Method and apparatus for matching clothing and articles based on artificial intelligence model

Abstract

This method for matching clothing and articles may comprise the steps of: acquiring user body information; acquiring user preference information; acquiring surrounding environment information; acquiring clothing and article information; and matching clothing or articles suitable for a user's body, user preference, and surrounding environment by inputting the user body information, the user preference information, the surrounding environment information, and the clothing and article information into an artificial intelligence model which matches the clothing or articles suitable for the user's body, user preference, and surrounding environment when the user body information, the user preference information, the surrounding environment information, and the clothing and article information are inputted.

Description

Method and device for matching clothing and supplies based on artificial intelligence models The present invention relates to a method and device for matching clothing and goods based on an artificial intelligence model. Recently, artificial intelligence (AI) technology has been utilized in various fields. In particular, various AI models are being researched and developed to achieve users' desired goals by utilizing the diverse data available from users. Typically, users have to try on clothes or accessories repeatedly to find the right fit. However, trying on clothes or accessories repeatedly can be cumbersome and time-consuming. Therefore, technology is required that can match users with clothing or accessories that are right for them through an artificial intelligence model. The present invention provides an artificial intelligence model-based clothing and product matching method and device that allows a user to conveniently match clothing or products that fit him or her and shorten the time required for matching. A method for matching clothes and goods based on an artificial intelligence model by an electronic device according to the present invention may include: a step of acquiring user body information including at least one of age, gender, height, weight, hair shape, upper body length, arm length, leg length, foot length, hand size, waist circumference, chest circumference, and shoulder width; a step of acquiring user preference information including at least one of fit, color, and design of clothes or goods liked by the user; a step of acquiring surrounding environment information including at least one of location information, season, temperature, and weather; a step of acquiring clothing and goods information including at least one of size, shape, material, and functionality; and a step of inputting the user body information, the user preference information, the surrounding environment information, and the clothing and goods information into an artificial intelligence model that matches clothes or goods that fit the user's body, the user's preference, and the surrounding environment when the user's body information, the user preference information, the surrounding environment information, and the clothing and goods information are input, thereby matching clothes or goods that fit the user's body, the user's preference, and the surrounding environment. According to one embodiment of the present invention, in the step of matching the clothing or articles, the clothing or articles can be matched in multiple numbers so that the user can select them. According to one embodiment of the present invention, in the step of matching the clothing or article, the matching result of the clothing or article can be provided in a state where the character formed using the user's body information is wearing the clothing or article. An electronic device for matching clothing and goods based on an artificial intelligence model according to the present invention comprises a memory for storing one or more instructions and at least one processor for executing the one or more instructions, wherein the processor executes the one or more instructions to obtain user body information including at least one of age, gender, height, weight, hair shape, upper body length, arm length, leg length, foot length, hand size, waist circumference, chest circumference, and shoulder width, obtain user tendency information including at least one of a user's favorite fit, color, and design, obtain surrounding environment information including at least one of a season, temperature, and weather, and obtain clothing and goods information including at least one of a size, shape, material, and functionality, and when the user body information, the user tendency information, the surrounding environment information, and the clothing and goods information are input, the user body information, the user tendency information, the surrounding environment information, and the clothing and goods information are input into an artificial intelligence model for matching clothing or goods that fit the user's body, the user's tendency, and the surrounding environment. The present invention matches clothing or products that fit the user's body, user tendencies, and surrounding environment through an artificial intelligence model, thereby enabling the user to conveniently match clothing or products that fit him or her, and shorten the time required for matching. FIG. 1 is a diagram illustrating a process in which an electronic device, according to one embodiment of the present invention, matches clothing and supplies based on an artificial intelligence model. FIG. 2 is a flowchart for explaining a clothing and goods matching method based on an artificial intelligence model according to one embodiment of the present invention. FIG. 3 is a block diagram illustrating an artificial intelligence model-based clothing and goods matching device according to one embodiment of the present invention. Figure 4 is a block diagram of a server according to one embodiment of the present invention. The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms. These embodiments are provided solely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. In connection with the description of the drawings, similar reference numerals may be used for similar components. In this document, the expressions "has," "may have," "includes," or "may include" indicate the presence of a feature (e.g., a number, function, operation, or component such as a part), but do not exclude the presence of additional features. In this document, the expressions "A or B," "at least one of A and / or B," or "one or more of A or / and B" can include all possible combinations of the listed items. For example, "A or B," "at least one of A and B," or "at least one of A or B" can all refer to (1) including at least one A, (3) including at least one B, or (3) including both at least one A and at least one B. The terms "first," "second," "first," or "second," as used herein, may describe various components, regardless of order and / or importance, and are only used to distinguish one component from another, without limiting the components. For example, a first user device and a second user device may represent different user devices, regardless of order or importance. For example, without departing from the scope of the rights set forth in this document, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component. When it is said that a component (e.g., a first component) is "(operatively or communicatively) coupled with / to" or "connected to" another component (e.g., a second component), it should be understood that the component is directly coupled to the other component, or can be connected via another component (e.g., a third component). Conversely, when it is said that a component (e.g., a first component) is "directly coupled to" or "directly connected to" another component (e.g., a second component), it should be understood that no other component (e.g., a third component) exists between the first component and the other component. The expression "configured to" as used herein can be used interchangeably with, for example, "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." The term "configured to" does not necessarily mean something is "specifically designed to" in hardware terms. Instead, in some contexts, the expression "a device configured to" can mean that the device, together with other devices or components, is "capable of." For example, the phrase "a processor configured (or set) to perform A, B, and C" may mean a dedicated processor (e.g., an embedded processor) for performing those operations, or a general-purpose processor (e.g., a CPU or application processor) that can perform those operations by executing one or more software programs stored in a memory device. The terms used in this document are used only to describe specific embodiments and may not be intended to limit the scope of other embodiments. The singular expression may include the plural expression unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by those of ordinary skill in the art described in this document. Terms defined in general dictionaries among the terms used in this document may be interpreted as having the same or similar meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this document. In some cases, even if a term is defined in this document, it cannot be interpreted to exclude the embodiments of this document. The artificial intelligence-related functions according to the present disclosure are operated via a processor and memory. The processor may be comprised of one or more processors. In this case, one or more processors may be a general-purpose processor such as a CPU, an AP, a Digital Signal Processor (DSP), a graphics-only processor such as a GPU or a Vision Processing Unit (VPU), or an artificial intelligence-only processor such as an NPU. One or more processors control the processing of input data according to predefined operating rules or artificial intelligence models stored in memory. Alternatively, if one or more processors are artificial intelligence-only processors, the artificial intelligence-only processor may be designed with a hardware structure specialized for processing a specific artificial intelligence model. The predefined operation rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that the basic artificial intelligence model is trained using a learning algorithm using a plurality of learning data, thereby creating a predefined operation rules or artificial intelligence model set to perform a desired characteristic (or purpose). This learning may be performed on the device itself on which the artificial intelligence according to the present disclosure is performed, or may be performed through a separate server and / or system. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning. An AI model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple nodes and weights, and performs neural network operations through operations between the computational results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers may be optimized based on the learning results of the AI ​​model. For example, the multiple weights may be updated during the learning process to reduce or minimize the loss or cost values ​​obtained from the AI ​​model. Furthermore, to minimize the loss or cost values, the multiple weights may be updated in a direction that minimizes the gradient associated with the loss or cost values. The artificial neural network may include a deep neural network (DNN), for example, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks, but is not limited to the examples described above. The features of each of the various embodiments of the present invention are partially or wholly mutually

[0036] It is possible to combine or combine, and various technical connections and operations are possible as can be fully understood by those skilled in the art, and each embodiment may be implemented independently of each other or may be implemented together in a related relationship. Hereinafter, various embodiments of the present invention will be described in detail with reference to the attached drawings. FIG. 1 is a diagram illustrating a process in which an electronic device, according to one embodiment of the present invention, matches clothing and supplies based on an artificial intelligence model. Referring to FIG. 1, the electronic device (100) can match clothing or items that fit the user's body, the user's tendencies, and the surrounding environment. The above clothing may include tops, bottoms, dresses, etc. The above tops may include shirts, t-shirts, knits, sweaters, cardigans, jackets, jumpers, vests, etc. The above bottoms may include pants, skirts, etc. The above items may include shoes, hats, accessories, various sporting goods, etc. An electronic device (100) according to one embodiment may be implemented in various forms. For example, the electronic device (100) may include, but is not limited to, a mobile terminal, a smart phone, a laptop computer, a tablet PC, an e-book terminal, a digital broadcasting terminal, a PDA (Personal Digital Assistant), etc. According to one embodiment, the electronic device (100) may include a clothing and goods matching artificial intelligence model (200). According to one embodiment, the artificial intelligence model used by the electronic device (100), for example, the clothing and goods matching artificial intelligence model (200), is an artificial neural network (ANN) model, which refers to a computing system inspired by a biological neural network. Examples of the artificial neural network model include a deep neural network (DNN), a convolution neural network (CNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), and deep Q-networks. Hereinafter, for convenience, the clothing and goods matching artificial intelligence model (200) according to the present disclosure will be described as an example of a deep neural network (DNN) among artificial neural network models. An electronic device (100) according to one embodiment may obtain user body information, user tendency information, surrounding environment information, and clothing and product information based on user input, and may match clothing or products that fit the user's body, user tendency, and surrounding environment using a clothing and product matching artificial intelligence model (200) based on the information. Specifically, in FIG. 1, the electronic device (100) can obtain the above information by inputting user body information, user tendency information, surrounding environment information, and clothing and product information into the clothing and product matching artificial intelligence model (200). The above information can be input by the user. The above user body information may include, but is not limited to, at least one of the user's age, gender, height, weight, hair shape, upper body length, arm length, leg length, foot length, hand size, waist circumference, chest circumference, and shoulder width. The above user preference information may include, but is not limited to, at least one of the fit, color, design, and style of clothing or goods that the user likes. Specifically, for upper garments, the fit can be categorized into slim fit, standard fit, and over fit, and for lower garments, the fit can be categorized into straight fit, wide fit, loose fit, and papered fit. The above colors may be distinguished as individual colors, or as chromatic or achromatic colors. The above design refers to the shape of the above clothing or article. The above style refers to the appearance when combining the above clothing and accessories. Examples of the above styles include casual, street, vintage, normcore, modern, feminine, dandy, minimalism, maximalism, layered, classic, sporty, aesthetic, and avant-garde. The above-mentioned surrounding environment information may include at least one of location information, season, temperature, and weather, but is not limited thereto. The above location information may be the user's current location information or location information for a location desired by the user. The information about the season, temperature, and weather may be based on the location information and the date the user wears the clothing or item. Additionally, the clothing and product information may include at least one of the size, shape, material, and functionality of the clothing or product. The above material refers to the fabric used to make the above clothing or product. Descriptions of the types and characteristics of the above material are identical to those of general materials, so they are omitted. The above functionality may include elasticity, windproofness, waterproofness, breathability, moisture permeability, heat generation, UV protection, etc. In particular, the above UV protection property can be expressed as a UV protection factor. The UV protection factor is an index indicating the degree to which UVA and B rays are blocked. In addition, the clothing and product matching artificial intelligence model (200) can match clothing or products that fit the user's body, the user's tendencies, and the surrounding environment based on the above information. Furthermore, the clothing and product matching artificial intelligence model (200) can match multiple clothing or products to enable the user to select the clothing or products when matching the clothing or products. In particular, the clothing and goods matching artificial intelligence model (200) can match multiple clothing or goods with a high matching rate with the user. In contrast, the clothing and goods matching artificial intelligence model (200) can match multiple clothing or goods based on the user's preference information. Additionally, the clothing and product matching artificial intelligence model (200) can use the user's body information to create a user's character and provide the matching results for the clothing or product while the character is wearing it. Therefore, the user can easily and accurately check the matching results for the clothing or product. Afterwards, the user can select one of the multiple matching clothes or items. FIG. 2 is a flowchart for explaining a clothing and goods matching method based on an artificial intelligence model according to one embodiment of the present invention. Referring to FIG. 2, first, the electronic device (100) can obtain the user's body information. (S110) The above user body information may include, but is not limited to, at least one of the user's age, gender, height, weight, hair shape, upper body length, arm length, leg length, foot length, hand size, waist circumference, chest circumference, and shoulder width. The above user body information can be obtained by the user's input to the electronic device (100) or the user's input to a server (not shown) connected to the electronic device (100). In particular, the above-mentioned hairstyle can be input in the form of an image or selected from among hairstyles preset in the electronic device (100) or a server (not shown) connected to the electronic device (100). Next, the electronic device (100) can obtain the user's preference information. (S120) The above user preference information may include, but is not limited to, at least one of the fit, color, design, and style of clothing or goods that the user likes. Specifically, for upper garments, the fit can be categorized into slim fit, standard fit, and over fit, and for lower garments, the fit can be categorized into straight fit, wide fit, loose fit, and papered fit. The above colors may be distinguished as individual colors, or as chromatic or achromatic colors. The above design refers to the shape of the above clothing or article. The above style refers to the appearance when combining the above clothing and accessories. Examples of the above styles include casual, street, vintage, normcore, modern, feminine, dandy, minimalism, maximalism, layered, classic, sporty, aesthetic, and avant-garde. The above user preference information can be obtained by a user's input to the electronic device (100) or a user's input to a server (not shown) connected to the electronic device (100). Next, the electronic device (100) can obtain information about the surrounding environment. (S130) The above-mentioned surrounding environment information may include at least one of location information, season, temperature, and weather, but is not limited thereto. The above location information may be the user's current location information or location information for a location desired by the user. The information about the season, temperature, and weather may be based on the location information and the date the user wears the clothing or item. The above-mentioned surrounding environment information can be obtained by a user's input to the electronic device (100) or by a user's input to a server (not shown) connected to the electronic device (100). In this case, the user's current location, season, temperature, and weather can be directly measured or confirmed and input by the user. In contrast, the above-mentioned surrounding environment information can be obtained through communication with an electronic device (100) or a server (not shown) connected to the electronic device (100) and a GPS satellite or a weather server. Next, the electronic device (100) can obtain clothing and product information. (S140) The above clothing and product information may include at least one of the size, shape, material, and functionality of the clothing or product. The above material refers to the fabric used to make the above clothing or product. Descriptions of the types and characteristics of the above material are identical to those of general materials, so they are omitted. The above functionality may include elasticity, windproofness, waterproofness, breathability, moisture permeability, heat generation, UV protection, etc. In particular, the above UV protection property can be expressed as a UV protection factor. The UV protection factor is an index indicating the degree to which UVA and B rays are blocked. The above user body information can be obtained by the user's input to the electronic device (100) or the user's input to a server (not shown) connected to the electronic device (100). When the electronic device (100) acquires the user's body information, the user's tendency information, the surrounding environment information, and the clothing and product information, the clothing and product matching artificial intelligence model (200) can match clothing or products that fit the user's body, the user's tendency, and the surrounding environment based on the acquired information. (S150) Furthermore, the clothing or goods can be matched in multiple ways so that the user can select from the clothing and goods matching artificial intelligence model (200). The clothing and goods matching artificial intelligence model (200) can match multiple clothing or goods with a high matching rate with the user. In contrast, the clothing and goods matching artificial intelligence model (200) can match multiple clothing or goods based on the user's preference information. Meanwhile, the clothing and product matching artificial intelligence model (200) can use the user's body information to create a user's character and provide the user with the matching results for the clothing or product while the character is wearing it. Therefore, the user can easily and accurately check the matching results for the clothing or product. Afterwards, the user can select one of the multiple matching clothes or items. The electronic device (100) can display the matching results of the clothing or goods performed by the clothing and goods matching artificial intelligence model (200) through a display. Additionally, the electronic device (100) can display the matching results of the clothing or product through a display frame while the character is wearing it. Therefore, the above artificial intelligence model-based clothing and product matching method allows users to conveniently match clothing or products that fit them, and shortens the time required for matching. FIG. 3 is a block diagram illustrating an artificial intelligence model-based clothing and goods matching device according to one embodiment of the present invention. Referring to FIG. 3, the electronic device (100) may include a processor (110) and a memory (120). However, not all of the illustrated components are essential components. The electronic device (100) may be implemented with more components than the illustrated components, or may be implemented with fewer components. For example, the electronic device (100) may further include a user input unit (130), a communication unit (140), and a display (150). The processor (110) controls the overall operation of the electronic device (100) by executing one or more instructions in the memory (120). For example, the processor (110) can control the user input unit (130), the communication unit (140), the display (150), etc., by executing one or more instructions stored in the memory (120). In addition, the processor (110) can perform the operations and functions of the electronic device (100) described with respect to FIGS. 1 and 2 by executing one or more instructions stored in the memory (120). The processor (110) may be composed of one or more processors, and the one or more processors may be a general-purpose processor such as a CPU, an AP, a DSP (Digital Signal Processor), a graphics-only processor such as a GPU, a VPU (Vision Processing Unit), or an artificial intelligence (AI)-only processor such as an NPU. According to one embodiment, when the processor (110) is implemented with a plurality of processors or graphics-only processors or artificial intelligence-only processors such as an NPU, at least some of the plurality of processors or graphics-only processors or artificial intelligence-only processors such as an NPU may be mounted on the electronic device (100) and other electronic devices or servers connected to the electronic device (100). According to one embodiment, the processor (110) may obtain user body information including at least one of age, gender, height, weight, hair shape, upper body length, arm length, leg length, foot length, hand size, waist circumference, chest circumference, and shoulder width by executing one or more instructions, obtain user preference information including at least one of fit, color, and design of clothing or goods liked by the user, obtain surrounding environment information including at least one of location information, season, temperature, and weather, obtain clothing and goods information including at least one of size, shape, material, functionality, and UV protection factor, and input the obtained information into an artificial intelligence model for matching clothing and goods, thereby matching clothing or goods that fit the user's body, the user's preference, and the surrounding environment. The specific description of the user body information, the user tendency information, the surrounding environment information, and the clothing and product information is substantially the same as the description of the user body information, the user tendency information, the surrounding environment information, and the clothing and product information with reference to FIGS. 1 and 2. According to another embodiment, the processor (110) may execute one or more instructions to match multiple clothing or items that fit the user's body, the user's tendencies, and the surrounding environment so that the user can select them. According to another embodiment, the processor (110) can match multiple clothing or items with a high degree of matching with the user by executing one or more instructions. According to another embodiment, the processor (110) may execute one or more instructions to match multiple items of clothing or equipment according to the user's preference information. According to another embodiment, the processor (110) may form a user's character using the user's body information by executing one or more instructions, and provide the matching result of the clothing or product while the character is wearing it. The memory (120) may include one or more instructions for controlling the operation of the electronic device (100). The memory (120) may include artificial intelligence models used by the electronic device (100), for example, a clothing and product matching artificial intelligence model (200). According to one embodiment, the memory (120) may include, but is not limited to, at least one type of storage medium among, for example, a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), a RAM (Random Access Memory), a SRAM (Static Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a PROM (Programmable Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk. The user input unit (130) can receive user input for controlling the operation of the electronic device (100). For example, the user input unit (130) can include, but is not limited to, a key pad, a dome switch, a touch pad (contact electrostatic capacitance type, pressure resistive film type, infrared detection type, surface ultrasonic conduction type, integral tension measurement type, piezo effect type, etc.), a jog wheel, a jog switch, etc. User body information, user tendency information, surrounding environment information, clothing and equipment information, etc. can be input through the user input unit (130). The communication unit (140) may include one or more communication modules for communication with a server (not shown), GPS satellites, a weather server, etc. For example, the communication unit (140) may include at least one of a short-range communication unit and a mobile communication unit. Accordingly, the electronic device (100) may obtain the surrounding environment information through the communication unit (140). The short-range wireless communication unit may include, but is not limited to, a Bluetooth communication unit, a BLE (Bluetooth Low Energy) communication unit, a near field communication unit, a WLAN (Wi-Fi) communication unit, a Zigbee communication unit, an infrared (IrDA, infrared Data Association) communication unit, a WFD (Wi-Fi Direct) communication unit, an UWB (ultra wideband) communication unit, an Ant+ communication unit, etc. The mobile communication unit transmits and receives wireless signals with at least one of a base station, an external terminal, or a server on a mobile communication network. Here, the wireless signals may include various forms of data, such as voice call signals, video call signals, or text / multimedia message transmission and reception. The display (150) can display and output information processed in the electronic device (100). For example, the display (150) can display an interface for controlling the electronic device (100), an interface for indicating the status of the electronic device (100), etc. In addition, the display (150) can display the matching results of the clothing or goods performed in the electronic device (100). Additionally, the display (150) may display the matching result of the clothing or product performed in the electronic device (100) while the character is wearing it. Figure 4 is a block diagram of a server according to one embodiment of the present invention. Referring to FIG. 4, according to one embodiment, a method of creating an application using a recommendation template provided based on an artificial intelligence model performed by an electronic device (100) can be performed in a server (300) that is connected to and capable of communication with the electronic device (100). The server (300) may include a communication interface (310), a database (320), and a processor (330). For example, the communication interface (310) of the server (300) according to the present disclosure may correspond to the communication unit (140) of the electronic device (100), the database (320) of the server (300) may correspond to the memory (110) of the electronic device (100), and the processor (330) of the server (300) may correspond to the processor (110) of the electronic device (100). In addition, the processor (330) of the server (300) may perform a method of matching clothes or products that fit the user's body, the user's tendencies, and the surrounding environment based on the artificial intelligence model described with reference to FIGS. 1 and 2. The present invention can match clothing or products that fit the user's body, user tendencies, and surrounding environment through an artificial intelligence model, so that the user can conveniently match clothing or products that fit him / her, and can shorten the time required for matching. Although the present invention has been described above with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various modifications and changes may be made to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below.

Claims

1. In a method for matching clothing and accessories based on an artificial intelligence model, A step of acquiring user body information including at least one of age, gender, height, weight, hair shape, upper body length, arm length, leg length, foot length, hand size, waist circumference, chest circumference, and shoulder width; A step of obtaining user preference information including at least one of the fit, color, and design of clothing or goods that the user likes; A step of acquiring surrounding environment information including at least one of location information, season, temperature, and weather; A step of obtaining clothing and article information including at least one of size, shape, material, and functionality; and A clothing and goods matching method comprising: a step of inputting user body information, user tendency information, surrounding environment information, and clothing and goods information into an artificial intelligence model that matches clothing or goods that fit the user's body, user tendency information, and surrounding environment when user body information, user tendency information, surrounding environment information, and clothing and goods information are input; 2. In paragraph 1, At the stage of matching the above clothing or items, A clothing and goods matching method characterized by matching multiple clothing or goods to enable a user to select.

3. In paragraph 1, At the stage of matching the above clothing or items, A clothing and product matching method characterized in that the matching result of the clothing or product is provided in a state where the character formed using the user's body information is wearing the clothing or product.

4. In an electronic device for matching clothing and accessories based on an artificial intelligence model, Memory that stores one or more instructions; and comprising at least one processor executing one or more of the above instructions; The processor executes one or more of the instructions, Obtain user body information including at least one of age, gender, height, weight, hair shape, upper body length, arm length, leg length, foot length, hand size, waist circumference, chest circumference, and shoulder width; Obtain user preference information including at least one of the user's favorite fit, color, and design; Obtaining environmental information including at least one of season, temperature, and weather, Obtain clothing and equipment information including at least one of size, shape, material, and functionality; A clothing and goods matching device characterized in that when user body information, user tendency information, surrounding environment information, clothing and goods information are input, the user body information, user tendency information, surrounding environment information, and clothing and goods information are input into an artificial intelligence model that matches clothing or goods that fit the user's body, user tendency, and surrounding environment, thereby matching clothing or goods that fit the user's body, user tendency, and surrounding environment.

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