system

Abstract

The system according to this embodiment aims to allow users to check the fit and appearance of clothes before actually trying them on. [Solution] The system according to the embodiment comprises an input unit, a generation unit, a selection unit, a fitting unit, a confirmation unit, and a simulation unit. The input unit receives the user's body data. The generation unit generates an avatar based on the information entered by the input unit. The selection unit allows the user to choose the clothes they want to try on. The fitting unit allows the avatar to try on the clothes selected by the selection unit. The confirmation unit confirms the fit and appearance of the clothes tried on by the fitting unit. The simulation unit performs a custom fit simulation based on the information confirmed by the confirmation unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to confirm the fit and appearance before actually trying on clothes.

[0005] The system according to the embodiment aims to enable a user to confirm the fit and appearance before actually trying on clothes.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a generation unit, a selection unit, a fitting unit, a confirmation unit, and a simulation unit. The input unit receives the user's body data. The generation unit generates an avatar based on the information entered by the input unit. The selection unit allows the user to choose the clothes they want to try on. The fitting unit allows the avatar to try on the clothes selected by the selection unit. The confirmation unit confirms the fit and appearance of the clothes tried on by the fitting unit. The simulation unit performs a custom fit simulation based on the information confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to check the fit and appearance of clothes before actually trying them on. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AR modeling system according to an embodiment of the present invention is an innovative content that utilizes the latest AR technology to allow users to try on various clothes on an avatar with the same build as themselves. This AR modeling system allows users to check the fit and appearance of clothes before actually purchasing them through a virtual try-on experience. First, the user inputs their body data. For example, they input information such as height, weight, shoulder width, and waist size. This information is input into the AR system, and an avatar with the same build as the user is generated. Next, the user selects the clothes they want to try on. For example, they select clothes they are interested in on an online shopping site and input that information into the AR system. The AR system has the avatar try on the selected clothes and displays it in real time. The user checks the fit and appearance of the clothes the avatar is trying on. For example, they can see the avatar rotating 360 degrees and walking while wearing the clothes. This allows users to check the fit and appearance of clothes without actually trying them on. Furthermore, the AR system performs a custom fit simulation based on the user's body data. For example, it adjusts the size of the clothes to match the user's body type to provide an optimal fit. This allows users to find clothes that fit perfectly, even if standard sizes don't suit them. This system enables users to virtually try on clothes in real time from the comfort of their homes, reducing the anxieties and return risks associated with online shopping. It also saves busy professionals time and effort, helping them efficiently select the best items. For those seeking custom fits, it provides a highly satisfying purchasing experience through accurate simulations. The AR modeling system generates an avatar based on the user's body data, allowing them to simulate trying on clothes and check the fit and appearance before purchasing.

[0029] The AR modeling system according to this embodiment comprises an input unit, a generation unit, a selection unit, a try-on unit, a confirmation unit, and a simulation unit. The input unit receives the user's physical data. The user's physical data includes, but is not limited to, height, weight, shoulder width, and waist size. For example, the user enters their height in centimeters. The input unit also allows the user to enter their weight in kilograms. Furthermore, the input unit allows the user to enter their shoulder width in centimeters. For example, the input unit allows the user to enter their waist size in centimeters. The generation unit generates an avatar based on the information entered by the input unit. For example, the generation unit generates an avatar with the same build as the user based on information such as the user's height, weight, shoulder width, and waist size. The generation unit can also generate an avatar using a 3D model. Furthermore, the generation unit can also generate an avatar using a 2D illustration. For example, the generation unit generates an avatar in real time based on the user's physical data. The selection unit allows the user to choose the clothes they want to try on. The selection unit, for example, selects clothing items of interest from an online shopping site and inputs that information into the AR system. The selection unit can also retrieve information about the clothing selected by the user from a database. Furthermore, the selection unit can input information about the clothing selected by the user into the AR system in real time. For example, the selection unit inputs information about the clothing selected by the user using a QR code (registered trademark). The try-on unit has the selected clothing try on an avatar and displays it in real time. For example, the try-on unit can have the selected clothing try on an avatar and display it rotated 360 degrees. The try-on unit can also have the selected clothing try on an avatar and display a walking animation. Furthermore, the try-on unit can have the selected clothing try on an avatar and adjust the size in real time. For example, the try-on unit has the selected clothing try on an avatar and displays the degree of match in color and design. The confirmation unit checks the fit and appearance of the clothing tried on by the try-on unit. For example, the confirmation unit checks how the avatar rotates 360 degrees and walks while wearing the clothing. Furthermore, the verification unit can also check how easily the avatar can move while wearing clothes.Furthermore, the verification unit can also check the degree of size matching when the avatar is wearing the clothes. For example, the verification unit can check the degree of matching in color and design when the avatar is wearing the clothes. The simulation unit performs a custom fit simulation based on the information verified by the verification unit. For example, the simulation unit adjusts the size of the clothes to match the user's body shape to provide an optimal fit. The simulation unit can also adjust the design of the clothes to match the user's body shape. Furthermore, the simulation unit can adjust the material of the clothes to match the user's body shape. For example, the simulation unit simulates a custom fit of the clothes to match the user's body shape. As a result, the AR modeling system according to this embodiment generates an avatar based on the user's body data and simulates trying on clothes, allowing the user to check the fit and appearance before purchasing.

[0030] The input section allows users to enter their physical data. This data may include, but is not limited to, height, weight, shoulder width, and waist size. For example, the user can enter their height in centimeters. The input section can also allow users to enter their weight in kilograms. Furthermore, the input section can allow users to enter their shoulder width in centimeters. For example, the input section can allow users to enter their waist size in centimeters. The input section provides an intuitive interface to make data entry easy. For example, users can select values ​​using sliders or dropdown menus. The input section also includes input verification features to prevent users from entering incorrect data. For example, it can set ranges for height and weight and display a warning if a value outside the range is entered. Furthermore, the input section saves and allows users to reuse previously entered data. This saves users the trouble of entering the same data every time. For example, it can automatically display previously entered height and weight data and allow users to correct it as needed. The input section protects user privacy by encrypting data and controlling access. For example, user data is stored encrypted and accessible only to authenticated users. This allows users to enter data with confidence.

[0031] The generation unit generates avatars based on information entered by the input unit. For example, the generation unit generates an avatar with the same build as the user based on information such as the user's height, weight, shoulder width, and waist size. The generation unit can also generate avatars using 3D models. Furthermore, the generation unit can generate avatars using 2D illustrations. For example, the generation unit generates avatars in real time based on the user's body data. The generation unit uses advanced algorithms to create detailed 3D models based on the user's body data. For example, when data such as the user's height, weight, shoulder width, and waist size are entered, the generation unit analyzes this data and generates a 3D avatar that is closest to the user's body type. The generation unit adjusts the avatar's proportions to match the user's body type, achieving a realistic appearance. The generation unit can also generate the avatar's face using the user's facial photograph. For example, when a user uploads a facial photograph, the generation unit uses facial recognition technology to extract facial features and reflect them in the avatar's face. This allows the user to create an avatar that resembles themselves. The generation unit also has the function of generating avatar movements and facial expressions in real time. For example, when a user operates the avatar, the generation unit generates the avatar's movements and facial expressions in real time, resulting in natural movements. This allows users to perform various simulations using the avatar.

[0032] The selection unit allows the user to choose clothes they want to try on. For example, the unit selects clothes they are interested in on an online shopping site and inputs that information into the AR system. The selection unit can also retrieve information about the clothes the user has selected from a database. Furthermore, the selection unit can input information about the clothes the user has selected into the AR system in real time. For example, the selection unit inputs information about the clothes the user has selected using a QR code. The selection unit provides an intuitive interface to make it easy for users to choose clothes. For example, when a user selects clothes on an online shopping site, the selection unit automatically retrieves that information and inputs it into the AR system. The selection unit can also retrieve information about the clothes the user has selected from a database and display it in real time. This allows the user to simulate trying on clothes while checking detailed information about the clothes they have selected. The selection unit also has the function to input information about clothes the user has selected using QR codes or barcodes. For example, if a user finds clothes they are interested in at a store, the selection unit scans the QR code of those clothes and inputs the information into the AR system. This allows the user to simulate trying on clothes they found in a store at home. The selection unit also has the function to save information about clothes the user has selected so that it can be reused later. For example, if a user selects multiple outfits and performs a try-on simulation, the selection section saves that information and can be used later when performing another try-on simulation. This allows users to perform try-on simulations more efficiently.

[0033] The fitting room allows users to try on selected clothing on an avatar and view the results in real time. For example, it can try on selected clothing on an avatar and display a 360-degree rotation. It can also display walking animations of the avatar trying on selected clothing. Furthermore, the fitting room can adjust the size of selected clothing in real time. For example, it can try on selected clothing on an avatar and display the degree of match in color and design. The fitting room uses advanced rendering technology to reproduce realistic textures and movements when the user tries on clothing selected by the user on an avatar. For example, the fitting room accurately reproduces the material and color of the selected clothing, realistically displaying how the avatar will look when wearing it. The fitting room also displays how the avatar moves while wearing the clothing in real time, allowing users to check the fit and ease of movement of the clothing. The fitting room also has a function to adjust the size of the clothing selected by the user in real time. For example, when a user changes the size of their clothing, the fitting room automatically adjusts the size of the clothing on their avatar and displays it in real time. This allows the user to find the size that best suits them. The fitting room also has a function to display how well the selected clothing matches the avatar's color and design. For example, it displays in real time how the color and design of the clothing selected by the user will look on their avatar, allowing the user to check the appearance of the clothing. This helps the user find clothes that suit them.

[0034] The verification unit checks the fit and appearance of the clothes tried on by the fitting unit. For example, the verification unit checks how the avatar rotates 360 degrees and walks while wearing the clothes. The verification unit can also check how easily the avatar can move while wearing the clothes. Furthermore, the verification unit can check the degree of size matching while the avatar is wearing the clothes. For example, the verification unit checks how well the colors and designs match while the avatar is wearing the clothes. The verification unit has a function to display the avatar from various viewpoints and angles so that users can check the results of the fitting simulation in detail. For example, users can rotate the avatar 360 degrees to check the fit and appearance of the clothes. The verification unit can also display how the avatar walks while wearing the clothes to check how easily it can move. This allows users to simulate the actual comfort of wearing the clothes. The verification unit also has a function to check the degree of size matching while the avatar is wearing the clothes. For example, it can display in real time how the size of the clothes selected by the user fits the avatar, allowing users to check the degree of size matching. This allows users to find the size that is best suited to them. The verification unit also includes a function to check the degree of color and design matching when the avatar is wearing clothes. For example, it displays in real time how the colors and designs of the clothes selected by the user will look on the avatar, allowing the user to check the appearance of the clothes. This allows users to find clothes that suit them.

[0035] The simulation unit performs a custom fit simulation based on the information verified by the verification unit. For example, the simulation unit adjusts the clothing size to match the user's body shape, providing an optimal fit. The simulation unit can also adjust the clothing design to match the user's body shape. Furthermore, the simulation unit can adjust the clothing material to match the user's body shape. For example, the simulation unit simulates a custom fit for the clothing based on the user's body shape. The simulation unit uses advanced algorithms to adjust the clothing size and design to provide the optimal fit for the user's body shape. For example, based on data such as the user's height, weight, shoulder width, and waist size, the simulation unit automatically adjusts the clothing size to provide an optimal fit. The simulation unit also has a function to adjust the clothing design to match the user's body shape. For example, it adjusts the silhouette and details of the clothing to match the user's body shape for a better appearance. The simulation unit also has a function to adjust the clothing material to match the user's body shape. For example, it adjusts the elasticity and thickness of the clothing material to match the user's body shape for optimal comfort. This allows the user to find the perfect clothing for themselves. The simulation unit also includes a function that allows users to save the results of their fitting simulations and reuse them later. For example, if a user simulates trying on multiple clothes, the simulation unit saves that information and can use it when performing another fitting simulation later. This allows users to perform fitting simulations efficiently.

[0036] The input section allows users to input information such as their height, weight, shoulder width, and waist size. For example, the user can input their height in centimeters. The input section can also allow the user to input their weight in kilograms. Furthermore, the input section can allow the user to input their shoulder width in centimeters. For example, the input section can allow the user to input their waist size in centimeters. This allows for the generation of a more accurate avatar by inputting detailed physical data of the user. Some or all of the above processing in the input section may be performed using AI, or not. For example, the input section can input the user's physical data into the AI, which can then analyze the data and extract the information necessary for avatar generation.

[0037] The generation unit can generate an avatar with the same build as the user based on the information input by the input unit. For example, the generation unit generates an avatar with the same build as the user based on information such as the user's height, weight, shoulder width, and waist size. The generation unit can also generate avatars using 3D models. Furthermore, the generation unit can generate avatars using 2D illustrations. For example, the generation unit generates an avatar in real time based on the user's physical data. This allows for the generation of an accurate avatar based on the user's physical data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's physical data into AI, and the AI ​​can analyze the data to generate an avatar.

[0038] The selection unit can choose clothes of interest from an online shopping site and input that information into the AR system. For example, the selection unit can choose clothes of interest from an online shopping site and input that information into the AR system. The selection unit can also retrieve information about the clothes selected by the user from a database. Furthermore, the selection unit can input information about the clothes selected by the user into the AR system in real time. For example, the selection unit can input information about the clothes selected by the user using a QR code. This allows the user to input the clothes they selected from the online shopping site into the AR system. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input information about the clothes selected by the user into AI, and the AI ​​can analyze the data and input it into the AR system.

[0039] The fitting room unit can have an avatar try on selected clothing and display the results in real time. For example, the fitting room unit can have an avatar try on selected clothing and display it rotated 360 degrees. The fitting room unit can also have an avatar try on selected clothing and display a walking animation. Furthermore, the fitting room unit can have an avatar try on selected clothing and adjust the size in real time. For example, the fitting room unit can have an avatar try on selected clothing and display the degree of match in color and design. This allows users to check the fit and appearance of selected clothing by having an avatar try it on and displaying the results in real time. Some or all of the above processes in the fitting room unit may be performed using AI, for example, or not. For example, the fitting room unit can input data of selected clothing into an AI, which can analyze the data and have the avatar try on the clothing.

[0040] The verification unit can check how the avatar rotates 360 degrees and walks while wearing clothes. For example, the verification unit can check how the avatar rotates 360 degrees and walks while wearing clothes. The verification unit can also check how easily the avatar can move while wearing clothes. Furthermore, the verification unit can check how well the avatar fits the clothes. For example, the verification unit can check how well the avatar fits the clothes in terms of color and design. This allows the user to check the fit and appearance of the clothes in detail by checking how the avatar rotates 360 degrees and walks while wearing clothes. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the avatar's movement data into AI, and the AI ​​can analyze the data to evaluate the fit and appearance.

[0041] The simulation unit can adjust the size of clothing to match the user's body shape, providing an optimal fit. For example, the simulation unit can adjust the size of clothing to match the user's body shape, providing an optimal fit. The simulation unit can also adjust the design of clothing to match the user's body shape. Furthermore, the simulation unit can adjust the material of clothing to match the user's body shape. For example, the simulation unit simulates a custom fit of clothing to match the user's body shape. This allows the user to find the perfect clothing by adjusting the size of clothing to match their body shape and providing an optimal fit. Some or all of the above processes in the simulation unit may be performed using AI, or not. For example, the simulation unit can input the user's body shape data into an AI, which can then analyze the data to provide an optimal fit.

[0042] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can automatically display as candidates physical data that the user has frequently entered in the past. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest physical data to be used at specific times based on the user's past input history. For example, the input unit can automatically display as candidates physical data that the user has frequently entered in the past. By analyzing the user's past input history, it provides the optimal input method and improves input efficiency. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input data into AI, which can then analyze the data and suggest the optimal input method.

[0043] The input unit can customize input fields based on the user's current physical condition and lifestyle. For example, if the user is tired, the input unit can reduce and simplify the input fields. It can also request detailed physical data if the user leads a healthy lifestyle. Furthermore, if the user has plans to attend a specific event, the input unit can prioritize inputting physical data related to that event. For example, if the user is tired, the input unit can reduce and simplify the input fields. This allows for an optimal input experience for the user by customizing input fields based on their current physical condition and lifestyle. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input the user's physical condition data into AI, which can then analyze the data and customize the input fields.

[0044] The input unit can prioritize displaying input items that are highly relevant to the user's geographical location during input. For example, if the user is in a cold region, the input unit will prioritize displaying input items related to body temperature and cold weather protection measures. Similarly, if the user is in a hot and humid region, the input unit can prioritize displaying input items related to sweating tendency and breathability. Furthermore, if the user is in an urban area, the input unit can prioritize displaying input items related to walking distance and commute time. This allows the system to provide the user with an optimal input experience by prioritizing the display of highly relevant input items based on their geographical location. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can input the user's geographical location information into AI, which can then analyze the data and display highly relevant input items.

[0045] The input unit can analyze the user's social media activity during input and suggest relevant input items. For example, if the user frequently posts about fitness, the input unit can suggest input items related to body fat percentage and muscle mass. Similarly, if the user frequently posts about fashion, it can suggest input items related to shoulder width and waist size. Furthermore, if the user frequently posts about travel, it can suggest input items related to weight and height. In this way, by analyzing the user's social media activity, relevant input items are suggested, improving the efficiency of input. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media data into AI, which can then analyze the data and suggest relevant input items.

[0046] The generation unit can select the optimal generation method by referring to the user's past avatar generation history during generation. For example, the generation unit can propose the optimal generation method based on the data of avatars the user has previously generated. The generation unit can also generate avatars that reflect the user's preferred style based on the user's past avatar generation history. Furthermore, the generation unit can analyze the user's past avatar generation history and propose the most efficient generation method. For example, the generation unit proposes the optimal generation method based on the data of avatars the user has previously generated. In this way, by referring to the user's past avatar generation history, the optimal generation method is provided, improving the efficiency of avatar generation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past avatar data into AI, and the AI ​​can analyze the data and propose the optimal generation method.

[0047] The generation unit can customize the avatar's details based on the user's current body shape and posture during generation. For example, if the user inputs their current body shape, the generation unit will customize the avatar based on that body shape. The generation unit can also customize the avatar based on the user's current posture if that posture is input. Furthermore, if the user inputs their current body shape and posture in real time, the generation unit can customize the avatar based on that information. For example, if the user inputs their current body shape, the generation unit will customize the avatar based on that body shape. This allows the generation unit to provide the user with the most suitable avatar by customizing the avatar's details based on the user's current body shape and posture. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's body shape data into an AI, which can then analyze the data and customize the avatar's details.

[0048] The generation unit can select the optimal avatar generation method based on the user's geographical location information during generation. For example, if the user is in a cold region, the generation unit will generate avatars related to body temperature and cold weather protection. Furthermore, if the user is in a hot and humid region, the generation unit can generate avatars related to sweating tendency and breathability. Additionally, if the user is in an urban area, the generation unit can generate avatars related to walking distance and commuting time. For example, if the user is in a cold region, the generation unit will generate avatars related to body temperature and cold weather protection. By selecting the optimal avatar generation method based on the user's geographical location information, the generation unit can provide the user with the most suitable avatar. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the user's geographical location information into AI, which can then analyze the data and select the optimal avatar generation method.

[0049] The generation unit can analyze the user's social media activity during generation and suggest relevant avatar generation methods. For example, if a user frequently posts about fitness, the generation unit can generate avatars related to body fat percentage and muscle mass. Similarly, if a user frequently posts about fashion, the generation unit can generate avatars related to shoulder width and waist size. Furthermore, if a user frequently posts about travel, the generation unit can generate avatars related to weight and height. By analyzing the user's social media activity, the generation unit can suggest relevant avatar generation methods and improve the efficiency of avatar generation. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into AI, which can then analyze the data and suggest relevant avatar generation methods.

[0050] The selection unit can select the optimal selection method by referring to the user's past selection history during the selection process. For example, the selection unit can suggest the optimal selection method based on data of clothes the user has previously selected. The selection unit can also provide a selection method that reflects the user's preferred style based on their past selection history. Furthermore, the selection unit can analyze the user's past selection history and suggest the most efficient selection method. For example, the selection unit suggests the optimal selection method based on data of clothes the user has previously selected. By referring to the user's past selection history, it provides the optimal selection method and improves the efficiency of the selection process. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past selection data into AI, which can then analyze the data and suggest the optimal selection method.

[0051] The selection unit can customize the options based on the user's current fashion trends when making a selection. For example, if the user inputs their current fashion trends, the selection unit will customize the options based on those trends. Furthermore, if the user inputs their current fashion trends in real time, the selection unit can also customize the options based on that information. Additionally, if the selection unit obtains the user's current fashion trends from social media, it can customize the options based on that information. For example, if the user inputs their current fashion trends, the selection unit will customize the options based on those trends. This allows the selection unit to provide the user with the best possible selection experience by customizing the options based on their current fashion trends. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input the user's fashion trend data into an AI, which can then analyze the data and customize the options.

[0052] The selection section can prioritize displaying highly relevant options based on the user's geographical location information when an option is selected. For example, if the user is in a cold region, the selection section will prioritize displaying options related to cold weather protection. Similarly, if the user is in a hot and humid region, the selection section can prioritize displaying options related to breathability. Furthermore, if the user is in an urban area, the selection section can prioritize displaying options related to walking distance and commute time. This allows the selection section to provide the user with an optimal selection experience by prioritizing highly relevant options based on their geographical location information. Some or all of the above processing in the selection section may be performed using AI, or not. For example, the selection section can input the user's geographical location information into an AI, which can then analyze the data and display highly relevant options.

[0053] The selection unit can analyze the user's social media activity and suggest relevant options when making a selection. For example, if the user frequently posts about fitness, the selection unit can suggest options related to sportswear. Similarly, if the user frequently posts about fashion, it can suggest options related to trends. Furthermore, if the user frequently posts about travel, it can suggest options related to travel clothing. In this way, by analyzing the user's social media activity, it suggests relevant options and improves the efficiency of the selection process. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media data into an AI, which can then analyze the data and suggest relevant options.

[0054] The fitting room unit can select the optimal fitting method by referring to the user's past fitting history during the fitting process. For example, the fitting room unit can suggest the optimal fitting method based on data of clothes the user has tried on in the past. Furthermore, the fitting room unit can provide a fitting method that reflects the user's preferred style based on their past fitting history. In addition, the fitting room unit can analyze the user's past fitting history and suggest the most efficient fitting method. For example, the fitting room unit suggests the optimal fitting method based on data of clothes the user has tried on in the past. This improves fitting efficiency by providing the optimal fitting method through reference to the user's past fitting history. Some or all of the above processing in the fitting room unit may be performed using AI, or without AI. For example, the fitting room unit can input the user's past fitting data into an AI, which can then analyze the data and suggest the optimal fitting method.

[0055] The fitting room unit can customize fitting details based on the user's current body shape and posture during the fitting process. For example, if the user inputs their current body shape, the fitting room unit will customize the fitting details based on that body shape. It can also customize fitting details based on the user's current posture if that posture is input. Furthermore, if the user inputs their current body shape and posture in real time, the fitting room unit can customize fitting details based on that information. For example, if the user inputs their current body shape, the fitting room unit will customize fitting details based on that body shape. This allows for the provision of an optimal fitting experience for the user by customizing fitting details based on their current body shape and posture. Some or all of the above processing in the fitting room unit may be performed using AI, or not. For example, the fitting room unit can input the user's body shape data into an AI, which can then analyze the data and customize the fitting details.

[0056] The fitting room unit can select the optimal fitting method based on the user's geographical location information during the fitting process. For example, if the user is in a cold region, the fitting room unit can provide a fitting method related to cold weather protection. It can also provide a fitting method related to breathability if the user is in a hot and humid region. Furthermore, if the user is in an urban area, the fitting room unit can provide a fitting method related to walking distance and commuting time. For example, if the user is in a cold region, the fitting room unit can provide a fitting method related to cold weather protection. By selecting the optimal fitting method based on the user's geographical location information, the system can provide the user with the best possible fitting experience. Some or all of the above processing in the fitting room unit may be performed using AI, or without AI. For example, the fitting room unit can input the user's geographical location information into AI, which can then analyze the data and select the optimal fitting method.

[0057] The fitting room unit can analyze the user's social media activity during the fitting process and suggest relevant fitting methods. For example, if the user frequently posts about fitness, the fitting room unit can suggest fitting methods related to sportswear. Similarly, if the user frequently posts about fashion, it can suggest fitting methods related to trends. Furthermore, if the user frequently posts about travel, it can suggest fitting methods related to travel clothing. In this way, by analyzing the user's social media activity, it can suggest relevant fitting methods and improve the efficiency of the fitting process. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's social media data into an AI, which can then analyze the data and suggest relevant fitting methods.

[0058] The verification unit can select the optimal verification method by referring to the user's past verification history during the verification process. For example, the verification unit can suggest the optimal verification method based on the user's past clothing verification data. The verification unit can also provide a verification method that reflects the user's preferred style based on their past verification history. Furthermore, the verification unit can analyze the user's past verification history and suggest the most efficient verification method. For example, the verification unit suggests the optimal verification method based on the user's past clothing verification data. By referring to the user's past verification history, it provides the optimal verification method and improves the efficiency of the verification process. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's past verification data into AI, which can then analyze the data and suggest the optimal verification method.

[0059] The verification unit can customize the verification details based on the user's current body shape and posture during verification. For example, if the user inputs their current body shape, the verification unit will customize the verification details based on that body shape. The verification unit can also customize the verification details based on the user's current posture if that posture is input. Furthermore, if the user inputs their current body shape and posture in real time, the verification unit can also customize the verification details based on that information. For example, if the user inputs their current body shape, the verification unit will customize the verification details based on that body shape. By customizing the verification details based on the user's current body shape and posture, the system can provide the user with the optimal verification experience. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's body shape data into AI, which can then analyze the data and customize the verification details.

[0060] The verification unit can select the optimal verification method based on the user's geographical location information during verification. For example, if the user is in a cold region, the verification unit can provide a verification method related to cold weather protection. It can also provide a verification method related to breathability if the user is in a hot and humid region. Furthermore, if the user is in an urban area, the verification unit can provide a verification method related to walking distance and commuting time. For example, if the user is in a cold region, the verification unit can provide a verification method related to cold weather protection. This allows the system to provide the user with the optimal verification experience by selecting the optimal verification method based on the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's geographical location information into AI, which can then analyze the data and select the optimal verification method.

[0061] The verification unit can analyze the user's social media activity during verification and suggest relevant verification methods. For example, if the user frequently posts about fitness, the verification unit can suggest verification methods related to sportswear. Similarly, if the user frequently posts about fashion, the verification unit can suggest verification methods related to trends. Furthermore, if the user frequently posts about travel, the verification unit can suggest verification methods related to travel clothing. In this way, by analyzing the user's social media activity, relevant verification methods are suggested, improving the efficiency of verification. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into AI, which can then analyze the data and suggest relevant verification methods.

[0062] The simulation unit can select the optimal simulation method by referring to the user's past simulation history during a simulation. For example, the simulation unit can propose the optimal simulation method based on the user's past simulation data for clothing. The simulation unit can also provide a simulation method that reflects the user's preferred style based on their past simulation history. Furthermore, the simulation unit can analyze the user's past simulation history and propose the most efficient simulation method. For example, the simulation unit proposes the optimal simulation method based on the user's past simulation data for clothing. By referring to the user's past simulation history, it provides the optimal simulation method and improves the efficiency of the simulation. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past simulation data into AI, which can then analyze the data and propose the optimal simulation method.

[0063] The simulation unit can customize the simulation details based on the user's current body shape and posture during the simulation. For example, if the user inputs their current body shape, the simulation unit will customize the simulation details based on that body shape. The simulation unit can also customize the simulation details based on the user's current posture if that posture is input. Furthermore, if the user inputs their current body shape and posture in real time, the simulation unit can customize the simulation details based on that information. For example, if the user inputs their current body shape, the simulation unit will customize the simulation details based on that body shape. By customizing the simulation details based on the user's current body shape and posture, the system can provide the user with the optimal simulation experience. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's body shape data into AI, which can then analyze the data and customize the simulation details.

[0064] The simulation unit can select the optimal simulation method based on the user's geographical location information during a simulation. For example, if the user is in a cold region, the simulation unit can provide a simulation method related to cold weather protection. The simulation unit can also provide a simulation method related to ventilation if the user is in a hot and humid region. Furthermore, if the user is in an urban area, the simulation unit can provide a simulation method related to walking distance and commuting time. For example, if the user is in a cold region, the simulation unit can provide a simulation method related to cold weather protection. This allows the system to provide the user with the optimal simulation experience by selecting the optimal simulation method based on the user's geographical location information. Some or all of the above processing in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input the user's geographical location information into the AI, which can then analyze the data and select the optimal simulation method.

[0065] The simulation unit can analyze the user's social media activity during a simulation and suggest relevant simulation methods. For example, if the user frequently posts about fitness, the simulation unit can suggest a simulation method related to sportswear. Similarly, if the user frequently posts about fashion, the simulation unit can suggest a simulation method related to trends. Furthermore, if the user frequently posts about travel, the simulation unit can suggest a simulation method related to travel clothing. In this way, by analyzing the user's social media activity, relevant simulation methods are suggested, improving the efficiency of the simulation. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's social media data into AI, which can then analyze the data and suggest relevant simulation methods.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The AR modeling system can analyze a user's past try-on history and suggest the optimal try-on method. For example, it can prioritize suggesting similar styles and sizes of clothing based on data from clothes the user has tried on in the past. It can also provide try-on options that reflect the user's preferred colors and designs based on their past try-on history. Furthermore, it can analyze the user's past try-on history and suggest the most efficient try-on method. This allows the system to provide the optimal try-on method by referring to the user's past try-on history, thereby improving the efficiency of the try-on process. Some or all of the above processing in the try-on section may be performed using AI or not.

[0068] The AR modeling system can customize the details of the try-on based on the user's current body shape and posture. For example, if the user inputs their current body shape, the system can customize the details of the try-on based on that body shape. Similarly, if the user inputs their current posture, the system can customize the details of the try-on based on that posture. Furthermore, if the user inputs their current body shape and posture in real time, the system can customize the details of the try-on based on that information. This allows the system to provide the user with the optimal try-on experience by customizing the details of the try-on based on their current body shape and posture. Some or all of the above processing in the try-on section may be performed using AI, or it may not.

[0069] The AR modeling system can customize fitting details based on the user's geographical location. For example, if the user is in a cold region, it can provide fitting options related to cold weather protection. Similarly, if the user is in a hot and humid region, it can provide fitting options related to breathability. Furthermore, if the user is in an urban area, it can provide fitting options related to walking distance and commute time. This allows for the provision of optimal fitting options based on the user's geographical location, thereby providing the user with the best possible fitting experience. Some or all of the above processing in the fitting section may be performed using AI, or without AI.

[0070] The AR modeling system can analyze a user's social media activity and suggest relevant try-on options. For example, if a user frequently posts about fitness, it can suggest try-on options related to sportswear. Similarly, if a user frequently posts about fashion, it can suggest try-on options related to trends. Furthermore, if a user frequently posts about travel, it can suggest try-on options related to travel clothes. By analyzing a user's social media activity, it is possible to suggest relevant try-on options and improve the efficiency of the try-on process. Some or all of the above processing in the try-on section may be performed using AI or not.

[0071] The AR modeling system can select the optimal generation method by referring to the user's past avatar generation history. For example, it can suggest the optimal generation method based on the data of avatars the user has previously generated. It can also generate avatars that reflect the user's preferred style based on their past avatar generation history. Furthermore, it can analyze the user's past avatar generation history and suggest the most efficient generation method. In this way, by referring to the user's past avatar generation history, the system can provide the optimal generation method and improve the efficiency of avatar generation. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0072] The AR modeling system can customize the details of an avatar based on the user's current body shape and posture. For example, if the user inputs their current body shape, the avatar can be customized based on that body shape. Similarly, if the user inputs their current posture, the avatar can be customized based on that posture. Furthermore, if the user inputs their current body shape and posture in real time, the avatar can be customized based on that information. This allows the system to provide the user with the most suitable avatar by customizing its details based on their current body shape and posture. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not.

[0073] The AR modeling system can customize avatar details based on the user's geographical location. For example, if the user is in a cold region, it can generate an avatar related to cold weather protection. If the user is in a hot and humid region, it can generate an avatar related to breathability. Furthermore, if the user is in an urban area, it can generate an avatar related to walking distance and commute time. This allows the system to provide the optimal avatar based on the user's geographical location, thereby providing the user with the best possible avatar generation experience. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The input section allows the user to enter their physical data. This data includes, for example, height, weight, shoulder width, and waist size. The input section allows the user to enter this data in centimeters or kilograms. Step 2: The generation unit generates an avatar based on the information entered by the input unit. The generation unit generates an avatar with the same build as the user based on information such as the user's height, weight, shoulder width, and waist size. The generation unit can generate avatars using 3D models or 2D illustrations. Step 3: The selection unit allows the user to choose the clothes they want to try on. The selection unit selects clothes of interest from an online shopping site and inputs that information into the AR system. The selection unit can also retrieve information about the clothes selected by the user from a database. Step 4: The fitting room section allows the avatar to try on the selected clothing and displays it in real time. The fitting room section can rotate the avatar 360 degrees while displaying the selected clothing. The fitting room section can also display a walking animation while the avatar is wearing the selected clothing. Step 5: The verification unit checks the fit and appearance of the clothes tried on by the fitting unit. The verification unit checks how the avatar rotates 360 degrees and walks while wearing the clothes. The verification unit can also check the ease of movement and size match of the avatar while wearing the clothes. Step 6: The simulation unit performs a custom fit simulation based on the information confirmed by the verification unit. The simulation unit adjusts the size, design, and material of the clothing to match the user's body shape, providing an optimal fit.

[0076] (Example of form 2) An AR modeling system according to an embodiment of the present invention is an innovative content that utilizes the latest AR technology to allow users to try on various clothes on an avatar with the same build as themselves. This AR modeling system allows users to check the fit and appearance of clothes before actually purchasing them through a virtual try-on experience. First, the user inputs their body data. For example, they input information such as height, weight, shoulder width, and waist size. This information is input into the AR system, and an avatar with the same build as the user is generated. Next, the user selects the clothes they want to try on. For example, they select clothes they are interested in on an online shopping site and input that information into the AR system. The AR system has the avatar try on the selected clothes and displays it in real time. The user checks the fit and appearance of the clothes the avatar is trying on. For example, they can see the avatar rotating 360 degrees and walking while wearing the clothes. This allows users to check the fit and appearance of clothes without actually trying them on. Furthermore, the AR system performs a custom fit simulation based on the user's body data. For example, it adjusts the size of the clothes to match the user's body type to provide an optimal fit. This allows users to find clothes that fit perfectly, even if standard sizes don't suit them. This system enables users to virtually try on clothes in real time from the comfort of their homes, reducing the anxieties and return risks associated with online shopping. It also saves busy professionals time and effort, helping them efficiently select the best items. For those seeking custom fits, it provides a highly satisfying purchasing experience through accurate simulations. The AR modeling system generates an avatar based on the user's body data, allowing them to simulate trying on clothes and check the fit and appearance before purchasing.

[0077] The AR modeling system according to this embodiment comprises an input unit, a generation unit, a selection unit, a try-on unit, a confirmation unit, and a simulation unit. The input unit receives the user's physical data. The user's physical data includes, but is not limited to, height, weight, shoulder width, and waist size. For example, the user enters their height in centimeters. The input unit also allows the user to enter their weight in kilograms. Furthermore, the input unit allows the user to enter their shoulder width in centimeters. For example, the input unit allows the user to enter their waist size in centimeters. The generation unit generates an avatar based on the information entered by the input unit. For example, the generation unit generates an avatar with the same build as the user based on information such as the user's height, weight, shoulder width, and waist size. The generation unit can also generate an avatar using a 3D model. Furthermore, the generation unit can also generate an avatar using a 2D illustration. For example, the generation unit generates an avatar in real time based on the user's physical data. The selection unit allows the user to choose the clothes they want to try on. The selection unit, for example, selects clothing items of interest from an online shopping site and inputs that information into the AR system. The selection unit can also retrieve information about the clothing selected by the user from a database. Furthermore, the selection unit can input information about the clothing selected by the user into the AR system in real time. For example, the selection unit inputs information about the clothing selected by the user using a QR code. The try-on unit has the selected clothing try on an avatar and displays it in real time. For example, the try-on unit can have the selected clothing try on an avatar and display it rotated 360 degrees. The try-on unit can also have the selected clothing try on an avatar and display a walking animation. Furthermore, the try-on unit can have the selected clothing try on an avatar and adjust the size in real time. For example, the try-on unit has the selected clothing try on an avatar and displays the degree of match in color and design. The confirmation unit checks the fit and appearance of the clothing tried on by the try-on unit. For example, the confirmation unit checks how the avatar rotates 360 degrees and walks while wearing the clothing. Furthermore, the verification unit can also check how easily the avatar can move while wearing clothes.Furthermore, the verification unit can also check the degree of size matching when the avatar is wearing the clothes. For example, the verification unit can check the degree of matching in color and design when the avatar is wearing the clothes. The simulation unit performs a custom fit simulation based on the information verified by the verification unit. For example, the simulation unit adjusts the size of the clothes to match the user's body shape to provide an optimal fit. The simulation unit can also adjust the design of the clothes to match the user's body shape. Furthermore, the simulation unit can adjust the material of the clothes to match the user's body shape. For example, the simulation unit simulates a custom fit of the clothes to match the user's body shape. As a result, the AR modeling system according to this embodiment generates an avatar based on the user's body data and simulates trying on clothes, allowing the user to check the fit and appearance before purchasing.

[0078] The input section allows users to enter their physical data. This data may include, but is not limited to, height, weight, shoulder width, and waist size. For example, the user can enter their height in centimeters. The input section can also allow users to enter their weight in kilograms. Furthermore, the input section can allow users to enter their shoulder width in centimeters. For example, the input section can allow users to enter their waist size in centimeters. The input section provides an intuitive interface to make data entry easy. For example, users can select values ​​using sliders or dropdown menus. The input section also includes input verification features to prevent users from entering incorrect data. For example, it can set ranges for height and weight and display a warning if a value outside the range is entered. Furthermore, the input section saves and allows users to reuse previously entered data. This saves users the trouble of entering the same data every time. For example, it can automatically display previously entered height and weight data and allow users to correct it as needed. The input section protects user privacy by encrypting data and controlling access. For example, user data is stored encrypted and accessible only to authenticated users. This allows users to enter data with confidence.

[0079] The generation unit generates avatars based on information entered by the input unit. For example, the generation unit generates an avatar with the same build as the user based on information such as the user's height, weight, shoulder width, and waist size. The generation unit can also generate avatars using 3D models. Furthermore, the generation unit can generate avatars using 2D illustrations. For example, the generation unit generates avatars in real time based on the user's body data. The generation unit uses advanced algorithms to create detailed 3D models based on the user's body data. For example, when data such as the user's height, weight, shoulder width, and waist size are entered, the generation unit analyzes this data and generates a 3D avatar that is closest to the user's body type. The generation unit adjusts the avatar's proportions to match the user's body type, achieving a realistic appearance. The generation unit can also generate the avatar's face using the user's facial photograph. For example, when a user uploads a facial photograph, the generation unit uses facial recognition technology to extract facial features and reflect them in the avatar's face. This allows the user to create an avatar that resembles themselves. The generation unit also has the function of generating avatar movements and facial expressions in real time. For example, when a user operates the avatar, the generation unit generates the avatar's movements and facial expressions in real time, resulting in natural movements. This allows users to perform various simulations using the avatar.

[0080] The selection unit allows the user to choose clothes they want to try on. For example, the unit selects clothes they are interested in on an online shopping site and inputs that information into the AR system. The selection unit can also retrieve information about the clothes the user has selected from a database. Furthermore, the selection unit can input information about the clothes the user has selected into the AR system in real time. For example, the selection unit inputs information about the clothes the user has selected using a QR code. The selection unit provides an intuitive interface to make it easy for users to choose clothes. For example, when a user selects clothes on an online shopping site, the selection unit automatically retrieves that information and inputs it into the AR system. The selection unit can also retrieve information about the clothes the user has selected from a database and display it in real time. This allows the user to simulate trying on clothes while checking detailed information about the clothes they have selected. The selection unit also has the function to input information about clothes the user has selected using QR codes or barcodes. For example, if a user finds clothes they are interested in at a store, the selection unit scans the QR code of those clothes and inputs the information into the AR system. This allows the user to simulate trying on clothes they found in a store at home. The selection unit also has the function to save information about clothes the user has selected so that it can be reused later. For example, if a user selects multiple outfits and performs a try-on simulation, the selection section saves that information and can be used later when performing another try-on simulation. This allows users to perform try-on simulations more efficiently.

[0081] The fitting room allows users to try on selected clothing on an avatar and view the results in real time. For example, it can try on selected clothing on an avatar and display a 360-degree rotation. It can also display walking animations of the avatar trying on selected clothing. Furthermore, the fitting room can adjust the size of selected clothing in real time. For example, it can try on selected clothing on an avatar and display the degree of match in color and design. The fitting room uses advanced rendering technology to reproduce realistic textures and movements when the user tries on clothing selected by the user on an avatar. For example, the fitting room accurately reproduces the material and color of the selected clothing, realistically displaying how the avatar will look when wearing it. The fitting room also displays how the avatar moves while wearing the clothing in real time, allowing users to check the fit and ease of movement of the clothing. The fitting room also has a function to adjust the size of the clothing selected by the user in real time. For example, when a user changes the size of their clothing, the fitting room automatically adjusts the size of the clothing on their avatar and displays it in real time. This allows the user to find the size that best suits them. The fitting room also has a function to display how well the selected clothing matches the avatar's color and design. For example, it displays in real time how the color and design of the clothing selected by the user will look on their avatar, allowing the user to check the appearance of the clothing. This helps the user find clothes that suit them.

[0082] The verification unit checks the fit and appearance of the clothes tried on by the fitting unit. For example, the verification unit checks how the avatar rotates 360 degrees and walks while wearing the clothes. The verification unit can also check how easily the avatar can move while wearing the clothes. Furthermore, the verification unit can check the degree of size matching while the avatar is wearing the clothes. For example, the verification unit checks how well the colors and designs match while the avatar is wearing the clothes. The verification unit has a function to display the avatar from various viewpoints and angles so that users can check the results of the fitting simulation in detail. For example, users can rotate the avatar 360 degrees to check the fit and appearance of the clothes. The verification unit can also display how the avatar walks while wearing the clothes to check how easily it can move. This allows users to simulate the actual comfort of wearing the clothes. The verification unit also has a function to check the degree of size matching while the avatar is wearing the clothes. For example, it can display in real time how the size of the clothes selected by the user fits the avatar, allowing users to check the degree of size matching. This allows users to find the size that is best suited to them. The verification unit also includes a function to check the degree of color and design matching when the avatar is wearing clothes. For example, it displays in real time how the colors and designs of the clothes selected by the user will look on the avatar, allowing the user to check the appearance of the clothes. This allows users to find clothes that suit them.

[0083] The simulation unit performs a custom fit simulation based on the information verified by the verification unit. For example, the simulation unit adjusts the clothing size to match the user's body shape, providing an optimal fit. The simulation unit can also adjust the clothing design to match the user's body shape. Furthermore, the simulation unit can adjust the clothing material to match the user's body shape. For example, the simulation unit simulates a custom fit for the clothing based on the user's body shape. The simulation unit uses advanced algorithms to adjust the clothing size and design to provide the optimal fit for the user's body shape. For example, based on data such as the user's height, weight, shoulder width, and waist size, the simulation unit automatically adjusts the clothing size to provide an optimal fit. The simulation unit also has a function to adjust the clothing design to match the user's body shape. For example, it adjusts the silhouette and details of the clothing to match the user's body shape for a better appearance. The simulation unit also has a function to adjust the clothing material to match the user's body shape. For example, it adjusts the elasticity and thickness of the clothing material to match the user's body shape for optimal comfort. This allows the user to find the perfect clothing for themselves. The simulation unit also includes a function that allows users to save the results of their fitting simulations and reuse them later. For example, if a user simulates trying on multiple clothes, the simulation unit saves that information and can use it when performing another fitting simulation later. This allows users to perform fitting simulations efficiently.

[0084] The input section allows users to input information such as their height, weight, shoulder width, and waist size. For example, the user can input their height in centimeters. The input section can also allow the user to input their weight in kilograms. Furthermore, the input section can allow the user to input their shoulder width in centimeters. For example, the input section can allow the user to input their waist size in centimeters. This allows for the generation of a more accurate avatar by inputting detailed physical data of the user. Some or all of the above processing in the input section may be performed using AI, or not. For example, the input section can input the user's physical data into the AI, which can then analyze the data and extract the information necessary for avatar generation.

[0085] The generation unit can generate an avatar with the same build as the user based on the information input by the input unit. For example, the generation unit generates an avatar with the same build as the user based on information such as the user's height, weight, shoulder width, and waist size. The generation unit can also generate avatars using 3D models. Furthermore, the generation unit can generate avatars using 2D illustrations. For example, the generation unit generates an avatar in real time based on the user's physical data. This allows for the generation of an accurate avatar based on the user's physical data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's physical data into AI, and the AI ​​can analyze the data to generate an avatar.

[0086] The selection unit can choose clothes of interest from an online shopping site and input that information into the AR system. For example, the selection unit can choose clothes of interest from an online shopping site and input that information into the AR system. The selection unit can also retrieve information about the clothes selected by the user from a database. Furthermore, the selection unit can input information about the clothes selected by the user into the AR system in real time. For example, the selection unit can input information about the clothes selected by the user using a QR code. This allows the user to input the clothes they selected from the online shopping site into the AR system. Some or all of the above processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input information about the clothes selected by the user into AI, and the AI ​​can analyze the data and input it into the AR system.

[0087] The fitting room unit can have an avatar try on selected clothing and display the results in real time. For example, the fitting room unit can have an avatar try on selected clothing and display it rotated 360 degrees. The fitting room unit can also have an avatar try on selected clothing and display a walking animation. Furthermore, the fitting room unit can have an avatar try on selected clothing and adjust the size in real time. For example, the fitting room unit can have an avatar try on selected clothing and display the degree of match in color and design. This allows users to check the fit and appearance of selected clothing by having an avatar try it on and displaying the results in real time. Some or all of the above processes in the fitting room unit may be performed using AI, for example, or not. For example, the fitting room unit can input data of selected clothing into an AI, which can analyze the data and have the avatar try on the clothing.

[0088] The verification unit can check how the avatar rotates 360 degrees and walks while wearing clothes. For example, the verification unit can check how the avatar rotates 360 degrees and walks while wearing clothes. The verification unit can also check how easily the avatar can move while wearing clothes. Furthermore, the verification unit can check how well the avatar fits the clothes. For example, the verification unit can check how well the avatar fits the clothes in terms of color and design. This allows the user to check the fit and appearance of the clothes in detail by checking how the avatar rotates 360 degrees and walks while wearing clothes. Some or all of the above processing in the verification unit may be performed using AI, for example, or not using AI. For example, the verification unit can input the avatar's movement data into AI, and the AI ​​can analyze the data to evaluate the fit and appearance.

[0089] The simulation unit can adjust the size of clothing to match the user's body shape, providing an optimal fit. For example, the simulation unit can adjust the size of clothing to match the user's body shape, providing an optimal fit. The simulation unit can also adjust the design of clothing to match the user's body shape. Furthermore, the simulation unit can adjust the material of clothing to match the user's body shape. For example, the simulation unit simulates a custom fit of clothing to match the user's body shape. This allows the user to find the perfect clothing by adjusting the size of clothing to match their body shape and providing an optimal fit. Some or all of the above processes in the simulation unit may be performed using AI, or not. For example, the simulation unit can input the user's body shape data into an AI, which can then analyze the data to provide an optimal fit.

[0090] The input unit can estimate the user's emotions and adjust the timing of input based on the estimated emotions. For example, if the user is stressed, the input unit can simplify the input and request only the minimum necessary information. If the user is relaxed, the input unit can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the input unit can prioritize voice input to allow for quick input of physical data. For example, if the user is stressed, the input unit reduces and simplifies the input items. By adjusting the timing of input based on the user's emotions, it is possible to reduce user stress and provide a comfortable input experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not using AI. For example, the input unit can input the user's facial expression data into a generative AI, which can analyze the data to estimate emotions.

[0091] The input unit can analyze the user's past input history and select the optimal input method. For example, the input unit can automatically display as candidates physical data that the user has frequently entered in the past. The input unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the input unit can predict and suggest physical data to be used at specific times based on the user's past input history. For example, the input unit can automatically display as candidates physical data that the user has frequently entered in the past. By analyzing the user's past input history, it provides the optimal input method and improves input efficiency. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's past input data into AI, which can then analyze the data and suggest the optimal input method.

[0092] The input unit can customize input fields based on the user's current physical condition and lifestyle. For example, if the user is tired, the input unit can reduce and simplify the input fields. It can also request detailed physical data if the user leads a healthy lifestyle. Furthermore, if the user has plans to attend a specific event, the input unit can prioritize inputting physical data related to that event. For example, if the user is tired, the input unit can reduce and simplify the input fields. This allows for an optimal input experience for the user by customizing input fields based on their current physical condition and lifestyle. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit can input the user's physical condition data into AI, which can then analyze the data and customize the input fields.

[0093] The input unit can estimate the user's emotions and prioritize input items based on the estimated emotions. For example, if the user is nervous, the input unit may prompt the user to enter the most important items first. Conversely, if the user is relaxed, the input unit may allow the user to postpone detailed items. Furthermore, if the user is in a hurry, the input unit may prompt the user to enter the simplest items first. This allows for the provision of an optimal input experience tailored to the user's situation by prioritizing input items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the input unit may be performed using AI, or not. For example, the input unit can input user facial expression data into a generative AI, which can analyze the data to estimate emotions.

[0094] The input unit can prioritize displaying input items that are highly relevant to the user's geographical location during input. For example, if the user is in a cold region, the input unit will prioritize displaying input items related to body temperature and cold weather protection measures. Similarly, if the user is in a hot and humid region, the input unit can prioritize displaying input items related to sweating tendency and breathability. Furthermore, if the user is in an urban area, the input unit can prioritize displaying input items related to walking distance and commute time. This allows the system to provide the user with an optimal input experience by prioritizing the display of highly relevant input items based on their geographical location. Some or all of the above processing in the input unit may be performed using AI, or without AI. For example, the input unit can input the user's geographical location information into AI, which can then analyze the data and display highly relevant input items.

[0095] The input unit can analyze the user's social media activity during input and suggest relevant input items. For example, if the user frequently posts about fitness, the input unit can suggest input items related to body fat percentage and muscle mass. Similarly, if the user frequently posts about fashion, it can suggest input items related to shoulder width and waist size. Furthermore, if the user frequently posts about travel, it can suggest input items related to weight and height. In this way, by analyzing the user's social media activity, relevant input items are suggested, improving the efficiency of input. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's social media data into AI, which can then analyze the data and suggest relevant input items.

[0096] The generation unit can estimate the user's emotions and adjust the avatar generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed avatar. It can also generate a simplified avatar if the user is in a hurry. Furthermore, if the user is excited, the generation unit can generate a visually stimulating avatar. For example, if the user is relaxed, the generation unit generates a detailed avatar. By adjusting the avatar generation method based on the user's emotions, the system can provide the user with the optimal avatar generation experience. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into the generation AI, which can analyze the data to estimate emotions and adjust the avatar generation method based on the results.

[0097] The generation unit can select the optimal generation method by referring to the user's past avatar generation history during generation. For example, the generation unit can propose the optimal generation method based on the data of avatars the user has previously generated. The generation unit can also generate avatars that reflect the user's preferred style based on the user's past avatar generation history. Furthermore, the generation unit can analyze the user's past avatar generation history and propose the most efficient generation method. For example, the generation unit proposes the optimal generation method based on the data of avatars the user has previously generated. In this way, by referring to the user's past avatar generation history, the optimal generation method is provided, improving the efficiency of avatar generation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past avatar data into AI, and the AI ​​can analyze the data and propose the optimal generation method.

[0098] The generation unit can customize the avatar's details based on the user's current body shape and posture during generation. For example, if the user inputs their current body shape, the generation unit will customize the avatar based on that body shape. The generation unit can also customize the avatar based on the user's current posture if that posture is input. Furthermore, if the user inputs their current body shape and posture in real time, the generation unit can customize the avatar based on that information. For example, if the user inputs their current body shape, the generation unit will customize the avatar based on that body shape. This allows the generation unit to provide the user with the most suitable avatar by customizing the avatar's details based on the user's current body shape and posture. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the user's body shape data into an AI, which can then analyze the data and customize the avatar's details.

[0099] The generation unit can estimate the user's emotions and adjust the avatar generation order based on the estimated emotions. For example, if the user is nervous, the generation unit can start generating from the most important parts. Conversely, if the user is relaxed, the generation unit can postpone generating the detailed parts. Furthermore, if the user is in a hurry, the generation unit can start generating from the simplest parts. This allows for an optimal avatar generation experience for the user by adjusting the avatar generation order based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input user facial expression data into a generation AI, which analyzes the data to estimate emotions and adjusts the avatar generation order based on the results.

[0100] The generation unit can select the optimal avatar generation method based on the user's geographical location information during generation. For example, if the user is in a cold region, the generation unit will generate avatars related to body temperature and cold weather protection. Furthermore, if the user is in a hot and humid region, the generation unit can generate avatars related to sweating tendency and breathability. Additionally, if the user is in an urban area, the generation unit can generate avatars related to walking distance and commuting time. For example, if the user is in a cold region, the generation unit will generate avatars related to body temperature and cold weather protection. By selecting the optimal avatar generation method based on the user's geographical location information, the generation unit can provide the user with the most suitable avatar. Some or all of the above-described processes in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the user's geographical location information into AI, which can then analyze the data and select the optimal avatar generation method.

[0101] The generation unit can analyze the user's social media activity during generation and suggest relevant avatar generation methods. For example, if a user frequently posts about fitness, the generation unit can generate avatars related to body fat percentage and muscle mass. Similarly, if a user frequently posts about fashion, the generation unit can generate avatars related to shoulder width and waist size. Furthermore, if a user frequently posts about travel, the generation unit can generate avatars related to weight and height. By analyzing the user's social media activity, the generation unit can suggest relevant avatar generation methods and improve the efficiency of avatar generation. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into AI, which can then analyze the data and suggest relevant avatar generation methods.

[0102] The selection unit can estimate the user's emotions and adjust the clothing selection method based on the estimated emotions. For example, if the user is relaxed, the selection unit can provide detailed selection options. It can also provide simplified selection options if the user is in a hurry. Furthermore, if the user is excited, the selection unit can provide visually stimulating selection options. For example, if the user is relaxed, the selection unit can provide detailed selection options. This allows for an optimal selection experience for the user by adjusting the clothing selection method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input user facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the clothing selection method based on the result.

[0103] The selection unit can select the optimal selection method by referring to the user's past selection history during the selection process. For example, the selection unit can suggest the optimal selection method based on data of clothes the user has previously selected. The selection unit can also provide a selection method that reflects the user's preferred style based on their past selection history. Furthermore, the selection unit can analyze the user's past selection history and suggest the most efficient selection method. For example, the selection unit suggests the optimal selection method based on data of clothes the user has previously selected. By referring to the user's past selection history, it provides the optimal selection method and improves the efficiency of the selection process. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's past selection data into AI, which can then analyze the data and suggest the optimal selection method.

[0104] The selection unit can customize the options based on the user's current fashion trends when making a selection. For example, if the user inputs their current fashion trends, the selection unit will customize the options based on those trends. Furthermore, if the user inputs their current fashion trends in real time, the selection unit can also customize the options based on that information. Additionally, if the selection unit obtains the user's current fashion trends from social media, it can customize the options based on that information. For example, if the user inputs their current fashion trends, the selection unit will customize the options based on those trends. This allows the selection unit to provide the user with the best possible selection experience by customizing the options based on their current fashion trends. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input the user's fashion trend data into an AI, which can then analyze the data and customize the options.

[0105] The selection unit can estimate the user's emotions and determine the priority of options based on the estimated emotions. For example, if the user is nervous, the selection unit will display the most important option first. If the user is relaxed, the selection unit can also postpone detailed options. Furthermore, if the user is in a hurry, the selection unit can display the simplest option first. For example, if the user is nervous, the selection unit will display the most important option first. This allows for an optimal selection experience tailored to the user's situation by prioritizing options based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input user facial expression data into a generative AI, which analyzes the data to estimate emotions and determines the priority of options based on the results.

[0106] The selection section can prioritize displaying highly relevant options based on the user's geographical location information when an option is selected. For example, if the user is in a cold region, the selection section will prioritize displaying options related to cold weather protection. Similarly, if the user is in a hot and humid region, the selection section can prioritize displaying options related to breathability. Furthermore, if the user is in an urban area, the selection section can prioritize displaying options related to walking distance and commute time. This allows the selection section to provide the user with an optimal selection experience by prioritizing highly relevant options based on their geographical location information. Some or all of the above processing in the selection section may be performed using AI, or not. For example, the selection section can input the user's geographical location information into an AI, which can then analyze the data and display highly relevant options.

[0107] The selection unit can analyze the user's social media activity and suggest relevant options when making a selection. For example, if the user frequently posts about fitness, the selection unit can suggest options related to sportswear. Similarly, if the user frequently posts about fashion, it can suggest options related to trends. Furthermore, if the user frequently posts about travel, it can suggest options related to travel clothing. In this way, by analyzing the user's social media activity, it suggests relevant options and improves the efficiency of the selection process. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media data into an AI, which can then analyze the data and suggest relevant options.

[0108] The fitting room unit can estimate the user's emotions and adjust the way the fitting room is displayed based on the estimated emotions. For example, if the user is relaxed, the fitting room unit can provide a detailed fitting room display. It can also provide a simplified fitting room display if the user is in a hurry. Furthermore, if the user is excited, the fitting room unit can provide a visually stimulating fitting room display. For example, if the user is relaxed, the fitting room unit provides a detailed fitting room display. This allows for an optimal fitting room experience for the user by adjusting the fitting room display based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fitting room unit may be performed using AI, or not. For example, the fitting room unit can input user facial expression data into a generative AI, which analyzes the data to estimate emotions and adjusts the fitting room display based on the result.

[0109] The fitting room unit can select the optimal fitting method by referring to the user's past fitting history during the fitting process. For example, the fitting room unit can suggest the optimal fitting method based on data of clothes the user has tried on in the past. Furthermore, the fitting room unit can provide a fitting method that reflects the user's preferred style based on their past fitting history. In addition, the fitting room unit can analyze the user's past fitting history and suggest the most efficient fitting method. For example, the fitting room unit suggests the optimal fitting method based on data of clothes the user has tried on in the past. This improves fitting efficiency by providing the optimal fitting method through reference to the user's past fitting history. Some or all of the above processing in the fitting room unit may be performed using AI, or without AI. For example, the fitting room unit can input the user's past fitting data into an AI, which can then analyze the data and suggest the optimal fitting method.

[0110] The fitting room unit can customize fitting details based on the user's current body shape and posture during the fitting process. For example, if the user inputs their current body shape, the fitting room unit will customize the fitting details based on that body shape. It can also customize fitting details based on the user's current posture if that posture is input. Furthermore, if the user inputs their current body shape and posture in real time, the fitting room unit can customize fitting details based on that information. For example, if the user inputs their current body shape, the fitting room unit will customize fitting details based on that body shape. This allows for the provision of an optimal fitting experience for the user by customizing fitting details based on their current body shape and posture. Some or all of the above processing in the fitting room unit may be performed using AI, or not. For example, the fitting room unit can input the user's body shape data into an AI, which can then analyze the data and customize the fitting details.

[0111] The fitting room unit can estimate the user's emotions and adjust the fitting order based on the estimated emotions. For example, if the user is nervous, the fitting room unit can start the fitting with the most important parts. If the user is relaxed, the fitting room unit can postpone the detailed parts. Furthermore, if the user is in a hurry, the fitting room unit can start the fitting with the simplest parts. For example, if the user is nervous, the fitting room unit can start the fitting with the most important parts. This allows the system to provide the user with the best possible fitting experience by adjusting the fitting order based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input the user's facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the fitting order based on the result.

[0112] The fitting room unit can select the optimal fitting method based on the user's geographical location information during the fitting process. For example, if the user is in a cold region, the fitting room unit can provide a fitting method related to cold weather protection. It can also provide a fitting method related to breathability if the user is in a hot and humid region. Furthermore, if the user is in an urban area, the fitting room unit can provide a fitting method related to walking distance and commuting time. For example, if the user is in a cold region, the fitting room unit can provide a fitting method related to cold weather protection. By selecting the optimal fitting method based on the user's geographical location information, the system can provide the user with the best possible fitting experience. Some or all of the above processing in the fitting room unit may be performed using AI, or without AI. For example, the fitting room unit can input the user's geographical location information into AI, which can then analyze the data and select the optimal fitting method.

[0113] The fitting room unit can analyze the user's social media activity during the fitting process and suggest relevant fitting methods. For example, if the user frequently posts about fitness, the fitting room unit can suggest fitting methods related to sportswear. Similarly, if the user frequently posts about fashion, it can suggest fitting methods related to trends. Furthermore, if the user frequently posts about travel, it can suggest fitting methods related to travel clothing. In this way, by analyzing the user's social media activity, it can suggest relevant fitting methods and improve the efficiency of the fitting process. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's social media data into an AI, which can then analyze the data and suggest relevant fitting methods.

[0114] The confirmation unit can estimate the user's emotions and adjust the display method of confirmation based on the estimated user emotions. For example, if the user is relaxed, the confirmation unit can provide a detailed confirmation display. It can also provide a simplified confirmation display if the user is in a hurry. Furthermore, if the user is excited, the confirmation unit can provide a visually stimulating confirmation display. For example, if the user is relaxed, the confirmation unit can provide a detailed confirmation display. This allows for the provision of an optimal confirmation experience for the user by adjusting the display method of confirmation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the confirmation unit may be performed using AI, or not. For example, the confirmation unit can input user facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the display method of confirmation based on the result.

[0115] The verification unit can select the optimal verification method by referring to the user's past verification history during the verification process. For example, the verification unit can suggest the optimal verification method based on the user's past clothing verification data. The verification unit can also provide a verification method that reflects the user's preferred style based on their past verification history. Furthermore, the verification unit can analyze the user's past verification history and suggest the most efficient verification method. For example, the verification unit suggests the optimal verification method based on the user's past clothing verification data. By referring to the user's past verification history, it provides the optimal verification method and improves the efficiency of the verification process. Some or all of the above-described processes in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's past verification data into AI, which can then analyze the data and suggest the optimal verification method.

[0116] The verification unit can customize the verification details based on the user's current body shape and posture during verification. For example, if the user inputs their current body shape, the verification unit will customize the verification details based on that body shape. The verification unit can also customize the verification details based on the user's current posture if that posture is input. Furthermore, if the user inputs their current body shape and posture in real time, the verification unit can also customize the verification details based on that information. For example, if the user inputs their current body shape, the verification unit will customize the verification details based on that body shape. By customizing the verification details based on the user's current body shape and posture, the system can provide the user with the optimal verification experience. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's body shape data into AI, which can then analyze the data and customize the verification details.

[0117] The verification unit can estimate the user's emotions and adjust the order of verification based on the estimated emotions. For example, if the user is nervous, the verification unit can start the verification from the most important part. Also, if the user is relaxed, the verification unit can postpone the detailed parts. Furthermore, if the user is in a hurry, the verification unit can start the verification from the simplest part. For example, if the user is nervous, the verification unit can start the verification from the most important part. In this way, by adjusting the order of verification based on the user's emotions, the system can provide the user with the optimal verification experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not using AI. For example, the verification unit can input the user's facial expression data into a generative AI, the generative AI can analyze the data to estimate emotions, and adjust the order of verification based on the result.

[0118] The verification unit can select the optimal verification method based on the user's geographical location information during verification. For example, if the user is in a cold region, the verification unit can provide a verification method related to cold weather protection. It can also provide a verification method related to breathability if the user is in a hot and humid region. Furthermore, if the user is in an urban area, the verification unit can provide a verification method related to walking distance and commuting time. For example, if the user is in a cold region, the verification unit can provide a verification method related to cold weather protection. This allows the system to provide the user with the optimal verification experience by selecting the optimal verification method based on the user's geographical location information. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's geographical location information into AI, which can then analyze the data and select the optimal verification method.

[0119] The verification unit can analyze the user's social media activity during verification and suggest relevant verification methods. For example, if the user frequently posts about fitness, the verification unit can suggest verification methods related to sportswear. Similarly, if the user frequently posts about fashion, the verification unit can suggest verification methods related to trends. Furthermore, if the user frequently posts about travel, the verification unit can suggest verification methods related to travel clothing. In this way, by analyzing the user's social media activity, relevant verification methods are suggested, improving the efficiency of verification. Some or all of the above processing in the verification unit may be performed using AI, for example, or without AI. For example, the verification unit can input the user's social media data into AI, which can then analyze the data and suggest relevant verification methods.

[0120] The simulation unit can estimate the user's emotions and adjust the simulation method based on the estimated emotions. For example, if the user is relaxed, the simulation unit can provide a detailed simulation. It can also provide a simplified simulation if the user is in a hurry. Furthermore, if the user is excited, the simulation unit can provide a visually stimulating simulation. For example, if the user is relaxed, the simulation unit can provide a detailed simulation. This allows for the provision of an optimal simulation experience for the user by adjusting the simulation method based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI or not. For example, the simulation unit can input user facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the simulation method based on the results.

[0121] The simulation unit can select the optimal simulation method by referring to the user's past simulation history during a simulation. For example, the simulation unit can propose the optimal simulation method based on the user's past simulation data for clothing. The simulation unit can also provide a simulation method that reflects the user's preferred style based on their past simulation history. Furthermore, the simulation unit can analyze the user's past simulation history and propose the most efficient simulation method. For example, the simulation unit proposes the optimal simulation method based on the user's past simulation data for clothing. By referring to the user's past simulation history, it provides the optimal simulation method and improves the efficiency of the simulation. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past simulation data into AI, which can then analyze the data and propose the optimal simulation method.

[0122] The simulation unit can customize the simulation details based on the user's current body shape and posture during the simulation. For example, if the user inputs their current body shape, the simulation unit will customize the simulation details based on that body shape. The simulation unit can also customize the simulation details based on the user's current posture if that posture is input. Furthermore, if the user inputs their current body shape and posture in real time, the simulation unit can customize the simulation details based on that information. For example, if the user inputs their current body shape, the simulation unit will customize the simulation details based on that body shape. By customizing the simulation details based on the user's current body shape and posture, the system can provide the user with the optimal simulation experience. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's body shape data into AI, which can then analyze the data and customize the simulation details.

[0123] The simulation unit can estimate the user's emotions and adjust the order of the simulation based on the estimated emotions. For example, if the user is nervous, the simulation unit can start the simulation from the most important parts. Also, if the user is relaxed, the simulation unit can postpone the detailed parts. Furthermore, if the user is in a hurry, the simulation unit can start the simulation from the simplest parts. For example, if the user is nervous, the simulation unit can start the simulation from the most important parts. This allows the system to provide the user with the optimal simulation experience by adjusting the order of the simulation based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI or not using AI. For example, the simulation unit can input user facial expression data into a generative AI, the generative AI can analyze the data to estimate emotions, and adjust the order of the simulation based on the results.

[0124] The simulation unit can select the optimal simulation method based on the user's geographical location information during a simulation. For example, if the user is in a cold region, the simulation unit can provide a simulation method related to cold weather protection. The simulation unit can also provide a simulation method related to ventilation if the user is in a hot and humid region. Furthermore, if the user is in an urban area, the simulation unit can provide a simulation method related to walking distance and commuting time. For example, if the user is in a cold region, the simulation unit can provide a simulation method related to cold weather protection. This allows the system to provide the user with the optimal simulation experience by selecting the optimal simulation method based on the user's geographical location information. Some or all of the above processing in the simulation unit may be performed using AI, or without AI. For example, the simulation unit can input the user's geographical location information into the AI, which can then analyze the data and select the optimal simulation method.

[0125] The simulation unit can analyze the user's social media activity during a simulation and suggest relevant simulation methods. For example, if the user frequently posts about fitness, the simulation unit can suggest a simulation method related to sportswear. Similarly, if the user frequently posts about fashion, the simulation unit can suggest a simulation method related to trends. Furthermore, if the user frequently posts about travel, the simulation unit can suggest a simulation method related to travel clothing. In this way, by analyzing the user's social media activity, relevant simulation methods are suggested, improving the efficiency of the simulation. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's social media data into AI, which can then analyze the data and suggest relevant simulation methods.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The AR modeling system can estimate the user's emotions and customize the fitting experience based on those emotions. For example, if the user is stressed, the fitting area can provide a simple and intuitive interface to expedite the fitting process. If the user is relaxed, the fitting area can provide detailed feedback and additional customization options. Furthermore, if the user is excited, the fitting area can add visually appealing animations and effects. This allows for the provision of an optimal fitting experience tailored to the user's emotions. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice data. Some or all of the above processing in the fitting area may be performed using AI or not.

[0128] The AR modeling system can analyze a user's past try-on history and suggest the optimal try-on method. For example, it can prioritize suggesting similar styles and sizes of clothing based on data from clothes the user has tried on in the past. It can also provide try-on options that reflect the user's preferred colors and designs based on their past try-on history. Furthermore, it can analyze the user's past try-on history and suggest the most efficient try-on method. This allows the system to provide the optimal try-on method by referring to the user's past try-on history, thereby improving the efficiency of the try-on process. Some or all of the above processing in the try-on section may be performed using AI or not.

[0129] The AR modeling system can customize the details of the try-on based on the user's current body shape and posture. For example, if the user inputs their current body shape, the system can customize the details of the try-on based on that body shape. Similarly, if the user inputs their current posture, the system can customize the details of the try-on based on that posture. Furthermore, if the user inputs their current body shape and posture in real time, the system can customize the details of the try-on based on that information. This allows the system to provide the user with the optimal try-on experience by customizing the details of the try-on based on their current body shape and posture. Some or all of the above processing in the try-on section may be performed using AI, or it may not.

[0130] The AR modeling system can customize fitting details based on the user's geographical location. For example, if the user is in a cold region, it can provide fitting options related to cold weather protection. Similarly, if the user is in a hot and humid region, it can provide fitting options related to breathability. Furthermore, if the user is in an urban area, it can provide fitting options related to walking distance and commute time. This allows for the provision of optimal fitting options based on the user's geographical location, thereby providing the user with the best possible fitting experience. Some or all of the above processing in the fitting section may be performed using AI, or without AI.

[0131] The AR modeling system can analyze a user's social media activity and suggest relevant try-on options. For example, if a user frequently posts about fitness, it can suggest try-on options related to sportswear. Similarly, if a user frequently posts about fashion, it can suggest try-on options related to trends. Furthermore, if a user frequently posts about travel, it can suggest try-on options related to travel clothes. By analyzing a user's social media activity, it is possible to suggest relevant try-on options and improve the efficiency of the try-on process. Some or all of the above processing in the try-on section may be performed using AI or not.

[0132] The AR modeling system can estimate the user's emotions and adjust the avatar generation method based on the estimated emotions. For example, if the user is relaxed, a detailed avatar can be generated. If the user is in a hurry, a simplified avatar can be generated. Furthermore, if the user is excited, a visually stimulating avatar can be generated. In this way, by adjusting the avatar generation method based on the user's emotions, the system can provide the user with the optimal avatar generation experience. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice data. Some or all of the above processing in the generation unit may be performed using AI or not.

[0133] The AR modeling system can select the optimal generation method by referring to the user's past avatar generation history. For example, it can suggest the optimal generation method based on the data of avatars the user has previously generated. It can also generate avatars that reflect the user's preferred style based on their past avatar generation history. Furthermore, it can analyze the user's past avatar generation history and suggest the most efficient generation method. In this way, by referring to the user's past avatar generation history, the system can provide the optimal generation method and improve the efficiency of avatar generation. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0134] The AR modeling system can customize the details of an avatar based on the user's current body shape and posture. For example, if the user inputs their current body shape, the avatar can be customized based on that body shape. Similarly, if the user inputs their current posture, the avatar can be customized based on that posture. Furthermore, if the user inputs their current body shape and posture in real time, the avatar can be customized based on that information. This allows the system to provide the user with the most suitable avatar by customizing its details based on their current body shape and posture. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not.

[0135] The AR modeling system can estimate the user's emotions and adjust the avatar generation order based on the estimated emotions. For example, if the user is nervous, generation can start from the most important parts. If the user is relaxed, detailed parts can be postponed. Furthermore, if the user is in a hurry, generation can start from the simplest parts. By adjusting the avatar generation order based on the user's emotions, the system can provide the user with the optimal avatar generation experience. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice data. Some or all of the above processing in the generation unit may be performed using AI or not.

[0136] The AR modeling system can customize avatar details based on the user's geographical location. For example, if the user is in a cold region, it can generate an avatar related to cold weather protection. If the user is in a hot and humid region, it can generate an avatar related to breathability. Furthermore, if the user is in an urban area, it can generate an avatar related to walking distance and commute time. This allows the system to provide the optimal avatar based on the user's geographical location, thereby providing the user with the best possible avatar generation experience. Some or all of the above-described processes in the generation unit may be performed using AI or not.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The input section allows the user to enter their physical data. This data includes, for example, height, weight, shoulder width, and waist size. The input section allows the user to enter this data in centimeters or kilograms. Step 2: The generation unit generates an avatar based on the information entered by the input unit. The generation unit generates an avatar with the same build as the user based on information such as the user's height, weight, shoulder width, and waist size. The generation unit can generate avatars using 3D models or 2D illustrations. Step 3: The selection unit allows the user to choose the clothes they want to try on. The selection unit selects clothes of interest from an online shopping site and inputs that information into the AR system. The selection unit can also retrieve information about the clothes selected by the user from a database. Step 4: The fitting room section allows the avatar to try on the selected clothing and displays it in real time. The fitting room section can rotate the avatar 360 degrees while displaying the selected clothing. The fitting room section can also display a walking animation while the avatar is wearing the selected clothing. Step 5: The verification unit checks the fit and appearance of the clothes tried on by the fitting unit. The verification unit checks how the avatar rotates 360 degrees and walks while wearing the clothes. The verification unit can also check the ease of movement and size match of the avatar while wearing the clothes. Step 6: The simulation unit performs a custom fit simulation based on the information confirmed by the verification unit. The simulation unit adjusts the size, design, and material of the clothing to match the user's body shape, providing an optimal fit.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0141] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the input unit, generation unit, selection unit, fitting unit, confirmation unit, and simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the input unit is implemented by the receiving device 38 of the smart device 14 and inputs the user's body data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an avatar based on the user's body data. The selection unit is implemented by the control unit 46A of the smart device 14 and allows the user to select the clothes they want to try on. The fitting unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows the avatar to try on the selected clothes. The confirmation unit is implemented by the output device 40 of the smart device 14 and confirms the fit and appearance of the tried-on clothes. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a custom fit simulation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the input unit, generation unit, selection unit, try-on unit, confirmation unit, and simulation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the smart glasses 214 and inputs the user's body data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an avatar based on the user's body data. The selection unit is implemented by the control unit 46A of the smart glasses 214 and allows the user to select the clothes they want to try on. The try-on unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows the avatar to try on the selected clothes. The confirmation unit is implemented by the speaker 240 of the smart glasses 214 and confirms the fit and appearance of the tried-on clothes. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs a custom fit simulation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the input unit, generation unit, selection unit, fitting unit, confirmation unit, and simulation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the headset terminal 314 and inputs the user's body data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an avatar based on the user's body data. The selection unit is implemented by, for example, the control unit 46A of the headset terminal 314 and allows the user to select the clothes they want to try on. The fitting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and allows the avatar to try on the selected clothes. The confirmation unit is implemented by, for example, the display 343 of the headset terminal 314 and allows the user to confirm the fit and appearance of the clothes that have been tried on. The simulation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs a custom fit simulation. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0177] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0178] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0179] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0181] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0182] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0183] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0184] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0185] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0186] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0187] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0188] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0189] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0190] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0191] Each of the multiple elements described above, including the input unit, generation unit, selection unit, fitting unit, confirmation unit, and simulation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit is implemented by the microphone 238 of the robot 414 and inputs the user's body data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an avatar based on the user's body data. The selection unit is implemented by, for example, the control unit 46A of the robot 414 and allows the user to select the clothes they want to try on. The fitting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and allows the avatar to try on the selected clothes. The confirmation unit is implemented by, for example, the speaker 240 of the robot 414 and confirms the fit and appearance of the tried-on clothes. The simulation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs a custom fit simulation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0192] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0193] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0194] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0195] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0196] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0197] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0199] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0200] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0201] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0202] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0203] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0204] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0205] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0206] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0207] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0208] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0209] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0210] (Note 1) An input section for entering the user's physical data, A generation unit that generates an avatar based on the information input by the aforementioned input unit, A selection section where the user chooses the clothes they want to try on, A fitting unit that allows the avatar to try on the clothes selected by the selection unit, A confirmation unit for checking the fit and appearance of the clothing tried on by the aforementioned fitting unit, The system includes a simulation unit that performs a custom fit simulation based on the information confirmed by the verification unit. A system characterized by the following features. (Note 2) The aforementioned input unit is Enter the user's height, weight, shoulder width, waist size, and other information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the information input by the aforementioned input unit, an avatar with the same build as the user is generated. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Select an item of clothing you like from an online shopping site and input that information into the AR system. The system described in Appendix 1, characterized by the features described herein. (Note 5) The fitting area is, The selected clothing is tried on by the avatar and displayed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned verification unit is We will observe how the avatar rotates 360 degrees and walks while wearing clothes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned simulation unit, The clothing size is adjusted to the user's body shape, providing an optimal fit. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is It estimates the user's emotions and adjusts the timing of input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is When users enter data, the input fields are customized based on their current health condition and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned input unit is When users enter data, the system prioritizes displaying the most relevant input fields based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned input unit is During input, the system analyzes the user's social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the avatar generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the system selects the optimal generation method by referring to the user's past avatar generation history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the avatar's details are customized based on the user's current body shape and posture. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the avatar generation order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the optimal avatar generation method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the system analyzes the user's social media activity and suggests relevant avatar generation methods. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is It estimates the user's emotions and adjusts the clothing selection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned selection unit is When making a selection, the system refers to the user's past selection history to determine the optimal selection method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned selection unit is When selecting an option, customize the choices based on the user's current fashion preferences. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned selection unit is It estimates the user's emotions and determines the priority of choices based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned selection unit is When making a selection, the system prioritizes displaying the most relevant options based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is When making a selection, the system analyzes the user's social media activity and suggests relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 26) The fitting area is, The system estimates the user's emotions and adjusts how the try-on display is based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The fitting area is, During the fitting process, the system selects the optimal fitting method by referring to the user's past fitting history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The fitting area is, During the fitting process, the fitting details are customized based on the user's current body shape and posture. The system described in Appendix 1, characterized by the features described herein. (Note 29) The fitting area is, It estimates the user's emotions and adjusts the try-on order based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The fitting area is, During the fitting process, the system selects the optimal fitting method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The fitting area is, During the try-on process, the system analyzes the user's social media activity and suggests relevant try-on methods. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned verification unit is The system estimates the user's emotions and adjusts how confirmations are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned verification unit is During verification, the system will refer to the user's past verification history to select the most suitable verification method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned verification unit is During verification, the verification details are customized based on the user's current body shape and posture. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned verification unit is It estimates the user's emotions and adjusts the order of confirmations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned verification unit is During verification, the optimal verification method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned verification unit is During verification, we analyze the user's social media activity and suggest relevant verification methods. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned simulation unit, During simulation, the system selects the optimal simulation method by referring to the user's past simulation history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned simulation unit, During the simulation, the simulation details are customized based on the user's current body shape and posture. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned simulation unit, During the simulation, the optimal simulation method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned simulation unit, During the simulation, we analyze the user's social media activity and propose relevant simulation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An input section for entering the user's physical data, A generation unit that generates an avatar based on the information input by the aforementioned input unit, A selection section where the user chooses the clothes they want to try on, A fitting unit that allows the avatar to try on the clothes selected by the selection unit, A confirmation unit for checking the fit and appearance of the clothing tried on by the aforementioned fitting unit, The system includes a simulation unit that performs a custom fit simulation based on the information confirmed by the verification unit. A system characterized by the following features.

2. The aforementioned input unit is Enter the user's height, weight, shoulder width, waist size, and other information. The system according to feature 1.

3. The generating unit is Based on the information input by the aforementioned input unit, an avatar with the same build as the user is generated. The system according to feature 1.

4. The aforementioned selection unit is Select an item of clothing you like from an online shopping site and input that information into the AR system. The system according to feature 1.

5. The fitting area is, The selected clothing is tried on by the avatar and displayed in real time. The system according to feature 1.

6. The aforementioned verification unit is We will observe how the avatar rotates 360 degrees and walks while wearing clothes. The system according to feature 1.

7. The aforementioned simulation unit, The clothing size is adjusted to the user's body shape, providing an optimal fit. The system according to feature 1.

8. The aforementioned input unit is It estimates the user's emotions and adjusts the timing of input based on the estimated emotions. The system according to feature 1.

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