system

Abstract

The system according to this embodiment aims to enable users to virtually try on fashion items that fit their body type. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a fitting unit, and a simulation unit. The reception unit receives the user's body shape data. The generation unit generates a 3D avatar based on the body shape data received by the reception unit. The fitting unit has the 3D avatar generated by the generation unit try on fashion items. The simulation unit simulates the fit and appearance of the fashion items tried on by the fitting unit in real time.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Means for Solving the Problems

[0007] The system according to this embodiment can enable users to virtually try on fashion items that fit their body type. [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 controls communication between multiple computers. Examples of communication standards applied 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) The virtual fitting system according to an embodiment of the present invention is a system that allows users to create their own avatars and virtually try on various fashion items. In this virtual fitting system, the user inputs their own body shape data, and AI generates a 3D avatar of the user based on that data. This avatar is created to be very close to the user's body shape. Next, the user selects various fashion items in a virtual fitting room and has the avatar try them on. The AI ​​simulates the fit and appearance of the selected items in real time and provides this to the user. This allows the user to check the fit and appearance of items while online shopping without actually trying them on. For example, if the user selects a dress, the AI ​​simulates how the dress will fit the user's body shape and displays it on the avatar. The user can check the fit and appearance of the dress from a 360-degree perspective. The AI ​​also provides personalized recommendations based on the user's body shape data. For example, it suggests other items that suit the user's body shape, or accessories that match the items that were tried on. This technology allows users to enjoy online shopping without worrying about choosing the right size. Furthermore, the risk of returns is reduced, and a more efficient shopping experience is provided. In addition, by analyzing the user's fitting data with AI, more accurate recommendations become possible, improving user satisfaction. Thus, the virtual fitting system can enhance the online shopping experience through the generation of 3D avatars based on the user's body shape data and real-time simulation of fashion items.

[0029] The virtual fitting system according to the embodiment comprises a reception unit, a generation unit, a fitting unit, and a simulation unit. The reception unit inputs the user's body shape data. The user's body shape data includes, but is not limited to, height, weight, body fat percentage, and 3D scan data. The reception unit provides, for example, a method for the user to manually input the data or a method for acquiring body shape data using a 3D scanner. The reception unit can also acquire body shape data from the user's smartphone or wearable device. The generation unit generates a 3D avatar based on the body shape data input by the reception unit. The generation unit generates a 3D avatar that is very close to the user's body shape, for example, using a generation AI. The generation AI takes the user's body shape data as input and outputs a 3D avatar. The generation unit generates a 3D avatar, for example, using a text generation AI (e.g., LLM). The generation unit can also generate a 3D avatar based on the user's body shape data using a multimodal generation AI. The fitting unit allows the 3D avatar generated by the generation unit to try on fashion items. The fitting unit, for example, allows a 3D avatar to try on fashion items selected by the user. The fitting unit can also try on a 3D avatar a dress selected by the user, or a suit selected by the user. The simulation unit simulates the fit and appearance of the fashion items tried on by the fitting unit in real time. The simulation unit can use AI to simulate the fit and appearance of the fashion items in real time. For example, the simulation unit can use text generation AI (e.g., LLM) to simulate the fit and appearance of the fashion items. Furthermore, the simulation unit can also use multimodal generation AI to simulate the fit and appearance of the fashion items. As a result, the virtual fitting system according to this embodiment can improve the online shopping experience through the generation of a 3D avatar based on the user's body shape data and the real-time simulation of fashion items.

[0030] The reception desk inputs the user's body shape data. This data may include, but is not limited to, height, weight, body fat percentage, and 3D scan data. The reception desk provides methods for users to input this data manually or to acquire it using a 3D scanner. Specifically, users can input their height and weight using a dedicated application. When using a 3D scanner, users can stand in front of a dedicated scanning device and have their entire body scanned to obtain detailed 3D data. Furthermore, the reception desk can also acquire body shape data from the user's smartphone or wearable device. For example, a full-body photo can be taken using the smartphone camera, and AI can analyze the body shape data from that photo. Wearable devices can also provide daily activity data and health data such as body fat percentage. This allows the reception desk to collect user body shape data in diverse ways and provide the information necessary to generate an accurate 3D avatar. In addition, the reception desk securely manages the collected data and implements security measures to protect user privacy. For example, the data is encrypted and can only be viewed by those with access rights. This allows users to confidently provide their body shape data.

[0031] The generation unit generates a 3D avatar based on body shape data entered by the reception unit. The generation unit generates a 3D avatar that is very close to the user's body shape, for example, using a generation AI. The generation AI takes the user's body shape data as input and outputs a 3D avatar. Specifically, the generation AI analyzes the user's height, weight, body fat percentage, 3D scan data, etc., and generates a 3D avatar that faithfully reproduces the user's body shape based on this data. The generation unit also generates a 3D avatar using a text generation AI (for example, LLM). The text generation AI takes the user's body shape data as input in text format and generates a 3D avatar based on that text data. Furthermore, the generation unit can also generate a 3D avatar based on the user's body shape data using a multimodal generation AI. The multimodal generation AI integrates and analyzes multiple data formats, such as image data and text data, to generate a more accurate 3D avatar. As a result, the generation unit can quickly and accurately generate a 3D avatar that is very close to the user's body shape. Furthermore, the generation unit provides a customization function for the generated 3D avatar. For example, users can customize their facial features, hairstyle, skin color, and other characteristics. This allows users to create 3D avatars that more closely resemble themselves, improving their virtual try-on experience.

[0032] The fitting unit allows the user to try on fashion items with the 3D avatar generated by the generation unit. For example, the fitting unit can have the 3D avatar try on fashion items selected by the user. Specifically, it can have the 3D avatar try on fashion items such as dresses, suits, and casual wear selected by the user from an online shop. The fitting unit retrieves 3D models of fashion items from a database and applies them to the generated 3D avatar. For example, when a user tries on a dress selected by the user, the 3D model of the dress is automatically adjusted to fit the avatar's body shape, resulting in a realistic fitting simulation. The fitting unit can also have the 3D avatar try on a suit selected by the user. The 3D model of the suit is adjusted to fit the avatar's shoulder width and waistline, simulating the actual fit. Furthermore, the fitting unit can have multiple fashion items tried on simultaneously. For example, a user can try on a shirt, pants, and shoes selected by the user on the 3D avatar at the same time to check the overall coordination. This allows the fitting room to allow users to virtually try on the fashion items they have selected, improving the online shopping experience.

[0033] The simulation unit simulates the fit and appearance of fashion items tried on by the fitting unit in real time. For example, the simulation unit uses AI to simulate the fit and appearance of fashion items in real time. Specifically, the simulation unit simulates the fit and appearance of items in detail based on the material and design of the fashion items, as well as the user's body shape data. For example, when simulating the fit and appearance of fashion items using text generation AI (e.g., LLM), the AI ​​analyzes the user's body shape data and the characteristics of the fashion items, and generates an evaluation of the fit and appearance in text format. The simulation unit can also simulate the fit and appearance of fashion items using multimodal generation AI. Multimodal generation AI integrates and analyzes image data and text data to provide more realistic simulation results. This allows the simulation unit to simulate the fit and appearance of the fashion items selected by the user in real time, providing the user with visual feedback. Furthermore, based on the simulation results, the simulation unit can suggest the optimal size and style to the user. For example, based on the simulation results, it can suggest fashion items in sizes that fit better and styles that suit the user's body shape. This allows the simulation unit to improve the user's online shopping experience and increase their satisfaction.

[0034] The simulation unit can simulate fit and appearance in real time based on the user's body shape data. For example, the simulation unit uses an AI model that takes the user's body shape data as input and outputs fit and appearance to perform real-time simulations. The simulation unit can also use a text generation AI (e.g., LLM) to simulate fit and appearance based on the user's body shape data. Furthermore, the simulation unit can use a multimodal generation AI to simulate fit and appearance based on the user's body shape data. This allows for real-time simulation of fit and appearance based on the user's body shape data. The specific definition and criteria of "real-time" include, but are not limited to, processing speed and latency.

[0035] The generation unit can generate a 3D avatar based on the user's body shape data. For example, the generation unit generates a 3D avatar using a generation AI that takes the user's body shape data as input and outputs a 3D avatar. Alternatively, the generation unit can generate a 3D avatar based on the user's body shape data using a text generation AI (e.g., LLM). Furthermore, the generation unit can also generate a 3D avatar based on the user's body shape data using a multimodal generation AI. This allows for the generation of a 3D avatar based on the user's body shape data. Specific methods and criteria for generation include, but are not limited to, the algorithms used and the details of the generation process.

[0036] The fitting room allows users to try on fashion items they have selected for a 3D avatar. For example, the fitting room can try on a 3D avatar wearing a fashion item selected by the user. For example, the fitting room can try on a 3D avatar wearing a dress selected by the user. The fitting room can also try on a 3D avatar wearing a suit selected by the user. This allows users to try on fashion items they have selected for a 3D avatar. The specific methods and criteria for trying on clothes include, for example, the simulation method for trying on clothes and the conditions during trying on clothes, but are not limited to these examples.

[0037] The simulation unit can simulate the fit and appearance of tried-on items from a 360-degree perspective. For example, the simulation unit can have a 3D avatar try on fashion items selected by the user and simulate the fit and appearance from a 360-degree perspective. The simulation unit can also use text generation AI (e.g., LLM) to simulate the fit and appearance of tried-on items from a 360-degree perspective. Furthermore, the simulation unit can use multimodal generation AI to simulate the fit and appearance of tried-on items from a 360-degree perspective. This allows for the simulation of the fit and appearance of tried-on items from a 360-degree perspective. Specific methods and criteria for achieving a 360-degree perspective include, but are not limited to, methods for switching perspectives and display devices.

[0038] The simulation unit may include a recommendation unit that analyzes the user's body shape data and provides personalized recommendations. The simulation unit may, for example, use an AI model that takes the user's body shape data as input and outputs personalized recommendations to provide recommendations. The simulation unit may, for example, use a text generation AI (e.g., LLM) to analyze the user's body shape data and provide personalized recommendations. Furthermore, the simulation unit may also use a multimodal generation AI to analyze the user's body shape data and provide personalized recommendations. This enables the analysis of the user's body shape data and the provision of personalized recommendations. Specific methods and criteria for personalized recommendations include, but are not limited to, recommendation algorithms and methods of using user data.

[0039] The reception desk can analyze the user's past body shape data and select the optimal input method. For example, the reception desk may prioritize suggesting input methods the user has used in the past (manual input, scanning, etc.). For example, the reception desk may present the most efficient input procedure based on the user's past input data. Furthermore, the reception desk can automatically input frequently used data items based on the user's past input history. This allows the system to analyze the user's past body shape data and select the optimal input method. Specific criteria and selection methods for the optimal input method include, but are not limited to, details of input devices and input processes.

[0040] The reception unit can filter body shape data based on the user's current health status and lifestyle. For example, the reception unit filters input data based on the user's current health status (weight, height, etc.). The reception unit adjusts input data considering the user's lifestyle (exercise frequency, diet, etc.). The reception unit can also automatically update body shape data based on data obtained from the user's health app. This allows for filtering of body shape data based on the user's current health status and lifestyle. Specific filtering methods and criteria include, but are not limited to, filtering algorithms and filtering conditions.

[0041] The reception desk can prioritize inputting highly relevant data when users enter body shape data, taking into account their geographical location. For example, if a user lives in a cold region, the reception desk can prioritize inputting winter clothing sizes. Similarly, if a user lives in an urban area, it can prioritize inputting business wear sizes. Furthermore, if a user lives in a resort area, it can prioritize inputting swimwear and casual wear sizes. This allows the system to prioritize inputting highly relevant data, taking into account the user's geographical location. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services.

[0042] The reception desk can analyze a user's social media activity and input relevant data when entering body shape data. For example, the reception desk can automatically input body shape data based on photos shared by the user on social media. For example, the reception desk can analyze the content of a user's social media posts and suggest relevant body shape data. The reception desk can also adjust the input data by referring to the body shape data of the user's social media friends. This allows the system to analyze the user's social media activity and input relevant data. Specific methods and criteria for analyzing social media activity include, but are not limited to, analyzing post content and follower analysis.

[0043] The generation unit can adjust the accuracy of 3D avatar generation based on the level of detail of the user's body shape data. For example, if the user provides detailed body shape data, the generation unit will generate a highly accurate 3D avatar. If the user provides only basic body shape data, the generation unit will generate a 3D avatar with standard accuracy. Furthermore, if the user does not provide some body shape data, the generation unit can generate a 3D avatar based on estimated data. This allows the generation accuracy to be adjusted based on the level of detail of the user's body shape data. Specific evaluation criteria and adjustment methods for level of detail include, but are not limited to, data resolution and information accuracy.

[0044] The generation unit can apply different generation algorithms to 3D avatars depending on the user's body type category. For example, if the user has a standard body type, the generation unit applies a general generation algorithm. If the user has an athletic body type, the generation unit applies a generation algorithm that reflects muscle details. The generation unit can also apply a generation algorithm that emphasizes body type features if the user is plus-size. This allows for the application of different generation algorithms depending on the user's body type category. Specific classification methods and criteria for body type categories include, but are not limited to, BMI, body fat percentage, and body shape.

[0045] The generation unit can determine the generation priority based on when the user's body shape data was acquired when generating 3D avatars. For example, the generation unit can generate the latest 3D avatar based on the user's most recently acquired body shape data. For example, the generation unit can generate a 3D avatar that reflects the user's past body shape based on the user's previously acquired body shape data. Furthermore, if the user provides multiple body shape data sets, the generation unit can prioritize the generation of the 3D avatar using the most relevant data. This allows the generation priority to be determined based on when the user's body shape data was acquired. Specific evaluation criteria and usage methods for acquisition timing include, but are not limited to, data freshness and acquisition timing.

[0046] The generation unit can adjust the generation order based on the relevance of the user's body shape data when generating 3D avatars. For example, the generation unit can prioritize generating 3D avatars based on the most important data among the body shape data provided by the user. For example, the generation unit can generate 3D avatars based on highly relevant data among the body shape data provided by the user. The generation unit can also prioritize generating 3D avatars based on data belonging to a specific category among the body shape data provided by the user. This allows the generation order to be adjusted based on the relevance of the user's body shape data. Specific evaluation criteria and adjustment methods for relevance include, but are not limited to, data matching rates and relevance scores.

[0047] The fitting room function can adjust the level of detail during fitting based on the importance of the fashion item. For example, it provides a detailed fitting simulation for expensive fashion items. For example, it provides a standard fitting simulation for everyday fashion items. It can also provide a special fitting simulation for fashion items for specific events. This allows the level of detail to be adjusted based on the importance of the fashion item. Specific criteria and adjustment methods for importance include, but are not limited to, the popularity and frequency of use of the item.

[0048] The fitting room can apply different fitting algorithms depending on the category of the fashion item during the fitting process. For example, in the case of a dress, the fitting room applies an algorithm that simulates the movement and fit of the fabric in detail. For example, in the case of a suit, the fitting room applies an algorithm that emphasizes the fit around the shoulders and chest. Furthermore, in the case of casual wear, the fitting room can also apply an algorithm that emphasizes the overall silhouette. This allows for the application of different fitting algorithms depending on the category of the fashion item. Specific classification methods and criteria for categories include, but are not limited to, types of clothing or types of accessories.

[0049] The fitting room system can prioritize fittings based on when the fashion items were selected. For example, it might prioritize items selected by the user for a specific event, or items selected according to the season. It could also prioritize items selected by the user during a sale. This allows for prioritizing fittings based on when the fashion items were selected. Specific evaluation criteria and usage methods for selection timing include, but are not limited to, purchase timing and fitting timing.

[0050] The fitting room system can adjust the order in which fashion items are tried on based on their relevance. For example, the fitting room system may prioritize trying on the most relevant items among those selected by the user. The fitting room system may determine the order of try-ons based on the relevance of the items selected by the user. The fitting room system may also prioritize trying on items belonging to a specific category among those selected by the user. This allows the fitting room system to adjust the order of try-ons based on the relevance of fashion items. Specific methods and criteria for adjusting the order include, but are not limited to, display order and try-on order.

[0051] The simulation unit can optimize the current simulation by referring to past simulation data during the simulation process. For example, the simulation unit can optimize the current simulation based on data from items the user has tried on in the past. For example, the simulation unit can analyze the user's past simulation history and propose the most effective simulation method. The simulation unit can also adjust the current simulation by referring to the fit of items the user has tried on in the past. This allows the current simulation to be optimized by referring to past simulation data. Specific methods and criteria for optimization include, but are not limited to, algorithm selection and parameter adjustment.

[0052] The simulation unit can apply different simulation methods to each category of fashion item during the simulation process. For example, in the case of a dress, the simulation unit applies a method that simulates the movement and fit of the fabric in detail. For example, in the case of a suit, the simulation unit applies a simulation method that emphasizes the fit of the shoulders and chest. Furthermore, in the case of casual wear, the simulation unit can also apply a simulation method that emphasizes the overall silhouette. This allows for the application of different simulation methods to each category of fashion item. Specific types of simulation methods and their application methods include, but are not limited to, physics-based simulations and data-driven simulations.

[0053] The simulation unit can analyze changes in the simulation based on the timing of fashion item selection. For example, the simulation unit may prioritize simulating items selected by the user for a specific event. For example, it may prioritize simulating items selected by the user according to the season. The simulation unit can also prioritize simulating items selected by the user during a sale period. This allows for analysis of changes in the simulation based on the timing of fashion item selection. Specific evaluation criteria and analysis methods for changes include, but are not limited to, changes over time and changes in trends.

[0054] The simulation unit can analyze simulations by referring to relevant market data for fashion items during the simulation process. For example, the simulation unit may prioritize simulations of items that are popular in the market. The simulation unit may adjust the content of the simulation based on market trend data, for example. The simulation unit can also determine the order of simulations by referring to market demand data. This allows for the analysis of simulations by referring to relevant market data for fashion items. Specific types and uses of relevant market data include, but are not limited to, market trend data and sales data.

[0055] The recommendation unit can analyze the user's past try-on data to select the optimal recommendation method. For example, the recommendation unit provides optimal recommendations based on data of items the user has tried on in the past. For example, the recommendation unit analyzes the user's past try-on history and proposes the most effective recommendation method. The recommendation unit can also adjust recommendations by referring to the fit of items the user has tried on in the past. This allows the system to analyze the user's past try-on data and select the optimal recommendation method. Specific selection methods and criteria for the optimal recommendation method include, but are not limited to, algorithm selection and parameter adjustment.

[0056] The recommendation unit can customize its recommendation methods based on the user's current body shape data. For example, the recommendation unit provides optimal recommendations based on the user's current body shape data. The recommendation unit can, for example, analyze the user's body shape data and propose the most effective recommendation method. Furthermore, the recommendation unit can adjust the content of recommendations based on the user's body shape data. This allows for the customization of recommendation methods based on the user's current body shape data. Specific methods and criteria for customization include, but are not limited to, adjustments and personalization based on user preferences.

[0057] The recommendation system can select the optimal recommendation method by considering the user's geographical location information when making recommendations. For example, if the user lives in a cold region, the recommendation system will prioritize recommending winter clothing. If the user lives in an urban area, the recommendation system will prioritize recommending business wear. Furthermore, if the user lives in a resort area, the recommendation system can prioritize recommending swimwear or casual wear. This allows the system to select the optimal recommendation method by considering the user's geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services.

[0058] The recommendation system can analyze a user's social media activity and suggest recommendation methods when making recommendations. For example, the recommendation system can customize recommendations based on photos shared by the user on social media. For example, the recommendation system can analyze the content of a user's social media posts and provide relevant recommendations. The recommendation system can also adjust recommendations by referencing the fashion styles of the user's social media friends. This allows the system to analyze a user's social media activity and suggest recommendation methods. Specific methods and criteria for analyzing social media activity include, but are not limited to, analyzing post content and follower analysis.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The virtual fitting system can analyze the user's past fitting data to provide an optimal fitting simulation for a 3D avatar generated based on the user's body shape data. For example, it can adjust the current fitting simulation based on the fit and appearance of items the user has tried on in the past. It can also analyze the user's past fitting history and suggest the most effective fitting method. Furthermore, it can optimize the current fitting simulation based on data of items the user has tried on in the past. This allows for the provision of more accurate fitting simulations by utilizing the user's past fitting data.

[0061] The virtual fitting system can provide fitting simulations for a 3D avatar generated based on the user's body shape data, taking into account the user's geographical location. For example, if the user lives in a cold region, it can prioritize fitting simulations of winter clothing. Similarly, if the user lives in an urban area, it can prioritize fitting simulations of business wear. Furthermore, if the user lives in a resort area, it can prioritize fitting simulations of swimwear or casual wear. This allows the system to provide more relevant fitting simulations by considering the user's geographical location.

[0062] The virtual try-on system can analyze the user's social media activity and provide relevant try-on simulations to a 3D avatar generated based on the user's body shape data. For example, it can automatically input body shape data based on photos the user shares on social media and perform a try-on simulation. It can also analyze the user's social media posts and suggest relevant fashion items. Furthermore, it can adjust the try-on simulation by referencing the body shape data of the user's social media friends. This allows for the provision of more personalized try-on simulations by leveraging the user's social media activity.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk inputs the user's body shape data. This data includes, for example, height, weight, body fat percentage, and 3D scan data. The reception desk provides methods for the user to manually input the data or to acquire it using a 3D scanner. It can also acquire body shape data from the user's smartphone or wearable device. Step 2: The generation unit generates a 3D avatar based on the body shape data entered by the reception unit. The generation unit uses a generation AI to generate a 3D avatar that is very close to the user's body shape. The generation AI takes the user's body shape data as input and outputs a 3D avatar. The generation unit can generate 3D avatars using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The fitting unit makes the 3D avatar generated by the generation unit try on fashion items. The fitting unit makes the 3D avatar try on fashion items selected by the user. For example, it can make the 3D avatar try on dresses or suits selected by the user. Step 4: The simulation unit simulates the fit and appearance of the fashion items tried on by the fitting unit in real time. The simulation unit uses AI to simulate the fit and appearance of the fashion items in real time. For example, simulations can be performed using text generation AI (e.g., LLM) or multimodal generation AI.

[0065] (Example of form 2) The virtual fitting system according to an embodiment of the present invention is a system that allows users to create their own avatars and virtually try on various fashion items. In this virtual fitting system, the user inputs their own body shape data, and AI generates a 3D avatar of the user based on that data. This avatar is created to be very close to the user's body shape. Next, the user selects various fashion items in a virtual fitting room and has the avatar try them on. The AI ​​simulates the fit and appearance of the selected items in real time and provides this to the user. This allows the user to check the fit and appearance of items while online shopping without actually trying them on. For example, if the user selects a dress, the AI ​​simulates how the dress will fit the user's body shape and displays it on the avatar. The user can check the fit and appearance of the dress from a 360-degree perspective. The AI ​​also provides personalized recommendations based on the user's body shape data. For example, it suggests other items that suit the user's body shape, or accessories that match the items that were tried on. This technology allows users to enjoy online shopping without worrying about choosing the right size. Furthermore, the risk of returns is reduced, and a more efficient shopping experience is provided. In addition, by analyzing the user's fitting data with AI, more accurate recommendations become possible, improving user satisfaction. Thus, the virtual fitting system can enhance the online shopping experience through the generation of 3D avatars based on the user's body shape data and real-time simulation of fashion items.

[0066] The virtual fitting system according to the embodiment comprises a reception unit, a generation unit, a fitting unit, and a simulation unit. The reception unit inputs the user's body shape data. The user's body shape data includes, but is not limited to, height, weight, body fat percentage, and 3D scan data. The reception unit provides, for example, a method for the user to manually input the data or a method for acquiring body shape data using a 3D scanner. The reception unit can also acquire body shape data from the user's smartphone or wearable device. The generation unit generates a 3D avatar based on the body shape data input by the reception unit. The generation unit generates a 3D avatar that is very close to the user's body shape, for example, using a generation AI. The generation AI takes the user's body shape data as input and outputs a 3D avatar. The generation unit generates a 3D avatar, for example, using a text generation AI (e.g., LLM). The generation unit can also generate a 3D avatar based on the user's body shape data using a multimodal generation AI. The fitting unit allows the 3D avatar generated by the generation unit to try on fashion items. The fitting unit, for example, allows a 3D avatar to try on fashion items selected by the user. The fitting unit can also try on a 3D avatar a dress selected by the user, or a suit selected by the user. The simulation unit simulates the fit and appearance of the fashion items tried on by the fitting unit in real time. The simulation unit can use AI to simulate the fit and appearance of the fashion items in real time. For example, the simulation unit can use text generation AI (e.g., LLM) to simulate the fit and appearance of the fashion items. Furthermore, the simulation unit can also use multimodal generation AI to simulate the fit and appearance of the fashion items. As a result, the virtual fitting system according to this embodiment can improve the online shopping experience through the generation of a 3D avatar based on the user's body shape data and the real-time simulation of fashion items.

[0067] The reception desk inputs the user's body shape data. This data may include, but is not limited to, height, weight, body fat percentage, and 3D scan data. The reception desk provides methods for users to input this data manually or to acquire it using a 3D scanner. Specifically, users can input their height and weight using a dedicated application. When using a 3D scanner, users can stand in front of a dedicated scanning device and have their entire body scanned to obtain detailed 3D data. Furthermore, the reception desk can also acquire body shape data from the user's smartphone or wearable device. For example, a full-body photo can be taken using the smartphone camera, and AI can analyze the body shape data from that photo. Wearable devices can also provide daily activity data and health data such as body fat percentage. This allows the reception desk to collect user body shape data in diverse ways and provide the information necessary to generate an accurate 3D avatar. In addition, the reception desk securely manages the collected data and implements security measures to protect user privacy. For example, the data is encrypted and can only be viewed by those with access rights. This allows users to confidently provide their body shape data.

[0068] The generation unit generates a 3D avatar based on body shape data entered by the reception unit. The generation unit generates a 3D avatar that is very close to the user's body shape, for example, using a generation AI. The generation AI takes the user's body shape data as input and outputs a 3D avatar. Specifically, the generation AI analyzes the user's height, weight, body fat percentage, 3D scan data, etc., and generates a 3D avatar that faithfully reproduces the user's body shape based on this data. The generation unit also generates a 3D avatar using a text generation AI (for example, LLM). The text generation AI takes the user's body shape data as input in text format and generates a 3D avatar based on that text data. Furthermore, the generation unit can also generate a 3D avatar based on the user's body shape data using a multimodal generation AI. The multimodal generation AI integrates and analyzes multiple data formats, such as image data and text data, to generate a more accurate 3D avatar. As a result, the generation unit can quickly and accurately generate a 3D avatar that is very close to the user's body shape. Furthermore, the generation unit provides a customization function for the generated 3D avatar. For example, users can customize their facial features, hairstyle, skin color, and other characteristics. This allows users to create 3D avatars that more closely resemble themselves, improving their virtual try-on experience.

[0069] The fitting unit allows the user to try on fashion items with the 3D avatar generated by the generation unit. For example, the fitting unit can have the 3D avatar try on fashion items selected by the user. Specifically, it can have the 3D avatar try on fashion items such as dresses, suits, and casual wear selected by the user from an online shop. The fitting unit retrieves 3D models of fashion items from a database and applies them to the generated 3D avatar. For example, when a user tries on a dress selected by the user, the 3D model of the dress is automatically adjusted to fit the avatar's body shape, resulting in a realistic fitting simulation. The fitting unit can also have the 3D avatar try on a suit selected by the user. The 3D model of the suit is adjusted to fit the avatar's shoulder width and waistline, simulating the actual fit. Furthermore, the fitting unit can have multiple fashion items tried on simultaneously. For example, a user can try on a shirt, pants, and shoes selected by the user on the 3D avatar at the same time to check the overall coordination. This allows the fitting room to allow users to virtually try on the fashion items they have selected, improving the online shopping experience.

[0070] The simulation unit simulates the fit and appearance of fashion items tried on by the fitting unit in real time. For example, the simulation unit uses AI to simulate the fit and appearance of fashion items in real time. Specifically, the simulation unit simulates the fit and appearance of items in detail based on the material and design of the fashion items, as well as the user's body shape data. For example, when simulating the fit and appearance of fashion items using text generation AI (e.g., LLM), the AI ​​analyzes the user's body shape data and the characteristics of the fashion items, and generates an evaluation of the fit and appearance in text format. The simulation unit can also simulate the fit and appearance of fashion items using multimodal generation AI. Multimodal generation AI integrates and analyzes image data and text data to provide more realistic simulation results. This allows the simulation unit to simulate the fit and appearance of the fashion items selected by the user in real time, providing the user with visual feedback. Furthermore, based on the simulation results, the simulation unit can suggest the optimal size and style to the user. For example, based on the simulation results, it can suggest fashion items in sizes that fit better and styles that suit the user's body shape. This allows the simulation unit to improve the user's online shopping experience and increase their satisfaction.

[0071] The simulation unit can simulate fit and appearance in real time based on the user's body shape data. For example, the simulation unit uses an AI model that takes the user's body shape data as input and outputs fit and appearance to perform real-time simulations. The simulation unit can also use a text generation AI (e.g., LLM) to simulate fit and appearance based on the user's body shape data. Furthermore, the simulation unit can use a multimodal generation AI to simulate fit and appearance based on the user's body shape data. This allows for real-time simulation of fit and appearance based on the user's body shape data. The specific definition and criteria of "real-time" include, but are not limited to, processing speed and latency.

[0072] The generation unit can generate a 3D avatar based on the user's body shape data. For example, the generation unit generates a 3D avatar using a generation AI that takes the user's body shape data as input and outputs a 3D avatar. Alternatively, the generation unit can generate a 3D avatar based on the user's body shape data using a text generation AI (e.g., LLM). Furthermore, the generation unit can also generate a 3D avatar based on the user's body shape data using a multimodal generation AI. This allows for the generation of a 3D avatar based on the user's body shape data. Specific methods and criteria for generation include, but are not limited to, the algorithms used and the details of the generation process.

[0073] The fitting room allows users to try on fashion items they have selected for a 3D avatar. For example, the fitting room can try on a 3D avatar wearing a fashion item selected by the user. For example, the fitting room can try on a 3D avatar wearing a dress selected by the user. The fitting room can also try on a 3D avatar wearing a suit selected by the user. This allows users to try on fashion items they have selected for a 3D avatar. The specific methods and criteria for trying on clothes include, for example, the simulation method for trying on clothes and the conditions during trying on clothes, but are not limited to these examples.

[0074] The simulation unit can simulate the fit and appearance of tried-on items from a 360-degree perspective. For example, the simulation unit can have a 3D avatar try on fashion items selected by the user and simulate the fit and appearance from a 360-degree perspective. The simulation unit can also use text generation AI (e.g., LLM) to simulate the fit and appearance of tried-on items from a 360-degree perspective. Furthermore, the simulation unit can use multimodal generation AI to simulate the fit and appearance of tried-on items from a 360-degree perspective. This allows for the simulation of the fit and appearance of tried-on items from a 360-degree perspective. Specific methods and criteria for achieving a 360-degree perspective include, but are not limited to, methods for switching perspectives and display devices.

[0075] The simulation unit may include a recommendation unit that analyzes the user's body shape data and provides personalized recommendations. The simulation unit may, for example, use an AI model that takes the user's body shape data as input and outputs personalized recommendations to provide recommendations. The simulation unit may, for example, use a text generation AI (e.g., LLM) to analyze the user's body shape data and provide personalized recommendations. Furthermore, the simulation unit may also use a multimodal generation AI to analyze the user's body shape data and provide personalized recommendations. This enables the analysis of the user's body shape data and the provision of personalized recommendations. Specific methods and criteria for personalized recommendations include, but are not limited to, recommendation algorithms and methods of using user data.

[0076] The reception desk can estimate the user's emotions and adjust the timing of body shape data input based on the estimated emotions. For example, the reception desk might use facial recognition or voice analysis technology to estimate the user's emotions. For instance, if the user is relaxed, the reception desk might send a notification prompting them to input body shape data. It could also offer the option to postpone input if the user is stressed. Furthermore, if the user is in a hurry, the reception desk could display a simplified input form. This allows for adjustment of the timing of body shape data input 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The reception desk can analyze the user's past body shape data and select the optimal input method. For example, the reception desk may prioritize suggesting input methods the user has used in the past (manual input, scanning, etc.). For example, the reception desk may present the most efficient input procedure based on the user's past input data. Furthermore, the reception desk can automatically input frequently used data items based on the user's past input history. This allows the system to analyze the user's past body shape data and select the optimal input method. Specific criteria and selection methods for the optimal input method include, but are not limited to, details of input devices and input processes.

[0078] The reception unit can filter body shape data based on the user's current health status and lifestyle. For example, the reception unit filters input data based on the user's current health status (weight, height, etc.). The reception unit adjusts input data considering the user's lifestyle (exercise frequency, diet, etc.). The reception unit can also automatically update body shape data based on data obtained from the user's health app. This allows for filtering of body shape data based on the user's current health status and lifestyle. Specific filtering methods and criteria include, but are not limited to, filtering algorithms and filtering conditions.

[0079] The reception desk can estimate the user's emotions and determine the priority of body shape data to be entered based on the estimated emotions. For example, the reception desk may use facial recognition technology or voice analysis technology to estimate the user's emotions. For instance, if the user is relaxed, the reception desk may prompt for detailed body shape data. Conversely, if the user is stressed, the reception desk may only require basic body shape data. Furthermore, if the user is in a hurry, the reception desk may prioritize the input of the most important data items. This allows the system to determine the priority of body shape data to be entered 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.

[0080] The reception desk can prioritize inputting highly relevant data when users enter body shape data, taking into account their geographical location. For example, if a user lives in a cold region, the reception desk can prioritize inputting winter clothing sizes. Similarly, if a user lives in an urban area, it can prioritize inputting business wear sizes. Furthermore, if a user lives in a resort area, it can prioritize inputting swimwear and casual wear sizes. This allows the system to prioritize inputting highly relevant data, taking into account the user's geographical location. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services.

[0081] The reception desk can analyze a user's social media activity and input relevant data when entering body shape data. For example, the reception desk can automatically input body shape data based on photos shared by the user on social media. For example, the reception desk can analyze the content of a user's social media posts and suggest relevant body shape data. The reception desk can also adjust the input data by referring to the body shape data of the user's social media friends. This allows the system to analyze the user's social media activity and input relevant data. Specific methods and criteria for analyzing social media activity include, but are not limited to, analyzing post content and follower analysis.

[0082] The generation unit can estimate the user's emotions and adjust the 3D avatar generation method based on the estimated user emotions. For example, the generation unit uses facial recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the generation unit generates a detailed 3D avatar. It can also generate a simplified 3D avatar if the user is stressed. Furthermore, if the user is in a hurry, the generation unit can provide a quickly generated 3D avatar. This allows the 3D avatar generation method to be adjusted based on the user's emotions. 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.

[0083] The generation unit can adjust the accuracy of 3D avatar generation based on the level of detail of the user's body shape data. For example, if the user provides detailed body shape data, the generation unit will generate a highly accurate 3D avatar. If the user provides only basic body shape data, the generation unit will generate a 3D avatar with standard accuracy. Furthermore, if the user does not provide some body shape data, the generation unit can generate a 3D avatar based on estimated data. This allows the generation accuracy to be adjusted based on the level of detail of the user's body shape data. Specific evaluation criteria and adjustment methods for level of detail include, but are not limited to, data resolution and information accuracy.

[0084] The generation unit can apply different generation algorithms to 3D avatars depending on the user's body type category. For example, if the user has a standard body type, the generation unit applies a general generation algorithm. If the user has an athletic body type, the generation unit applies a generation algorithm that reflects muscle details. The generation unit can also apply a generation algorithm that emphasizes body type features if the user is plus-size. This allows for the application of different generation algorithms depending on the user's body type category. Specific classification methods and criteria for body type categories include, but are not limited to, BMI, body fat percentage, and body shape.

[0085] The generation unit can estimate the user's emotions and adjust the 3D avatar generation speed based on the estimated emotions. For example, the generation unit uses facial recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the generation unit takes time to generate a detailed 3D avatar. Conversely, if the user is in a hurry, the generation unit can provide a quickly generated 3D avatar. Furthermore, if the user is stressed, the generation unit can adjust the generation speed to alleviate stress. This allows the 3D avatar generation speed to be adjusted based on the user's emotions. 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.

[0086] The generation unit can determine the generation priority based on when the user's body shape data was acquired when generating 3D avatars. For example, the generation unit can generate the latest 3D avatar based on the user's most recently acquired body shape data. For example, the generation unit can generate a 3D avatar that reflects the user's past body shape based on the user's previously acquired body shape data. Furthermore, if the user provides multiple body shape data sets, the generation unit can prioritize the generation of the 3D avatar using the most relevant data. This allows the generation priority to be determined based on when the user's body shape data was acquired. Specific evaluation criteria and usage methods for acquisition timing include, but are not limited to, data freshness and acquisition timing.

[0087] The generation unit can adjust the generation order based on the relevance of the user's body shape data when generating 3D avatars. For example, the generation unit can prioritize generating 3D avatars based on the most important data among the body shape data provided by the user. For example, the generation unit can generate 3D avatars based on highly relevant data among the body shape data provided by the user. The generation unit can also prioritize generating 3D avatars based on data belonging to a specific category among the body shape data provided by the user. This allows the generation order to be adjusted based on the relevance of the user's body shape data. Specific evaluation criteria and adjustment methods for relevance include, but are not limited to, data matching rates and relevance scores.

[0088] The fitting room unit can estimate the user's emotions and adjust the fitting room's presentation based on those emotions. For example, the fitting room unit uses facial recognition technology or voice analysis technology to estimate the user's emotions. For instance, if the user is relaxed, the fitting room unit can provide a detailed fitting room simulation. If the user is stressed, it can provide a simplified fitting room simulation. Furthermore, if the user is in a hurry, the fitting room unit can perform a rapid fitting room simulation. This allows the fitting room's presentation to be adjusted 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The fitting room function can adjust the level of detail during fitting based on the importance of the fashion item. For example, it provides a detailed fitting simulation for expensive fashion items. For example, it provides a standard fitting simulation for everyday fashion items. It can also provide a special fitting simulation for fashion items for specific events. This allows the level of detail to be adjusted based on the importance of the fashion item. Specific criteria and adjustment methods for importance include, but are not limited to, the popularity and frequency of use of the item.

[0090] The fitting room can apply different fitting algorithms depending on the category of the fashion item during the fitting process. For example, in the case of a dress, the fitting room applies an algorithm that simulates the movement and fit of the fabric in detail. For example, in the case of a suit, the fitting room applies an algorithm that emphasizes the fit around the shoulders and chest. Furthermore, in the case of casual wear, the fitting room can also apply an algorithm that emphasizes the overall silhouette. This allows for the application of different fitting algorithms depending on the category of the fashion item. Specific classification methods and criteria for categories include, but are not limited to, types of clothing or types of accessories.

[0091] The fitting room unit can estimate the user's emotions and adjust the length of the fitting room based on those emotions. For example, the fitting room unit uses facial recognition technology or voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the fitting room unit provides a detailed fitting simulation. It can also perform a rapid fitting simulation if the user is in a hurry. Furthermore, if the user is stressed, the fitting room unit can provide a simplified fitting simulation. This allows the fitting room unit to adjust the length of the fitting room 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The fitting room system can prioritize fittings based on when the fashion items were selected. For example, it might prioritize items selected by the user for a specific event, or items selected according to the season. It could also prioritize items selected by the user during a sale. This allows for prioritizing fittings based on when the fashion items were selected. Specific evaluation criteria and usage methods for selection timing include, but are not limited to, purchase timing and fitting timing.

[0093] The fitting room system can adjust the order in which fashion items are tried on based on their relevance. For example, the fitting room system may prioritize trying on the most relevant items among those selected by the user. The fitting room system may determine the order of try-ons based on the relevance of the items selected by the user. The fitting room system may also prioritize trying on items belonging to a specific category among those selected by the user. This allows the fitting room system to adjust the order of try-ons based on the relevance of fashion items. Specific methods and criteria for adjusting the order include, but are not limited to, display order and try-on order.

[0094] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated user emotions. For example, the simulation unit uses facial recognition technology and voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the simulation unit will provide a detailed simulation. The simulation unit can also provide a simplified simulation if the user is stressed. Furthermore, the simulation unit can perform a simulation quickly if the user is in a hurry. This allows the display method of the simulation to be adjusted based on the user's 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.

[0095] The simulation unit can optimize the current simulation by referring to past simulation data during the simulation process. For example, the simulation unit can optimize the current simulation based on data from items the user has tried on in the past. For example, the simulation unit can analyze the user's past simulation history and propose the most effective simulation method. The simulation unit can also adjust the current simulation by referring to the fit of items the user has tried on in the past. This allows the current simulation to be optimized by referring to past simulation data. Specific methods and criteria for optimization include, but are not limited to, algorithm selection and parameter adjustment.

[0096] The simulation unit can apply different simulation methods to each category of fashion item during the simulation process. For example, in the case of a dress, the simulation unit applies a method that simulates the movement and fit of the fabric in detail. For example, in the case of a suit, the simulation unit applies a simulation method that emphasizes the fit of the shoulders and chest. Furthermore, in the case of casual wear, the simulation unit can also apply a simulation method that emphasizes the overall silhouette. This allows for the application of different simulation methods to each category of fashion item. Specific types of simulation methods and their application methods include, but are not limited to, physics-based simulations and data-driven simulations.

[0097] The simulation unit can estimate the user's emotions and adjust the importance of the simulation based on the estimated emotions. For example, the simulation unit uses facial recognition technology and voice analysis technology to estimate the user's emotions. For example, if the user is relaxed, the simulation unit provides a detailed simulation. It can also perform a rapid simulation if the user is in a hurry. Furthermore, if the user is stressed, the simulation unit can provide a simplified simulation. This allows the importance of the simulation to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The simulation unit can analyze changes in the simulation based on the timing of fashion item selection. For example, the simulation unit may prioritize simulating items selected by the user for a specific event. For example, it may prioritize simulating items selected by the user according to the season. The simulation unit can also prioritize simulating items selected by the user during a sale period. This allows for analysis of changes in the simulation based on the timing of fashion item selection. Specific evaluation criteria and analysis methods for changes include, but are not limited to, changes over time and changes in trends.

[0099] The simulation unit can analyze simulations by referring to relevant market data for fashion items during the simulation process. For example, the simulation unit may prioritize simulations of items that are popular in the market. The simulation unit may adjust the content of the simulation based on market trend data, for example. The simulation unit can also determine the order of simulations by referring to market demand data. This allows for the analysis of simulations by referring to relevant market data for fashion items. Specific types and uses of relevant market data include, but are not limited to, market trend data and sales data.

[0100] The recommendation unit can estimate the user's emotions and adjust its recommendation method based on those emotions. For example, the recommendation unit might use facial recognition or voice analysis technology to estimate the user's emotions. For instance, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is stressed, it can provide simplified recommendations. Furthermore, if the user is in a hurry, the recommendation unit can provide quick recommendations. This allows the recommendation method to be adjusted 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.

[0101] The recommendation unit can analyze the user's past try-on data to select the optimal recommendation method. For example, the recommendation unit provides optimal recommendations based on data of items the user has tried on in the past. For example, the recommendation unit analyzes the user's past try-on history and proposes the most effective recommendation method. The recommendation unit can also adjust recommendations by referring to the fit of items the user has tried on in the past. This allows the system to analyze the user's past try-on data and select the optimal recommendation method. Specific selection methods and criteria for the optimal recommendation method include, but are not limited to, algorithm selection and parameter adjustment.

[0102] The recommendation unit can customize its recommendation methods based on the user's current body shape data. For example, the recommendation unit provides optimal recommendations based on the user's current body shape data. The recommendation unit can, for example, analyze the user's body shape data and propose the most effective recommendation method. Furthermore, the recommendation unit can adjust the content of recommendations based on the user's body shape data. This allows for the customization of recommendation methods based on the user's current body shape data. Specific methods and criteria for customization include, but are not limited to, adjustments and personalization based on user preferences.

[0103] The recommendation unit can estimate the user's emotions and determine the priority of recommendations based on those emotions. For example, the recommendation unit might use facial recognition or voice analysis technology to estimate the user's emotions. For instance, if the user is relaxed, the recommendation unit can provide detailed recommendations. If the user is stressed, it can provide simplified recommendations. Furthermore, if the user is in a hurry, the recommendation unit can provide quick recommendations. This allows for the prioritization of recommendations 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.

[0104] The recommendation system can select the optimal recommendation method by considering the user's geographical location information when making recommendations. For example, if the user lives in a cold region, the recommendation system will prioritize recommending winter clothing. If the user lives in an urban area, the recommendation system will prioritize recommending business wear. Furthermore, if the user lives in a resort area, the recommendation system can prioritize recommending swimwear or casual wear. This allows the system to select the optimal recommendation method by considering the user's geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services.

[0105] The recommendation system can analyze a user's social media activity and suggest recommendation methods when making recommendations. For example, the recommendation system can customize recommendations based on photos shared by the user on social media. For example, the recommendation system can analyze the content of a user's social media posts and provide relevant recommendations. The recommendation system can also adjust recommendations by referencing the fashion styles of the user's social media friends. This allows the system to analyze a user's social media activity and suggest recommendation methods. Specific methods and criteria for analyzing social media activity include, but are not limited to, analyzing post content and follower analysis.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] A virtual try-on system can estimate the user's emotions based on a 3D avatar generated from the user's body shape data, and provide try-on feedback based on those emotions. For example, if the user expresses positive emotions towards an item they tried on, the system can recommend other items related to that item. Conversely, if the user expresses negative emotions, the system can suggest alternatives to that item. Furthermore, if the user is feeling stressed during the try-on process, the system can simplify the process to help the user relax. This allows for a personalized try-on experience based on the user's emotions, providing a more satisfying shopping experience.

[0108] The virtual fitting system can analyze the user's past fitting data to provide an optimal fitting simulation for a 3D avatar generated based on the user's body shape data. For example, it can adjust the current fitting simulation based on the fit and appearance of items the user has tried on in the past. It can also analyze the user's past fitting history and suggest the most effective fitting method. Furthermore, it can optimize the current fitting simulation based on data of items the user has tried on in the past. This allows for the provision of more accurate fitting simulations by utilizing the user's past fitting data.

[0109] The virtual fitting system can provide fitting simulations for a 3D avatar generated based on the user's body shape data, taking into account the user's geographical location. For example, if the user lives in a cold region, it can prioritize fitting simulations of winter clothing. Similarly, if the user lives in an urban area, it can prioritize fitting simulations of business wear. Furthermore, if the user lives in a resort area, it can prioritize fitting simulations of swimwear or casual wear. This allows the system to provide more relevant fitting simulations by considering the user's geographical location.

[0110] The virtual try-on system can analyze the user's social media activity and provide relevant try-on simulations to a 3D avatar generated based on the user's body shape data. For example, it can automatically input body shape data based on photos the user shares on social media and perform a try-on simulation. It can also analyze the user's social media posts and suggest relevant fashion items. Furthermore, it can adjust the try-on simulation by referencing the body shape data of the user's social media friends. This allows for the provision of more personalized try-on simulations by leveraging the user's social media activity.

[0111] The virtual fitting system can estimate the user's emotions based on a 3D avatar generated from the user's body shape data, and adjust the length of the fitting based on those emotions. For example, if the user is relaxed, it can provide a detailed fitting simulation. If the user is in a hurry, it can also provide a quick fitting simulation. Furthermore, if the user is stressed, it can provide a simplified fitting simulation. This allows the system to adjust the length of the fitting based on the user's emotions and provide a fitting experience tailored to the user's needs.

[0112] The virtual fitting system can estimate the user's emotions based on a 3D avatar generated from the user's body shape data, and adjust the fitting experience based on those emotions. For example, if the user is relaxed, it can provide a detailed fitting simulation. If the user is stressed, it can provide a simplified fitting simulation. Furthermore, if the user is in a hurry, it can perform a quick fitting simulation. This allows the system to adjust the fitting experience based on the user's emotions and provide a fitting experience tailored to the user's needs.

[0113] The virtual fitting system can estimate the user's emotions based on a 3D avatar generated from the user's body shape data, and then prioritize fittings based on those emotions. For example, if the user is relaxed, a detailed fitting simulation can be prioritized. If the user is stressed, a simplified fitting simulation can be prioritized. Furthermore, if the user is in a hurry, a quick fitting simulation can be performed. This allows the system to prioritize fittings based on the user's emotions and provide a fitting experience tailored to the user's needs.

[0114] The virtual try-on system can estimate the user's emotions based on a 3D avatar generated from the user's body shape data, and adjust the level of detail in the try-on based on those emotions. For example, if the user is relaxed, it can provide a detailed try-on simulation. If the user is in a hurry, it can perform a quick try-on simulation. Furthermore, if the user is stressed, it can provide a simplified try-on simulation. This allows the system to adjust the level of detail in the try-on based on the user's emotions, providing a try-on experience that meets the user's needs.

[0115] A virtual try-on system can estimate the user's emotions based on a 3D avatar generated from the user's body shape data, and provide try-on feedback based on those emotions. For example, if the user expresses positive emotions towards an item they tried on, the system can recommend other items related to that item. Conversely, if the user expresses negative emotions, the system can suggest alternatives to that item. Furthermore, if the user is feeling stressed during the try-on process, the system can simplify the process to help the user relax. This allows for a personalized try-on experience based on the user's emotions, providing a more satisfying shopping experience.

[0116] The virtual fitting system can estimate the user's emotions based on a 3D avatar generated from the user's body shape data, and adjust the length of the fitting based on those emotions. For example, if the user is relaxed, it can provide a detailed fitting simulation. If the user is in a hurry, it can also provide a quick fitting simulation. Furthermore, if the user is stressed, it can provide a simplified fitting simulation. This allows the system to adjust the length of the fitting based on the user's emotions and provide a fitting experience tailored to the user's needs.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The reception desk inputs the user's body shape data. This data includes, for example, height, weight, body fat percentage, and 3D scan data. The reception desk provides methods for the user to manually input the data or to acquire it using a 3D scanner. It can also acquire body shape data from the user's smartphone or wearable device. Step 2: The generation unit generates a 3D avatar based on the body shape data entered by the reception unit. The generation unit uses a generation AI to generate a 3D avatar that is very close to the user's body shape. The generation AI takes the user's body shape data as input and outputs a 3D avatar. The generation unit can generate 3D avatars using a text generation AI (e.g., LLM) or a multimodal generation AI. Step 3: The fitting unit makes the 3D avatar generated by the generation unit try on fashion items. The fitting unit makes the 3D avatar try on fashion items selected by the user. For example, it can make the 3D avatar try on dresses or suits selected by the user. Step 4: The simulation unit simulates the fit and appearance of the fashion items tried on by the fitting unit in real time. The simulation unit uses AI to simulate the fit and appearance of the fashion items in real time. For example, simulations can be performed using text generation AI (e.g., LLM) or multimodal generation AI.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the reception unit, generation unit, fitting unit, and simulation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit inputs the user's body shape data using the reception device 38 of the smart device 14. The generation unit generates a 3D avatar using the specific processing unit 290 of the data processing unit 12. The fitting unit allows the 3D avatar to try on fashion items using the control unit 46A of the smart device 14. The simulation unit simulates the fit and appearance of the fashion items in real time using the specific processing unit 290 of the data processing unit 12. 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.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the reception unit, generation unit, fitting unit, and simulation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit inputs the user's body shape data using the microphone 238 of the smart glasses 214. The generation unit generates a 3D avatar using the specific processing unit 290 of the data processing unit 12. The fitting unit allows the 3D avatar to try on fashion items using the control unit 46A of the smart glasses 214. The simulation unit simulates the fit and appearance of the fashion items in real time using the specific processing unit 290 of the data processing unit 12. 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.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the reception unit, generation unit, fitting 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 reception unit inputs the user's body shape data using the microphone 238 of the headset terminal 314. The generation unit generates a 3D avatar using the specific processing unit 290 of the data processing unit 12. The fitting unit allows the 3D avatar to try on fashion items using the control unit 46A of the headset terminal 314. The simulation unit simulates the fit and appearance of the fashion items in real time using the specific processing unit 290 of the data processing unit 12. 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.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the reception unit, generation unit, fitting 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 reception unit inputs the user's body shape data using the microphone 238 of the robot 414. The generation unit generates a 3D avatar using the specific processing unit 290 of the data processing unit 12. The fitting unit makes the 3D avatar try on fashion items using the control unit 46A of the robot 414. The simulation unit simulates the fit and appearance of the fashion items in real time using the specific processing unit 290 of the data processing unit 12. 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A reception area where users input their body shape data, A generation unit generates a 3D avatar based on body shape data input by the reception unit, A fitting unit that allows the 3D avatar generated by the generation unit to try on fashion items, The system includes a simulation unit that simulates the fit and appearance of fashion items tried on by the fitting unit in real time. A system characterized by the following features. (Note 2) The aforementioned simulation unit, It simulates the fit and appearance in real time based on the user's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate a 3D avatar based on the user's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The fitting area is, The user can try on fashion items selected by the user onto a 3D avatar. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, The fit and appearance of the tried-on items are simulated from a 360-degree perspective. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned simulation unit, It includes a recommendation unit that analyzes the user's body shape data and provides personalized recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of body shape data input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past body shape data and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering body shape data, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of body shape data to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering body shape data, the system prioritizes inputting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering body shape data, the system analyzes the user's social media activity and inputs relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the 3D avatar generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating 3D avatars, the accuracy of the generation is adjusted based on the level of detail in the user's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating 3D avatars, different generation algorithms are applied depending on the user's body type category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the 3D avatar generation speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating 3D avatars, the generation priority is determined based on when the user's body shape data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating 3D avatars, the generation order is adjusted based on the relevance of the user's body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The fitting area is, The system estimates the user's emotions and adjusts the way the try-on experience is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The fitting area is, During fitting, the level of detail in the fitting is adjusted based on the importance of the fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 21) The fitting area is, When trying on clothes, different fitting algorithms are applied depending on the category of the fashion item. The system described in Appendix 1, characterized by the features described herein. (Note 22) The fitting area is, It estimates the user's emotions and adjusts the length of the try-on based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The fitting area is, When trying on clothes, prioritize the items you try on based on when you made your fashion selection. The system described in Appendix 1, characterized by the features described herein. (Note 24) The fitting area is, When trying on clothes, adjust the order in which you try on items based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During the simulation, past simulation data is referenced to optimize the current simulation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned simulation unit, During the simulation, different simulation methods are applied to each category of fashion item. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned simulation unit, It estimates the user's emotions and adjusts the importance of the simulation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned simulation unit, During the simulation, we analyze how the simulation changes based on when fashion items were selected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned simulation unit, During the simulation, we analyze the simulation by referring to relevant market data for fashion items. The system described in Appendix 1, characterized by the features described herein. (Note 31) The recommendation unit is, It estimates the user's emotions and adjusts the recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The recommendation unit is, When making recommendations, the system analyzes the user's past try-on data to select the most suitable recommendation method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The recommendation unit is, When making recommendations, customize the recommendation method based on the user's current body shape data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The recommendation unit is, It estimates the user's emotions and determines the priority of recommendations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The recommendation unit is, When making recommendations, the system selects the optimal recommendation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The recommendation unit is, When making recommendations, the system analyzes the user's social media activity to suggest appropriate recommendation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 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. A reception area where users input their body shape data, A generation unit generates a 3D avatar based on body shape data input by the reception unit, A fitting unit that allows the 3D avatar generated by the generation unit to try on fashion items, The system includes a simulation unit that simulates the fit and appearance of fashion items tried on by the fitting unit in real time. A system characterized by the following features.

2. The aforementioned simulation unit, It simulates the fit and appearance in real time based on the user's body shape data. The system according to feature 1.

3. The generating unit is Generate a 3D avatar based on the user's body shape data. The system according to feature 1.

4. The fitting area is, The user can try on fashion items selected by the user onto a 3D avatar. The system according to feature 1.

5. The aforementioned simulation unit, The fit and appearance of the tried-on items are simulated from a 360-degree perspective. The system according to feature 1.

6. The aforementioned simulation unit, It includes a recommendation unit that analyzes the user's body shape data and provides personalized recommendations. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of body shape data input based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past body shape data and selects the optimal input method. The system according to feature 1.

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