System

Abstract

An object of a system according to an embodiment is to make it easier for a user to select clothing.SOLUTION: A system according to an embodiment includes an image generation unit, a clothing selection unit, and a try-on support unit. The image generation unit receives a photograph of a user as an input and generates an image of the user wearing various clothes based on the photograph. The clothing selector selects clothing that the user likes from the catalog generated by the image generator. The try-on support unit provides support for actually trying on the clothing selected by the clothing selection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that trying on clothes takes a lot of time and effort.

[0005] The system according to the embodiment aims to make it easier for the user to select an outfit. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation unit, a costume selection unit, and a try-on support unit. The image generation unit receives a user's photo as input and generates images of the user wearing various costumes based on the photo. The costume selection unit selects a costume that the user likes from the catalog generated by the image generation unit. The try-on support unit supports the user in actually trying on the costume selected by the costume selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it easier for the user to select an outfit. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The costume selection system according to the embodiment of the present invention uses an image generation AI to generate images of the user wearing various costumes based on the user's photographs, and creates a catalogue of these images. This allows the user to efficiently find costumes that suit them.

[0029] A costume selection system according to an embodiment includes an image generation unit, a costume selection unit, and a try-on support unit. The image generation unit receives a user's photo as input and generates images of the user wearing various costumes based on the photo. For example, the image generation unit analyzes the user's body shape and facial features to generate realistic costume images. Furthermore, when a user uploads a photo of themselves and enters keywords such as "furisode for Coming of Age Day" or "wedding dress," the image generation unit generates images of the user wearing those costumes based on prompts containing the instructions. The costume selection unit selects a costume that the user likes from a catalog generated by the image generation unit. For example, the costume selection unit compares furisode and wedding dresses of different designs and colors included in the generated catalog, allowing the user to select one. The try-on support unit provides support for actually trying on the costume selected by the costume selection unit. For example, the try-on support unit provides support for reducing the number of visits and the effort required for the user to try on the costume selected from the catalog. This allows the costume selection system according to an embodiment to efficiently find a costume that suits them.

[0030] The image generation unit can analyze the user's movements and postures and generate costume images in different poses. For example, the image generation unit uses a generation AI to generate costume images in different poses based on the user's photograph. For example, it provides images of furisode or wedding dresses in various poses, such as standing, sitting, and walking. Furthermore, when the user specifies a specific pose, the image generation unit generates a costume image that matches that pose. For example, when the user specifies a "dance pose," it generates an image of a dress that matches that pose. Furthermore, the image generation unit uses a generation AI to analyze the user's movements and generate costume images in dynamic poses. For example, it recreates dynamic scenes such as spinning or jumping while wearing a furisode. This provides costume images in different poses, allowing for a more realistic try-on experience.

[0031] The image generation unit automatically adjusts the user's skin color and hairstyle, providing a more realistic try-on image. For example, the image generation unit uses a generation AI to analyze the user's skin color and reflect it in the outfit image. For example, the image generation unit adjusts the color of a long-sleeved kimono or wedding dress to match the user's skin color. Furthermore, if the user wishes to change their hairstyle, the image generation unit generates outfit images that match that hairstyle. For example, it provides outfit images with different hairstyles, such as updos and downdos. Furthermore, the image generation unit uses a generation AI to automatically recognize the user's skin color and hairstyle and generate the optimal outfit image. For example, it adjusts the pattern and color of a long-sleeved kimono to match the user's skin color, providing a more realistic try-on image. This allows the unit to provide realistic try-on images that match the user's skin color and hairstyle.

[0032] The image generation unit can also import photos of the user's family and friends and generate images of trying on outfits as a group. For example, the image generation unit uses a generation AI to import photos of the user's family and friends and generate images of trying on outfits as a group. For example, it can provide images of the whole family wearing long-sleeved kimonos or suits. If the user wants to check images of trying on outfits together with friends, the image generation unit can import photos of the friends and generate images of trying on outfits as a group. For example, it can provide images of the whole family wearing wedding dresses or tuxedos. The image generation unit can also use a generation AI to analyze photos of the user's family and friends and generate images of trying on outfits as a group. For example, it can provide images of the whole family wearing long-sleeved kimonos with different designs. This makes it possible to provide images of trying on outfits as a group.

[0033] The image generation unit can simulate different seasons and weather conditions and generate corresponding costume images. For example, the image generation unit uses a generation AI to simulate different seasons and generate corresponding costume images. For example, it provides images of a furisode kimono under cherry blossoms in spring or a wedding dress in a snowy winter landscape. When a user specifies specific weather conditions, the image generation unit generates costume images that match those conditions. For example, it provides images of dresses suitable for rainy days or sunny days. The image generation unit also uses a generation AI to automatically recognize seasons and weather conditions and generate costume images that match them. For example, it provides images of a furisode kimono on a summer beach or a wedding dress among autumn leaves. This makes it possible to provide costume images that match different seasons and weather conditions.

[0034] The image generation unit can simulate different backgrounds and situations and provide corresponding outfit trying-on images. For example, the image generation unit uses a generation AI to simulate different backgrounds and provide outfit trying-on images. For example, it provides images of furisode kimonos and wedding dresses in specific situations, such as wedding halls and coming-of-age ceremony venues. Furthermore, when a user specifies a specific situation, the image generation unit generates outfit images that match that situation. For example, it provides images of a wedding on the beach or a coming-of-age ceremony in a garden. Furthermore, the image generation unit uses a generation AI to automatically recognize different backgrounds and situations and generate outfit images that match them. For example, it provides images of dresses that match an evening party or a daytime event. This makes it possible to provide outfit trying-on images that match different backgrounds and situations.

[0035] The image generation unit can simulate changes in the user's body shape and generate clothing images that match the user's future body shape. For example, the image generation unit uses a generation AI to simulate changes in the user's body shape and generate clothing images that match the user's future body shape. For example, it provides images of long-sleeved kimonos and wedding dresses that match the body shape after dieting or muscle training. Furthermore, when the user specifies a specific change in body shape, the image generation unit generates clothing images that match that body shape. For example, it provides images of dresses that match the body shape during pregnancy or after childbirth. Furthermore, the image generation unit uses a generation AI to automatically recognize changes in the user's body shape and generate clothing images that match the changes. For example, it provides images of long-sleeved kimonos and wedding dresses that match changes in age and lifestyle. This makes it possible to provide clothing images that match the user's future body shape.

[0036] The image generation unit can incorporate different cultures and traditional costumes to provide the user with a variety of options. For example, the generation AI of the image generation unit incorporates traditional costumes from different cultures to provide the user with a variety of options. For example, it can provide not only Japanese furisode kimonos and Western wedding dresses, but also Indian saris and Chinese cheongsams. Furthermore, when a user specifies a specific culture or traditional costume, the image generation unit generates an image that matches that costume. For example, if a user specifies "Indian wedding," the generation AI generates an image of the person wearing a sari. Furthermore, the image generation unit can automatically recognize different cultures and traditional costumes and generate costume images that correspond to them. For example, if a user specifies "traditional African costume," the generation AI generates an image of the person wearing that costume. This allows the user to provide a variety of options that incorporate different cultures and traditional costumes.

[0037] The image generation unit can analyze the user's past fashion history and make outfit suggestions based on that. For example, the image generation unit uses a generation AI to analyze the user's past fashion history and make outfit suggestions based on that. For example, new suggestions are made based on designs of furisode or wedding dresses chosen in the past. Furthermore, when the user inputs data on outfits worn in the past, the image generation unit suggests optimal outfits based on that data. For example, it analyzes past fashion styles and suggests designs that suit them. Furthermore, the image generation unit uses a generation AI to automatically recognize the user's past fashion history and make outfit suggestions based on that. For example, it suggests new furisode or wedding dresses based on colors and designs chosen in the past. This makes it possible to make optimal outfit suggestions based on the user's past fashion history.

[0038] The fitting support unit can automatically manage the user's fitting schedule and suggest the optimal fitting time. For example, the generation AI in the fitting support unit analyzes the user's calendar and schedule to suggest the optimal fitting time. For example, it automatically makes a fitting reservation based on the user's free time. Furthermore, when the user inputs the desired fitting date and time, the fitting support unit suggests a fitting schedule that matches that date and time. For example, it automatically adjusts the fitting location and time. Furthermore, the fitting support unit builds a system in which the generation AI analyzes the user's schedule in real time and suggests the optimal fitting time. For example, it readjusts the fitting time in response to changes in the user's plans. In this way, the user's fitting schedule can be automatically managed and the optimal fitting time can be suggested.

[0039] The fitting support unit can collect feedback during fitting in real time and reflect it in the next fitting. In the fitting support unit, for example, the generation AI collects feedback during fitting by the user in real time and reflects the results in the next fitting. For example, the next outfit is suggested based on the impressions and evaluations of the try-on. The fitting support unit also builds a system in which the generation AI analyzes the feedback entered by the user during fitting and reflects the results in the next fitting. For example, an outfit is selected based on comments and evaluations made during fitting. In addition, the fitting support unit can collect feedback during fitting in real time using the generation AI and reflect the results in the next fitting. For example, the emotional reactions of the user during fitting are analyzed and used to suggest the next outfit. This allows the feedback during fitting to be reflected in the next fitting.

[0040] The fitting support unit automatically reserves a place to try on clothes, saving the user time and effort. For example, the generation AI analyzes the user's schedule and automatically reserves the most suitable place to try on clothes. For example, it automatically reserves a place to try on clothes based on the user's free time. The fitting support unit also automatically reserves a place to try on clothes when the user inputs their desired place to try on clothes. For example, it checks the availability of the fitting place and makes a reservation. The fitting support unit also builds a system in which the generation AI automatically reserves a place to try on clothes, saving the user time and effort. For example, it reserves a place to try on clothes based on the user's schedule. This automatically reserves a place to try on clothes, saving the user time and effort.

[0041] The fitting support unit can provide a guide to support putting on and taking off clothes when trying on. For example, the fitting support unit provides a guide to support the generation AI in putting on and taking off clothes when trying on. For example, it explains how to put on and take off a furisode or wedding dress using videos and images. The fitting support unit also provides a guide to support the user in real time when they have trouble putting on or taking off clothes when trying on. For example, it suggests an appropriate way to put on or take off clothes in response to a user's question. The fitting support unit also automatically generates a guide to support the generation AI in putting on and taking off clothes when trying on, and provides it to the user. For example, it suggests the optimal way to put on or take off clothes depending on the user's fitting situation. In this way, it is possible to provide a guide to support putting on and taking off clothes when trying on.

[0042] The costume selection unit can suggest costumes based on the theme and color code of an event. For example, the generation AI of the costume selection unit suggests the most suitable costume based on the theme and color code of an event. For example, it suggests a furisode that matches the theme color of Coming of Age Day, or a wedding dress that matches the theme color of a wedding ceremony. Furthermore, when a user inputs the theme and color code of a specific event, the costume selection unit suggests the most suitable costume based on that information. For example, it suggests a hakama that matches the theme color of a graduation ceremony. Furthermore, the costume selection unit suggests the most suitable costume based on the event theme and color code using the generation AI, automatically recognizing the theme and color code of an event. For example, it suggests a dress that matches the theme color of a party. This makes it possible to suggest the most suitable costume based on the theme and color code of an event.

[0043] The costume selection unit can suggest the optimal timing to choose a costume according to the event schedule. For example, the generation AI analyzes the event schedule and suggests the optimal timing to choose a costume. For example, it suggests the timing to choose a costume according to the schedule of Coming of Age Day or a wedding. Furthermore, when a user inputs the schedule of a specific event, the costume selection unit suggests the optimal timing to choose a costume according to that schedule. For example, it suggests the timing to choose a costume according to the schedule of a graduation ceremony or a party. Furthermore, the generation AI automatically recognizes the event schedule and suggests the optimal timing to choose a costume based on that. For example, it suggests the timing to choose a costume according to the preparation period for the event. This makes it possible to suggest the optimal timing to choose a costume according to the event schedule.

[0044] The costume selection unit can simulate different events and make costume suggestions based on the results. For example, the generation AI simulates different events and makes costume suggestions based on the results. For example, the generation AI simulates Coming of Age Day, weddings, graduation ceremonies, etc., and suggests the most suitable costumes for each. Furthermore, when a user specifies a specific event, the costume selection unit simulates the event and suggests the most suitable costumes. For example, the generation AI simulates a party or business event and suggests dresses to match it. Furthermore, the generation AI automatically simulates different events and makes costume suggestions based on the results. For example, the generation AI suggests costumes that match seasonal events or specific themes. This makes it possible to suggest the most suitable costumes based on simulations of different events.

[0045] The costume selection unit can analyze the user's past event participation history and make costume suggestions based on that. For example, the generation AI of the costume selection unit analyzes the user's past event participation history and makes costume suggestions based on that. For example, new suggestions are made based on data from Coming of Age Day or weddings that the user has attended in the past. Furthermore, when the costume selection unit inputs data from events the user has attended in the past, it suggests the most suitable costume based on that data. For example, suggestions are made based on the designs of costumes worn at past events. Furthermore, the generation AI of the costume selection unit automatically recognizes the user's past event participation history and makes costume suggestions based on that. For example, new suggestions are made based on the themes and color codes of events that the user has attended in the past. This makes it possible to suggest the most suitable costume based on the user's past event participation history.

[0046] The costume selection unit can suggest customization options according to the user's preferences in real time. For example, the generation AI of the costume selection unit suggests customization options according to the user's preferences in real time. For example, detailed customization is possible, such as the pattern and color of the furisode, the design of the wedding dress, and accessories. Furthermore, when the user specifies a specific customization option, the costume selection unit generates a costume image that matches that option. For example, if the user specifies a "red furisode," it generates an image of the furisode that matches that color. Furthermore, the costume selection unit constructs a system in which the generation AI analyzes the user's preferences and suggests customization options based on them in real time. For example, it suggests optimal customization options based on the user's past selection history. This makes it possible to suggest customization options according to the user's preferences in real time.

[0047] The outfit selection unit can provide customization options based on the user's body type and facial features. For example, the outfit selection unit uses a generation AI to analyze the user's body type and facial features and provide customization options based on that. For example, it can suggest a furisode design that matches the user's body type or wedding dress accessories that match their facial features. When a user inputs a specific body type or facial features, the outfit selection unit provides customization options based on that information. For example, if a user inputs characteristics such as "tall" or "round face," it can suggest outfits that match those characteristics. The outfit selection unit also builds a system in which the generation AI automatically recognizes the user's body type and facial features and provides customization options based on those. For example, it can analyze the user's photo and suggest optimal customization options. This makes it possible to provide customization options based on the user's body type and facial features.

[0048] The costume selection unit can provide customization options based on different cultures and traditions. For example, the generation AI provides customization options based on different cultures and traditions. For example, it provides not only Japanese furisode kimonos and Western wedding dresses, but also Indian saris and Chinese cheongsams. When a user specifies a customization option based on a specific culture or tradition, the costume selection unit generates an outfit image that matches that option. For example, if a user specifies "Indian wedding," the generation AI generates an image of the person wearing a sari. The costume selection unit also automatically recognizes customization options based on different cultures and traditions and generates an outfit image that corresponds to that option. For example, if a user specifies "traditional African clothing," the generation AI generates an image of the person wearing that clothing. This makes it possible to provide customization options based on different cultures and traditions.

[0049] The outfit selection unit can analyze the user's past fashion history and suggest customization options based on that. For example, the generation AI of the outfit selection unit analyzes the user's past fashion history and suggests customization options based on that. For example, new suggestions are made based on designs of furisode or wedding dresses chosen in the past. In addition, when the user inputs data on outfits worn in the past, the outfit selection unit suggests optimal customization options based on that data. For example, it analyzes past fashion styles and suggests designs that suit them. In addition, the generation AI of the outfit selection unit automatically recognizes the user's past fashion history and suggests customization options based on that. For example, it suggests new furisode or wedding dresses based on colors and designs chosen in the past. This makes it possible to suggest optimal customization options based on the user's past fashion history.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The outfit selection system can also analyze a user's past fashion history and make outfit suggestions based on that. For example, it can make new suggestions based on the designs of furisode or wedding dresses chosen in the past. Also, when a user inputs data on outfits they have worn in the past, it can suggest the most suitable outfit based on that data. For example, it can analyze past fashion styles and suggest designs that suit them. The generation AI can also automatically recognize a user's past fashion history and make outfit suggestions based on that. For example, it can suggest new furisode or wedding dresses based on colors and designs chosen in the past. This makes it possible to make optimal outfit suggestions based on a user's past fashion history.

[0052] The costume selection system can also incorporate costumes from different cultures and traditions, providing users with a wider variety of options. For example, it can offer not only Japanese furisode (long-sleeved kimono) and Western wedding dresses, but also Indian saris and Chinese cheongsams. Furthermore, when a user specifies a specific culture or traditional costume, it generates an image that matches that costume. For example, if a user specifies "Indian wedding," the generation AI generates an image of the person wearing a sari. Furthermore, the generation AI can automatically recognize different cultures and traditional costumes and generate costume images that match them. For example, if a user specifies "traditional African costume," it generates an image of the person wearing that costume. This allows for a wider variety of options incorporating different cultures and traditional costumes.

[0053] The outfit selection system can also simulate changes in the user's body shape and generate outfit images that fit the user's future body shape. For example, it can provide images of long-sleeved kimonos and wedding dresses that fit the body shape after dieting or muscle training. In addition, if the user specifies a specific body shape change, it can generate outfit images that fit that body shape. For example, it can provide images of dresses that fit the body shape during pregnancy or after childbirth. The generation AI can also automatically recognize changes in the user's body shape and generate outfit images that match them. For example, it can provide images of long-sleeved kimonos and wedding dresses that fit changes in age and lifestyle. This makes it possible to provide outfit images that fit the user's future body shape.

[0054] The costume selection system can also simulate different seasons and weather conditions and generate costume images accordingly. For example, it can provide images of a furisode under cherry blossoms in spring or a wedding dress against a snowy winter landscape. In addition, when the user specifies specific weather conditions, it can generate costume images that match those conditions. For example, it can provide images of dresses suitable for rainy or sunny days. The generation AI can also automatically recognize seasons and weather conditions and generate costume images that match them. For example, it can provide images of a furisode on a summer beach or a wedding dress among autumn leaves. This makes it possible to provide costume images that suit different seasons and weather conditions.

[0055] The costume selection system can also import photos of the user's family and friends to generate group try-on images. For example, it can provide images of the whole family wearing long-sleeved kimonos or suits. If the user wants to see what it will look like when trying on outfits together with friends, it can import photos of friends and generate group try-on images. For example, it can provide images of the whole family wearing wedding dresses or tuxedos. The generation AI can also analyze photos of the user's family and friends to generate group try-on images. For example, it can provide images of the whole family wearing long-sleeved kimonos with different designs. This allows it to provide group try-on images.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image generation unit receives a user's photo as input and generates images of the user wearing various outfits based on the photo. For example, the image generation unit analyzes the user's body shape and facial features to generate realistic outfit images. Also, when a user uploads a photo of themselves and enters keywords such as "coming-of-age day furisode" or "wedding dress," the unit generates images of the user wearing those outfits based on prompts containing those instructions. Step 2: The costume selection unit selects a costume that the user likes from the catalog generated by the image generation unit. For example, the costume selection unit compares furisode and wedding dresses of different designs and colors included in the generated catalog, allowing the user to select one. Step 3: The fitting support unit provides support for actually trying on the outfit selected by the outfit selection unit. For example, the fitting support unit provides support for reducing the number of visits required for the user to try on the outfit selected from the catalog and the effort required for dressing.

[0058] (Example 2) The costume selection system according to the embodiment of the present invention uses an image generation AI to generate images of the user wearing various costumes based on the user's photographs, and creates a catalogue of these images. This allows the user to efficiently find costumes that suit them.

[0059] A costume selection system according to an embodiment includes an image generation unit, a costume selection unit, and a try-on support unit. The image generation unit receives a user's photo as input and generates images of the user wearing various costumes based on the photo. For example, the image generation unit analyzes the user's body shape and facial features to generate realistic costume images. Furthermore, when a user uploads a photo of themselves and enters keywords such as "furisode for Coming of Age Day" or "wedding dress," the image generation unit generates images of the user wearing those costumes based on prompts containing the instructions. The costume selection unit selects a costume that the user likes from a catalog generated by the image generation unit. For example, the costume selection unit compares furisode and wedding dresses of different designs and colors included in the generated catalog, allowing the user to select one. The try-on support unit provides support for actually trying on the costume selected by the costume selection unit. For example, the try-on support unit provides support for reducing the number of visits and the effort required for the user to try on the costume selected from the catalog. This allows the costume selection system according to an embodiment to efficiently find a costume that suits them.

[0060] The image generation unit can analyze the user's movements and postures and generate costume images in different poses. For example, the image generation unit uses a generation AI to generate costume images in different poses based on the user's photograph. For example, it provides images of furisode or wedding dresses in various poses, such as standing, sitting, and walking. Furthermore, when the user specifies a specific pose, the image generation unit generates a costume image that matches that pose. For example, when the user specifies a "dance pose," it generates an image of a dress that matches that pose. Furthermore, the image generation unit uses a generation AI to analyze the user's movements and generate costume images in dynamic poses. For example, it recreates dynamic scenes such as spinning or jumping while wearing a furisode. This provides costume images in different poses, allowing for a more realistic try-on experience.

[0061] The image generation unit automatically adjusts the user's skin color and hairstyle, providing a more realistic try-on image. For example, the image generation unit uses a generation AI to analyze the user's skin color and reflect it in the outfit image. For example, the image generation unit adjusts the color of a long-sleeved kimono or wedding dress to match the user's skin color. Furthermore, if the user wishes to change their hairstyle, the image generation unit generates outfit images that match that hairstyle. For example, it provides outfit images with different hairstyles, such as updos and downdos. Furthermore, the image generation unit uses a generation AI to automatically recognize the user's skin color and hairstyle and generate the optimal outfit image. For example, it adjusts the pattern and color of a long-sleeved kimono to match the user's skin color, providing a more realistic try-on image. This allows the unit to provide realistic try-on images that match the user's skin color and hairstyle.

[0062] The image generation unit can use the emotion estimation function to analyze the user's facial expression and generate an outfit image that brings out the most positive expression. For example, the image generation unit uses a generation AI to analyze the user's facial expression and generate an outfit image that brings out a positive expression. For example, it provides images of long-sleeved kimono or wedding dresses that emphasize smiles or expressions of joy. Furthermore, when the user specifies a specific expression, the image generation unit generates an outfit image that matches that expression. For example, when the user specifies "smile," it generates an image of a dress that matches that expression. Furthermore, the image generation unit uses the emotion estimation function to analyze the facial expression that the user has the most positive emotion and generate an outfit image that brings out that expression. For example, it provides an image that brings out the most positive expression when the user is wearing a long-sleeved kimono. In this way, it is possible to provide outfit images that bring out the user's positive expression.

[0063] The image generation unit can also import photos of the user's family and friends and generate images of trying on outfits as a group. For example, the image generation unit uses a generation AI to import photos of the user's family and friends and generate images of trying on outfits as a group. For example, it can provide images of the whole family wearing long-sleeved kimonos or suits. If the user wants to check images of trying on outfits together with friends, the image generation unit can import photos of the friends and generate images of trying on outfits as a group. For example, it can provide images of the whole family wearing wedding dresses or tuxedos. The image generation unit can also use a generation AI to analyze photos of the user's family and friends and generate images of trying on outfits as a group. For example, it can provide images of the whole family wearing long-sleeved kimonos with different designs. This makes it possible to provide images of trying on outfits as a group.

[0064] The image generation unit can simulate different seasons and weather conditions and generate corresponding costume images. For example, the image generation unit uses a generation AI to simulate different seasons and generate corresponding costume images. For example, it provides images of a furisode kimono under cherry blossoms in spring or a wedding dress in a snowy winter landscape. When a user specifies specific weather conditions, the image generation unit generates costume images that match those conditions. For example, it provides images of dresses suitable for rainy days or sunny days. The image generation unit also uses a generation AI to automatically recognize seasons and weather conditions and generate costume images that match them. For example, it provides images of a furisode kimono on a summer beach or a wedding dress among autumn leaves. This makes it possible to provide costume images that match different seasons and weather conditions.

[0065] The image generation unit uses the emotion estimation function to provide real-time feedback on the emotional reaction of the user when they see a specific outfit, and can suggest the most suitable outfit. For example, the image generation unit uses the emotion estimation function to analyze the emotional reaction of the user when they see a specific outfit in real time, and suggests the most suitable outfit based on the results. For example, the suggestion is made based on the emotion score when the user sees a furisode kimono. The image generation unit also provides real-time feedback on the emotional reaction of the user when they see a specific outfit, and builds a system that suggests the most suitable outfit based on the results. For example, the image generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The image generation unit also uses the emotion estimation function to monitor the emotional reaction of the user when they see a specific outfit in real time, and suggests the most suitable outfit based on the results. For example, the suggestion is made based on the emotion score when the user sees a wedding dress. This makes it possible to suggest the most suitable outfit based on the user's emotional reaction.

[0066] The image generation unit can simulate different backgrounds and situations and provide corresponding outfit trying-on images. For example, the image generation unit uses a generation AI to simulate different backgrounds and provide outfit trying-on images. For example, it provides images of furisode kimonos and wedding dresses in specific situations, such as wedding halls and coming-of-age ceremony venues. Furthermore, when a user specifies a specific situation, the image generation unit generates outfit images that match that situation. For example, it provides images of a wedding on the beach or a coming-of-age ceremony in a garden. Furthermore, the image generation unit uses a generation AI to automatically recognize different backgrounds and situations and generate outfit images that match them. For example, it provides images of dresses that match an evening party or a daytime event. This makes it possible to provide outfit trying-on images that match different backgrounds and situations.

[0067] The image generation unit can simulate changes in the user's body shape and generate clothing images that match the user's future body shape. For example, the image generation unit uses a generation AI to simulate changes in the user's body shape and generate clothing images that match the user's future body shape. For example, it provides images of long-sleeved kimonos and wedding dresses that match the body shape after dieting or muscle training. Furthermore, when the user specifies a specific change in body shape, the image generation unit generates clothing images that match that body shape. For example, it provides images of dresses that match the body shape during pregnancy or after childbirth. Furthermore, the image generation unit uses a generation AI to automatically recognize changes in the user's body shape and generate clothing images that match the changes. For example, it provides images of long-sleeved kimonos and wedding dresses that match changes in age and lifestyle. This makes it possible to provide clothing images that match the user's future body shape.

[0068] The image generation unit can use the emotion estimation function to analyze the emotion a user feels when viewing a specific outfit and suggest an outfit that elicits the most positive emotion. For example, the image generation unit uses the emotion estimation function to analyze the emotion a user feels when viewing a specific outfit and, based on the results, suggests an outfit that elicits the most positive emotion. For example, the suggestion is made based on the emotion score when the user views a furisode kimono. The image generation unit also analyzes the emotion a user feels when viewing a specific outfit in real time and, based on the results, builds a system that suggests an outfit that elicits the most positive emotion. For example, the image generation unit analyzes the emotion a user feels when viewing a specific outfit and, based on the results, calculates an emotion score by analyzing the user's facial expressions and voice. The image generation unit also uses the emotion estimation function to monitor the emotion a user feels when viewing a specific outfit in real time and, based on the results, suggests an outfit that elicits the most positive emotion. For example, the suggestion is made based on the emotion score when the user views a wedding dress. This makes it possible to suggest optimal outfits based on the user's emotions.

[0069] The image generation unit can incorporate different cultures and traditional costumes to provide the user with a variety of options. For example, the generation AI of the image generation unit incorporates traditional costumes from different cultures to provide the user with a variety of options. For example, it can provide not only Japanese furisode kimonos and Western wedding dresses, but also Indian saris and Chinese cheongsams. Furthermore, when a user specifies a specific culture or traditional costume, the image generation unit generates an image that matches that costume. For example, if a user specifies "Indian wedding," the generation AI generates an image of the person wearing a sari. Furthermore, the image generation unit can automatically recognize different cultures and traditional costumes and generate costume images that correspond to them. For example, if a user specifies "traditional African costume," the generation AI generates an image of the person wearing that costume. This allows the user to provide a variety of options that incorporate different cultures and traditional costumes.

[0070] The image generation unit can analyze the user's past fashion history and make outfit suggestions based on that. For example, the image generation unit uses a generation AI to analyze the user's past fashion history and make outfit suggestions based on that. For example, new suggestions are made based on designs of furisode or wedding dresses chosen in the past. Furthermore, when the user inputs data on outfits worn in the past, the image generation unit suggests optimal outfits based on that data. For example, it analyzes past fashion styles and suggests designs that suit them. Furthermore, the image generation unit uses a generation AI to automatically recognize the user's past fashion history and make outfit suggestions based on that. For example, it suggests new furisode or wedding dresses based on colors and designs chosen in the past. This makes it possible to make optimal outfit suggestions based on the user's past fashion history.

[0071] The image generation unit can use the emotion estimation function to compare the emotional reactions of the user when viewing different outfits and select the most suitable outfit. For example, the image generation unit uses the emotion estimation function to analyze the emotional reactions of the user when viewing different outfits in real time and select the most suitable outfit based on the results. For example, the selection is made based on the emotional score when the user views a furisode kimono. The image generation unit also constructs a system that feeds back the emotional reactions of the user when viewing different outfits in real time and selects the most suitable outfit based on the results. For example, the image generation unit analyzes the user's facial expressions and voice and calculates an emotional score. The image generation unit also uses the emotion estimation function to monitor the emotional reactions of the user when viewing different outfits in real time and selects the most suitable outfit based on the results. For example, the selection is made based on the emotional score when the user views a wedding dress. This makes it possible to select the most suitable outfit based on the user's emotional reactions.

[0072] The fitting support unit can automatically manage the user's fitting schedule and suggest the optimal fitting time. For example, the generation AI in the fitting support unit analyzes the user's calendar and schedule to suggest the optimal fitting time. For example, it automatically makes a fitting reservation based on the user's free time. Furthermore, when the user inputs the desired fitting date and time, the fitting support unit suggests a fitting schedule that matches that date and time. For example, it automatically adjusts the fitting location and time. Furthermore, the fitting support unit builds a system in which the generation AI analyzes the user's schedule in real time and suggests the optimal fitting time. For example, it readjusts the fitting time in response to changes in the user's plans. In this way, the user's fitting schedule can be automatically managed and the optimal fitting time can be suggested.

[0073] The fitting support unit can collect feedback during fitting in real time and reflect it in the next fitting. In the fitting support unit, for example, the generation AI collects feedback during fitting by the user in real time and reflects the results in the next fitting. For example, the next outfit is suggested based on the impressions and evaluations of the try-on. The fitting support unit also builds a system in which the generation AI analyzes the feedback entered by the user during fitting and reflects the results in the next fitting. For example, an outfit is selected based on comments and evaluations made during fitting. In addition, the fitting support unit can collect feedback during fitting in real time using the generation AI and reflect the results in the next fitting. For example, the emotional reactions of the user during fitting are analyzed and used to suggest the next outfit. This allows the feedback during fitting to be reflected in the next fitting.

[0074] The fitting support unit can use the emotion estimation function to analyze the user's emotions when trying on clothes and suggest a fitting method that will elicit the most positive emotions. For example, the fitting support unit can use the emotion estimation function to analyze the user's emotions when trying on clothes in real time and, based on the results, suggest a fitting method that will elicit the most positive emotions. For example, it can suggest an outfit that will evoke the most positive emotions when the user tries on clothes. The fitting support unit can also analyze the emotions felt by the user when trying on clothes in real time and, based on the results, build a system that suggests an optimal fitting method. For example, it can analyze facial expressions and voices when trying on clothes and calculate an emotion score. The fitting support unit can also use the emotion estimation function to monitor the user's emotions when trying on clothes in real time and, based on the results, suggest an optimal fitting method. For example, it can provide a fitting environment that will evoke the most positive emotions when the user tries on clothes. This makes it possible to suggest an optimal fitting method based on the user's emotions when trying on clothes.

[0075] The fitting support unit automatically reserves a place to try on clothes, saving the user time and effort. For example, the generation AI analyzes the user's schedule and automatically reserves the most suitable place to try on clothes. For example, it automatically reserves a place to try on clothes based on the user's free time. The fitting support unit also automatically reserves a place to try on clothes when the user inputs their desired place to try on clothes. For example, it checks the availability of the fitting place and makes a reservation. The fitting support unit also builds a system in which the generation AI automatically reserves a place to try on clothes, saving the user time and effort. For example, it reserves a place to try on clothes based on the user's schedule. This automatically reserves a place to try on clothes, saving the user time and effort.

[0076] The fitting support unit can provide a guide to support putting on and taking off clothes when trying on. For example, the fitting support unit provides a guide to support the generation AI in putting on and taking off clothes when trying on. For example, it explains how to put on and take off a furisode or wedding dress using videos and images. The fitting support unit also provides a guide to support the user in real time when they have trouble putting on or taking off clothes when trying on. For example, it suggests an appropriate way to put on or take off clothes in response to a user's question. The fitting support unit also automatically generates a guide to support the generation AI in putting on and taking off clothes when trying on, and provides it to the user. For example, it suggests the optimal way to put on or take off clothes depending on the user's fitting situation. In this way, it is possible to provide a guide to support putting on and taking off clothes when trying on.

[0077] The fitting support unit can use the emotion estimation function to monitor the user's emotional reactions when trying on clothes and provide an optimal fitting environment. For example, the fitting support unit can use the emotion estimation function to monitor the user's emotional reactions when trying on clothes in real time and provide an optimal fitting environment based on the results. For example, an environment in which the user feels the most positive emotions when trying on clothes can be provided. The fitting support unit can also analyze the emotions felt by the user when trying on clothes in real time and build a system that provides an optimal fitting environment based on the results. For example, the fitting support unit can analyze facial expressions and voices when trying on clothes and calculate an emotion score. The fitting support unit can also use the emotion estimation function to monitor the user's emotional reactions when trying on clothes in real time and provide an optimal fitting environment based on the results. For example, a fitting location in which the user feels the most positive emotions when trying on clothes can be provided. This allows an optimal fitting environment to be provided based on the user's emotional reactions when trying on clothes.

[0078] The costume selection unit can suggest costumes based on the theme and color code of an event. For example, the generation AI of the costume selection unit suggests the most suitable costume based on the theme and color code of an event. For example, it suggests a furisode that matches the theme color of Coming of Age Day, or a wedding dress that matches the theme color of a wedding ceremony. Furthermore, when a user inputs the theme and color code of a specific event, the costume selection unit suggests the most suitable costume based on that information. For example, it suggests a hakama that matches the theme color of a graduation ceremony. Furthermore, the costume selection unit suggests the most suitable costume based on the event theme and color code using the generation AI, automatically recognizing the theme and color code of an event. For example, it suggests a dress that matches the theme color of a party. This makes it possible to suggest the most suitable costume based on the theme and color code of an event.

[0079] The costume selection unit can suggest the optimal timing to choose a costume according to the event schedule. For example, the generation AI analyzes the event schedule and suggests the optimal timing to choose a costume. For example, it suggests the timing to choose a costume according to the schedule of Coming of Age Day or a wedding. Furthermore, when a user inputs the schedule of a specific event, the costume selection unit suggests the optimal timing to choose a costume according to that schedule. For example, it suggests the timing to choose a costume according to the schedule of a graduation ceremony or a party. Furthermore, the generation AI automatically recognizes the event schedule and suggests the optimal timing to choose a costume based on that. For example, it suggests the timing to choose a costume according to the preparation period for the event. This makes it possible to suggest the optimal timing to choose a costume according to the event schedule.

[0080] The costume selection unit can use the emotion estimation function to analyze the user's emotions toward an event and suggest an outfit that will elicit the most positive emotions. For example, the costume selection unit can use the emotion estimation function to analyze the user's emotions toward an event in real time and suggest an outfit that will elicit the most positive emotions based on the results. For example, the costume selection unit can suggest a furisode kimono that will evoke the most positive emotions for the user on Coming of Age Day. The costume selection unit can also build a system that analyzes the user's emotions toward a specific event in real time and suggests the most appropriate outfit based on the results. For example, the costume selection unit can analyze the user's facial expressions and voice and calculate an emotion score. The costume selection unit can also use the emotion estimation function to monitor the user's emotions toward an event in real time and suggest the most appropriate outfit based on the results. For example, the costume selection unit can suggest a wedding dress that will evoke the most positive emotions for a wedding. This allows the costume selection unit to suggest the most appropriate outfit based on the user's emotions toward an event.

[0081] The costume selection unit can simulate different events and make costume suggestions based on the results. For example, the generation AI simulates different events and makes costume suggestions based on the results. For example, the generation AI simulates Coming of Age Day, weddings, graduation ceremonies, etc., and suggests the most suitable costumes for each. Furthermore, when a user specifies a specific event, the costume selection unit simulates the event and suggests the most suitable costumes. For example, the generation AI simulates a party or business event and suggests dresses to match it. Furthermore, the generation AI automatically simulates different events and makes costume suggestions based on the results. For example, the generation AI suggests costumes that match seasonal events or specific themes. This makes it possible to suggest the most suitable costumes based on simulations of different events.

[0082] The costume selection unit can analyze the user's past event participation history and make costume suggestions based on that. For example, the generation AI of the costume selection unit analyzes the user's past event participation history and makes costume suggestions based on that. For example, new suggestions are made based on data from Coming of Age Day or weddings that the user has attended in the past. Furthermore, when the costume selection unit inputs data from events the user has attended in the past, it suggests the most suitable costume based on that data. For example, suggestions are made based on the designs of costumes worn at past events. Furthermore, the generation AI of the costume selection unit automatically recognizes the user's past event participation history and makes costume suggestions based on that. For example, new suggestions are made based on the themes and color codes of events that the user has attended in the past. This makes it possible to suggest the most suitable costume based on the user's past event participation history.

[0083] The costume selection unit can use the emotion estimation function to compare the user's emotional responses for each event and select the most appropriate costume. For example, the costume selection unit uses the emotion estimation function to analyze the user's emotional responses for different events in real time and select the most appropriate costume based on the results. For example, the costume selection unit suggests an outfit that will elicit the most positive emotions from the user for each event, such as Coming of Age Day, a wedding, or a graduation ceremony. The costume selection unit also builds a system that analyzes the user's emotional responses to different events in real time and selects the most appropriate costume based on the results. For example, the costume selection unit analyzes the user's facial expressions and voice and calculates an emotion score. The costume selection unit also uses the emotion estimation function to monitor the user's emotional responses for each event in real time and selects the most appropriate costume based on the results. For example, the costume selection unit suggests a wedding dress that will evoke the most positive emotions from the user for a wedding. This allows the most appropriate costume to be selected based on the user's emotional responses for each event.

[0084] The costume selection unit can suggest customization options according to the user's preferences in real time. For example, the generation AI of the costume selection unit suggests customization options according to the user's preferences in real time. For example, detailed customization is possible, such as the pattern and color of the furisode, the design of the wedding dress, and accessories. Furthermore, when the user specifies a specific customization option, the costume selection unit generates a costume image that matches that option. For example, if the user specifies a "red furisode," it generates an image of the furisode that matches that color. Furthermore, the costume selection unit constructs a system in which the generation AI analyzes the user's preferences and suggests customization options based on them in real time. For example, it suggests optimal customization options based on the user's past selection history. This makes it possible to suggest customization options according to the user's preferences in real time.

[0085] The outfit selection unit can provide customization options based on the user's body type and facial features. For example, the outfit selection unit uses a generation AI to analyze the user's body type and facial features and provide customization options based on that. For example, it can suggest a furisode design that matches the user's body type or wedding dress accessories that match their facial features. When a user inputs a specific body type or facial features, the outfit selection unit provides customization options based on that information. For example, if a user inputs characteristics such as "tall" or "round face," it can suggest outfits that match those characteristics. The outfit selection unit also builds a system in which the generation AI automatically recognizes the user's body type and facial features and provides customization options based on those. For example, it can analyze the user's photo and suggest optimal customization options. This makes it possible to provide customization options based on the user's body type and facial features.

[0086] The costume selection unit can use the emotion estimation function to analyze the user's emotions regarding the customization options and suggest the option that elicits the most positive emotions. For example, the costume selection unit uses the emotion estimation function to analyze the user's emotions regarding the customization options in real time and, based on the results, suggest the option that elicits the most positive emotions. For example, the costume selection unit makes suggestions based on the emotion score when the user selects the pattern and color of a furisode. The costume selection unit also analyzes the emotions of the user when selecting customization options in real time and, based on the results, builds a system that suggests the optimal option. For example, the costume selection unit analyzes the user's facial expressions and voice and calculates the emotion score. The costume selection unit also uses the emotion estimation function to monitor the user's emotions regarding the customization options in real time and, based on the results, suggests the optimal option. For example, the costume selection unit makes suggestions based on the emotion score when the user selects a wedding dress design. This makes it possible to suggest the optimal option based on the user's emotions regarding the customization options.

[0087] The costume selection unit can provide customization options based on different cultures and traditions. For example, the generation AI provides customization options based on different cultures and traditions. For example, it provides not only Japanese furisode kimonos and Western wedding dresses, but also Indian saris and Chinese cheongsams. When a user specifies a customization option based on a specific culture or tradition, the costume selection unit generates an outfit image that matches that option. For example, if a user specifies "Indian wedding," the generation AI generates an image of the person wearing a sari. The costume selection unit also automatically recognizes customization options based on different cultures and traditions and generates an outfit image that corresponds to that option. For example, if a user specifies "traditional African clothing," the generation AI generates an image of the person wearing that clothing. This makes it possible to provide customization options based on different cultures and traditions.

[0088] The outfit selection unit can analyze the user's past fashion history and suggest customization options based on that. For example, the generation AI of the outfit selection unit analyzes the user's past fashion history and suggests customization options based on that. For example, new suggestions are made based on designs of furisode or wedding dresses chosen in the past. In addition, when the user inputs data on outfits worn in the past, the outfit selection unit suggests optimal customization options based on that data. For example, it analyzes past fashion styles and suggests designs that suit them. In addition, the generation AI of the outfit selection unit automatically recognizes the user's past fashion history and suggests customization options based on that. For example, it suggests new furisode or wedding dresses based on colors and designs chosen in the past. This makes it possible to suggest optimal customization options based on the user's past fashion history.

[0089] The costume selection unit can use the emotion estimation function to compare the user's emotional reactions to the customization options and select the optimal option. For example, the costume selection unit uses the emotion estimation function to analyze the user's emotional reactions to the customization options in real time and select the optimal option based on the results. For example, the selection is made based on the emotion score when the user selects the pattern and color of a furisode. The costume selection unit also constructs a system that provides real-time feedback of the user's emotional reactions when selecting a customization option and selects the optimal option based on the results. For example, the costume selection unit analyzes the user's facial expressions and voice and calculates an emotion score. The costume selection unit also uses the emotion estimation function to monitor the user's emotional reactions to the customization options in real time and selects the optimal option based on the results. For example, the selection is made based on the emotion score when the user selects a wedding dress design. This makes it possible to select the optimal option based on the user's emotional reactions to the customization options.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The outfit selection system can also analyze a user's past fashion history and make outfit suggestions based on that. For example, it can make new suggestions based on the designs of furisode or wedding dresses chosen in the past. Also, when a user inputs data on outfits they have worn in the past, it can suggest the most suitable outfit based on that data. For example, it can analyze past fashion styles and suggest designs that suit them. The generation AI can also automatically recognize a user's past fashion history and make outfit suggestions based on that. For example, it can suggest new furisode or wedding dresses based on colors and designs chosen in the past. This makes it possible to make optimal outfit suggestions based on a user's past fashion history.

[0092] The costume selection system can also incorporate costumes from different cultures and traditions, providing users with a wider variety of options. For example, it can offer not only Japanese furisode (long-sleeved kimono) and Western wedding dresses, but also Indian saris and Chinese cheongsams. Furthermore, when a user specifies a specific culture or traditional costume, it generates an image that matches that costume. For example, if a user specifies "Indian wedding," the generation AI generates an image of the person wearing a sari. Furthermore, the generation AI can automatically recognize different cultures and traditional costumes and generate costume images that match them. For example, if a user specifies "traditional African costume," it generates an image of the person wearing that costume. This allows for a wider variety of options incorporating different cultures and traditional costumes.

[0093] The outfit selection system can also simulate changes in the user's body shape and generate outfit images that fit the user's future body shape. For example, it can provide images of long-sleeved kimonos and wedding dresses that fit the body shape after dieting or muscle training. In addition, if the user specifies a specific body shape change, it can generate outfit images that fit that body shape. For example, it can provide images of dresses that fit the body shape during pregnancy or after childbirth. The generation AI can also automatically recognize changes in the user's body shape and generate outfit images that match them. For example, it can provide images of long-sleeved kimonos and wedding dresses that fit changes in age and lifestyle. This makes it possible to provide outfit images that fit the user's future body shape.

[0094] The costume selection system can also simulate different seasons and weather conditions and generate costume images accordingly. For example, it can provide images of a furisode under cherry blossoms in spring or a wedding dress against a snowy winter landscape. In addition, when the user specifies specific weather conditions, it can generate costume images that match those conditions. For example, it can provide images of dresses suitable for rainy or sunny days. The generation AI can also automatically recognize seasons and weather conditions and generate costume images that match them. For example, it can provide images of a furisode on a summer beach or a wedding dress among autumn leaves. This makes it possible to provide costume images that suit different seasons and weather conditions.

[0095] The costume selection system can also import photos of the user's family and friends to generate group try-on images. For example, it can provide images of the whole family wearing long-sleeved kimonos or suits. If the user wants to see what it will look like when trying on outfits together with friends, it can import photos of friends and generate group try-on images. For example, it can provide images of the whole family wearing wedding dresses or tuxedos. The generation AI can also analyze photos of the user's family and friends to generate group try-on images. For example, it can provide images of the whole family wearing long-sleeved kimonos with different designs. This allows it to provide group try-on images.

[0096] The costume selection system can further use an emotion estimation function to provide real-time feedback on the emotional response of the user when they see a specific costume, and suggest the most suitable costume. For example, suggestions can be made based on the emotional score when the user sees a furisode kimono. A system can also be constructed that provides real-time feedback on the emotional response of the user when they see a specific costume, and suggests the most suitable costume based on the results. For example, the system can analyze the user's facial expressions and voice to calculate an emotional score. The emotion estimation function can also be used to monitor the emotional response of the user when they see a specific costume in real time, and suggest the most suitable costume based on the results. For example, suggestions can be made based on the emotional score when the user sees a wedding dress. This makes it possible to suggest the most suitable costume based on the user's emotional response.

[0097] The costume selection system can further use an emotion estimation function to compare the emotional reactions of the user when they see different costumes and select the most suitable costume. For example, the selection is made based on the emotional score when the user sees a furisode kimono. In addition, a system is constructed that provides real-time feedback on the emotional reactions of the user when they see different costumes and selects the most suitable costume based on the results. For example, the system analyzes the user's facial expressions and voice and calculates an emotional score. In addition, the emotion estimation function is used to monitor the emotional reactions of the user when they see different costumes in real time and selects the most suitable costume based on the results. For example, the system makes a selection based on the emotional score when the user sees a wedding dress. In this way, the most suitable costume can be selected based on the user's emotional reactions.

[0098] The costume selection system can further use an emotion estimation function to analyze a user's emotions toward an event and suggest an outfit that will evoke the most positive emotions. For example, a furisode kimono that evokes the most positive emotions for a user's Coming of Age Day can be suggested. A system can also be constructed that analyzes a user's emotions toward a specific event in real time and suggests the most suitable outfit based on the results. For example, the system can analyze a user's facial expressions and voice and calculate an emotion score. The emotion estimation function can also be used to monitor a user's emotions toward an event in real time and suggest the most suitable outfit based on the results. For example, a wedding dress that evokes the most positive emotions for a wedding can be suggested. This makes it possible to suggest the most suitable outfit based on the user's emotions toward an event.

[0099] The costume selection system can further use an emotion estimation function to analyze the user's emotions regarding the customization options and suggest the option that elicits the most positive emotions. For example, suggestions can be made based on the emotion score when the user selects the pattern and color of a furisode. A system can also be constructed that analyzes the emotions of the user when selecting customization options in real time and suggests the optimal option based on the results. For example, the system can analyze the user's facial expressions and voice and calculate an emotion score. The emotion estimation function can also be used to monitor the user's emotions regarding the customization options in real time and suggest the optimal option based on the results. For example, suggestions can be made based on the emotion score when the user selects a wedding dress design. This makes it possible to suggest the optimal option based on the user's emotions regarding the customization options.

[0100] The costume selection system can further use an emotion estimation function to compare the user's emotional responses for each event and select the most appropriate costume. For example, it can suggest the costume that will evoke the most positive emotions in the user for each event, such as Coming of Age Day, weddings, and graduation ceremonies. It can also build a system that analyzes the user's emotional responses to different events in real time and selects the most appropriate costume based on the results. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. It can also use the emotion estimation function to monitor the user's emotional responses for each event in real time and select the most appropriate costume based on the results. For example, it can suggest the wedding dress that evokes the most positive emotions in the user's wedding. This allows it to select the most appropriate costume based on the user's emotional responses for each event.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The image generation unit receives a user's photo as input and generates images of the user wearing various outfits based on the photo. For example, the image generation unit analyzes the user's body shape and facial features to generate realistic outfit images. Also, when a user uploads a photo of themselves and enters keywords such as "coming-of-age day furisode" or "wedding dress," the unit generates images of the user wearing those outfits based on prompts containing those instructions. Step 2: The costume selection unit selects a costume that the user likes from the catalog generated by the image generation unit. For example, the costume selection unit compares furisode and wedding dresses of different designs and colors included in the generated catalog, allowing the user to select one. Step 3: The fitting support unit provides support for actually trying on the outfit selected by the outfit selection unit. For example, the fitting support unit provides support for reducing the number of visits required for the user to try on the outfit selected from the catalog and the effort required for dressing.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image generation unit that receives a user's photo as an input and generates images of the user wearing various outfits based on the photo; a costume selection unit that selects a costume that the user likes from the catalog generated by the image generation unit; a try-on support unit for actually trying on the outfit selected by the outfit selection unit; A system characterized by:

2. The image generation unit Analyzing the user's movements and postures and generating images of the clothes in different poses 2. The system of claim 1.

3. The image generation unit Automatically adjusts the user's skin color and hairstyle to provide a more realistic try-on look.

2. The system of claim 1.

4. The image generation unit Analyze the user's facial expression and generate an outfit image that brings out the most positive expression.

2. The system of claim 1.

5. The image generation unit Photos of the user's family and friends are also imported to generate group outfit try-on images.

2. The system of claim 1.

6. The image generation unit Simulate different seasons and weather conditions and generate appropriate outfit images 2. The system of claim 1.

7. The image generation unit The emotional reaction of the user when seeing a particular outfit is fed back in real time, and the most suitable outfit is suggested.

2. The system of claim 1.

8. The image generation unit Simulate different backgrounds and situations and provide images of how to try on outfits accordingly 2. The system of claim 1.

Images