor 123 (eg, image signal processor or communication processor) may be implemented as part of other functionally related components (eg, camera module 180 or communication module 190). there is. According to an embodiment, the auxiliary processor 123 (eg, a neural network processing device) may include a hardware structure specialized for processing an artificial intelligence model. AI models can be created through machine learning. Such learning may be performed, for example, in the electronic device 101 itself where the artificial intelligence model is performed, or may be performed through a separate server (eg, the server 108). The learning algorithm may include, for example, supervised learning, unsupervised learning, semi-supervised learning or reinforcement learning, but in the above example Not limited. The artificial intelligence model may include a plurality of artificial neural network layers. Artificial neural networks include deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), restricted boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), It may be one of deep Q-networks or a combination of two or more of the foregoing, but is not limited to the foregoing examples. The artificial intelligence model may include, in addition or alternatively, software structures in addition to hardware structures.

The memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101 . The data may include, for example, input data or output data for software (eg, program 140) and commands related thereto. The memory 130 may include volatile memory 132 or non-volatile memory 134 .

The program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142 , middleware 144 , or an application 146 .

The input module 150 may receive a command or data to be used by a component (eg, the processor 120) of the electronic device 101 from the outside of the electronic device 101 (eg, a user). The input module 150 may include, for example, a microphone, a mouse, a keyboard, a key (eg, a button), or a digital pen (eg, a stylus pen).

The sound output module 155 may output sound signals to the outside of the electronic device 101 . The sound output module 155 may include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback or recording playback. A receiver may be used to receive an incoming call. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.

The display module 160 may visually provide information to the outside of the electronic device 101 (eg, a user). The display module 160 may include, for example, a display, a hologram device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module 160 may include a touch sensor set to detect a touch or a pressure sensor set to measure the intensity of force generated by the touch.

The audio module 170 may convert sound into an electrical signal or vice versa. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device connected directly or wirelessly to the electronic device 101 (eg: Sound may be output through the electronic device 102 (eg, a speaker or a headphone).

The sensor module 176 detects an operating state (eg, power or temperature) of the electronic device 101 or an external environmental state (eg, a user state), and generates an electrical signal or data value corresponding to the detected state. can do. According to one embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a bio sensor, It may include a temperature sensor, humidity sensor, or light sensor.

The interface 177 may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device 101 to an external electronic device (eg, the electronic device 102). According to one embodiment, the interface 177 may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

The connection terminal 178 may include a connector through which the electronic device 101 may be physically connected to an external electronic device (eg, the electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).

The haptic module 179 may convert electrical signals into mechanical stimuli (eg, vibration or motion) or electrical stimuli that a user may perceive through tactile or kinesthetic senses. According to one embodiment, the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

The camera module 180 may capture still images and moving images. According to one embodiment, the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.

The power management module 188 may manage power supplied to the electronic device 101 . According to one embodiment, the power management module 188 may be implemented as at least part of a power management integrated circuit (PMIC), for example.

The battery 189 may supply power to at least one component of the electronic device 101 . According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary cell, a rechargeable secondary cell, or a fuel cell.

The communication module 190 is a direct (eg, wired) communication channel or a wireless communication channel between the electronic device 101 and an external electronic device (eg, the electronic device 102, the electronic device 104, or the server 108). Establishment and communication through the established communication channel may be supported. The communication module 190 may include one or more communication processors that operate independently of the processor 120 (eg, an application processor) and support direct (eg, wired) communication or wireless communication. According to one embodiment, the communication module 190 is a wireless communication module 192 (eg, a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module 194 (eg, : a local area network (LAN) communication module or a power line communication module). Among these communication modules, a corresponding communication module is a first network 198 (eg, a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network 199 (eg, legacy It may communicate with the external electronic device 104 through a cellular network, a 5G network, a next-generation communication network, the Internet, or a telecommunications network such as a computer network (eg, a LAN or a WAN). These various types of communication modules may be integrated as one component (eg, a single chip) or implemented as a plurality of separate components (eg, multiple chips). The wireless communication module 192 uses subscriber information (eg, International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199. The electronic device 101 may be identified or authenticated.

The wireless communication module 192 may support a 5G network after a 4G network and a next-generation communication technology, for example, NR access technology (new radio access technology). NR access technologies include high-speed transmission of high-capacity data (enhanced mobile broadband (eMBB)), minimization of terminal power and access of multiple terminals (massive machine type communications (mMTC)), or high reliability and low latency (ultra-reliable and low latency (URLLC)). -latency communications)) can be supported. The wireless communication module 192 may support a high frequency band (eg, mmWave band) to achieve a high data transmission rate. The wireless communication module 192 uses various technologies for securing performance in a high frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. Technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna may be supported. The wireless communication module 192 may support various requirements defined for the electronic device 101, an external electronic device (eg, the electronic device 104), or a network system (eg, the second network 199). According to one embodiment, the wireless communication module 192 is a peak data rate for eMBB realization (eg, 20 Gbps or more), a loss coverage for mMTC realization (eg, 164 dB or less), or a U-plane latency for URLLC realization (eg, Example: downlink (DL) and uplink (UL) each of 0.5 ms or less, or round trip 1 ms or less) may be supported.

The antenna module 197 may transmit or receive signals or power to the outside (eg, an external electronic device). According to one embodiment, the antenna module 197 may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (eg, PCB). According to one embodiment, the antenna module 197 may include a plurality of antennas (eg, an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network such as the first network 198 or the second network 199 is selected from the plurality of antennas by the communication module 190, for example. can be chosen A signal or power may be transmitted or received between the communication module 190 and an external electronic device through the selected at least one antenna. According to some embodiments, other components (eg, a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module 197 in addition to the radiator.

According to various embodiments, the antenna module 197 may form a mmWave antenna module. According to one embodiment, the mmWave antenna module includes a printed circuit board, an RFIC disposed on or adjacent to a first surface (eg, a lower surface) of the printed circuit board and capable of supporting a designated high frequency band (eg, mmWave band); and a plurality of antennas (eg, array antennas) disposed on or adjacent to a second surface (eg, a top surface or a side surface) of the printed circuit board and capable of transmitting or receiving signals of the designated high frequency band. can do.

At least some of the components are connected to each other through a communication method between peripheral devices (eg, a bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)) and signal ( e.g. commands or data) can be exchanged with each other.

According to an embodiment, commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199 . Each of the external electronic devices 102 or 104 may be the same as or different from the electronic device 101 . According to an embodiment, all or part of operations executed in the electronic device 101 may be executed in one or more external electronic devices among the external electronic devices 102 , 104 , or 108 . For example, when the electronic device 101 needs to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device 101 instead of executing the function or service by itself. Alternatively or additionally, one or more external electronic devices may be requested to perform the function or at least part of the service. One or more external electronic devices receiving the request may execute at least a part of the requested function or service or an additional function or service related to the request, and deliver the execution result to the electronic device 101 . The electronic device 101 may provide the result as at least part of a response to the request as it is or additionally processed. To this end, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used. The electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an internet of things (IoT) device. Server 108 may be an intelligent server using machine learning and/or neural networks. According to one embodiment, the external electronic device 104 or server 108 may be included in the second network 199 . The electronic device 101 may be applied to intelligent services (eg, smart home, smart city, smart car, or health care) based on 5G communication technology and IoT-related technology.

The server 108 is connected to the electronic device 101 and can provide a service to the connected electronic device 101 . In addition, the server 108 may proceed with a membership sign-up procedure, store and manage various types of information of users subscribed as members, and provide various purchase and payment functions related to services. In addition, the server 108 may share execution data of service applications executed in each of the plurality of electronic devices 101 in real time so that the service can be shared among users. This server 108 may have the same configuration as a conventional web server or WAP server in terms of hardware. However, in terms of software, it may include a program module that is implemented through any language such as C, C++, Java, Visual Basic, or Visual C and performs various functions. In addition, the server 108 is generally connected to an unspecified number of clients and/or other servers through an open computer network such as the Internet, and receives requests from clients or other servers to perform tasks and derives and provides work results. It means a computer system and the computer software (server program) installed for it. In addition, the server 108, in addition to the above-described server program, a series of application programs operating on the server 108 and various databases (DB: Database, hereinafter referred to as It should be understood as a broad concept including DB"). Accordingly, the server 108 classifies member registration information and various information and data about games, stores them in a DB, and manages them. This DB may be implemented inside or outside the server 108 . In addition, the server 108 uses a server program provided in various ways according to operating systems such as DOS, Windows, Linux, UNIX, and Macintosh in general server hardware. As a representative example, a website used in a Windows environment, Internet Information Server (IIS), and CERN, NCSA, APPACH, etc. used in a Unix environment can be used. In addition, the server 108 may interoperate with an authentication system and a payment system for user authentication of services or payment for purchases related to services.

The first network 198 and the second network 199 are a connection structure capable of exchanging information between nodes such as terminals and servers, or a network connecting the server 108 and the electronic devices 101 and 104. means (Network). The first network 198 and the second network 199 include Internet, Local Area Network (LAN), Wireless Local Area Network (Wireless Local Area Network), Wide Area Network (WAN), Personal Area Network (PAN), and 3G , 4G, LTE, 5G, Wi-Fi, etc., but are not limited thereto. The first network 198 and the second network 199 may be closed first networks 198 and second networks 199 such as LAN and WAN, but are preferably open such as the Internet. The Internet is based on the TCP/IP protocol and several services that exist on its upper layer, such as HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP ( Simple Network Management Protocol), Network File Service (NFS), and Network Information Service (NIS).

A database may have a general data structure implemented in a storage space (hard disk or memory) of a computer system using a database management program (DBMS). The database may have a data storage form in which data can be freely searched for (extracted), deleted, edited, added, and the like. Databases are relational database management systems (RDBMS) such as Oracle, Informix, Sybase, and DB2, or object-oriented database management such as Gemston, Orion, and O2. It can be implemented according to the purpose of an embodiment of the present disclosure using a system (OODBMS) and XML Native Databases such as Excelon, Tamino, and Sekaiju, and its functions may have appropriate fields or elements to achieve.

2 is a diagram showing the configuration of a program according to an embodiment.

2 is a block diagram 200 illustrating a program 140 according to various embodiments. According to one embodiment, the program 140 includes an operating system 142, middleware 144, or an application 146 executable in the operating system 142 for controlling one or more resources of the electronic device 101. can include The operating system 142 may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs 140 are, for example, preloaded in the electronic device 101 at the time of manufacture, or when used by a user, an external electronic device (eg, the electronic device 102 or 104), or a server ( 108)) can be downloaded or updated. All or part of program 140 may include a neural network.

The operating system 142 may control management (eg, allocation or reclamation) of one or more system resources (eg, process, memory, or power) of the electronic device 101 . Operating system 142 may additionally or alternatively include other hardware devices of electronic device 101 , such as input module 150 , sound output module 155 , display module 160 , audio module 170 . , sensor module 176, interface 177, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196, or It may include one or more driver programs for driving the antenna module 197.

The middleware 144 may provide various functions to the application 146 so that the function or information provided from one or more resources of the electronic device 101 may be used by the application 146 . The middleware 144 includes, for example, the application manager 201, the window manager 203, the multimedia manager 205, the resource manager 207, the power manager 209, the database manager 211, and the package manager 213. ), connectivity manager 215, notification manager 217, location manager 219, graphics manager 221, security manager 223, call manager 225, or voice recognition manager 227. can

The application manager 201 may manage the life cycle of the application 146 , for example. The window manager 203 may manage one or more GUI resources used in a screen, for example. The multimedia manager 205 identifies, for example, one or more formats necessary for reproducing media files, and encodes or decodes a corresponding media file among the media files using a codec suitable for the selected format. can be done The resource manager 207 may manage a source code of the application 146 or a memory space of the memory 130 . The power manager 209 may manage, for example, the capacity, temperature, or power of the battery 189, and determine or provide related information necessary for the operation of the electronic device 101 by using corresponding information among them. . According to an embodiment, the power manager 209 may interoperate with a basic input/output system (BIOS) (not shown) of the electronic device 101 .

The database manager 211 may create, search, or change a database to be used by the application 146, for example. The package manager 213 may manage installation or update of applications distributed in the form of package files, for example. The connectivity manager 215 may manage, for example, a wireless connection or a direct connection between the electronic device 101 and an external electronic device. The notification manager 217 may provide a function for notifying a user of occurrence of a designated event (eg, an incoming call, message, or alarm), for example. The location manager 219 may manage location information of the electronic device 101, for example. The graphic manager 221 may manage, for example, one or more graphic effects to be provided to a user or a user interface related thereto.

Security manager 223 may provide system security or user authentication, for example. The telephony manager 225 may manage, for example, a voice call function or a video call function provided by the electronic device 101 . The voice recognition manager 227 transmits, for example, the user's voice data to the server 108, and at least partially based on the voice data, a command corresponding to a function to be performed in the electronic device 101; Alternatively, text data converted at least partially based on the voice data may be received from the server 108 . According to one embodiment, the middleware 244 may dynamically delete some existing components or add new components. According to one embodiment, at least part of the middleware 144 may be included as part of the operating system 142 or may be implemented as separate software different from the operating system 142 .

The application 146 includes, for example, a home 251, a dialer 253, an SMS/MMS 255, an instant message (IM) 257, a browser 259, a camera 261, and an alarm 263. , Contacts (265), Voice Recognition (267), Email (269), Calendar (271), Media Player (273), Albums (275), Watch (277), Health (279) (e.g. exercise or blood sugar) measurement of biometric information) or environmental information 281 (eg, measurement of atmospheric pressure, humidity, or temperature information). According to an embodiment, the application 146 may further include an information exchange application (not shown) capable of supporting information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit designated information (eg, a call, message, or alarm) to an external electronic device, or a device management application configured to manage an external electronic device. there is. The notification relay application, for example, transmits notification information corresponding to a designated event (eg, mail reception) generated in another application (eg, the email application 269) of the electronic device 101 to an external electronic device. can Additionally or alternatively, the notification relay application may receive notification information from an external electronic device and provide the notification information to the user of the electronic device 101 .

The device management application is, for example, a power source (eg, turn-on or turn-off) of an external electronic device that communicates with the electronic device 101 or some component thereof (eg, a display module or a camera module of the external electronic device). ) or functions (eg brightness, resolution, or focus). The device management application may additionally or alternatively support installation, deletion, or update of an application operating in an external electronic device.

Throughout this specification, a neural network, a neural network, and a network function may be used interchangeably. A neural network may consist of a set of interconnected computational units, which may be generally referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network includes at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

In a neural network, two or more nodes connected through a link may form a relative relationship of an input node and an output node. The concept of an input node and an output node is relative, and any node in an output node relationship with one node may have an input node relationship with another node, and vice versa. As described above, the input node to output node relationship can be created around the link. More than one output node can be connected to one input node through a link, and vice versa.

In a relationship between an input node and an output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable, and may be changed by a user or an algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected by respective links to one output node, the output node is set to a link corresponding to values input to input nodes connected to the output node and respective input nodes. An output node value may be determined based on the weight.

As described above, in a neural network, two or more nodes are interconnected through one or more links to form an input node and output node relationship in the neural network. Characteristics of the neural network may be determined according to the number of nodes and links in the neural network, an association between the nodes and links, and a weight value assigned to each link. For example, when there are two neural networks having the same number of nodes and links and different weight values between the links, the two neural networks may be recognized as different from each other.

3 is a schematic diagram illustrating a system for recommending clothes and sizes according to an exemplary embodiment.

According to one embodiment, the system 300 for recommending apparel design and size comprises a server 310, a database 311, a network 320 (eg, the second network 199 of FIG. 1), one or more electronic Devices 330 and 340 (eg, the electronic device 100 of FIG. 1 ) may be included. Here, one or more electronic devices 330 and 340 may include a user terminal 330 and a plurality of local user terminals 340 . Hereinafter, the user terminal 330 and the plurality of local user terminals 340 may be referred to as a user and a plurality of local users, respectively.

The server 310 may select a recommended clothing category for the user. The recommended clothing category may correspond to any one of bottoms, tops, coats, and shoes. Also, in detail, the recommended clothing category may correspond to any one of jackets, jumpers, coats, cardigans, vests, T-shirts, blouses, shirts, knitwear, pants, skirts, one-pieces, two-pieces, and training suits. The server 310 may recommend clothes that the user actually needs by recommending clothes of a category that has not been purchased much recently to the user according to the user's purchase history.

Also, the degree of taste may correspond to a value obtained by quantifying a user's preference for specific clothes. The degree of preference may correspond to a value reflecting a user's purchasing tendency. Also, the degree of preference may represent a user's brand preference.

The relationship score may correspond to a digitized value of matching suitability between clothes already purchased by the user and recommended clothes to be purchased. For example, when the recommended clothing corresponds to a top, a value indicating matching and/or matching degree to a previously purchased bottom may correspond to a relationship score.

Preferred clothing fit information may indicate a preferred clothing wearing fit. Preferred clothing fit information may correspond to any one of tight fit, overfit, basic fit, and loose fit.

The matching color degree may correspond to a value obtained by digitizing the degree of matching of clothing color in comparison with the user's skin color and hair color. For example, if the user's hair color is black, black clothes may correspond to a matching color degree of 9. In addition, when the user's skin color corresponds to copper, light green clothing may have a matching color degree of 4 or less.

The brand similarity may represent a similarity between a brand of clothing previously purchased by the user and a brand of recommended clothing. Brand similarity can be determined by the value of the brand, the selling price of clothing, and the purchasing age group. Also, brand similarity may correspond to a natural number between 1 and 100. In addition, the lower the brand similarity value, the more similar the brands may be.

The carryover preference may correspond to a digitized value of a user's degree of preference for carried over clothing. Also, the carryover preference may be calculated based on the release year and the purchase year of purchased clothing in the clothing purchase information.

The regional tendency score may indicate purchasing tendency of people in the region where the user resides. For example, when a user lives in a cold region, a high regional tendency score may be calculated for thick wool coats, gloves, scarves, and the like.

A user 350 and a plurality of local users 340 can communicate with the server as clients and participate in the method. The client is capable of exchanging information through communication with a server, and may be a general user terminal. The user terminal means a hardware device including a wireless communication function including at least one processor, and may be understood as encompassing a software configuration operating in a corresponding hardware device according to an embodiment. For example, a user terminal may be understood as including a smartphone, a mobile phone, a hand-held device, a tablet PC, a desktop computer, a laptop computer, and user clients and applications running on each device. It is not limited thereto.

4 is a flowchart illustrating an example of an operation of managing a clothing design project according to an exemplary embodiment.

According to an embodiment, in operation S401, the server 310 may receive user personal data of a user account from a user terminal through a transceiver. The user's personal data may include the user's name, age, gender, height, weight, and the like. In addition, the server 310 may store the received user personal data in the database 311 .

In operation S402 , the server 310 may select a recommended clothing category based on clothing purchase information including a user's clothing purchase history by the clothing category selector. The clothing purchase history may include information on clothing purchased by the user within a preset period. For example, the clothing purchase history may include a purchase price of the purchased clothing, a clothing category, and the like. Also, the clothing purchase information may include user interest product information and click product information. The product of interest may include clothes that the user did not purchase but added to the interest list. In addition, the click product information may include the number of times the user clicks to view detailed product information.

In an embodiment, the server 310 may select a recommended clothing category based on the clothing purchase history, the interest product information, and click product information by the clothing category selector.

In addition, the server 310 may calculate the category determination score for each clothing category by Equation 1 below.

[Equation 1]

In Equation 1, Category Decision Score represents a category decision score, C is an identifier identifying purchased clothing of a specific category of the user, M_p represents a purchase price of clothing corresponding to the identifier C, and InterItems may represent the number of items of interest for clothes corresponding to the user's specific category, and N_c may represent the number of clicks on clothes corresponding to the user's specific category.

For example, the server 310 may calculate the category decision score for each category, such as tops, bottoms, shoes, and coats. Any one of top, bottom, shoes, and outerwear may correspond to the specific category. Here, the server 310 may select a clothing category having a high category determination score as the recommended clothing category.

The server 310 adds up the purchase prices of the purchased clothes of the specific category and subtracts them from the category determination score, thereby excluding categories that the user has purchased relatively more from the recommendation category. Through this, the category determination score may be calculated low for clothes that the user spends a lot on or buys a lot of times. Contrary to purchased clothing, the server 310 may recommend a clothing category to which a product the user is interested in or wants to purchase belongs, by reflecting the product of interest and the number of clicks to view to the category determination score. there is. The server 310 may recommend clothes of a category in which the user is interested and/or interested by accurately reflecting the user's current consumption status and clothes of interest to the category determination score.

In operation S403, the server 310 may calculate a preference degree of each clothing of the recommended clothing category based on user information including the clothing purchase information and the user's personal information by a user preference calculator. there is. The server 310 may calculate the degree of preference for each of the clothes belonging to the recommended clothing category. A method of calculating the degree of preference will be described in detail with reference to FIG. 7 .

In operation S404, the server 310 may generate a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category based on the degree of preference by the user taste reflecting unit. The server 310 may create a first recommended clothing set by arranging the plurality of first recommended clothing items in order of preference.

5 is a flowchart illustrating an example of an operation of generating a second recommended clothing set and a relationship score according to an embodiment.

According to an embodiment, in operation S501, the server 310 selects the first recommended clothing set in a plurality of sets based on the taste and regional information including the user's residence information by the regional trend reflecting unit. A second recommended clothing set including second recommended clothing may be created.

Here, the server 310 may calculate a regional tendency score of each of the plurality of first recommended clothes included in the first recommended clothing set based on the taste and the residence information by the regional trend reflecting unit. .

In addition, the server 310 may select a resident with a preference based on the degree of taste and the residence information. Specifically, the server 310 classifies into a plurality of local residents residing in the area included in the area information, compares the taste level of each of the plurality of area residents with the user's taste level, and among the plurality of area residents A local resident having a difference in taste within a preset range from the user may be set as a nearby taste resident. The neighborhood preference residents may include one or more local residents.

In addition, the server 310 may reset residents having the same sex, age, height as the user, or a difference within a preset range from the user as proximity preference residents based on the user's gender, age, and height included in the user's personal data. there is.

The server 310 may request resident purchase information from the resident terminal of the resident of the neighboring preference. The resident terminal may correspond to an electronic device used by a proximity taste resident, and the resident terminal may include a plurality of proximity taste residents and/or a plurality of terminals.

The server 310 receives the resident purchase information from the resident terminal, and based on the resident purchase information, the plurality of first recommendations included in the first recommended clothing set in the order of clothes most purchased by the residents of the proximity taste. A regional trend score for each of the garments can be calculated.

Also, the server 310 may generate a second recommended clothing set excluding the first clothing from the first recommended clothing set based on the regional tendency score by the regional trend reflecting unit. The first clothing may correspond to clothing having a regional tendency score equal to or less than a threshold value.

In addition, the server 310 may generate a second recommended clothing set by adding a second clothing to the first recommended clothing set based on the regional tendency score by the regional trend reflecting unit. The second clothing may correspond to clothing whose regional tendency score exceeds a threshold value.

For example, the server 310 includes first recommended clothing purchased by the proximate taste residents exceeding a threshold value among the first recommended clothing sets in a second recommended clothing set, and the proximate taste residents are less than or equal to the threshold value. The first recommended clothing purchased with may be excluded from the second recommended clothing set.

As described above, the server 310 selects residents of the area where the user resides, and generates the first recommended clothing set as the second recommended clothing set based on the purchase history of the residents, thereby providing information about the area where the user resides. We can recommend clothing that reflects fashion trends. In addition, clothing that meets the user's taste may be recommended by selecting residents having the same or similar taste level as the user rather than simple local residents as neighboring taste residents.

In addition, the server 310 primarily selects residents who live in the same residence as the user, and secondarily selects residents based on the degree of taste and selects residents with close taste. We can help you find clothes that match your taste without spending too much time or money. In addition, the present invention can contribute to the development of online shopping malls by increasing the probability that a user will purchase clothes.

In operation S502, the server 310 may calculate a relationship score representing a degree of matching between the clothing previously purchased by the user and the second recommended clothing based on the clothing purchase history by the relationship score calculation unit. The clothing purchase information including the clothing purchase history may include information on a first color, a first material, and a first dress code of clothing purchased by the user within a predetermined period of time.

In one embodiment, the server 310 is based on the first color, first dress code information and the second color, second material, and second dress code information of the second recommended clothing by the relationship score calculation unit. The relationship score can be calculated.

The server 310 may calculate the relationship score using Equation 2 below by the relationship score calculation unit.

[Equation 2]

In Equation 2, RelScore is a relationship score, i is a natural number identifying the second recommended clothing, n is the total number of the second recommended clothing, RelScore is a relationship score, Col is a color relationship score, Tex is a texture relationship score, DrCode is a dress code score, t_c is the current time, and t_p is the ith second recommended clothing It can indicate the time of purchase.

The server 310 may calculate the degree of color matching between the first color and the second color as the color relationship score based on the reference color table. The reference color table may correspond to a reference table pre-stored in the server. Also, the reference color table may be set as a reference table for calculating a matching score according to color comparison. For example, if black color is the reference color, white color may be set close to the reference table and calculated as a plus score. Also, if the yellow color is the reference color, the red color may be set away from the reference table and counted as a negative score. The server 310 may calculate the material relation score in the same way as the color relation score was calculated based on the pre-stored reference material table.

The server 310 may calculate the dress code score based on the dress code of each garment. The dress code may include "party", "business meeting", "travel", "interview", "seminar", "date", "wedding", and the like. For example, when the first dress code corresponds to "party" and the second dress code corresponds to "business meeting," the server 310 may calculate the dress code score as a negative value. In addition, the server 310 may calculate the dress code score as a positive value when the first dress code corresponds to "work meeting" and the second dress code corresponds to "seminar".

The server 310 calculates a relationship score with pre-purchased clothes together with the second set of recommended clothes and provides the calculated score to the user, so that the user can purchase suitable and/or matching clothes without looking at the clothes already purchased. can In addition, the present invention not only classifies and recommends clothes according to the user's taste, but also compares the recommended clothes with previously purchased clothes and provides a relationship score, helping the user to wear the purchased clothes frequently. . Through this, the user's satisfaction with purchased clothes may be improved. In addition, by increasing the user's purchase possibility, the present invention can activate the online clothing sales market.

In operation S503, the server 310 may transmit the second recommended clothing set and the relationship score to the user terminal of the user.

6 is a flowchart illustrating an example of an operation of calculating a recommended size according to an embodiment.

According to an embodiment, in operation S601, the server 310 receives a selection signal for at least one of the plurality of second recommended clothes included in the second recommended clothing set and a preferred clothing fit from the user terminal. information can be received. The selection signal may include selection information on clothing selected by the user through the user terminal. The preferred clothing fit information may include tight fit, overfit, basic fit, and loose fit.

In operation S602, the server 310 may calculate a recommended size of the selected clothing product included in the selection signal based on the user information and the preferred clothing fit information. The server 310 may calculate an appropriate size based on the user's height and weight included in the user information and review information on the selected clothing product. The review information may correspond to product buyers' wearing reviews. The server 310 may calculate an appropriate clothing size based on the user's height and weight. For example, if the user's height is 163 and the weight is 60, the server 310 selects an appropriate user-customized size among sizes such as "XS", "S", "M", "L", and "XL". It can be calculated as "M". The review information may include wearing reviews for sizes such as "come out small", "come out large", "appropriate", and the server 310, when "come out small" corresponds to the review information, the user You can set the custom size one size larger.

Also, the server 310 may calculate a recommended size obtained by adjusting the appropriate size based on the preferred clothing fit information. For example, when a user selects a tight fit, the server 310 may set the recommended size smaller than the proper size by one step. In addition, in the case of an overfit, the server 310 may set the recommended size one step larger than the proper size.

The server 310 sets the recommended size based on the user's body shape and other users' wearing reviews as well as the user's preferred fit information, so the present invention can give the user greater satisfaction than a method of recommending a general size. there is.

In operation S603, the server 310 may transmit the recommended size to the user terminal through the transceiver.

7 is a flowchart illustrating an example of a specific operation of calculating a preference degree according to an exemplary embodiment.

According to an embodiment, in operation S701, the server 310 indicates a color contrast of each of the first recommended clothes based on the skin color and hair color of the user information by the user taste reflecting unit. A matching color degree can be calculated. The degree of contrast of the colors may mean a degree of color matching. The user taste reflecting unit sets a reference table for calculating a matching color degree according to color comparison in relation to skin color and hair color, and the user taste reflecting unit sets a color group matching the user's skin color and hair color. A matching degree for each color of the first recommended clothes may be determined based on the set reference table.

Also, based on the reference table, a matching color degree for any one color of the first recommended clothes in comparison with the skin color and hair color of the user may be calculated in correspondence with the matching degree. The matching color degree may correspond to numbers from 1 to 10.

In operation S702, the server 310 may calculate a brand similarity of each of the first recommended clothes based on the price and brand information of the clothes to be purchased in the clothes purchase information by the user taste reflecting unit.

The server 310 may calculate the brand similarity using Equation 3 below by the user taste reflecting unit.

[Equation 3]

In Equation 3, Brand similarity represents brand similarity, P_p is the average selling price of the user's main purchased clothing brand, P_s is the price of the first recommended clothing, V is the brand of the first recommended clothing, A_p may represent the age group of the main customer group of the user's main purchased clothing brand, and A_s may represent the age group of the main customer group of the first recommended clothing.

In operation S703 , the server 310 may calculate the carryover preference of the user based on the release year and purchase year of purchased clothing in the clothing purchase information by the user preference reflection unit. The server 310 may calculate the carryover period of the purchased clothes by subtracting the release year from the purchase year of the purchased clothes.

The server may calculate the carryover preference using Equation 4 below, by the user taste reflection unit.

[Equation 4]

In Equation 4, Carryover preference represents a carryover preference, i is an identifier of clothes purchased by the user, n is the total number of clothes purchased by the user within a predetermined period, and Y_ip is the i-th purchase by the user. The clothing purchase year, Y_ir, may indicate a release year of the i-th clothing purchased by the user.

In operation S704, the server 310 may calculate a preference based on the user matching color degree, the brand similarity, and the carryover preference by the user taste reflection unit. The degree of preference may be calculated for each piece of clothing in the recommended clothing category. The server 310 may calculate the degree of preference using Equation 5 below by the user taste reflecting unit.

[Equation 5]

In Equation 5, T represents taste, M_c represents the user matching color, Y_c represents the current year, Y_r represents the release year of the first recommended clothing, C represents the user's carryover preference, B may indicate brand similarity.

In this way, the present invention can recommend clothing similar to the user's taste by calculating a high degree of preference for the brand that the user mainly purchases. In addition, by reflecting the carryover preference to the degree of taste, the user's consumption habit and preferred clothing may be accurately determined, and clothing suitable for the user's taste and consumption may be recommended.

8 is a diagram illustrating a recommended clothing set displayed on a display of a user terminal according to an exemplary embodiment.

The user terminal 800 may receive a second recommended clothing set from the server 300 and display the second recommended clothing set on the display 801 . The second recommended clothing set may be arranged in an ascending order of a relationship score indicating a degree of matching between the clothing previously purchased by the user and the second recommended clothing. Score of FIG. 8 may represent the relationship score. The user terminal 800 may display the second recommended clothing set together with the relationship score, so that the user can easily select clothes matching and/or suitable for recently purchased clothes.

9 is a diagram illustrating an example in which a user terminal outputs a recommended size of a selected clothing product according to an exemplary embodiment.

FIG. 9 is a view showing detailed information about the clothing selected by the user received from the server 300 by the user terminal 900 when the user selects clothing displayed at the top of FIG. 8 . The preferred clothing fit information may correspond to the preferred fit shown on the display 901 . The user may select and/or touch any one of "tight", "over", "basic", and "loose" through the user terminal 900 . 9 may correspond to a case where the user selects a basic fit as a preferred fit. When a preferred fit is selected by the user, the server 300 may calculate a recommended size based on user information and preferred clothing fit information, and transmit the recommended size to the user terminal 900 . The user terminal 900 may display the recommended size received from the server as "M".

10 is a diagram illustrating a configuration of a server 310 according to an embodiment.

The server 1001 (eg, the server 108 of FIG. 1 or the server 310 of FIG. 3 ) may include a memory 1001 , a processor 1002 , and a transceiver 1003 . The transceiver 1003 may include a transceiver.

The memory 1001 includes ROM, RAM, non-volatile memory, volatile memory, hard disk drive (HDD), solid state drive (SSD), USB memory, flash memory card, and various types of removable and portable devices corresponding thereto. A storage medium (eg, a secure digital (SD) memory card, a microSD memory card, an ISO 7816 standard storage device, etc.), but is not limited thereto, and may include all types of storage devices for storing data. The memory 1001 may be referred to as a storage device.

The processor 1002 may include a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit (PLU), a microprocessor, or instructions. Like any other device capable of executing and responding, it may be implemented using one or more general purpose or special purpose computers. The processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

The embodiments described above may be implemented as hardware components, software components, and/or a combination of hardware components and software components. For example, the devices, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), programmable logic units (PLUs), microprocessors, or any other device capable of executing and responding to instructions. A processing device may run an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of software. For convenience of understanding, there are cases in which one processing device is used, but those skilled in the art will understand that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that it can include. For example, a processing device may include a plurality of processors or a processor and a controller. Other processing configurations are also possible, such as parallel processors.

Software may include a computer program, code, instructions, or a combination of one or more of the foregoing, which configures a processing device to operate as desired or processes independently or collectively. The device can be commanded. Software and/or data may be any tangible machine, component, physical device, virtual equipment, computer storage medium or device, intended to be interpreted by or provide instructions or data to a processing device. , or may be permanently or temporarily embodied in a signal wave to be transmitted. Software may be distributed on networked computer systems and stored or executed in a distributed manner. Software and data may be stored on one or more computer readable media.

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

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

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

Claims (10)

receiving, by the transceiver, user personal data of the user account from the user terminal;
selecting a recommended clothing category based on clothing purchase information including a user's clothing purchase history by a clothing category selector;
calculating, by a user taste degree calculation unit, a taste level of each piece of clothing in the recommended clothing category based on user information including the clothing purchase information and the user's personal information;
generating a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category based on the degree of preference by a user taste reflecting unit;
generating a second recommended clothing set including a plurality of second recommended clothing sets from the first recommended clothing set based on the regional information including the taste and the user's residence information by a regional trend reflector;
calculating, by a relationship score calculation unit, a relationship score representing a degree of matching between the clothing previously purchased by the user and the second recommended clothing based on the clothing purchase history;
The transceiver transmits the second recommended clothing set and the relationship score to a user terminal of the user, and from the user terminal, a selection signal for at least one of the plurality of second recommended clothing and a preferred clothing fit receiving information;
recommending, by a size recommendation unit, a recommended size of a selected clothing product included in the selection signal based on the user information and the preferred clothing fit information; and
transmitting, by the transceiver, the recommended size to the user terminal;
Apparel and size recommendation method including.
According to claim 1,
The operation of calculating the degree of preference,
calculating, by the user taste reflector, a user matching color degree indicating a degree of contrast between colors of the first recommended clothes based on the skin color and hair color of the user information;
calculating a brand similarity of each of the first recommended clothes based on the price and brand information of the clothes to be purchased in the clothes purchase information by the user taste reflecting unit;
calculating a carryover preference of the user based on a release year and a purchase year of purchased clothing in the clothing purchase information by the user preference reflecting unit; and
calculating a preference degree based on the user matching color degree, the brand similarity degree, and the carryover preference by the user taste reflection unit;
Apparel and size recommendation method including.
According to claim 1,
The operation of generating the first recommended clothing set as the second recommended clothing set,
calculating a regional tendency score of each of the plurality of first recommended clothes included in the first recommended clothing set based on the taste and the residence information by the regional tendency reflecting unit;
generating a second recommended clothing set excluding the first clothing from the first recommended clothing set based on the regional tendency score by the regional tendency reflecting unit; and
generating a second recommended clothing set by adding a second clothing from the first recommended clothing set based on the regional tendency score by the regional tendency reflecting unit; Including more,
The first clothing corresponds to clothing having the regional tendency score equal to or less than the threshold value, and the second clothing corresponds to clothing having the regional tendency score exceeding the threshold value.
How to recommend clothing and sizes.
According to claim 1,
The clothing purchase information includes information on a first color, a first material, and a first dress code of clothing purchased by the user within a predetermined period of time;
The operation of calculating the relationship score,
The relationship score is calculated by the relationship score calculation unit based on the first color, first material, and first dress code information and the second color, second material, and second dress code information of the second recommended clothing. action;
Apparel and size recommendation method including.
According to claim 1,
The preferred clothing fit information includes tight fit, over fit, basic fit, and loose fit,
The operation of recommending the recommended size is,
calculating an appropriate size based on the height and weight of the user included in the user information and review information on the selected clothing product; and
calculating a recommended size obtained by adjusting the appropriate size based on the preferred clothing fit information;
Apparel and size recommendation method including.
According to claim 1,
transceiver;
apparel category selection department;
a user preference degree calculation unit;
user taste reflection unit;
Local trend reflection unit;
a relationship score calculator; and
Including a size calculator,
The transceiver receives user personal data of a user account from a user terminal;
The clothing category selector selects a recommended clothing category based on clothing purchase information including a user's clothing purchase history;
The user preference degree calculation unit calculates a preference degree of each clothing of the recommended clothing category based on user information including the clothing purchase information and the user's personal information;
The user taste reflecting unit generates a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category based on the degree of preference;
The regional trend reflecting unit generates a second recommended clothing set including a plurality of second recommended clothing sets from the first recommended clothing set based on the taste and regional information including the user's residence information;
The relationship score calculation unit calculates a relationship score representing a degree of matching between the clothing previously purchased by the user and the second recommended clothing based on the clothing purchase history;
The transceiver transmits the second recommended clothing set and the relationship score to the user terminal of the user, and receives a selection signal for at least one of the plurality of second recommended clothing and preferred clothing fit information from the user terminal. receive,
The size recommendation unit recommends a recommended size of a selected clothing product included in the selection signal based on the user information and the preferred clothing fit information;
The transceiver transmits the recommended size to the user terminal.
server.
sending, by the transceiver, user personal data of the user account stored in the memory to the server;
receiving, by the transceiver, a second recommended clothing set and a relationship score recommended from the server;
transmitting, by the transceiver, a selection signal for at least one garment among a plurality of second recommended garments and preferred garment fit information;
receiving, by the transceiver, a recommended size from the server; and
and displaying the second recommended clothing set and the relationship score on a display,
The server selects a recommended clothing category based on clothing purchase information including a user's clothing purchase history, and clothing of the recommended clothing category based on the clothing purchase information and user information including the user's personal information. Each preference level is calculated, a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category is generated based on the preference level, and the preference level and the user's residence information are generated. Based on region information, the first recommended clothing set is generated as the second recommended clothing set including a plurality of second recommended clothing sets, and based on the clothing purchase history, the user's pre-purchased clothing and the second recommended clothing set. The relationship score representing the matching suitability between clothes is calculated, and the recommended size of the selected clothing product included in the selection signal is recommended based on the user information and the preferred clothing fit information.
Apparel and size recommendation method including.
processor;
transceiver;
Memory; and
including a display;
The transceiver transmits the user personal data of the user account stored in the memory to the server;
The transceiver receives a second recommended clothing set and a relationship score recommended from the server;
The transceiver transmits a selection signal and preferred clothing fit information for at least one of a plurality of second recommended clothing;
The transceiver receives a recommended size from the server,
The display displays the second recommended clothing set and the relationship score;
The server selects a recommended clothing category based on clothing purchase information including a user's clothing purchase history, and clothing of the recommended clothing category based on the clothing purchase information and user information including the user's personal information. Each preference level is calculated, a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category is generated based on the preference level, and the preference level and the user's residence information are generated. Based on region information, the first recommended clothing set is generated as the second recommended clothing set including a plurality of second recommended clothing sets, and based on the clothing purchase history, the user's pre-purchased clothing and the second recommended clothing set. The relationship score representing the matching suitability between clothes is calculated, and the recommended size of the selected clothing product included in the selection signal is recommended based on the user information and the preferred clothing fit information.
user terminal.
According to claim 1,
The clothing purchase information includes the user's interest product information and click product information;
The operation of selecting the recommended clothing category is
calculating, by the clothing category selecting unit, a category determination score for each of the clothing categories including tops, bottoms, shoes, and outerwear based on the clothing purchase history, the interest product information, and the click product information; and
selecting, by the clothing category selector, a clothing category having the highest category determination score as the recommended clothing category; Including more,
The clothing category selection unit calculates the category determination score by Equation 1 below,
[Equation 1]

In Equation 1, Category Decision Score represents the category decision score, C is an identifier for identifying purchased clothes of a specific category of the user, M_p represents the purchase price of clothes corresponding to C, and InterItems is the user's Represents the number of interested products of clothing corresponding to a specific category, and N_c represents the number of clicks on clothing corresponding to the specific category of the user,
How to recommend clothing and sizes.
user terminal; and
including the server;
The user terminal transmits the user's personal data of the user account stored in the memory to the server;
The server selects a recommended clothing category based on clothing purchase information including a user's clothing purchase history, and selects clothing items of each recommended clothing category based on user information including the clothing purchase information and personal information of the user. A preference degree is calculated, a first recommended clothing set including a plurality of first recommended clothing belonging to the recommended clothing category is generated based on the taste level, and local information including the taste degree and the user's residence information. Based on the above, the first recommended clothing set is generated as a second recommended clothing set including a plurality of second recommended clothing sets, and a relationship between clothing pre-purchased by the user and the second recommended clothing based on the clothing purchase history is generated. Calculating a relationship score indicating matching suitability, and transmitting the second recommended clothing set and the relationship score to the user's user terminal;
The user terminal transmits a selection signal and preferred clothing fit information for at least one of the plurality of second recommended clothing to a server;
The server recommends a recommended size of a selected clothing product included in the selection signal based on the user information and the preferred clothing fit information, and transmits the recommended size to the user terminal.
Apparel and size recommendation system.

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