system

Abstract

The system according to the embodiment aims to enable online shopping users to check the size and coordination of clothes before purchasing them. [Solution] A system according to an embodiment includes a clothing reproduction unit, an avatar registration unit, a synthesis unit, and an editing unit. The clothing reproduction unit reproduces clothing. The avatar registration unit has an avatar try on the clothing reproduced by the clothing reproduction unit. The synthesis unit synthesizes the clothing tried on by the avatar registration unit with an image of the real user. The editing unit uses AI to edit the image synthesized by the synthesis unit.

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 technology has had the problem that it is difficult to check the size and coordination of clothes before purchasing them when shopping online.

[0005] The system according to the embodiment aims to enable online shopping users to check the size and coordination of clothes before purchasing them. [Means for solving the problem]

[0006] The system according to the embodiment includes a clothing reproduction unit, an avatar registration unit, a synthesis unit, and an editing unit. The clothing reproduction unit reproduces clothing. The avatar registration unit has an avatar try on the clothing reproduced by the clothing reproduction unit. The synthesis unit synthesizes the clothing tried on by the avatar registration unit with an image of the real user. The editing unit uses AI to edit the image synthesized by the synthesis unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to check the size and coordination of clothes before purchasing them when shopping online. [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 metaverse system according to an embodiment of the present invention reproduces clothes in the same size and color as in real life. Users can register avatars that reflect their measurements, allowing them to try on clothes and coordinate outfits before purchasing them. This metaverse system allows users to upload real-life images of themselves, which are then combined with the selected clothes in the metaverse to create an image that looks like they would wear them in real life. Users can also consult with AI to edit the image to create a different look. For example, a user can use a smartphone camera to capture their own measurements and apply them to an avatar. Next, they can select clothes in the metaverse that are reproduced in the same size and color as in real life and have the avatar try them on. This allows users to check the fit and coordination of the outfits. Furthermore, users can upload real-life images of themselves and combine them with the selected clothes in the metaverse. For example, an image of themselves wearing the clothes selected by the user is generated. This allows users to view images that look like they would wear them in real life. Users can also consult with AI to edit the image to create a different look. For example, if a user requests a more casual look, the AI ​​will edit the image based on that instruction. In this way, users can try out various styles. This allows the metaverse system to allow users to try on clothes in a realistic way and edit the images.

[0029] The metaverse system according to the embodiment includes a clothing reproduction unit, an avatar registration unit, a synthesis unit, and an editing unit. The clothing reproduction unit reproduces clothing in the same size and color as in real life. For example, 3D modeling technology and texture mapping can be used to realistically reproduce the shape and color of clothing. Fabric texture simulation and a physical movement engine can also be used to reproduce the texture and movement of clothing. The avatar registration unit registers an avatar that reflects the user's measurement data. For example, a user can obtain their own measurement data using a smartphone camera and reflect that data in the avatar. The avatar registration unit generates an avatar tailored to the user's body type and is used to check the fit and coordination. The synthesis unit synthesizes a real-life image uploaded by the user with clothing selected in the metaverse. For example, image processing technology and synthesis algorithms can be used to synthesize a real-life image of the user with clothing in the metaverse. This allows users to view images that look as if they were wearing the clothing in real life. The editing unit uses AI to edit images based on the user's instructions. For example, if a user instructs the AI ​​to "make the image look more casual," the AI ​​can edit the image based on that instruction. The editing department uses techniques such as image filtering and adding effects to edit the image in accordance with the user's instructions. This allows the Metaverse system to allow users to have a realistic experience of trying on clothes and editing the image.

[0030] The clothing reproduction unit can reproduce clothing in the same size and color as in real life. Specific standards and methods for achieving the same size and color include color calibration and size scaling. For example, 3D modeling technology can be used to realistically reproduce the shape and size of clothing. Texture mapping technology can also be used to accurately reproduce the color and pattern of clothing. This allows users to have a realistic clothing trying-on experience in the metaverse. Furthermore, the clothing reproduction unit can use fabric texture simulation and a motion physics engine to reproduce the texture and movement of clothing. For example, fabric texture simulation can be used to realistically reproduce the texture of clothing materials. A motion physics engine can also be used to realistically reproduce the movement of clothing. This allows users to have a realistic clothing trying-on experience in the metaverse.

[0031] The avatar registration unit can register an avatar that reflects the user's measurement data. Specific methods and standards for acquiring measurement data include the measurement method for each body part and the data format. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. The avatar registration unit is used to generate an avatar that matches the user's body type and check the fit and coordination of the outfit. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. This allows the user to experience trying on clothes in a realistic way in the metaverse. Furthermore, the avatar registration unit can generate an avatar that matches the user's body type and check the fit and coordination of the outfit. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. This allows the user to experience trying on clothes in a realistic way in the metaverse.

[0032] The synthesis unit can synthesize a real image of the user uploaded by the user with clothing selected on the metaverse. Specific methods and standards for acquiring the real image of the user include image resolution and shooting conditions. For example, the user can take a photo of themselves using a smartphone camera and upload that image. The synthesis unit can synthesize the real image of the user with clothing on the metaverse using image processing technology and synthesis algorithms. This allows the user to view images that look as if the user were wearing the clothing in real life. For example, the synthesis unit can synthesize the real image of the user with clothing on the metaverse using image processing technology. The synthesis unit can also synthesize the real image of the user with clothing on the metaverse using synthesis algorithms. This allows the user to view images that look as if the user were wearing the clothing in real life. Furthermore, the synthesis unit can also build a system for synthesizing the real image of the user uploaded by the user with clothing selected on the metaverse. For example, the synthesis unit can synthesize the real image of the user with clothing on the metaverse using image processing technology and synthesis algorithms. This allows the user to view images that look as if the user were wearing the clothing in real life.

[0033] The editorial department can use AI to edit images based on user instructions. The specific content and acceptance methods of user instructions include voice commands and text input. For example, if a user requests a more casual look, the AI ​​can edit the image based on that instruction. The editorial department uses techniques such as image filtering and effect addition to edit images in accordance with user instructions. For example, image filtering techniques can be used to adjust the color and brightness of an image. Effect addition techniques can also be used to add special effects to images. This allows users to edit images while experiencing a realistic clothing fitting experience in the metaverse. Furthermore, the editorial department can build a system for AI to edit images based on user instructions. For example, it can provide an interface that accepts voice commands and text input, allowing the AI ​​to edit images based on those instructions. This allows users to experience a realistic clothing fitting experience in the metaverse while editing images.

[0034] The clothing reproduction unit can reproduce the texture and movement of clothing. Specific methods and standards for reproducing the texture and movement include cloth texture simulation and a movement physics engine. For example, the texture of clothing can be realistically reproduced using cloth texture simulation. Furthermore, the movement of clothing can be realistically reproduced using a movement physics engine. This allows users to have a realistic clothing trying-on experience in the metaverse. Furthermore, the clothing reproduction unit can also build a system for reproducing the texture and movement of clothing. For example, the texture of clothing can be realistically reproduced using cloth texture simulation and a movement physics engine. This allows users to have a realistic clothing trying-on experience in the metaverse. Furthermore, the clothing reproduction unit can also build a system for reproducing the texture and movement of clothing. For example, the texture of clothing can be realistically reproduced using cloth texture simulation and a movement physics engine. This allows users to have a realistic clothing trying-on experience in the metaverse.

[0035] The avatar registration unit can acquire measurement data using a smartphone camera. Methods and standards for acquiring measurement data using a smartphone camera include the camera's resolution and shooting angle. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. The avatar registration unit is used to generate an avatar that matches the user's body type and check the fit and coordination of the clothes. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. This allows the user to have a realistic experience of trying on clothes in the metaverse. Furthermore, the avatar registration unit can generate an avatar that matches the user's body type and check the fit and coordination of the clothes. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. This allows the user to have a realistic experience of trying on clothes in the metaverse.

[0036] The editorial department can accept user instructions, such as "I want a casual style." Specific definitions and standards for casual style include specific elements and examples of casual styles. For example, if a user instructs, "I want a more casual look," the AI ​​can edit the image based on that instruction. The editorial department uses techniques such as image filtering and effect addition to edit images in accordance with the user's instructions. For example, image filtering techniques can be used to adjust the color and brightness of an image. Effect addition techniques can also be used to add special effects to images. This allows users to experience realistic clothing fitting in the metaverse and edit images. Furthermore, the editorial department can build a system that allows the AI ​​to edit images based on user instructions. For example, an interface can be provided that accepts voice instructions or text input, and the AI ​​can edit images based on those instructions. This allows users to experience realistic clothing fitting in the metaverse and edit images.

[0037] When recreating clothes, the clothing reproduction unit can select the optimal clothing reproduction method by referring to the user's past purchase history. Specific methods and criteria for referencing the past purchase history include a purchase history database and a data acquisition method. For example, the clothing reproduction unit can reproduce similar styles of clothes based on the color and design of clothes purchased by the user in the past. The clothing reproduction unit can also prioritize recreating new products from a brand based on the brand the user has previously purchased. Furthermore, the clothing reproduction unit can reproduce clothes that reflect seasonal trends based on the user's past purchase history. This makes it possible to reproduce optimal clothes based on the user's past purchase history. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the clothing reproduction unit can input the user's past purchase history data into the generation AI and cause the generation AI to execute the optimal clothing reproduction method.

[0038] The clothing reproduction unit can dynamically adjust the size of the reproduced clothing, taking into account changes in the user's body shape when reproducing the clothing. Specific methods and criteria for considering changes in body shape include body shape change data and size adjustment algorithms. For example, the clothing reproduction unit can automatically adjust the size of the clothing based on the user's latest measurement data. The clothing reproduction unit can also analyze the user's past body shape change data and predict future body shape changes to adjust the size of the clothing. Furthermore, if the user changes their body shape for a specific event, the clothing reproduction unit can adjust the size of the clothing to reflect those changes. This makes it possible to adjust the size of the clothing in accordance with changes in the user's body shape. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, AI, or may be performed without AI. For example, the clothing reproduction unit can input the user's body shape change data into a generation AI and have the generation AI adjust the size of the clothing.

[0039] When recreating the clothes, the clothing reproduction unit can reproduce regional fashion by taking into account the user's geographical location information. Specific methods and standards for acquiring geographical location information include GPS data and location information services. For example, if the user is in an urban area, the clothing reproduction unit can reproduce the latest urban fashion. Also, if the user is in a resort area, the clothing reproduction unit can reproduce resort fashion. Also, if the user is in a cold region, the clothing reproduction unit can reproduce clothing made of warm material. This makes it possible to reproduce regional fashion. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the clothing reproduction unit can input the user's geographical location information to the generation AI and cause the generation AI to execute regional fashion.

[0040] When recreating clothes, the clothing reproduction unit can analyze the user's social media activity and recreate clothes based on trends. Specific analysis methods and criteria for social media activity include analyzing posted content and trend analysis. For example, the clothing reproduction unit can recreate trendy clothes based on the fashion of influencers the user follows. The clothing reproduction unit can also recreate similar styles of clothes based on the fashion in posts that the user has "liked." The clothing reproduction unit can also analyze the content of the user's social media posts and recreate clothes that match trends. This makes it possible to recreate clothes based on social media trends. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the clothing reproduction unit can input the user's social media activity data into a generation AI and cause the generation AI to recreate clothes based on trends.

[0041] When registering an avatar, the avatar registration unit can generate an optimal avatar by referencing the user's past avatar history. Specific methods and standards for referencing the past avatar history include an avatar database and a method for acquiring history data. For example, the avatar registration unit can generate an avatar with a similar style based on the design of an avatar used by the user in the past. The avatar registration unit can also generate an optimal avatar based on the facial expressions and poses of avatars used by the user in the past. The avatar registration unit can also generate an avatar that reflects seasonal trends from the user's past avatar history. This allows the optimal avatar to be generated based on the user's past avatar history. Some or all of the above-described processing in the avatar registration unit may be performed using, or without, AI. For example, the avatar registration unit can input the user's past avatar history data into a generation AI and cause the generation AI to generate an optimal avatar.

[0042] The avatar registration unit can dynamically adjust the size of the avatar, taking into account changes in the user's body shape, when registering the avatar. Specific methods and standards for adjusting the avatar size include size adjustment algorithms and methods for reflecting body shape data. For example, the avatar registration unit can automatically adjust the avatar size based on the user's latest measurement data. The avatar registration unit can also analyze the user's past body shape change data and predict future body shape changes to adjust the avatar size. The avatar registration unit can also adjust the avatar size to reflect changes in the user's body shape when the user changes their body shape for a specific event. This enables the avatar size to be adjusted in accordance with changes in the user's body shape. Some or all of the above-described processing in the avatar registration unit may be performed using, or without, AI. For example, the avatar registration unit can input the user's body shape change data into a generation AI and have the generation AI adjust the avatar size.

[0043] The avatar registration unit can generate a region-specific avatar by taking into account the user's geographical location information when registering the avatar. Specific methods and standards for generating a region-specific avatar include methods that reflect the culture and fashion of the region. For example, if the user is in an urban area, the avatar registration unit can generate an avatar that reflects the latest urban fashion. Also, if the user is in a resort area, the avatar registration unit can generate an avatar that reflects resort fashion. Also, if the user is in a cold region, the avatar registration unit can generate an avatar wearing clothes made of warm material. This allows the generation of a region-specific avatar. Some or all of the above-described processing in the avatar registration unit may be performed using, or without, AI. For example, the avatar registration unit can input the user's geographical location information into the generation AI and cause the generation AI to generate a region-specific avatar.

[0044] When registering an avatar, the avatar registration unit can analyze the user's social media activity and generate an avatar based on trends. Specific analysis methods and criteria for social media activity include analyzing posted content and trend analysis. For example, the avatar registration unit can generate a trendy avatar based on the fashion of influencers the user follows. The avatar registration unit can also generate an avatar with a similar style based on the fashion of posts the user has "liked." The avatar registration unit can also analyze the content of the user's social media posts and generate an avatar that matches a trend. This makes it possible to generate an avatar based on social media trends. Some or all of the above-described processing in the avatar registration unit may be performed using, or without, AI. For example, the avatar registration unit can input the user's social media activity data into a generation AI and cause the generation AI to generate an avatar based on trends.

[0045] The compositing unit can select the optimal compositing method by referring to the user's past image history during compositing. Specific methods and standards for referencing the past image history include an image database and a method for acquiring history data. For example, the compositing unit can composite images with a similar style based on the style of images the user has previously composited. The compositing unit can also select the optimal compositing method based on backgrounds and effects used by the user in the past. The compositing unit can also composite images that reflect seasonal trends from the user's past image history. This makes it possible to select the optimal compositing method based on the user's past image history. Some or all of the above-described processing in the compositing unit may be performed using, or without, AI. For example, the compositing unit can input the user's past image history data into a generation AI and cause the generation AI to execute the optimal compositing method.

[0046] The synthesis unit can dynamically adjust the size of the synthesized image during synthesis, taking into account changes in the user's body shape. Specific methods and standards for adjusting the image size include size adjustment algorithms and methods for reflecting body shape data. For example, the synthesis unit can automatically adjust the image size based on the user's latest measurement data. The synthesis unit can also analyze the user's past body shape change data and predict future body shape changes to adjust the image size. The synthesis unit can also adjust the image size to reflect changes in the user's body shape when the user changes their body shape for a specific event. This makes it possible to adjust the image size in accordance with changes in the user's body shape. Some or all of the above-described processing in the synthesis unit may be performed using, for example, AI, or may be performed without AI. For example, the synthesis unit can input the user's body shape change data into the generation AI and have the generation AI adjust the image size.

[0047] During synthesis, the synthesis unit can synthesize a region-specific background by taking into account the user's geographical location information. Specific synthesis methods and standards for region-specific backgrounds include methods that reflect the scenery and culture of the region. For example, if the user is in an urban area, the synthesis unit can synthesize an urban landscape as the background. Also, if the user is in a resort area, the synthesis unit can synthesize a resort landscape as the background. Also, if the user is in a cold region, the synthesis unit can synthesize a snowy landscape as the background. In this way, a region-specific background can be synthesized. Some or all of the above-described processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input the user's geographical location information to the generation AI and cause the generation AI to execute a region-specific background.

[0048] During synthesis, the synthesis unit can analyze the user's social media activity and synthesize an image based on trends. Specific analysis methods and criteria for social media activity include analyzing the content of posts and trend analysis. For example, the synthesis unit can synthesize trending images based on the style of influencers the user follows. The synthesis unit can also synthesize images with a similar style based on the style of posts the user has "liked." The synthesis unit can also analyze the content of the user's social media posts and synthesize images that match trends. This makes it possible to synthesize images based on social media trends. Some or all of the above-described processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input the user's social media activity data into the generation AI and cause the generation AI to synthesize an image based on trends.

[0049] When editing, the editing department can select the optimal editing method by referring to the user's past editing history. Specific methods and criteria for referring to the past editing history include an editing database and a method for acquiring history data. For example, the editing department can edit an image into a similar style based on the style of images the user has edited in the past. The editing department can also select the optimal editing method based on filters and effects the user has used in the past. The editing department can also edit images to reflect seasonal trends based on the user's past editing history. This makes it possible to select the optimal editing method based on the user's past editing history. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's past editing history data into a generation AI and have the generation AI execute the optimal editing method.

[0050] The editing department can dynamically adjust the size of the image to be edited during editing, taking into account changes in the user's body shape. Specific methods and standards for adjusting the image size include size adjustment algorithms and methods for reflecting body shape data. For example, the editing department can automatically adjust the image size based on the user's latest measurement data. The editing department can also analyze the user's past body shape change data and predict future body shape changes to adjust the image size. If the user changes their body shape for a specific event, the editing department can also adjust the image size to reflect those changes. This makes it possible to adjust the image size in accordance with changes in the user's body shape. Some or all of the above-mentioned processing by the editing department may be performed using, for example, AI, or may be performed without AI. For example, the editing department can input the user's body shape change data into a generation AI and have the generation AI adjust the image size.

[0051] During editing, the editorial department can edit a region-specific style taking into account the user's geographical location information. Specific editing methods and standards for a region-specific style include methods that reflect the culture and fashion of the region. For example, if the user is in an urban area, the editorial department can edit the style to reflect urban trends. Also, if the user is in a resort area, the editorial department can edit the style to reflect resort trends. Also, if the user is in a cold region, the editorial department can edit the style to reflect clothing made of warm materials. In this way, a region-specific style can be edited. Some or all of the above-mentioned processing by the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input the user's geographical location information into the generation AI and have the generation AI execute a region-specific style.

[0052] During editing, the editorial department can analyze the user's social media activity and edit images based on trends. Specific analysis methods and criteria for social media activity include analyzing post content and trend analysis. For example, the editorial department can edit images to be trendy based on the style of influencers the user follows. The editorial department can also edit images in a similar style based on the style of posts the user has "liked." The editorial department can also analyze the content of the user's social media posts and edit images that match trends. This makes it possible to edit images based on social media trends. Some or all of the above-described processing by the editorial department may be performed using, or without, AI. For example, the editorial department can input the user's social media activity data into a generation AI and have the generation AI edit images based on trends.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The metaverse system can also include a health suggestion unit that acquires the user's health data and suggests clothing based on the user's health condition. For example, it can monitor the user's heart rate and stress level and suggest clothing made from comfortable materials when relaxation is needed. It can also suggest breathable clothing after exercise. Furthermore, the health suggestion unit can analyze the user's sleep data and suggest pajamas that promote comfortable sleep. This makes it possible to suggest optimal clothing based on the user's health condition.

[0055] The metaverse system may further include a hobby suggestion unit that suggests clothing based on the user's hobbies and interests. For example, if the user likes outdoor activities, clothing suitable for outdoor activities may be suggested. Also, if the user plans to attend a music festival, clothing suitable for the event may be suggested. Furthermore, if the user likes a particular movie or character, the hobby suggestion unit may suggest costumes based on that theme. This makes it possible to suggest optimal clothing according to the user's hobbies and interests.

[0056] The metaverse system can also include a purchase suggestion unit that analyzes a user's purchasing history and suggests new clothing based on past purchasing trends. For example, it can suggest new items with similar designs based on the style and color of clothing the user has previously purchased. Also, if a user has a preference for a particular brand, it can preferentially suggest new items from that brand. Furthermore, the purchase suggestion unit can suggest clothing that reflects seasonal trends based on the user's past purchasing history. This makes it possible to suggest optimal clothing based on the user's purchasing history.

[0057] The metaverse system can further include a region suggestion unit that suggests region-specific fashion in consideration of the user's geographical location information. For example, if the user is in an urban area, the latest urban fashion can be suggested. If the user is in a resort area, resort fashion can be suggested. Furthermore, if the user is in a cold area, clothing made of warm materials can be suggested. This makes it possible to suggest region-specific fashion.

[0058] The metaverse system can further include a trend suggestion unit that analyzes a user's social media activity and suggests clothing based on trends. For example, trendy clothing can be suggested based on the fashion of influencers the user follows. It can also suggest clothing with a similar style based on the fashion in posts that the user has "liked." Furthermore, the trend suggestion unit can analyze the content of a user's social media posts and suggest clothing that matches the trend. This makes it possible to suggest clothing based on social media trends.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The clothing reproduction section reproduces the clothing in the same size and color as the real thing. For example, 3D modeling technology and texture mapping can be used to realistically reproduce the shape and color of the clothing. Additionally, a fabric texture simulation and a physical movement engine can be used to reproduce the texture and movement of the clothing. Step 2: The avatar registration unit registers an avatar that reflects the user's measurement data. For example, the user can use a smartphone camera to obtain their own measurement data and reflect that data in the avatar. The avatar registration unit generates an avatar that matches the user's body type and is used to check the size and coordination. Step 3: The synthesis unit synthesizes the real image of the user uploaded with the clothes selected in the Metaverse. For example, image processing technology and synthesis algorithms can be used to synthesize the real image of the user with the clothes in the Metaverse. This allows the user to view an image that looks like they are wearing it in real life. Step 4: The editorial department uses AI to edit the image based on the user's instructions. For example, if the user requests a more casual look, the AI ​​can edit the image based on that instruction. The editorial department uses techniques such as image filtering and adding effects to edit the image in accordance with the user's instructions.

[0061] (Example 2) The metaverse system according to an embodiment of the present invention reproduces clothes in the same size and color as in real life. Users can register avatars that reflect their measurements, allowing them to try on clothes and coordinate outfits before purchasing them. This metaverse system allows users to upload real-life images of themselves, which are then combined with the selected clothes in the metaverse to create an image that looks like they would wear them in real life. Users can also consult with AI to edit the image to create a different look. For example, a user can use a smartphone camera to capture their own measurements and apply them to an avatar. Next, they can select clothes in the metaverse that are reproduced in the same size and color as in real life and have the avatar try them on. This allows users to check the fit and coordination of the outfits. Furthermore, users can upload real-life images of themselves and combine them with the selected clothes in the metaverse. For example, an image of themselves wearing the clothes selected by the user is generated. This allows users to view images that look like they would wear them in real life. Users can also consult with AI to edit the image to create a different look. For example, if a user requests a more casual look, the AI ​​will edit the image based on that instruction. In this way, users can try out various styles. This allows the metaverse system to allow users to try on clothes in a realistic way and edit the images.

[0062] The metaverse system according to the embodiment includes a clothing reproduction unit, an avatar registration unit, a synthesis unit, and an editing unit. The clothing reproduction unit reproduces clothing in the same size and color as in real life. For example, 3D modeling technology and texture mapping can be used to realistically reproduce the shape and color of clothing. Fabric texture simulation and a physical movement engine can also be used to reproduce the texture and movement of clothing. The avatar registration unit registers an avatar that reflects the user's measurement data. For example, a user can obtain their own measurement data using a smartphone camera and reflect that data in the avatar. The avatar registration unit generates an avatar tailored to the user's body type and is used to check the fit and coordination. The synthesis unit synthesizes a real-life image uploaded by the user with clothing selected in the metaverse. For example, image processing technology and synthesis algorithms can be used to synthesize a real-life image of the user with clothing in the metaverse. This allows users to view images that look as if they were wearing the clothing in real life. The editing unit uses AI to edit images based on the user's instructions. For example, if a user instructs the AI ​​to "make the image look more casual," the AI ​​can edit the image based on that instruction. The editing department uses techniques such as image filtering and adding effects to edit the image in accordance with the user's instructions. This allows the Metaverse system to allow users to have a realistic experience of trying on clothes and editing the image.

[0063] The clothing reproduction unit can reproduce clothing in the same size and color as in real life. Specific standards and methods for achieving the same size and color include color calibration and size scaling. For example, 3D modeling technology can be used to realistically reproduce the shape and size of clothing. Texture mapping technology can also be used to accurately reproduce the color and pattern of clothing. This allows users to have a realistic clothing trying-on experience in the metaverse. Furthermore, the clothing reproduction unit can use fabric texture simulation and a motion physics engine to reproduce the texture and movement of clothing. For example, fabric texture simulation can be used to realistically reproduce the texture of clothing materials. A motion physics engine can also be used to realistically reproduce the movement of clothing. This allows users to have a realistic clothing trying-on experience in the metaverse.

[0064] The avatar registration unit can register an avatar that reflects the user's measurement data. Specific methods and standards for acquiring measurement data include the measurement method for each body part and the data format. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. The avatar registration unit is used to generate an avatar that matches the user's body type and check the fit and coordination of the outfit. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. This allows the user to experience trying on clothes in a realistic way in the metaverse. Furthermore, the avatar registration unit can generate an avatar that matches the user's body type and check the fit and coordination of the outfit. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. This allows the user to experience trying on clothes in a realistic way in the metaverse.

[0065] The synthesis unit can synthesize a real image of the user uploaded by the user with clothing selected on the metaverse. Specific methods and standards for acquiring the real image of the user include image resolution and shooting conditions. For example, the user can take a photo of themselves using a smartphone camera and upload that image. The synthesis unit can synthesize the real image of the user with clothing on the metaverse using image processing technology and synthesis algorithms. This allows the user to view images that look as if the user were wearing the clothing in real life. For example, the synthesis unit can synthesize the real image of the user with clothing on the metaverse using image processing technology. The synthesis unit can also synthesize the real image of the user with clothing on the metaverse using synthesis algorithms. This allows the user to view images that look as if the user were wearing the clothing in real life. Furthermore, the synthesis unit can also build a system for synthesizing the real image of the user uploaded by the user with clothing selected on the metaverse. For example, the synthesis unit can synthesize the real image of the user with clothing on the metaverse using image processing technology and synthesis algorithms. This allows the user to view images that look as if the user were wearing the clothing in real life.

[0066] The editorial department can use AI to edit images based on user instructions. The specific content and acceptance methods of user instructions include voice commands and text input. For example, if a user requests a more casual look, the AI ​​can edit the image based on that instruction. The editorial department uses techniques such as image filtering and effect addition to edit images in accordance with user instructions. For example, image filtering techniques can be used to adjust the color and brightness of an image. Effect addition techniques can also be used to add special effects to images. This allows users to edit images while experiencing a realistic clothing fitting experience in the metaverse. Furthermore, the editorial department can build a system for AI to edit images based on user instructions. For example, it can provide an interface that accepts voice commands and text input, allowing the AI ​​to edit images based on those instructions. This allows users to experience a realistic clothing fitting experience in the metaverse while editing images.

[0067] The clothing reproduction unit can reproduce the texture and movement of clothing. Specific methods and standards for reproducing the texture and movement include cloth texture simulation and a movement physics engine. For example, the texture of clothing can be realistically reproduced using cloth texture simulation. Furthermore, the movement of clothing can be realistically reproduced using a movement physics engine. This allows users to have a realistic clothing trying-on experience in the metaverse. Furthermore, the clothing reproduction unit can also build a system for reproducing the texture and movement of clothing. For example, the texture of clothing can be realistically reproduced using cloth texture simulation and a movement physics engine. This allows users to have a realistic clothing trying-on experience in the metaverse. Furthermore, the clothing reproduction unit can also build a system for reproducing the texture and movement of clothing. For example, the texture of clothing can be realistically reproduced using cloth texture simulation and a movement physics engine. This allows users to have a realistic clothing trying-on experience in the metaverse.

[0068] The avatar registration unit can acquire measurement data using a smartphone camera. Methods and standards for acquiring measurement data using a smartphone camera include the camera's resolution and shooting angle. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. The avatar registration unit is used to generate an avatar that matches the user's body type and check the fit and coordination of the clothes. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. This allows the user to have a realistic experience of trying on clothes in the metaverse. Furthermore, the avatar registration unit can generate an avatar that matches the user's body type and check the fit and coordination of the clothes. For example, a user can acquire their own measurement data using a smartphone camera and reflect that data in an avatar. This allows the user to have a realistic experience of trying on clothes in the metaverse.

[0069] The editorial department can accept user instructions, such as "I want a casual style." Specific definitions and standards for casual style include specific elements and examples of casual styles. For example, if a user instructs, "I want a more casual look," the AI ​​can edit the image based on that instruction. The editorial department uses techniques such as image filtering and effect addition to edit images in accordance with the user's instructions. For example, image filtering techniques can be used to adjust the color and brightness of an image. Effect addition techniques can also be used to add special effects to images. This allows users to experience realistic clothing fitting in the metaverse and edit images. Furthermore, the editorial department can build a system that allows the AI ​​to edit images based on user instructions. For example, an interface can be provided that accepts voice instructions or text input, and the AI ​​can edit images based on those instructions. This allows users to experience realistic clothing fitting in the metaverse and edit images.

[0070] The metaverse system further includes a clothing reproduction unit that estimates the user's emotions and adjusts the way the clothing is reproduced based on the estimated user's emotions. For example, if the user is relaxed, the clothing reproduction unit reproduces clothing that emphasizes soft colors and textures. For example, if the user is excited, the clothing reproduction unit can reproduce clothing that emphasizes vibrant colors and dynamic designs. For example, if the user is stressed, the clothing reproduction unit can reproduce clothing with muted colors and simple designs. This enables the reproduction of clothing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the clothing reproduction unit may input the user's emotion data into the generation AI and cause the generation AI to reproduce clothing based on the emotion.

[0071] When recreating clothes, the clothing reproduction unit can select the optimal clothing reproduction method by referring to the user's past purchase history. Specific methods and criteria for referencing the past purchase history include a purchase history database and a data acquisition method. For example, the clothing reproduction unit can reproduce similar styles of clothes based on the color and design of clothes purchased by the user in the past. The clothing reproduction unit can also prioritize recreating new products from a brand based on the brand the user has previously purchased. Furthermore, the clothing reproduction unit can reproduce clothes that reflect seasonal trends based on the user's past purchase history. This makes it possible to reproduce optimal clothes based on the user's past purchase history. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the clothing reproduction unit can input the user's past purchase history data into the generation AI and cause the generation AI to execute the optimal clothing reproduction method.

[0072] The clothing reproduction unit can dynamically adjust the size of the reproduced clothing, taking into account changes in the user's body shape when reproducing the clothing. Specific methods and criteria for considering changes in body shape include body shape change data and size adjustment algorithms. For example, the clothing reproduction unit can automatically adjust the size of the clothing based on the user's latest measurement data. The clothing reproduction unit can also analyze the user's past body shape change data and predict future body shape changes to adjust the size of the clothing. Furthermore, if the user changes their body shape for a specific event, the clothing reproduction unit can adjust the size of the clothing to reflect those changes. This makes it possible to adjust the size of the clothing in accordance with changes in the user's body shape. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, AI, or may be performed without AI. For example, the clothing reproduction unit can input the user's body shape change data into a generation AI and have the generation AI adjust the size of the clothing.

[0073] The clothing reproduction unit can estimate the user's emotions and determine the priority of the clothing to be reproduced based on the estimated user's emotions. Specific methods and criteria for determining the priority include emotion scoring and a priority algorithm. For example, if the user is relaxed, the clothing reproduction unit can prioritize reproducing casual clothing. Also, if the user is excited, the clothing reproduction unit can prioritize reproducing clothing with a flashy design. Also, if the user is stressed, the clothing reproduction unit can prioritize reproducing clothing with a simple, subdued design. This allows the prioritization of clothing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the clothing reproduction unit can be performed using, for example, an AI, or without an AI. For example, the clothing reproduction unit can input the user's emotion data into the generation AI and cause the generation AI to prioritize clothing based on the emotion.

[0074] When recreating the clothes, the clothing reproduction unit can reproduce regional fashion by taking into account the user's geographical location information. Specific methods and standards for acquiring geographical location information include GPS data and location information services. For example, if the user is in an urban area, the clothing reproduction unit can reproduce the latest urban fashion. Also, if the user is in a resort area, the clothing reproduction unit can reproduce resort fashion. Also, if the user is in a cold region, the clothing reproduction unit can reproduce clothing made of warm material. This makes it possible to reproduce regional fashion. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the clothing reproduction unit can input the user's geographical location information to the generation AI and cause the generation AI to execute regional fashion.

[0075] When recreating clothes, the clothing reproduction unit can analyze the user's social media activity and recreate clothes based on trends. Specific analysis methods and criteria for social media activity include analyzing posted content and trend analysis. For example, the clothing reproduction unit can recreate trendy clothes based on the fashion of influencers the user follows. The clothing reproduction unit can also recreate similar styles of clothes based on the fashion in posts that the user has "liked." The clothing reproduction unit can also analyze the content of the user's social media posts and recreate clothes that match trends. This makes it possible to recreate clothes based on social media trends. Some or all of the above-described processing in the clothing reproduction unit may be performed using, for example, AI, or may be performed without using AI. For example, the clothing reproduction unit can input the user's social media activity data into a generation AI and cause the generation AI to recreate clothes based on trends.

[0076] The avatar registration unit can estimate the user's emotions and adjust the avatar's appearance based on the estimated user emotions. Specific methods and criteria for adjusting the avatar's appearance include appearance parameters and adjustment algorithms. For example, if the user is relaxed, the avatar registration unit can generate an avatar with a soft expression and casual clothing. If the user is excited, the avatar registration unit can generate an avatar with a bright expression and flashy clothing. If the user is stressed, the avatar registration unit can generate an avatar with a calm expression and simple clothing. This enables the avatar's appearance to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the avatar registration unit can be performed using AI, for example, or without AI. For example, the avatar registration unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the avatar's appearance based on the emotion.

[0077] When registering an avatar, the avatar registration unit can generate an optimal avatar by referencing the user's past avatar history. Specific methods and standards for referencing the past avatar history include an avatar database and a method for acquiring history data. For example, the avatar registration unit can generate an avatar with a similar style based on the design of an avatar used by the user in the past. The avatar registration unit can also generate an optimal avatar based on the facial expressions and poses of avatars used by the user in the past. The avatar registration unit can also generate an avatar that reflects seasonal trends from the user's past avatar history. This allows the optimal avatar to be generated based on the user's past avatar history. Some or all of the above-described processing in the avatar registration unit may be performed using, or without, AI. For example, the avatar registration unit can input the user's past avatar history data into a generation AI and cause the generation AI to generate an optimal avatar.

[0078] The avatar registration unit can dynamically adjust the size of the avatar, taking into account changes in the user's body shape, when registering the avatar. Specific methods and standards for adjusting the avatar size include size adjustment algorithms and methods for reflecting body shape data. For example, the avatar registration unit can automatically adjust the avatar size based on the user's latest measurement data. The avatar registration unit can also analyze the user's past body shape change data and predict future body shape changes to adjust the avatar size. The avatar registration unit can also adjust the avatar size to reflect changes in the user's body shape when the user changes their body shape for a specific event. This enables the avatar size to be adjusted in accordance with changes in the user's body shape. Some or all of the above-described processing in the avatar registration unit may be performed using, or without, AI. For example, the avatar registration unit can input the user's body shape change data into a generation AI and have the generation AI adjust the avatar size.

[0079] The avatar registration unit can estimate the user's emotions and prioritize avatars based on the estimated user emotions. Specific methods and criteria for determining avatar priorities include emotion scoring and priority algorithms. For example, if the user is relaxed, the avatar registration unit can prioritize generating a casual avatar. Also, if the user is excited, the avatar registration unit can prioritize generating an avatar with a flashy design. Also, if the user is stressed, the avatar registration unit can prioritize generating an avatar with a simple, calm design. This allows avatar priorities to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the avatar registration unit can be performed using AI, for example, or without AI. For example, the avatar registration unit can input user emotion data into the generation AI and cause the generation AI to prioritize avatars based on emotions.

[0080] The avatar registration unit can generate a region-specific avatar by taking into account the user's geographical location information when registering the avatar. Specific methods and standards for generating a region-specific avatar include methods that reflect the culture and fashion of the region. For example, if the user is in an urban area, the avatar registration unit can generate an avatar that reflects the latest urban fashion. Also, if the user is in a resort area, the avatar registration unit can generate an avatar that reflects resort fashion. Also, if the user is in a cold region, the avatar registration unit can generate an avatar wearing clothes made of warm material. This allows the generation of a region-specific avatar. Some or all of the above-described processing in the avatar registration unit may be performed using, or without, AI. For example, the avatar registration unit can input the user's geographical location information into the generation AI and cause the generation AI to generate a region-specific avatar.

[0081] When registering an avatar, the avatar registration unit can analyze the user's social media activity and generate an avatar based on trends. Specific analysis methods and criteria for social media activity include analyzing posted content and trend analysis. For example, the avatar registration unit can generate a trendy avatar based on the fashion of influencers the user follows. The avatar registration unit can also generate an avatar with a similar style based on the fashion of posts the user has "liked." The avatar registration unit can also analyze the content of the user's social media posts and generate an avatar that matches a trend. This makes it possible to generate an avatar based on social media trends. Some or all of the above-described processing in the avatar registration unit may be performed using, or without, AI. For example, the avatar registration unit can input the user's social media activity data into a generation AI and cause the generation AI to generate an avatar based on trends.

[0082] The synthesis unit can estimate the user's emotions and adjust the style of the synthesized image based on the estimated user's emotions. Specific methods and criteria for adjusting the image style include style parameters and adjustment algorithms. For example, if the user is relaxed, the synthesis unit can synthesize an image with soft colors and a casual style. If the user is excited, the synthesis unit can synthesize an image with bright colors and a flashy style. If the user is stressed, the synthesis unit can synthesize an image with subdued colors and a simple style. This enables image style adjustment according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the synthesis unit can be performed using AI, for example, or without AI. For example, the synthesis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the image style based on the emotion.

[0083] The compositing unit can select the optimal compositing method by referring to the user's past image history during compositing. Specific methods and standards for referencing the past image history include an image database and a method for acquiring history data. For example, the compositing unit can composite images with a similar style based on the style of images the user has previously composited. The compositing unit can also select the optimal compositing method based on backgrounds and effects used by the user in the past. The compositing unit can also composite images that reflect seasonal trends from the user's past image history. This makes it possible to select the optimal compositing method based on the user's past image history. Some or all of the above-described processing in the compositing unit may be performed using, or without, AI. For example, the compositing unit can input the user's past image history data into a generation AI and cause the generation AI to execute the optimal compositing method.

[0084] The synthesis unit can dynamically adjust the size of the synthesized image during synthesis, taking into account changes in the user's body shape. Specific methods and standards for adjusting the image size include size adjustment algorithms and methods for reflecting body shape data. For example, the synthesis unit can automatically adjust the image size based on the user's latest measurement data. The synthesis unit can also analyze the user's past body shape change data and predict future body shape changes to adjust the image size. The synthesis unit can also adjust the image size to reflect changes in the user's body shape when the user changes their body shape for a specific event. This makes it possible to adjust the image size in accordance with changes in the user's body shape. Some or all of the above-described processing in the synthesis unit may be performed using, for example, AI, or may be performed without AI. For example, the synthesis unit can input the user's body shape change data into the generation AI and have the generation AI adjust the image size.

[0085] The synthesis unit can estimate the user's emotions and determine the priority of images to be synthesized based on the estimated user emotions. Specific methods and criteria for determining the priority of images include emotion scoring and priority algorithms. For example, if the user is relaxed, the synthesis unit can prioritize synthesizing casual-style images. Also, if the user is excited, the synthesis unit can prioritize flashy-style images. Also, if the user is stressed, the synthesis unit can prioritize simple and calm-style images. This allows the priority of images to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the synthesis unit can be performed using AI, or without AI. For example, the synthesis unit can input the user's emotion data into the generation AI and cause the generation AI to prioritize images based on the emotion.

[0086] During synthesis, the synthesis unit can synthesize a region-specific background by taking into account the user's geographical location information. Specific synthesis methods and standards for region-specific backgrounds include methods that reflect the scenery and culture of the region. For example, if the user is in an urban area, the synthesis unit can synthesize an urban landscape as the background. Also, if the user is in a resort area, the synthesis unit can synthesize a resort landscape as the background. Also, if the user is in a cold region, the synthesis unit can synthesize a snowy landscape as the background. In this way, a region-specific background can be synthesized. Some or all of the above-described processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input the user's geographical location information to the generation AI and cause the generation AI to execute a region-specific background.

[0087] During synthesis, the synthesis unit can analyze the user's social media activity and synthesize an image based on trends. Specific analysis methods and criteria for social media activity include analyzing the content of posts and trend analysis. For example, the synthesis unit can synthesize trending images based on the style of influencers the user follows. The synthesis unit can also synthesize images with a similar style based on the style of posts the user has "liked." The synthesis unit can also analyze the content of the user's social media posts and synthesize images that match trends. This makes it possible to synthesize images based on social media trends. Some or all of the above-described processing in the synthesis unit may be performed using, for example, AI, or may be performed without using AI. For example, the synthesis unit can input the user's social media activity data into the generation AI and cause the generation AI to synthesize an image based on trends.

[0088] The editing department can estimate the user's emotions and adjust the style of the image to be edited based on the estimated user's emotions. Specific methods and criteria for adjusting the image style include style parameters and adjustment algorithms. For example, if the user is relaxed, the editing department can edit the image to a soft color scheme and a casual style. If the user is excited, the editing department can edit the image to a bright color scheme and a flashy style. If the user is stressed, the editing department can edit the image to a subdued color scheme and a simple style. This enables the image style to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the editing department can be performed using AI, for example, or without AI. For example, the editing department can input the user's emotion data into the generation AI and have the generation AI adjust the image style based on the emotion.

[0089] When editing, the editing department can select the optimal editing method by referring to the user's past editing history. Specific methods and criteria for referring to the past editing history include an editing database and a method for acquiring history data. For example, the editing department can edit an image into a similar style based on the style of images the user has edited in the past. The editing department can also select the optimal editing method based on filters and effects the user has used in the past. The editing department can also edit images to reflect seasonal trends based on the user's past editing history. This makes it possible to select the optimal editing method based on the user's past editing history. Some or all of the above-mentioned processing in the editing department may be performed using, for example, AI, or may be performed without using AI. For example, the editing department can input the user's past editing history data into a generation AI and have the generation AI execute the optimal editing method.

[0090] The editing department can dynamically adjust the size of the image to be edited during editing, taking into account changes in the user's body shape. Specific methods and standards for adjusting the image size include size adjustment algorithms and methods for reflecting body shape data. For example, the editing department can automatically adjust the image size based on the user's latest measurement data. The editing department can also analyze the user's past body shape change data and predict future body shape changes to adjust the image size. If the user changes their body shape for a specific event, the editing department can also adjust the image size to reflect those changes. This makes it possible to adjust the image size in accordance with changes in the user's body shape. Some or all of the above-mentioned processing by the editing department may be performed using, for example, AI, or may be performed without AI. For example, the editing department can input the user's body shape change data into a generation AI and have the generation AI adjust the image size.

[0091] The editorial department can estimate the user's emotions and prioritize the images to be edited based on the estimated user emotions. Specific methods and criteria for determining the image priorities include emotion scoring and priority algorithms. For example, if the user is relaxed, the editorial department can prioritize editing images in a casual style. Also, if the user is excited, the editorial department can prioritize editing images in a flashy style. Also, if the user is stressed, the editorial department can prioritize editing images in a simple, calm style. This allows the prioritization of images according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the editorial department can be performed using AI, for example, or without AI. For example, the editorial department can input the user's emotion data into the generation AI and have the generation AI prioritize images based on the emotion.

[0092] During editing, the editorial department can edit a region-specific style taking into account the user's geographical location information. Specific editing methods and standards for a region-specific style include methods that reflect the culture and fashion of the region. For example, if the user is in an urban area, the editorial department can edit the style to reflect urban trends. Also, if the user is in a resort area, the editorial department can edit the style to reflect resort trends. Also, if the user is in a cold region, the editorial department can edit the style to reflect clothing made of warm materials. In this way, a region-specific style can be edited. Some or all of the above-mentioned processing by the editorial department may be performed using, for example, AI, or may be performed without using AI. For example, the editorial department can input the user's geographical location information into the generation AI and have the generation AI execute a region-specific style.

[0093] During editing, the editorial department can analyze the user's social media activity and edit images based on trends. Specific analysis methods and criteria for social media activity include analyzing post content and trend analysis. For example, the editorial department can edit images to be trendy based on the style of influencers the user follows. The editorial department can also edit images in a similar style based on the style of posts the user has "liked." The editorial department can also analyze the content of the user's social media posts and edit images that match trends. This makes it possible to edit images based on social media trends. Some or all of the above-described processing by the editorial department may be performed using, or without, AI. For example, the editorial department can input the user's social media activity data into a generation AI and have the generation AI edit images based on trends. === Hard Collateral 1-1 === Each of the multiple elements, including the clothing reproduction unit, avatar registration unit, synthesis unit, and editing unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the clothing reproduction unit is implemented by the control unit 46A of the smart device 14 and uses 3D modeling technology and texture mapping to realistically reproduce the shape and color of clothing. The avatar registration unit acquires the user's measurement data using the camera 42 of the smart device 14 and reflects the data in the avatar. The synthesis unit is implemented by the control unit 46A of the smart device 14 and synthesizes a real image of the user uploaded by the user with clothing selected in the metaverse. The editing unit is implemented by the specific processing unit 290 of the data processing device 12 and uses AI to edit the image based on the user's instructions. Furthermore, the clothing reproduction unit is implemented by the specific processing unit 290 of the data processing device 12 and recreates clothing according to the user's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the clothing reproduction unit, avatar registration unit, synthesis unit, and editing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the clothing reproduction unit is realized by the control unit 46A of the smart glasses 214 and realistically reproduces the shape and color of clothing using 3D modeling technology and texture mapping. The avatar registration unit acquires the user's measurement data using the camera 42 of the smart glasses 214 and reflects the data in the avatar. The synthesis unit is realized by the control unit 46A of the smart glasses 214 and synthesizes a real image of the user uploaded by the user with clothing selected on the metaverse. The editing unit is realized by the specific processing unit 290 of the data processing device 12 and uses AI to edit the image based on the user's instructions. Furthermore, the clothing reproduction unit is realized by the specific processing unit 290 of the data processing device 12 and reproduces clothing according to the user's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the clothing reproduction unit, avatar registration unit, synthesis unit, and editing unit, described above, is implemented, for example, by at least one of the headset-type device 314 and the data processing device 12. For example, the clothing reproduction unit is implemented by the control unit 46A of the headset-type device 314 and uses 3D modeling technology and texture mapping to realistically reproduce the shape and color of clothing. The avatar registration unit acquires the user's measurement data using the camera 42 of the headset-type device 314 and reflects the data in the avatar. The synthesis unit is implemented by the control unit 46A of the headset-type device 314 and synthesizes a real image of the user uploaded by the user with clothing selected in the metaverse. The editing unit is implemented by the specific processing unit 290 of the data processing device 12 and uses AI to edit the image based on the user's instructions. Furthermore, the clothing reproduction unit is implemented by the specific processing unit 290 of the data processing device 12 and recreates clothing according to the user's emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the clothing reproduction unit, avatar registration unit, synthesis unit, and editing unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the clothing reproduction unit is realized by the control unit 46A of the robot 414 and realistically reproduces the shape and color of clothing using 3D modeling technology and texture mapping. The avatar registration unit acquires the user's measurement data using the camera 42 of the robot 414 and reflects the data in the avatar. The synthesis unit is realized by the control unit 46A of the robot 414 and synthesizes a real image of the user uploaded by the user with clothing selected on the Metaverse. The editing unit is realized by the specific processing unit 290 of the data processing device 12 and uses AI to edit the image based on the user's instructions. Furthermore, the clothing reproduction unit is realized by the specific processing unit 290 of the data processing device 12 and reproduces clothing according to the user's emotions.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The metaverse system can also include a health suggestion unit that acquires the user's health data and suggests clothing based on the user's health condition. For example, it can monitor the user's heart rate and stress level and suggest clothing made from comfortable materials when relaxation is needed. It can also suggest breathable clothing after exercise. Furthermore, the health suggestion unit can analyze the user's sleep data and suggest pajamas that promote comfortable sleep. This makes it possible to suggest optimal clothing based on the user's health condition.

[0096] The metaverse system may further include a hobby suggestion unit that suggests clothing based on the user's hobbies and interests. For example, if the user likes outdoor activities, clothing suitable for outdoor activities may be suggested. Also, if the user plans to attend a music festival, clothing suitable for the event may be suggested. Furthermore, if the user likes a particular movie or character, the hobby suggestion unit may suggest costumes based on that theme. This makes it possible to suggest optimal clothing according to the user's hobbies and interests.

[0097] The metaverse system can also include a purchase suggestion unit that analyzes a user's purchasing history and suggests new clothing based on past purchasing trends. For example, it can suggest new items with similar designs based on the style and color of clothing the user has previously purchased. Also, if a user has a preference for a particular brand, it can preferentially suggest new items from that brand. Furthermore, the purchase suggestion unit can suggest clothing that reflects seasonal trends based on the user's past purchasing history. This makes it possible to suggest optimal clothing based on the user's purchasing history.

[0098] The metaverse system can further include a region suggestion unit that suggests region-specific fashion in consideration of the user's geographical location information. For example, if the user is in an urban area, the latest urban fashion can be suggested. If the user is in a resort area, resort fashion can be suggested. Furthermore, if the user is in a cold area, clothing made of warm materials can be suggested. This makes it possible to suggest region-specific fashion.

[0099] The metaverse system can further include a trend suggestion unit that analyzes a user's social media activity and suggests clothing based on trends. For example, trendy clothing can be suggested based on the fashion of influencers the user follows. It can also suggest clothing with a similar style based on the fashion in posts that the user has "liked." Furthermore, the trend suggestion unit can analyze the content of a user's social media posts and suggest clothing that matches the trend. This makes it possible to suggest clothing based on social media trends.

[0100] The Metaverse system can also estimate the user's emotions and dynamically change the avatar's facial expression based on the estimated user's emotions. For example, if the user is relaxed, the avatar's facial expression can be made calm. If the user is excited, the avatar's facial expression can be made brighter. Furthermore, if the user is stressed, the avatar's facial expression can be made calmer. This makes it possible for the avatar's facial expression to change according to the user's emotions.

[0101] The Metaverse system can also estimate the user's emotions and adjust the avatar's movements based on the estimated user emotions. For example, if the user is relaxed, the avatar's movements can be made slower. If the user is excited, the avatar's movements can be made more active. Furthermore, if the user is stressed, the avatar's movements can be made calmer. This makes it possible to adjust the avatar's movements according to the user's emotions.

[0102] The metaverse system can also estimate the user's emotions and dynamically change the avatar's clothing based on the estimated user's emotions. For example, if the user is relaxed, the avatar can wear casual clothing. If the user is excited, the avatar can wear flashy clothing. If the user is stressed, the avatar can wear subdued clothing. This makes it possible for the avatar's clothing to change according to the user's emotions.

[0103] The Metaverse system can also estimate the user's emotions and dynamically change the avatar's background based on the estimated user's emotions. For example, if the user is relaxed, a natural landscape can be used as the background. If the user is excited, a bright urban landscape can be used as the background. Furthermore, if the user is stressed, a calm indoor landscape can be used as the background. This makes it possible to change the avatar's background according to the user's emotions.

[0104] The Metaverse system can also estimate the user's emotions and dynamically change the avatar's voice based on the estimated user emotions. For example, if the user is relaxed, the avatar's voice can be made calm. If the user is excited, the avatar's voice can be made brighter. If the user is stressed, the avatar's voice can be made calmer. This makes it possible for the avatar's voice to change according to the user's emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The clothing reproduction section reproduces the clothing in the same size and color as the real thing. For example, 3D modeling technology and texture mapping can be used to realistically reproduce the shape and color of the clothing. Additionally, a fabric texture simulation and a physical movement engine can be used to reproduce the texture and movement of the clothing. Step 2: The avatar registration unit registers an avatar that reflects the user's measurement data. For example, the user can use a smartphone camera to obtain their own measurement data and reflect that data in the avatar. The avatar registration unit generates an avatar that matches the user's body type and is used to check the size and coordination. Step 3: The synthesis unit synthesizes the real image of the user uploaded with the clothes selected in the Metaverse. For example, image processing technology and synthesis algorithms can be used to synthesize the real image of the user with the clothes in the Metaverse. This allows the user to view an image that looks like they are wearing it in real life. Step 4: The editorial department uses AI to edit the image based on the user's instructions. For example, if the user requests a more casual look, the AI ​​can edit the image based on that instruction. The editorial department uses techniques such as image filtering and adding effects to edit the image in accordance with the user's instructions.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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. The clothing reproduction part, an avatar registration unit that allows an avatar to try on the clothes reproduced by the clothes reproduction unit; a synthesis unit that synthesizes the clothes tried on by the avatar registration unit with an image of the user's real self; An editing unit in which AI edits the image synthesized by the synthesis unit. A system characterized by:

2. The reproduction of the clothes is as follows: Reproduce clothes in the same size and color as in real life 2. The system of claim 1.

3. The avatar registration unit Register an avatar that reflects the user's measurement data 2. The system of claim 1.

4. The synthesis unit The system combines real-life images uploaded by users with clothing selected in the metaverse.

2. The system of claim 1.

5. The editorial department AI edits images based on user instructions 2. The system of claim 1.

6. The reproduction of the clothes is as follows: Reproducing the texture and movement of clothing 2. The system of claim 1.

7. The avatar registration unit Use your smartphone camera to capture measurements 2. The system of claim 1.

Images