system

Abstract

The system according to this embodiment aims to suggest the optimal outfit based on the clothes the user owns, and to support virtual try-on and purchase. [Solution] The system according to the embodiment comprises a registration unit, a suggestion unit, a fitting unit, and a purchase unit. The registration unit registers the clothes the user owns. The suggestion unit suggests the most suitable outfit to the user based on the clothing data registered by the registration unit. The fitting unit virtually tries on the outfit suggested by the suggestion unit. The purchase unit purchases the clothes suggested by the suggestion unit from an e-commerce site.

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

[0004] In the conventional technology, sufficient means for solving the user's troubles regarding clothing coordination and purchase are not provided, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal coordination based on the clothes the user has and assist in virtual fitting and purchase.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a registration unit, a suggestion unit, a fitting unit, and a purchase unit. The registration unit registers the clothes the user owns. The suggestion unit suggests the most suitable outfit to the user based on the clothing data registered by the registration unit. The fitting unit virtually tries on the outfit suggested by the suggestion unit. The purchase unit purchases the clothes suggested by the suggestion unit from an e-commerce site. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the optimal outfit based on the clothes the user owns, and can support virtual try-on and purchase. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The clothing selection support system according to an embodiment of the present invention is a system that solves the problem of choosing clothes by utilizing generative AI. In this clothing selection support system, when a user registers the clothes they own and looks into a mirror while wearing AR glasses, the generative AI suggests outfits by voice. For example, it may suggest, "This shirt and these pants would suit you today." In addition, users can virtually try on clothes (generated images) according to their mood of the day. Furthermore, the generative AI can link with e-commerce sites to suggest clothes that suit the user, and users can purchase them on the spot. Through this mechanism, users can eliminate the problem of choosing clothes and choose clothes efficiently. For example, a user registers the clothes they own. In this case, the user takes pictures of the clothes and uploads them to the system. For example, items such as shirts, pants, and jackets can be registered. In this way, the system builds a database of the clothes the user owns. Next, when the user puts on AR glasses and looks into a mirror, the generative AI suggests outfits by voice. For example, it may suggest, "This shirt and these pants would suit you today." The generative AI suggests the optimal outfit considering the user's age, body type, preferences, etc. This allows users to easily choose clothes that suit them. Furthermore, they can virtually try on clothes (generated images) to match their mood of the day. For example, if a user requests, "I want a casual style today," the generating AI will suggest a casual outfit, which they can virtually try on. This allows users to try on various styles without actually trying on clothes. The generating AI also integrates with e-commerce sites, suggesting clothes that suit the user and allowing them to purchase them on the spot. For example, if a user says, "I like this shirt," the generating AI will search for that shirt on an e-commerce site and provide a purchase link. This allows users to easily buy clothes. This system eliminates the hassle of choosing clothes and allows users to choose clothes efficiently. For example, even users who are too busy to go to a store can easily choose clothes from the comfort of their home.Furthermore, even users who have had bad experiences with online shopping can choose clothes that suit them through virtual try-ons. In addition, users who are unsure of what clothes suit their age and body type can find suitable clothing through suggestions generated by AI. In this way, the clothing selection support system solves users' worries about choosing clothes and allows them to choose clothes efficiently.

[0029] The clothing selection support system according to this embodiment comprises a registration unit, a suggestion unit, a fitting unit, and a purchase unit. The registration unit registers the clothes owned by the user. The clothes owned by the user include, but are not limited to, shirts, pants, and jackets. The registration unit registers clothes, for example, when the user takes a picture of the clothes and uploads it to the system. The registration unit also allows the user to manually input information about the clothes. For example, the user can input information such as the brand, size, and color of the clothes. Furthermore, the registration unit also allows the user to register clothes by scanning a barcode or QR code (registered trademark). For example, the system automatically obtains information about the clothes by scanning the barcode attached to the clothing tag. The suggestion unit uses a generation AI to suggest the best outfit for the user based on the clothing data registered by the registration unit. The suggestion unit suggests outfits considering, for example, the user's age, body type, and preferences. For example, the generation AI suggests suitable clothing combinations based on the user's age and body type. The suggestion unit can also suggest outfits according to the user's mood. For example, if a user requests a casual style, the generating AI will suggest a casual outfit. The fitting section allows the user to virtually try on the outfit suggested by the suggestion section. The fitting section can use AR technology, for example, to allow the user to virtually try on clothes when they look into a mirror. For example, if a user wears AR glasses and looks into a mirror, the outfit suggested by the generating AI will be displayed virtually. The fitting section can also use 3D modeling technology to perform virtual try-ons tailored to the user's body shape. For example, the user's body shape can be scanned to create a 3D model, and then the clothes can be dressed on that model. The purchasing section allows the user to purchase the clothes suggested by the suggestion section from an e-commerce site. The purchasing section can search for the clothes suggested by the generating AI on an e-commerce site and provide a purchase link. For example, if a user says, "I like this shirt," the generating AI will search for that shirt on an e-commerce site and provide a purchase link. The purchasing section can also allow the user to purchase directly from the e-commerce site.For example, when a user clicks on a suggested outfit, they are redirected to an e-commerce site to complete the purchase. Thus, the clothing selection support system according to this embodiment allows the user to register their existing wardrobe, receive suggestions for optimal outfits, virtually try them on, and purchase them through the e-commerce site.

[0030] The registration function allows users to register their clothing. This includes, but is not limited to, items such as shirts, pants, and jackets. The registration function allows users to register clothing by, for example, taking photos of their clothes and uploading them to the system. Specifically, users take photos of their clothes using a smartphone or digital camera and upload them to the system via a dedicated application or web interface. The system uses image recognition technology to automatically identify the type and characteristics of the clothing and register them in the database. The registration function also allows users to manually input information about their clothing. For example, they can input information such as the brand, size, and color of the clothing. Users can use text input fields to fill in this information in detail and save it in the system. Furthermore, the registration function also allows users to register clothing by scanning barcodes or QR codes. For example, scanning a barcode on a clothing tag automatically retrieves the clothing information. Users scan barcodes or QR codes using their smartphone camera, and the system analyzes the information and registers it in the database. This allows users to quickly and easily register information about their clothing. Furthermore, the registration department centrally manages information about the clothes owned by users and can link with other systems and departments as needed. For example, registered clothing information is stored on a cloud server and can be accessed by the suggestion department and the fitting department. The registration department also has a function to automatically update information when a user purchases new clothes. This allows users to always register the latest clothing information in the system and manage it efficiently.

[0031] The suggestion department uses generative AI to propose the most suitable outfit to the user based on clothing data registered by the registration department. The suggestion department proposes outfits considering, for example, the user's age, body type, and preferences. Specifically, the generative AI analyzes the database of clothing registered by the user and proposes suitable clothing combinations based on the user's age and body type. The generative AI learns from past outfit data and fashion trend information to generate the most suitable outfit for the user. The suggestion department can also propose outfits according to the user's mood. For example, if the user requests, "I want a casual style today," the generative AI will propose a casual outfit. Based on the user's request, the generative AI selects and proposes clothing combinations that suit a casual style. Furthermore, the suggestion department can also propose outfits according to the season and weather. For example, it will suggest clothing made of warm materials in cold seasons and clothing made of cool materials in hot seasons. The generative AI analyzes weather data to generate the optimal outfit. In this way, the suggestion department can propose the most suitable outfit according to the user's individual needs and circumstances, and support the user in choosing clothes. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by providing evaluations and comments on suggested outfits, the generating AI learns from this feedback and incorporates it into future suggestions. This allows the suggestion department to consistently provide users with the most optimal outfits and improve their satisfaction.

[0032] The fitting room allows users to virtually try on outfits suggested by the suggestion room. For example, using AR technology, the fitting room allows users to virtually try on clothes when they look into a mirror. Specifically, when a user wears AR glasses and looks into a mirror, the outfit suggested by the generating AI is displayed virtually. AR technology provides a realistic fitting experience by overlaying virtual clothing onto the user's actual image. The fitting room can also use 3D modeling technology to perform virtual fittings tailored to the user's body shape. For example, the user's body shape is scanned to create a 3D model, and then clothes are dressed on that model. Users scan their body shape using a smartphone or a dedicated scanner, and the system generates a 3D model based on that data. This allows users to virtually try on clothes that fit their body perfectly. Furthermore, the fitting room also has a function that allows users to view the clothes from different angles. For example, users can rotate the screen and zoom in and out during the virtual fitting to check the details and fit of the clothes. This allows the fitting room to provide users with a realistic fitting experience and improve the accuracy of their clothing selection. Furthermore, the fitting room also has a function to save images and videos of the clothes the user has tried on, allowing them to review them later. This enables users to compare multiple outfits and make the best choice.

[0033] The purchasing unit buys the clothing suggested by the suggestion unit from e-commerce sites. For example, the purchasing unit searches for clothing suggested by the generation AI on e-commerce sites and provides a purchase link. Specifically, if a user says, "I like this shirt," the generation AI searches for that shirt on e-commerce sites and provides a purchase link. The generation AI crawls multiple e-commerce sites, checks the best price and availability, and suggests the best purchase option to the user. The purchasing unit also allows users to purchase directly from e-commerce sites. For example, if a user clicks on a suggested piece of clothing, they are redirected to the e-commerce site and can proceed with the purchase. The purchasing unit optimizes its interface to ensure a smooth purchase process for users. For example, when a user clicks on a purchase link, they are directly redirected to the e-commerce site's purchase page, and the necessary information is automatically entered. The purchasing unit also manages the user's purchase history, which can be used as a reference for future purchases. This allows users to combine previously purchased clothing with new clothing to create outfits. Furthermore, the purchasing unit has a function to automatically add information about the clothing purchased by the user to the registration unit. This eliminates the need for users to manually enter information every time they purchase new clothes. The purchasing section offers multiple payment and delivery options to enhance the user's shopping experience. For example, users can choose from payment methods such as credit cards, debit cards, and e-money, and options such as expedited delivery and scheduled delivery are also available. In this way, the purchasing section can provide users with a convenient and comfortable shopping experience and increase user satisfaction as part of the clothing selection support system.

[0034] The suggestion department can propose outfits that take into account the user's age, body type, preferences, and other factors. For example, based on the user's age, the suggestion department can suggest a suitable clothing style. For instance, it might suggest trendy styles for younger users and more subdued styles for middle-aged and older users. The suggestion department can also suggest clothing combinations that flatter the user's body type. For example, it might suggest clothing that fits well to the user's figure. Furthermore, the suggestion department can suggest clothing outfits that match the user's preferences. For example, it might suggest casual outfits for users who prefer casual styles and formal outfits for users who prefer formal styles. This allows the department to propose the optimal outfit considering the user's age, body type, preferences, and other factors.

[0035] The fitting room allows users to virtually try on clothes. For example, using AR technology, the fitting room allows users to virtually try on clothes when they look into a mirror. For instance, when a user wears AR glasses and looks into a mirror, a virtual outfit suggested by a generating AI is displayed. The fitting room can also use 3D modeling technology to perform virtual try-ons tailored to the user's body shape. For example, the user's body shape is scanned to create a 3D model, and then clothes are dressed on that model. This allows for virtual try-on.

[0036] The purchasing function allows users to purchase clothing in conjunction with e-commerce sites. For example, the purchasing function searches for clothing suggested by the AI ​​on e-commerce sites and provides purchase links. If a user says, "I like this shirt," the AI ​​will search for that shirt on the e-commerce site and provide a purchase link. The purchasing function also allows users to purchase directly from e-commerce sites. For example, if a user clicks on a suggested piece of clothing, they will be redirected to the e-commerce site to complete the purchase. This enables users to purchase clothing in conjunction with e-commerce sites.

[0037] The suggestion function can propose outfits tailored to the user's mood. For example, if a user requests, "I want a casual style today," the AI ​​will suggest a casual outfit. Similarly, if a user requests, "I want a formal style today," the AI ​​can suggest a formal outfit. This allows the system to suggest the most suitable outfit based on the user's mood.

[0038] The fitting room allows users to virtually try on clothes using AR glasses. For example, when a user puts on AR glasses and looks into a mirror, the AI-generated outfit suggestions are displayed virtually. The fitting room can also use 3D modeling technology to perform virtual try-ons tailored to the user's body shape. For example, the user's body shape is scanned to create a 3D model, and then clothes are dressed on that model. This enables virtual try-ons using AR glasses.

[0039] The registration unit can analyze a user's past registration history and select the optimal registration method. For example, the registration unit can prioritize suggesting registration methods that the user has frequently used in the past (such as photo uploads or manual input). The registration unit can also send notifications to encourage registration at specific times based on the user's past registration history. Furthermore, the registration unit can analyze the categories of clothing that the user has registered in the past and automatically categorize similar clothing items. This allows the system to analyze the user's past registration history and select the optimal registration method. Some or all of the above processes in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's past registration data into a generating AI and have the generating AI select the optimal registration method.

[0040] The registration unit can filter clothing items based on the user's current fashion trends and areas of interest when registering them. For example, the registration unit can analyze the style of clothing the user has recently purchased and prioritize registering similar styles. The registration unit can also suggest relevant clothing items by referencing the styles of fashion influencers the user follows on social media. Furthermore, the registration unit can filter and register relevant clothing items based on fashion keywords the user has searched for in the past. This allows for the registration of clothing items filtered based on the user's current fashion trends and areas of interest. Some or all of the above processes in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's fashion trend data into a generating AI and have the generating AI perform the filtering.

[0041] The registration unit can prioritize registering clothing that is highly relevant to the user's geographical location when registering clothing. For example, if the user lives in a cold region, the registration unit will prioritize registering winter clothing. It can also prioritize registering business casual clothing if the user lives in an urban area. Furthermore, if the user lives in a resort area, the registration unit can prioritize registering resort wear. This allows the system to prioritize registering clothing that is highly relevant to the user's geographical location. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant clothing.

[0042] The registration unit can analyze a user's social media activity when registering clothing and register relevant clothing items. For example, if a user frequently posts "#OOTD (Outfit of the Day)" on Instagram, the registration unit will prioritize registering clothing items that match that style. The registration unit can also analyze fashion boards saved by the user on Pinterest and register relevant clothing items. Furthermore, the registration unit can prioritize registering new arrivals from fashion brands that the user follows on Twitter. This allows the system to analyze a user's social media activity and register relevant clothing items. Some or all of the above processing in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's social media data into a generating AI and have the generating AI select relevant clothing items.

[0043] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing items. For example, for an important business meeting, the suggestion unit's generating AI can provide a detailed suggestion. For a casual everyday outing, the suggestion unit can also provide a simplified suggestion. Furthermore, for a special event, the suggestion unit can provide a detailed suggestion that includes accessories and shoes. This allows for adjustment of the level of detail in the suggestions based on the importance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input clothing importance data into the generating AI and have the generating AI adjust the level of detail in the suggestions.

[0044] The suggestion unit can apply different suggestion algorithms depending on the clothing category when suggesting outfits. For example, in the case of formal wear, the suggestion unit's generating AI can apply a formal outfit coordination algorithm. Similarly, in the case of sportswear, the suggestion unit can apply a sporty outfit coordination algorithm. Furthermore, in the case of casual wear, the suggestion unit can apply a casual outfit coordination algorithm. This allows for the application of different suggestion algorithms depending on the clothing category. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing category data into the generating AI and have the generating AI execute the application of the suggestion algorithm.

[0045] The suggestion unit can determine the priority of suggestions based on when the clothing was registered when suggesting outfits. For example, the suggestion unit may prioritize suggesting recently registered clothing. It can also prioritize suggesting clothing appropriate for the season. Furthermore, it can prioritize suggesting clothing that the user has not used for a long time. This allows the suggestion unit to determine the priority of suggestions based on when the clothing was registered. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input clothing registration date data into a generating AI and have the generating AI perform the determination of suggestion priority.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items when providing coordinate suggestions. For example, the suggestion unit may prioritize suggesting clothing from the same brand. It can also prioritize suggesting clothing of the same color scheme. Furthermore, it can prioritize suggesting clothing of the same style. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0047] The fitting room unit can analyze the user's past fitting history during virtual try-on to select the optimal fitting method. For example, the fitting room unit can prioritize trying on similar styles of clothing based on the styles the user has tried on in the past. It can also prioritize trying on clothing from a specific brand based on the user's past fitting history. Furthermore, the fitting room unit can analyze the user's past fitting history and prioritize trying on the styles that have been tried on the most times. This allows the fitting room unit to analyze the user's past fitting history and select the optimal fitting method. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's past fitting data into a generating AI and have the generating AI select the optimal fitting method.

[0048] The fitting room unit can customize the fitting process based on the user's current fashion trends during virtual try-on. For example, it can try on similar styles of clothing based on the user's recent purchases. It can also try on related clothing based on the styles of fashion influencers the user follows on social media. Furthermore, it can try on related clothing based on fashion keywords the user has searched for in the past. This allows the fitting room unit to customize the fitting process based on the user's current fashion trends. Some or all of the above processes in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's fashion trend data into a generating AI and have the generating AI perform the customization of the fitting process.

[0049] The fitting room unit can select the optimal fitting method during virtual try-on, taking into account the user's geographical location. For example, if the user lives in a cold region, the fitting room unit will prioritize trying on winter clothing. Similarly, if the user lives in an urban area, the fitting room unit can prioritize trying on business casual attire. Furthermore, if the user lives in a resort area, the fitting room unit can prioritize trying on resort wear. This allows the system to select the optimal fitting method considering the user's geographical location. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal fitting method.

[0050] The fitting room unit can analyze the user's social media activity during virtual try-on and suggest ways to try on clothes. For example, if a user frequently posts "#OOTD (Outfit of the Day)" on Instagram, the fitting room unit will prioritize trying on clothes that match that style. The fitting room unit can also analyze fashion boards saved by the user on Pinterest and try on related clothes. Furthermore, the fitting room unit can prioritize trying on new arrivals from fashion brands that the user follows on Twitter. This allows the fitting room unit to analyze the user's social media activity and suggest ways to try on clothes. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or not. For example, the fitting room unit can input the user's social media data into a generating AI and have the generating AI suggest ways to try on clothes.

[0051] The purchasing department can analyze a user's past purchase history to select the optimal purchase method at the time of purchase. For example, the purchasing department can prioritize suggesting purchase methods that the user has frequently used in the past (such as credit cards or electronic money). The purchasing department can also send notifications to encourage purchases at specific times based on the user's past purchase history. Furthermore, the purchasing department can analyze the brands and styles that the user has purchased in the past and suggest related products. This allows the purchasing department to analyze the user's past purchase history and select the optimal purchase method. Some or all of the above processes in the purchasing department may be performed using AI, for example, or not. For example, the purchasing department can input the user's past purchase data into a generating AI and have the generating AI select the optimal purchase method.

[0052] The purchasing function can customize the purchasing process based on the user's current fashion trends. For example, it can prioritize suggesting similar styles of clothing based on the user's recent purchases. It can also suggest related products based on the styles of fashion influencers the user follows on social media. Furthermore, it can suggest related products based on fashion keywords the user has searched for in the past. This allows for the customization of the purchasing process based on the user's current fashion trends. Some or all of the above processes in the purchasing function may be performed using AI, for example, or not. For example, the purchasing function can input the user's fashion trend data into a generating AI and have the generating AI perform the customization of the purchasing process.

[0053] The purchasing department can select the optimal purchasing method by considering the user's geographical location information at the time of purchase. For example, if the user lives in a cold region, the purchasing department can prioritize suggesting winter items. Similarly, if the user lives in an urban area, the purchasing department can prioritize suggesting business casual items. Furthermore, if the user lives in a resort area, the purchasing department can prioritize suggesting resort wear. This allows the purchasing department to select the optimal purchasing method by considering the user's geographical location information. Some or all of the above processing in the purchasing department may be performed using AI, for example, or without AI. For example, the purchasing department can input the user's geographical location data into a generating AI and have the generating AI select the optimal purchasing method.

[0054] The purchasing department can analyze a user's social media activity and suggest purchasing options at the time of purchase. For example, if a user frequently posts "#OOTD (Outfit of the Day)" on Instagram, the purchasing department will prioritize suggesting products that match that style. The purchasing department can also analyze fashion boards saved by the user on Pinterest and suggest related products. Furthermore, the purchasing department can prioritize suggesting new arrivals from fashion brands that the user follows on Twitter. In this way, the purchasing department can analyze a user's social media activity and suggest purchasing options. Some or all of the above processing in the purchasing department may be performed using AI, for example, or not. For example, the purchasing department can input the user's social media data into a generating AI and have the generating AI suggest purchasing options.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The suggestion unit can analyze the user's past outfit history and propose the optimal outfit. For example, it can suggest outfits in a similar style based on styles the user has preferred to wear in the past. It can also suggest outfits in a different style based on styles the user has avoided in the past. Furthermore, it can suggest outfits suitable for similar events based on outfits the user has worn at specific events. In this way, the system can analyze the user's past outfit history and propose the optimal outfit. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past outfit data into a generating AI and have the generating AI propose the optimal outfit.

[0057] The suggestion unit can propose outfits considering the user's geographical location. For example, if the user lives in a cold region, it can suggest winter clothing. If the user lives in an urban area, it can suggest business casual clothing. Furthermore, if the user lives in a resort area, it can suggest resort wear. This allows the system to propose the most suitable outfit considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI propose the most suitable outfit.

[0058] The fitting room unit can analyze the user's past fitting history during virtual fitting to select the optimal fitting method. For example, it can prioritize trying on similar styles of clothing based on the styles the user has tried on in the past. It can also prioritize trying on clothing from a specific brand based on the user's past fitting history. Furthermore, it can analyze the user's past fitting history and prioritize trying on the styles that have been tried on the most. This allows the system to analyze the user's past fitting history and select the optimal fitting method. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's past fitting data into a generating AI and have the generating AI select the optimal fitting method.

[0059] The purchasing unit can analyze a user's past purchase history to select the optimal purchase method at the time of purchase. For example, it can prioritize suggesting purchase methods that the user has frequently used in the past (credit card, electronic money, etc.). It can also send notifications to encourage purchases at specific times based on the user's past purchase history. Furthermore, it can analyze the brands and styles the user has purchased in the past and suggest related products. This allows the purchasing unit to analyze the user's past purchase history and select the optimal purchase method. Some or all of the above processes in the purchasing unit may be performed using AI, for example, or not. For example, the purchasing unit can input the user's past purchase data into a generating AI and have the generating AI select the optimal purchase method.

[0060] The suggestion unit can adjust the level of detail in its suggestions based on the importance of each piece of clothing. For example, for an important business meeting, the generating AI can provide a detailed suggestion. For a casual everyday outing, the generating AI can provide a simplified suggestion. Furthermore, for a special event, the generating AI can provide a detailed suggestion that includes accessories and shoes. This allows the level of detail in the suggestion unit to be adjusted based on the importance of each piece of clothing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input clothing importance data into the generating AI and have the generating AI adjust the level of detail in the suggestions.

[0061] The fitting room unit can select the optimal fitting method during virtual try-on, taking into account the user's geographical location. For example, if the user lives in a cold region, it can prioritize trying on winter clothing. If the user lives in an urban area, it can prioritize trying on business casual clothing. Furthermore, if the user lives in a resort area, it can prioritize trying on resort wear. This allows the system to select the optimal fitting method considering the user's geographical location. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal fitting method.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The registration section allows users to register their clothing. Users can either take photos of their clothes and upload them to the system, or manually enter clothing information (brand, size, color, etc.). They can also register their clothes by scanning barcodes or QR codes. Step 2: The suggestion unit uses generational AI to propose the most suitable outfit to the user based on the clothing data registered by the registration unit. The suggestion unit proposes outfits considering the user's age, body type, preferences, mood, etc. Step 3: The fitting room allows users to virtually try on the outfits suggested by the suggestion room. The fitting room uses AR technology and 3D modeling technology to allow users to virtually try on different clothes. Step 4: The purchasing department purchases the clothing suggested by the suggestion department from an e-commerce site. The purchasing department searches for the clothing suggested by the generation AI on the e-commerce site and provides a purchase link, or the user directly completes the purchase process on the e-commerce site.

[0064] (Example of form 2) The clothing selection support system according to an embodiment of the present invention is a system that solves the problem of choosing clothes by utilizing generative AI. In this clothing selection support system, when a user registers the clothes they own and looks into a mirror while wearing AR glasses, the generative AI suggests outfits by voice. For example, it may suggest, "This shirt and these pants would suit you today." In addition, users can virtually try on clothes (generated images) according to their mood of the day. Furthermore, the generative AI can link with e-commerce sites to suggest clothes that suit the user, and users can purchase them on the spot. Through this mechanism, users can eliminate the problem of choosing clothes and choose clothes efficiently. For example, a user registers the clothes they own. In this case, the user takes pictures of the clothes and uploads them to the system. For example, items such as shirts, pants, and jackets can be registered. In this way, the system builds a database of the clothes the user owns. Next, when the user puts on AR glasses and looks into a mirror, the generative AI suggests outfits by voice. For example, it may suggest, "This shirt and these pants would suit you today." The generative AI suggests the optimal outfit considering the user's age, body type, preferences, etc. This allows users to easily choose clothes that suit them. Furthermore, they can virtually try on clothes (generated images) to match their mood of the day. For example, if a user requests, "I want a casual style today," the generating AI will suggest a casual outfit, which they can virtually try on. This allows users to try on various styles without actually trying on clothes. The generating AI also integrates with e-commerce sites, suggesting clothes that suit the user and allowing them to purchase them on the spot. For example, if a user says, "I like this shirt," the generating AI will search for that shirt on an e-commerce site and provide a purchase link. This allows users to easily buy clothes. This system eliminates the hassle of choosing clothes and allows users to choose clothes efficiently. For example, even users who are too busy to go to a store can easily choose clothes from the comfort of their home.Furthermore, even users who have had bad experiences with online shopping can choose clothes that suit them through virtual try-ons. In addition, users who are unsure of what clothes suit their age and body type can find suitable clothing through suggestions generated by AI. In this way, the clothing selection support system solves users' worries about choosing clothes and allows them to choose clothes efficiently.

[0065] The clothing selection support system according to this embodiment comprises a registration unit, a suggestion unit, a try-on unit, and a purchase unit. The registration unit registers the clothes owned by the user. The clothes owned by the user include, but are not limited to, shirts, pants, and jackets. The registration unit registers clothes, for example, when the user takes a picture of the clothes and uploads it to the system. The registration unit also allows the user to manually input information about the clothes. For example, the user can input information such as the brand, size, and color of the clothes. Furthermore, the registration unit also allows the user to register clothes by scanning a barcode or QR code. For example, by scanning the barcode attached to the clothing tag, the system automatically obtains information about the clothes. The suggestion unit uses a generation AI to suggest the best outfit for the user based on the clothing data registered by the registration unit. The suggestion unit suggests outfits considering, for example, the user's age, body type, and preferences. For example, the generation AI suggests a combination of clothes that suits the user based on the user's age and body type. The suggestion unit can also suggest outfits according to the user's mood. For example, if the user requests, "I want a casual style today," the generation AI will suggest a casual outfit. The fitting section allows users to virtually try on outfits suggested by the suggestion section. For example, using AR technology, the fitting section allows users to virtually try on clothes when they look into a mirror. For instance, when a user wears AR glasses and looks into a mirror, the outfit suggested by the generative AI is displayed virtually. The fitting section can also use 3D modeling technology to perform virtual try-ons tailored to the user's body shape. For example, the user's body shape is scanned to create a 3D model, and then the model is dressed in clothes. The purchase section allows users to purchase the clothes suggested by the suggestion section from e-commerce sites. For example, the purchase section searches for the clothes suggested by the generative AI on e-commerce sites and provides a purchase link. For instance, if a user says, "I like this shirt," the generative AI searches for that shirt on e-commerce sites and provides a purchase link. The purchase section also allows users to purchase directly from e-commerce sites.For example, when a user clicks on a suggested outfit, they are redirected to an e-commerce site to complete the purchase. Thus, the clothing selection support system according to this embodiment allows the user to register their existing wardrobe, receive suggestions for optimal outfits, virtually try them on, and purchase them through the e-commerce site.

[0066] The registration function allows users to register their clothing. This includes, but is not limited to, items such as shirts, pants, and jackets. The registration function allows users to register clothing by, for example, taking photos of their clothes and uploading them to the system. Specifically, users take photos of their clothes using a smartphone or digital camera and upload them to the system via a dedicated application or web interface. The system uses image recognition technology to automatically identify the type and characteristics of the clothing and register them in the database. The registration function also allows users to manually input information about their clothing. For example, they can input information such as the brand, size, and color of the clothing. Users can use text input fields to fill in this information in detail and save it in the system. Furthermore, the registration function also allows users to register clothing by scanning barcodes or QR codes. For example, scanning a barcode on a clothing tag automatically retrieves the clothing information. Users scan barcodes or QR codes using their smartphone camera, and the system analyzes the information and registers it in the database. This allows users to quickly and easily register information about their clothing. Furthermore, the registration department centrally manages information about the clothes owned by users and can link with other systems and departments as needed. For example, registered clothing information is stored on a cloud server and can be accessed by the suggestion department and the fitting department. The registration department also has a function to automatically update information when a user purchases new clothes. This allows users to always register the latest clothing information in the system and manage it efficiently.

[0067] The suggestion department uses generative AI to propose the most suitable outfit to the user based on clothing data registered by the registration department. The suggestion department proposes outfits considering, for example, the user's age, body type, and preferences. Specifically, the generative AI analyzes the database of clothing registered by the user and proposes suitable clothing combinations based on the user's age and body type. The generative AI learns from past outfit data and fashion trend information to generate the most suitable outfit for the user. The suggestion department can also propose outfits according to the user's mood. For example, if the user requests, "I want a casual style today," the generative AI will propose a casual outfit. Based on the user's request, the generative AI selects and proposes clothing combinations that suit a casual style. Furthermore, the suggestion department can also propose outfits according to the season and weather. For example, it will suggest clothing made of warm materials in cold seasons and clothing made of cool materials in hot seasons. The generative AI analyzes weather data to generate the optimal outfit. In this way, the suggestion department can propose the most suitable outfit according to the user's individual needs and circumstances, and support the user in choosing clothes. Furthermore, the suggestion department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, by providing evaluations and comments on suggested outfits, the generating AI learns from this feedback and incorporates it into future suggestions. This allows the suggestion department to consistently provide users with the most optimal outfits and improve their satisfaction.

[0068] The fitting room allows users to virtually try on outfits suggested by the suggestion room. For example, using AR technology, the fitting room allows users to virtually try on clothes when they look into a mirror. Specifically, when a user wears AR glasses and looks into a mirror, the outfit suggested by the generating AI is displayed virtually. AR technology provides a realistic fitting experience by overlaying virtual clothing onto the user's actual image. The fitting room can also use 3D modeling technology to perform virtual fittings tailored to the user's body shape. For example, the user's body shape is scanned to create a 3D model, and then clothes are dressed on that model. Users scan their body shape using a smartphone or a dedicated scanner, and the system generates a 3D model based on that data. This allows users to virtually try on clothes that fit their body perfectly. Furthermore, the fitting room also has a function that allows users to view the clothes from different angles. For example, users can rotate the screen and zoom in and out during the virtual fitting to check the details and fit of the clothes. This allows the fitting room to provide users with a realistic fitting experience and improve the accuracy of their clothing selection. Furthermore, the fitting room also has a function to save images and videos of the clothes the user has tried on, allowing them to review them later. This enables users to compare multiple outfits and make the best choice.

[0069] The purchasing unit buys the clothing suggested by the suggestion unit from e-commerce sites. For example, the purchasing unit searches for clothing suggested by the generation AI on e-commerce sites and provides a purchase link. Specifically, if a user says, "I like this shirt," the generation AI searches for that shirt on e-commerce sites and provides a purchase link. The generation AI crawls multiple e-commerce sites, checks the best price and availability, and suggests the best purchase option to the user. The purchasing unit also allows users to purchase directly from e-commerce sites. For example, if a user clicks on a suggested piece of clothing, they are redirected to the e-commerce site and can proceed with the purchase. The purchasing unit optimizes its interface to ensure a smooth purchase process for users. For example, when a user clicks on a purchase link, they are directly redirected to the e-commerce site's purchase page, and the necessary information is automatically entered. The purchasing unit also manages the user's purchase history, which can be used as a reference for future purchases. This allows users to combine previously purchased clothing with new clothing to create outfits. Furthermore, the purchasing unit has a function to automatically add information about the clothing purchased by the user to the registration unit. This eliminates the need for users to manually enter information every time they purchase new clothes. The purchasing section offers multiple payment and delivery options to enhance the user's shopping experience. For example, users can choose from payment methods such as credit cards, debit cards, and e-money, and options such as expedited delivery and scheduled delivery are also available. In this way, the purchasing section can provide users with a convenient and comfortable shopping experience and increase user satisfaction as part of the clothing selection support system.

[0070] The suggestion department can propose outfits that take into account the user's age, body type, preferences, and other factors. For example, based on the user's age, the suggestion department can suggest a suitable clothing style. For instance, it might suggest trendy styles for younger users and more subdued styles for middle-aged and older users. The suggestion department can also suggest clothing combinations that flatter the user's body type. For example, it might suggest clothing that fits well to the user's figure. Furthermore, the suggestion department can suggest clothing outfits that match the user's preferences. For example, it might suggest casual outfits for users who prefer casual styles and formal outfits for users who prefer formal styles. This allows the department to propose the optimal outfit considering the user's age, body type, preferences, and other factors.

[0071] The fitting room allows users to virtually try on clothes. For example, using AR technology, the fitting room allows users to virtually try on clothes when they look into a mirror. For instance, when a user wears AR glasses and looks into a mirror, a virtual outfit suggested by a generating AI is displayed. The fitting room can also use 3D modeling technology to perform virtual try-ons tailored to the user's body shape. For example, the user's body shape is scanned to create a 3D model, and then clothes are dressed on that model. This allows for virtual try-on.

[0072] The purchasing function allows users to purchase clothing in conjunction with e-commerce sites. For example, the purchasing function searches for clothing suggested by the AI ​​on e-commerce sites and provides purchase links. If a user says, "I like this shirt," the AI ​​will search for that shirt on the e-commerce site and provide a purchase link. The purchasing function also allows users to purchase directly from e-commerce sites. For example, if a user clicks on a suggested piece of clothing, they will be redirected to the e-commerce site to complete the purchase. This enables users to purchase clothing in conjunction with e-commerce sites.

[0073] The suggestion function can propose outfits tailored to the user's mood. For example, if a user requests, "I want a casual style today," the AI ​​will suggest a casual outfit. Similarly, if a user requests, "I want a formal style today," the AI ​​can suggest a formal outfit. This allows the system to suggest the most suitable outfit based on the user's mood.

[0074] The fitting room allows users to virtually try on clothes using AR glasses. For example, when a user puts on AR glasses and looks into a mirror, the AI-generated outfit suggestions are displayed virtually. The fitting room can also use 3D modeling technology to perform virtual try-ons tailored to the user's body shape. For example, the user's body shape is scanned to create a 3D model, and then clothes are dressed on that model. This enables virtual try-ons using AR glasses.

[0075] The registration unit can estimate the user's emotions and adjust the timing of clothing registration based on the estimated emotions. For example, if the user is stressed, the system can automatically postpone clothing registration and notify the user again when they are relaxed. The registration unit can also encourage clothing registration and suggest entering detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the system can provide a simplified registration procedure to allow for quick clothing registration. This allows the timing of clothing registration to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not. For example, the registration unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The registration unit can analyze a user's past registration history and select the optimal registration method. For example, the registration unit can prioritize suggesting registration methods that the user has frequently used in the past (such as photo uploads or manual input). The registration unit can also send notifications to encourage registration at specific times based on the user's past registration history. Furthermore, the registration unit can analyze the categories of clothing that the user has registered in the past and automatically categorize similar clothing items. This allows the system to analyze the user's past registration history and select the optimal registration method. Some or all of the above processes in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's past registration data into a generating AI and have the generating AI select the optimal registration method.

[0077] The registration unit can filter clothing items based on the user's current fashion trends and areas of interest when registering them. For example, the registration unit can analyze the style of clothing the user has recently purchased and prioritize registering similar styles. The registration unit can also suggest relevant clothing items by referencing the styles of fashion influencers the user follows on social media. Furthermore, the registration unit can filter and register relevant clothing items based on fashion keywords the user has searched for in the past. This allows for the registration of clothing items filtered based on the user's current fashion trends and areas of interest. Some or all of the above processes in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's fashion trend data into a generating AI and have the generating AI perform the filtering.

[0078] The registration unit can estimate the user's emotions and determine the priority of clothing to register based on the estimated emotions. For example, if the user is excited, the system may prioritize registering trendy clothing. If the user is calm, the system may prioritize registering classic style clothing. Furthermore, if the user is tired, the system may prioritize registering clothing made of comfortable materials. This allows the system to determine the priority of clothing to register 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not using AI. For example, the registration unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0079] The registration unit can prioritize registering clothing that is highly relevant to the user's geographical location when registering clothing. For example, if the user lives in a cold region, the registration unit will prioritize registering winter clothing. It can also prioritize registering business casual clothing if the user lives in an urban area. Furthermore, if the user lives in a resort area, the registration unit can prioritize registering resort wear. This allows the system to prioritize registering clothing that is highly relevant to the user's geographical location. Some or all of the above processing in the registration unit may be performed using AI, for example, or without AI. For example, the registration unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant clothing.

[0080] The registration unit can analyze a user's social media activity when registering clothing and register relevant clothing items. For example, if a user frequently posts "#OOTD (Outfit of the Day)" on Instagram, the registration unit will prioritize registering clothing items that match that style. The registration unit can also analyze fashion boards saved by the user on Pinterest and register relevant clothing items. Furthermore, the registration unit can prioritize registering new arrivals from fashion brands that the user follows on Twitter. This allows the system to analyze a user's social media activity and register relevant clothing items. Some or all of the above processing in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input the user's social media data into a generating AI and have the generating AI select relevant clothing items.

[0081] The suggestion unit can estimate the user's emotions and adjust the way the outfit is presented based on the estimated emotions. For example, if the user is relaxed, the suggestion unit's generating AI can suggest a relaxed outfit. If the user is excited, the suggestion unit's generating AI can suggest an outfit with vibrant colors. Furthermore, if the user is calm, the suggestion unit's generating AI can suggest a simple and classic outfit. This allows the way the outfit is presented 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 a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input the user's facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0082] The suggestion unit can adjust the level of detail in its suggestions based on the importance of the clothing items. For example, for an important business meeting, the suggestion unit's generating AI can provide a detailed suggestion. For a casual everyday outing, the suggestion unit can also provide a simplified suggestion. Furthermore, for a special event, the suggestion unit can provide a detailed suggestion that includes accessories and shoes. This allows for adjustment of the level of detail in the suggestions based on the importance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input clothing importance data into the generating AI and have the generating AI adjust the level of detail in the suggestions.

[0083] The suggestion unit can apply different suggestion algorithms depending on the clothing category when suggesting outfits. For example, in the case of formal wear, the suggestion unit's generating AI can apply a formal outfit coordination algorithm. Similarly, in the case of sportswear, the suggestion unit can apply a sporty outfit coordination algorithm. Furthermore, in the case of casual wear, the suggestion unit can apply a casual outfit coordination algorithm. This allows for the application of different suggestion algorithms depending on the clothing category. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing category data into the generating AI and have the generating AI execute the application of the suggestion algorithm.

[0084] The suggestion unit can estimate the user's emotions and adjust the length of the suggested outfit based on those emotions. For example, if the user is in a hurry, the suggestion unit's generating AI can suggest an outfit that can be completed quickly. If the user is relaxed, the suggestion unit's generating AI can also suggest a more detailed outfit. Furthermore, if the user is excited, the suggestion unit's generating AI can also suggest a visually stimulating outfit. This allows the length of the outfit 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 a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0085] The suggestion unit can determine the priority of suggestions based on when the clothing was registered when suggesting outfits. For example, the suggestion unit may prioritize suggesting recently registered clothing. It can also prioritize suggesting clothing appropriate for the season. Furthermore, it can prioritize suggesting clothing that the user has not used for a long time. This allows the suggestion unit to determine the priority of suggestions based on when the clothing was registered. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input clothing registration date data into a generating AI and have the generating AI perform the determination of suggestion priority.

[0086] The suggestion unit can adjust the order of suggestions based on the relevance of the clothing items when providing coordinate suggestions. For example, the suggestion unit may prioritize suggesting clothing from the same brand. It can also prioritize suggesting clothing of the same color scheme. Furthermore, it can prioritize suggesting clothing of the same style. This allows the suggestion unit to adjust the order of suggestions based on the relevance of the clothing items. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input clothing relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0087] The fitting room unit can estimate the user's emotions and adjust the virtual fitting method based on the estimated emotions. For example, if the user is relaxed, the fitting room unit can provide a detailed virtual fitting. If the user is in a hurry, it can also provide a simplified virtual fitting. Furthermore, if the user is excited, it can provide a visually stimulating virtual fitting. This allows the virtual fitting method 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. Some or all of the above processing in the fitting room unit may be performed using AI or not using AI. For example, the fitting room unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The fitting room unit can analyze the user's past fitting history during virtual try-on to select the optimal fitting method. For example, the fitting room unit can prioritize trying on similar styles of clothing based on the styles the user has tried on in the past. It can also prioritize trying on clothing from a specific brand based on the user's past fitting history. Furthermore, the fitting room unit can analyze the user's past fitting history and prioritize trying on the styles that have been tried on the most times. This allows the fitting room unit to analyze the user's past fitting history and select the optimal fitting method. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's past fitting data into a generating AI and have the generating AI select the optimal fitting method.

[0089] The fitting room unit can customize the fitting process based on the user's current fashion trends during virtual try-on. For example, it can try on similar styles of clothing based on the user's recent purchases. It can also try on related clothing based on the styles of fashion influencers the user follows on social media. Furthermore, it can try on related clothing based on fashion keywords the user has searched for in the past. This allows the fitting room unit to customize the fitting process based on the user's current fashion trends. Some or all of the above processes in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's fashion trend data into a generating AI and have the generating AI perform the customization of the fitting process.

[0090] The fitting room unit can estimate the user's emotions and determine the priority of virtual try-ons based on those emotions. For example, if the user is excited, the system may prioritize trying on trendy clothing. If the user is calm, the system may prioritize trying on classic styles of clothing. Furthermore, if the user is tired, the system may prioritize trying on clothing made of comfortable materials. This allows the system to determine the priority of virtual try-ons based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The fitting room unit can select the optimal fitting method during virtual try-on, taking into account the user's geographical location. For example, if the user lives in a cold region, the fitting room unit will prioritize trying on winter clothing. Similarly, if the user lives in an urban area, the fitting room unit can prioritize trying on business casual attire. Furthermore, if the user lives in a resort area, the fitting room unit can prioritize trying on resort wear. This allows the system to select the optimal fitting method considering the user's geographical location. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal fitting method.

[0092] The fitting room unit can analyze the user's social media activity during virtual try-on and suggest ways to try on clothes. For example, if a user frequently posts "#OOTD (Outfit of the Day)" on Instagram, the fitting room unit will prioritize trying on clothes that match that style. The fitting room unit can also analyze fashion boards saved by the user on Pinterest and try on related clothes. Furthermore, the fitting room unit can prioritize trying on new arrivals from fashion brands that the user follows on Twitter. This allows the fitting room unit to analyze the user's social media activity and suggest ways to try on clothes. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or not. For example, the fitting room unit can input the user's social media data into a generating AI and have the generating AI suggest ways to try on clothes.

[0093] The purchasing unit can estimate the user's emotions and adjust the purchasing method based on those emotions. For example, if the user is relaxed, the purchasing unit can provide detailed purchasing instructions and suggest customizable options. If the user is in a hurry, the purchasing unit can provide simplified purchasing instructions to allow for a quick purchase. Furthermore, if the user is excited, the purchasing unit can provide a visually appealing purchasing interface. This allows the purchasing method 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. Some or all of the above processing in the purchasing unit may be performed using AI or not. For example, the purchasing unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The purchasing department can analyze a user's past purchase history to select the optimal purchase method at the time of purchase. For example, the purchasing department can prioritize suggesting purchase methods that the user has frequently used in the past (such as credit cards or electronic money). The purchasing department can also send notifications to encourage purchases at specific times based on the user's past purchase history. Furthermore, the purchasing department can analyze the brands and styles that the user has purchased in the past and suggest related products. This allows the purchasing department to analyze the user's past purchase history and select the optimal purchase method. Some or all of the above processes in the purchasing department may be performed using AI, for example, or not. For example, the purchasing department can input the user's past purchase data into a generating AI and have the generating AI select the optimal purchase method.

[0095] The purchasing function can customize the purchasing process based on the user's current fashion trends. For example, it can prioritize suggesting similar styles of clothing based on the user's recent purchases. It can also suggest related products based on the styles of fashion influencers the user follows on social media. Furthermore, it can suggest related products based on fashion keywords the user has searched for in the past. This allows for the customization of the purchasing process based on the user's current fashion trends. Some or all of the above processes in the purchasing function may be performed using AI, for example, or not. For example, the purchasing function can input the user's fashion trend data into a generating AI and have the generating AI perform the customization of the purchasing process.

[0096] The purchasing department can estimate the user's emotions and determine purchase priorities based on those emotions. For example, if the user is excited, the system may prioritize suggesting trendy items. If the user is calm, the system may prioritize suggesting classic style items. Furthermore, if the user is tired, the system may prioritize suggesting items made of comfortable materials. This allows the system to determine purchase priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the purchasing department may be performed using AI or not. For example, the purchasing department can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The purchasing department can select the optimal purchasing method by considering the user's geographical location information at the time of purchase. For example, if the user lives in a cold region, the purchasing department can prioritize suggesting winter items. Similarly, if the user lives in an urban area, the purchasing department can prioritize suggesting business casual items. Furthermore, if the user lives in a resort area, the purchasing department can prioritize suggesting resort wear. This allows the purchasing department to select the optimal purchasing method by considering the user's geographical location information. Some or all of the above processing in the purchasing department may be performed using AI, for example, or without AI. For example, the purchasing department can input the user's geographical location data into a generating AI and have the generating AI select the optimal purchasing method.

[0098] The purchasing department can analyze a user's social media activity and suggest purchasing options at the time of purchase. For example, if a user frequently posts "#OOTD (Outfit of the Day)" on Instagram, the purchasing department will prioritize suggesting products that match that style. The purchasing department can also analyze fashion boards saved by the user on Pinterest and suggest related products. Furthermore, the purchasing department can prioritize suggesting new arrivals from fashion brands that the user follows on Twitter. In this way, the purchasing department can analyze a user's social media activity and suggest purchasing options. Some or all of the above processing in the purchasing department may be performed using AI, for example, or not. For example, the purchasing department can input the user's social media data into a generating AI and have the generating AI suggest purchasing options.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The suggestion unit can estimate the user's emotions and adjust the color scheme of the outfit based on the estimated emotions. For example, if the user is relaxed, the generating AI can suggest an outfit with calm colors. If the user is excited, the generating AI can suggest an outfit with vibrant colors. Furthermore, if the user is sad, the generating AI can suggest an outfit with bright colors. In this way, the color scheme of the outfit can be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into the generating AI and have the generating AI perform emotion estimation.

[0101] The suggestion unit can analyze the user's past outfit history and propose the optimal outfit. For example, it can suggest outfits in a similar style based on styles the user has preferred to wear in the past. It can also suggest outfits in a different style based on styles the user has avoided in the past. Furthermore, it can suggest outfits suitable for similar events based on outfits the user has worn at specific events. In this way, the system can analyze the user's past outfit history and propose the optimal outfit. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past outfit data into a generating AI and have the generating AI propose the optimal outfit.

[0102] The fitting room unit can estimate the user's emotions and adjust the virtual fitting feedback based on the estimated emotions. For example, if the user is relaxed, it can provide detailed feedback. If the user is in a hurry, it can provide simplified feedback. Furthermore, if the user is excited, it can provide visually appealing feedback. This allows the virtual fitting feedback 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. Some or all of the above processing in the fitting room unit may be performed using AI or not. For example, the fitting room unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0103] The purchasing unit can estimate the user's emotions and adjust the purchasing interface based on those emotions. For example, if the user is relaxed, it can provide a detailed purchasing interface. If the user is in a hurry, it can provide a simplified purchasing interface. Furthermore, if the user is excited, it can provide a visually appealing purchasing interface. This allows the purchasing interface 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. Some or all of the above processing in the purchasing unit may be performed using AI, for example, or not using AI. For example, the purchasing unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The suggestion unit can estimate the user's emotions and suggest accessories based on those emotions. For example, if the user is relaxed, it can suggest simple accessories. If the user is excited, it can suggest more elaborate accessories. Furthermore, if the user is calm, it can suggest classic accessories. This allows the suggestion unit to suggest accessories based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0105] The suggestion unit can propose outfits considering the user's geographical location. For example, if the user lives in a cold region, it can suggest winter clothing. If the user lives in an urban area, it can suggest business casual clothing. Furthermore, if the user lives in a resort area, it can suggest resort wear. This allows the system to propose the most suitable outfit considering the user's geographical location. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location data into a generating AI and have the generating AI propose the most suitable outfit.

[0106] The fitting room unit can analyze the user's past fitting history during virtual fitting to select the optimal fitting method. For example, it can prioritize trying on similar styles of clothing based on the styles the user has tried on in the past. It can also prioritize trying on clothing from a specific brand based on the user's past fitting history. Furthermore, it can analyze the user's past fitting history and prioritize trying on the styles that have been tried on the most. This allows the system to analyze the user's past fitting history and select the optimal fitting method. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's past fitting data into a generating AI and have the generating AI select the optimal fitting method.

[0107] The purchasing unit can analyze a user's past purchase history to select the optimal purchase method at the time of purchase. For example, it can prioritize suggesting purchase methods that the user has frequently used in the past (credit card, electronic money, etc.). It can also send notifications to encourage purchases at specific times based on the user's past purchase history. Furthermore, it can analyze the brands and styles the user has purchased in the past and suggest related products. This allows the purchasing unit to analyze the user's past purchase history and select the optimal purchase method. Some or all of the above processes in the purchasing unit may be performed using AI, for example, or not. For example, the purchasing unit can input the user's past purchase data into a generating AI and have the generating AI select the optimal purchase method.

[0108] The suggestion unit can adjust the level of detail in its suggestions based on the importance of each piece of clothing. For example, for an important business meeting, the generating AI can provide a detailed suggestion. For a casual everyday outing, the generating AI can provide a simplified suggestion. Furthermore, for a special event, the generating AI can provide a detailed suggestion that includes accessories and shoes. This allows the level of detail in the suggestion unit to be adjusted based on the importance of each piece of clothing. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input clothing importance data into the generating AI and have the generating AI adjust the level of detail in the suggestions.

[0109] The fitting room unit can select the optimal fitting method during virtual try-on, taking into account the user's geographical location. For example, if the user lives in a cold region, it can prioritize trying on winter clothing. If the user lives in an urban area, it can prioritize trying on business casual clothing. Furthermore, if the user lives in a resort area, it can prioritize trying on resort wear. This allows the system to select the optimal fitting method considering the user's geographical location. Some or all of the above processing in the fitting room unit may be performed using AI, for example, or without AI. For example, the fitting room unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal fitting method.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The registration section allows users to register their clothing. Users can either take photos of their clothes and upload them to the system, or manually enter clothing information (brand, size, color, etc.). They can also register their clothes by scanning barcodes or QR codes. Step 2: The suggestion unit uses generational AI to propose the most suitable outfit to the user based on the clothing data registered by the registration unit. The suggestion unit proposes outfits considering the user's age, body type, preferences, mood, etc. Step 3: The fitting room allows users to virtually try on the outfits suggested by the suggestion room. The fitting room uses AR technology and 3D modeling technology to allow users to virtually try on different clothes. Step 4: The purchasing department purchases the clothing suggested by the suggestion department from an e-commerce site. The purchasing department searches for the clothing suggested by the generation AI on the e-commerce site and provides a purchase link, or the user directly completes the purchase process on the e-commerce site.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the registration unit, suggestion unit, try-on unit, and purchase unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by taking a picture of clothing using the camera 42 of the smart device 14 and uploading it to the system by the control unit 46A. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and uses generation AI to suggest the optimal outfit to the user. The try-on unit is implemented, for example, by the control unit 46A of the smart device 14, and uses AR technology to perform a virtual try-on. The purchase unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides a purchase link in cooperation with an e-commerce site. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the registration unit, suggestion unit, try-on unit, and purchase unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by taking a picture of clothing using the camera 42 of the smart glasses 214 and uploading it to the system by the control unit 46A. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests the optimal outfit to the user using generating AI. The try-on unit is implemented by, for example, the control unit 46A of the smart glasses 214 and performs virtual try-on using AR technology. The purchase unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a purchase link in cooperation with an e-commerce site. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the registration unit, suggestion unit, fitting unit, and purchase unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by taking a picture of clothing using the camera 42 of the headset terminal 314 and uploading it to the system by the control unit 46A. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and uses generation AI to suggest the optimal outfit to the user. The fitting unit is implemented by, for example, the control unit 46A of the headset terminal 314, and uses AR technology to perform a virtual fitting. The purchase unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and provides a purchase link in cooperation with an e-commerce site. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the registration unit, suggestion unit, fitting unit, and purchase unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by taking a picture of clothing using the camera 42 of the robot 414 and uploading it to the system by the control unit 46A. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests the optimal outfit to the user using generating AI. The fitting unit is implemented by, for example, the control unit 46A of the robot 414 and performs virtual fitting using AR technology. The purchase unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a purchase link in cooperation with an e-commerce site. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A registration section where users register the clothes they own, Based on the clothing data registered by the registration unit, the suggestion unit proposes the most suitable outfit for the user. A fitting room where the outfit proposed by the aforementioned proposal room is virtually tried on, The system includes a purchasing unit that purchases the clothing proposed by the proposal unit from an e-commerce site. A system characterized by the following features. (Note 2) The aforementioned proposal section is, We suggest outfits that take into account the user's age, body type, preferences, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The fitting area is, Dress up in clothes virtually The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned purchasing department, Purchase clothes through an e-commerce site. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We suggest outfits that match the user's mood. The system described in Appendix 1, characterized by the features described herein. (Note 6) The fitting area is, Try on clothes virtually using AR glasses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of clothing registration based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is Analyze the user's past registration history and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When registering clothing items, filtering is performed based on the user's current fashion trends and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is It estimates the user's emotions and determines the priority of clothing items to register based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering clothing items, the system prioritizes registering clothing items that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When registering clothing items, the system analyzes the user's social media activity and registers related clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the outfit is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When suggesting outfit combinations, adjust the level of detail based on the importance of each garment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When suggesting outfit combinations, different suggestion algorithms are applied depending on the clothing category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the outfit based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When suggesting outfit combinations, the priority of suggestions is determined based on when the clothing items were registered. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When suggesting outfit combinations, adjust the order of suggestions based on the relevance of the clothing items. The system described in Appendix 1, characterized by the features described herein. (Note 19) The fitting area is, It estimates the user's emotions and adjusts the virtual try-on method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The fitting area is, During virtual try-on, the system analyzes the user's past try-on history to select the optimal try-on method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The fitting area is, During virtual try-on, the try-on process is customized based on the user's current fashion trends. 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 determines the priority of virtual try-ons based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The fitting area is, During virtual try-on, the system selects the optimal try-on method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The fitting area is, During virtual try-ons, the system analyzes the user's social media activity to suggest ways to try on clothes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned purchasing department, It estimates the user's emotions and adjusts the purchasing method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned purchasing department, At the time of purchase, the system analyzes the user's past purchase history to select the optimal purchase method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned purchasing department, At the time of purchase, the purchase method is customized based on the user's current fashion trends. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned purchasing department, It estimates user emotions and determines purchase priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned purchasing department, When making a purchase, the system will select the most suitable purchase method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned purchasing department, When a user makes a purchase, we analyze their social media activity and suggest ways to make that purchase. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 registration section where users register the clothes they own, Based on the clothing data registered by the registration unit, the suggestion unit proposes the most suitable outfit for the user. A fitting room where the outfit proposed by the aforementioned proposal room is virtually tried on, The system includes a purchasing unit that purchases the clothing proposed by the proposal unit from an e-commerce site. A system characterized by the following features.

2. The aforementioned proposal section is, We suggest outfits that take into account the user's age, body type, preferences, etc. The system according to feature 1.

3. The fitting area is, Dress up in clothes virtually The system according to feature 1.

4. The aforementioned purchasing department, Purchase clothes through an e-commerce site. The system according to feature 1.

5. The aforementioned proposal section is, We suggest outfits that match the user's mood. The system according to feature 1.

6. The fitting area is, Try on clothes virtually using AR glasses. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and adjusts the timing of clothing registration based on those emotions. The system according to feature 1.

8. The aforementioned registration unit is Analyze the user's past registration history and select the optimal registration method. The system according to feature 1.

9. The aforementioned registration unit is When registering clothing items, filtering is performed based on the user's current fashion trends and areas of interest. The system according to feature 1.

10. The aforementioned registration unit is It estimates the user's emotions and determines the priority of clothing items to register based on the estimated user emotions. The system according to feature 1.

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