System

Abstract

An object of a system according to an embodiment is to easily customize and try on clothing that matches the body shape of a user.SOLUTION: A system includes a body shape data entry part, a design customization part, and a 3D try-on part. The physique data input unit inputs physique data of a user. The design customization unit customizes the design of the garment based on the figure data input by the figure data input unit. The 3D try-on unit generates a 3D model based on the design of the garment customized by the design customization unit, and tries on the garment.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to easily customize and try on clothes that fit a user's body type.

[0005] The system according to the embodiment aims to allow a user to easily customize and try on clothes that fit the user's body type. [Means for solving the problem]

[0006] The system according to the embodiment includes a body type data input unit, a design customization unit, and a 3D fitting unit. The body type data input unit inputs a user's body type data. The design customization unit customizes the design of the clothes based on the body type data input by the body type data input unit. The 3D fitting unit generates a 3D model based on the design of the clothes customized by the design customization unit and allows the user to try on the clothes. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to easily customize and try on clothes that fit their body type. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 custom-made tool according to the embodiment of the present invention is a tool that allows users to customize their favorite clothes to fit their own body type, try them on in 3D, and easily and casually have the clothes made to order. This allows the custom-made tool to easily order clothes that fit perfectly to the user.

[0029] The custom-made tool according to the embodiment includes a body data input unit, a design customization unit, and a 3D fitting unit. The body data input unit inputs a user's body data, such as height, weight, chest circumference, waist circumference, and hip circumference. The body data input unit can also use generative AI to predict changes in body shape in real time and suggest clothing that fits the user's future body shape. The design customization unit customizes clothing designs based on the body data input by the body data input unit. For example, the user can select colors, patterns, materials, and styles. The design customization unit can also use generative AI to analyze the user's past fashion history and suggest designs based on the user's preferences. The 3D fitting unit generates a 3D model based on the clothing design customized by the design customization unit and allows the user to try on the clothing. For example, the user can rotate the 3D model on the screen and zoom in and out to check the appearance of the clothing from various angles. The 3D fitting unit can also use generative AI to provide a real-time 3D fitting simulation that responds to the user's movements, allowing the user to check the fit according to their movements. As a result, the custom-made tool according to the embodiment allows users to easily customize clothes that fit their body type, try them on in 3D, and have them made to order. For example, users can create their own original clothes to match special events or everyday fashion. It also enhances the online shopping experience, allowing users to find clothes that fit them perfectly without having to go to a physical store.

[0030] When the user inputs their body data, the body data input unit uses generative AI to predict changes in body shape in real time and suggest clothing that will fit their future body shape. For example, when the user inputs body data such as height, weight, chest circumference, waist, and hips, the body data input unit uses generative AI to predict changes in body shape based on past data and health status. For example, if the user is on a diet, the generative AI predicts their future body shape and suggests clothing sizes and designs that will fit that. This makes it possible to suggest clothing that will fit the user's future body shape.

[0031] The body type data input unit can combine the user's input body type data with health data to suggest the optimal clothing for the user's health condition. For example, when the user inputs body type data, the generation AI combines this with health data such as the number of steps and heart rate obtained from a smartwatch or fitness tracker. For example, for users who exercise a lot, clothing made of materials and designs that are easy to move in can be suggested. This makes it possible to suggest the optimal clothing for the user's health condition.

[0032] The body type data input unit adds posture data to the input of the user's body type data, and can suggest optimal clothing according to the posture. For example, when the user inputs body type data, the body type data input unit uses the generation AI to acquire additional posture data. For example, the unit analyzes the user's posture using a smartphone camera and suggests optimal clothing according to the posture. This makes it possible to suggest optimal clothing according to the user's posture.

[0033] The body type data input unit supports voice input or gesture input for inputting the user's body type data, enabling more intuitive operation. For example, the body type data input unit allows the user to use voice input when inputting body type data. For example, if the user simply speaks, "height 170 cm, weight 65 kg," the generation AI will input that data. This allows the user to input body type data more intuitively.

[0034] The design customization unit uses a generation AI to analyze a user's past fashion history and propose designs based on their preferences. For example, the generation AI analyzes a user's past fashion history and proposes designs based on their preferences. For example, the design customization unit generates designs that the user prefers based on the designs, colors, and materials of clothes purchased in the past. This makes it possible to propose designs based on the user's preferences.

[0035] The design customization department can combine seasonal or weather data to suggest optimal materials and styles. For example, the design customization department uses generative AI to analyze seasonal and weather data and suggest optimal materials and styles. For example, it suggests breathable materials and light designs in summer, and highly insulating materials and warm designs in winter. This makes it possible to suggest optimal materials and styles according to the season and weather.

[0036] The design customization unit can combine the user's cultural background and event information to propose designs suited to special occasions. For example, the design customization unit uses a generative AI to analyze the user's cultural background and propose designs suited to special occasions. For example, it can propose designs suitable for traditional festivals or weddings. This makes it possible to propose designs suited to special occasions.

[0037] The design customization unit uses the generation AI to display feedback from other users in real time on a design selected by a user, thereby promoting improvements to the design. For example, the design customization unit uses the generation AI to display feedback from other users in real time on a design selected by a user. For example, comments and ratings on the design may be displayed to help the user improve the design. This allows the user to receive feedback from other users in real time and improve the design.

[0038] The 3D fitting unit can provide a real-time 3D fitting simulation that responds to the user's movements, allowing the user to check the fit that matches their movements. For example, the 3D fitting unit uses a generative AI to analyze the user's movements in real time and provide a 3D fitting simulation. For example, the user can check the fit of the clothes in accordance with their walking and sitting movements. This allows the user to check the fit that matches their movements.

[0039] The 3D fitting section can simulate different lighting conditions or backgrounds, allowing users to check how the garment will look in various scenes. For example, the generative AI can simulate different lighting conditions, allowing users to check how the garment will look in various scenes using the 3D fitting function. For example, users can check how the garment will look in different lighting conditions, such as natural light during the day and artificial light at night. This allows users to check how the garment will look in various scenes.

[0040] The 3D fitting room can combine virtual reality (VR) technology to provide a more immersive fitting experience. For example, the 3D fitting room uses generative AI to combine 3D fitting functionality with virtual reality (VR) technology to provide a more immersive fitting experience for users. For example, using a VR headset provides an experience that makes it feel as if you are actually trying on the clothes. This allows for a more immersive fitting experience.

[0041] The 3D try-on unit can enable sharing with friends and family and receiving feedback from others in real time. The 3D try-on unit, for example, enables the generative AI to share the 3D try-on function with friends and family and receiving feedback from others in real time. For example, images and videos of clothes tried on can be shared and comments and ratings can be received. This allows sharing with friends and family and receiving feedback from others in real time.

[0042] The custom-made ordering unit uses a generation AI to analyze the user's past order history and make suggestions for repeat orders and related products. For example, the custom-made ordering unit uses a generation AI to analyze the user's past order history and make suggestions for repeat orders and related products. For example, based on the design and size of clothes the user has previously purchased, the unit makes suggestions for products with the same design or related products. This makes it possible to make suggestions for repeat orders and related products.

[0043] The custom-made ordering unit can use generative AI to suggest eco-friendly materials and manufacturing methods to the user, providing environmentally conscious options. For example, the custom-made ordering unit can use generative AI to suggest eco-friendly materials and manufacturing methods to the user, providing environmentally conscious options. For example, it can suggest designs that use recycled materials or organic cotton. This can provide environmentally conscious options.

[0044] The made-to-order ordering department can introduce a subscription model and develop a service that periodically provides new designs of clothing. For example, the generative AI can introduce a subscription model and develop a service that periodically provides users with new designs of clothing. For example, new designs of clothing can be delivered to users every month. This makes it possible to provide a service that periodically provides new designs of clothing.

[0045] The custom order unit adds a function that allows users to place orders jointly with other users, thereby providing the benefits of group discounts and joint purchases. For example, the custom order unit provides a function that allows the generation AI to place custom orders jointly with other users and applies group discounts. For example, by ordering together with friends or family, users can purchase at a discounted price. This allows users to receive the benefits of group discounts and joint purchases.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The custom-made tool supports voice or gesture input when inputting the user's body type data, allowing for more intuitive operation. For example, if the user simply speaks, "I'm 170 cm tall and weigh 65 kg," the generation AI will input that data. Gesture input also allows the user to input data with hand movements, allowing the user to input body type data more intuitively.

[0048] The custom-made tool can add posture data to the input of the user's body type data and suggest the most suitable clothes for that posture. For example, it can analyze the user's posture using a smartphone camera and suggest the most suitable clothes for that posture. This makes it possible to suggest the most suitable clothes for the user's posture.

[0049] The custom-made tool can combine health data with the user's input body data to suggest the best clothing for their health condition. For example, it can combine health data such as the number of steps taken and heart rate obtained from a smartwatch or fitness tracker. For users who exercise a lot, it can suggest clothing made of materials and with designs that allow for easy movement. This makes it possible to suggest the best clothing for each user's health condition.

[0050] The custom-made tool can combine the user's cultural background and event information to suggest designs suited to special occasions. For example, the generative AI analyzes the user's cultural background and suggests designs suitable for traditional festivals or weddings. This makes it possible to suggest designs tailored to special occasions.

[0051] The custom-made tool uses generative AI to display feedback from other users in real time on the design selected by the user, promoting the improvement of the design. For example, the generative AI can display comments and ratings from other users in real time on the design selected by the user, which the user can use as a reference for improving the design. This allows the user to receive feedback from other users in real time and improve the design.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The body data input unit inputs the user's body data, such as height, weight, chest circumference, waist circumference, and hip circumference. The body data input unit can also use generative AI to predict changes in body shape in real time and suggest clothing that will fit the user's future body shape. Step 2: The design customization unit customizes the clothing design based on the body data entered by the body data input unit. For example, the user can select the color, pattern, material, style, etc. The design customization unit can also use generation AI to analyze the user's past fashion history and suggest designs based on their preferences. Step 3: The 3D fitting unit generates a 3D model based on the clothing design customized by the design customization unit and allows the user to try it on. For example, the user can rotate the 3D model on the screen and zoom in and out to check how the clothing will look from various angles. The 3D fitting unit can also use generative AI to provide a real-time 3D fitting simulation that responds to the user's movements, allowing the user to check the fit according to their movements.

[0054] (Example 2) The custom-made tool according to the embodiment of the present invention is a tool that allows users to customize their favorite clothes to fit their own body type, try them on in 3D, and easily and casually have the clothes made to order. This allows the custom-made tool to easily order clothes that fit perfectly to the user.

[0055] The custom-made tool according to the embodiment includes a body data input unit, a design customization unit, and a 3D fitting unit. The body data input unit inputs a user's body data, such as height, weight, chest circumference, waist circumference, and hip circumference. The body data input unit can also use generative AI to predict changes in body shape in real time and suggest clothing that fits the user's future body shape. The design customization unit customizes clothing designs based on the body data input by the body data input unit. For example, the user can select colors, patterns, materials, and styles. The design customization unit can also use generative AI to analyze the user's past fashion history and suggest designs based on the user's preferences. The 3D fitting unit generates a 3D model based on the clothing design customized by the design customization unit and allows the user to try on the clothing. For example, the user can rotate the 3D model on the screen and zoom in and out to check the appearance of the clothing from various angles. The 3D fitting unit can also use generative AI to provide a real-time 3D fitting simulation that responds to the user's movements, allowing the user to check the fit according to their movements. As a result, the custom-made tool according to the embodiment allows users to easily customize clothes that fit their body type, try them on in 3D, and have them made to order. For example, users can create their own original clothes to match special events or everyday fashion. It also enhances the online shopping experience, allowing users to find clothes that fit them perfectly without having to go to a physical store.

[0056] When the user inputs their body data, the body data input unit uses generative AI to predict changes in body shape in real time and suggest clothing that will fit their future body shape. For example, when the user inputs body data such as height, weight, chest circumference, waist, and hips, the body data input unit uses generative AI to predict changes in body shape based on past data and health status. For example, if the user is on a diet, the generative AI predicts their future body shape and suggests clothing sizes and designs that will fit that. This makes it possible to suggest clothing that will fit the user's future body shape.

[0057] The body type data input unit can combine the user's input body type data with health data to suggest the optimal clothing for the user's health condition. For example, when the user inputs body type data, the generation AI combines this with health data such as the number of steps and heart rate obtained from a smartwatch or fitness tracker. For example, for users who exercise a lot, clothing made of materials and designs that are easy to move in can be suggested. This makes it possible to suggest the optimal clothing for the user's health condition.

[0058] The body type data input unit can use the emotion estimation function to analyze the emotions felt when the user inputs body type data and provide an interface for reducing stress. For example, when the user inputs body type data, the generation AI analyzes the user's facial expressions and voice to estimate the emotion. For example, for a user who is feeling stressed, a relaxing interface or an encouraging message can be displayed. This can provide an interface that reduces the user's stress.

[0059] The body type data input unit adds posture data to the input of the user's body type data, and can suggest optimal clothing according to the posture. For example, when the user inputs body type data, the body type data input unit uses the generation AI to acquire additional posture data. For example, the unit analyzes the user's posture using a smartphone camera and suggests optimal clothing according to the posture. This makes it possible to suggest optimal clothing according to the user's posture.

[0060] The body type data input unit supports voice input or gesture input for inputting the user's body type data, enabling more intuitive operation. For example, the body type data input unit allows the user to use voice input when inputting body type data. For example, if the user simply speaks, "height 170 cm, weight 65 kg," the generation AI will input that data. This allows the user to input body type data more intuitively.

[0061] The body data input unit uses an emotion estimation function to provide real-time feedback on the emotions felt when the user enters body data, making suggestions that elicit positive emotions. For example, when the user enters body data, the body data input unit uses the generation AI to provide real-time feedback on emotions. For example, if the user is feeling stressed, the unit displays relaxing suggestions or encouraging messages. This makes it possible to make suggestions that elicit positive emotions from the user.

[0062] The design customization unit uses a generation AI to analyze a user's past fashion history and propose designs based on their preferences. For example, the generation AI analyzes a user's past fashion history and proposes designs based on their preferences. For example, the design customization unit generates designs that the user prefers based on the designs, colors, and materials of clothes purchased in the past. This makes it possible to propose designs based on the user's preferences.

[0063] The design customization department can combine seasonal or weather data to suggest optimal materials and styles. For example, the design customization department uses generative AI to analyze seasonal and weather data and suggest optimal materials and styles. For example, it suggests breathable materials and light designs in summer, and highly insulating materials and warm designs in winter. This makes it possible to suggest optimal materials and styles according to the season and weather.

[0064] The design customization unit uses the emotion estimation function to analyze the emotions of users when customizing a design and can propose designs that elicit positive emotions. For example, when a user customizes a design, the design customization unit uses a generative AI to analyze the emotions and propose designs that elicit positive emotions. For example, if the user is having fun, the unit will propose an even more enjoyable design. This makes it possible to propose designs that elicit positive emotions in the user.

[0065] The design customization unit can combine the user's cultural background and event information to propose designs suited to special occasions. For example, the design customization unit uses a generative AI to analyze the user's cultural background and propose designs suited to special occasions. For example, it can propose designs suitable for traditional festivals or weddings. This makes it possible to propose designs suited to special occasions.

[0066] The design customization unit uses the generation AI to display feedback from other users in real time on a design selected by a user, thereby promoting improvements to the design. For example, the design customization unit uses the generation AI to display feedback from other users in real time on a design selected by a user. For example, comments and ratings on the design may be displayed to help the user improve the design. This allows the user to receive feedback from other users in real time and improve the design.

[0067] The design customization unit uses the emotion estimation function to provide real-time feedback on the emotions of users as they customize their designs, making suggestions that elicit positive emotions. For example, when a user customizes a design, the design customization unit uses the generation AI to provide real-time feedback on emotions. For example, if the user is enjoying themselves, the unit will suggest an even more enjoyable design. This makes it possible to make suggestions that elicit positive emotions from the user.

[0068] The 3D fitting unit can provide a real-time 3D fitting simulation that responds to the user's movements, allowing the user to check the fit that matches their movements. For example, the 3D fitting unit uses a generative AI to analyze the user's movements in real time and provide a 3D fitting simulation. For example, the user can check the fit of the clothes in accordance with their walking and sitting movements. This allows the user to check the fit that matches their movements.

[0069] The 3D fitting section can simulate different lighting conditions or backgrounds, allowing users to check how the garment will look in various scenes. For example, the generative AI can simulate different lighting conditions, allowing users to check how the garment will look in various scenes using the 3D fitting function. For example, users can check how the garment will look in different lighting conditions, such as natural light during the day and artificial light at night. This allows users to check how the garment will look in various scenes.

[0070] The 3D fitting room can combine virtual reality (VR) technology to provide a more immersive fitting experience. For example, the 3D fitting room uses generative AI to combine 3D fitting functionality with virtual reality (VR) technology to provide a more immersive fitting experience for users. For example, using a VR headset provides an experience that makes it feel as if you are actually trying on the clothes. This allows for a more immersive fitting experience.

[0071] The 3D try-on unit can enable sharing with friends and family and receiving feedback from others in real time. The 3D try-on unit, for example, enables the generative AI to share the 3D try-on function with friends and family and receiving feedback from others in real time. For example, images and videos of clothes tried on can be shared and comments and ratings can be received. This allows sharing with friends and family and receiving feedback from others in real time.

[0072] The 3D fitting unit uses an emotion estimation function to provide real-time feedback on the emotions of the user as they try on 3D clothing, and can make suggestions that elicit positive emotions. For example, when a user tries on 3D clothing, the 3D fitting unit uses a generation AI to provide real-time feedback on emotions. For example, if the user is enjoying themselves, the unit can provide an even more enjoyable fitting experience. This makes it possible to make suggestions that elicit positive emotions from the user.

[0073] The custom-made ordering unit uses a generation AI to analyze the user's past order history and make suggestions for repeat orders and related products. For example, the custom-made ordering unit uses a generation AI to analyze the user's past order history and make suggestions for repeat orders and related products. For example, based on the design and size of clothes the user has previously purchased, the unit makes suggestions for products with the same design or related products. This makes it possible to make suggestions for repeat orders and related products.

[0074] The custom-made ordering unit can use generative AI to suggest eco-friendly materials and manufacturing methods to the user, providing environmentally conscious options. For example, the custom-made ordering unit can use generative AI to suggest eco-friendly materials and manufacturing methods to the user, providing environmentally conscious options. For example, it can suggest designs that use recycled materials or organic cotton. This can provide environmentally conscious options.

[0075] The custom-made ordering unit uses the emotion estimation function to analyze the emotions of the user when placing an order and can provide an ordering experience that elicits positive emotions. For example, when a user places an order, the custom-made ordering unit uses a generation AI to analyze the emotions and provide an ordering experience that elicits positive emotions. For example, if the user is having fun, an even more enjoyable ordering experience is provided. This makes it possible to provide an ordering experience that elicits positive emotions in the user.

[0076] The made-to-order ordering department can introduce a subscription model and develop a service that periodically provides new designs of clothing. For example, the generative AI can introduce a subscription model and develop a service that periodically provides users with new designs of clothing. For example, new designs of clothing can be delivered to users every month. This makes it possible to provide a service that periodically provides new designs of clothing.

[0077] The custom order unit adds a function that allows users to place orders jointly with other users, thereby providing the benefits of group discounts and joint purchases. For example, the custom order unit provides a function that allows the generation AI to place custom orders jointly with other users and applies group discounts. For example, by ordering together with friends or family, users can purchase at a discounted price. This allows users to receive the benefits of group discounts and joint purchases.

[0078] The custom-made ordering unit uses the emotion estimation function to provide real-time feedback on the emotions a user feels when placing an order, and can make suggestions that elicit positive emotions. For example, when a user places an order, the custom-made ordering unit uses the generation AI to provide real-time feedback on emotions. For example, if the user is enjoying themselves, the unit can provide an even more enjoyable ordering experience. This makes it possible to make suggestions that elicit positive emotions from the user.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The custom-made tool supports voice or gesture input when inputting the user's body type data, allowing for more intuitive operation. For example, if the user simply speaks, "I'm 170 cm tall and weigh 65 kg," the generation AI will input that data. Gesture input also allows the user to input data with hand movements, allowing the user to input body type data more intuitively.

[0081] The custom-made tool can add posture data to the input of the user's body type data and suggest the most suitable clothes for that posture. For example, it can analyze the user's posture using a smartphone camera and suggest the most suitable clothes for that posture. This makes it possible to suggest the most suitable clothes for the user's posture.

[0082] The custom-made tool can combine health data with the user's input body data to suggest the best clothing for their health condition. For example, it can combine health data such as the number of steps taken and heart rate obtained from a smartwatch or fitness tracker. For users who exercise a lot, it can suggest clothing made of materials and with designs that allow for easy movement. This makes it possible to suggest the best clothing for each user's health condition.

[0083] The custom-made tool can combine the user's cultural background and event information to suggest designs suited to special occasions. For example, the generative AI analyzes the user's cultural background and suggests designs suitable for traditional festivals or weddings. This makes it possible to suggest designs tailored to special occasions.

[0084] The custom-made tool uses generative AI to display feedback from other users in real time on the design selected by the user, promoting the improvement of the design. For example, the generative AI can display comments and ratings from other users in real time on the design selected by the user, which the user can use as a reference for improving the design. This allows the user to receive feedback from other users in real time and improve the design.

[0085] The custom-made tool uses an emotion estimation function to analyze the emotions felt when a user enters body data, and can provide an interface to reduce stress. For example, when a user enters body data, the generative AI analyzes their facial expressions and voice to estimate their emotions. For users who are feeling stressed, it can display a relaxing interface or an encouraging message. This allows the tool to provide an interface that reduces the user's stress.

[0086] The custom-made tool uses an emotion estimation function to analyze the emotions of users when customizing a design and can propose designs that elicit positive emotions. For example, when a user customizes a design, the generative AI analyzes their emotions and proposes designs that elicit positive emotions. If the user is enjoying the design, it will suggest even more enjoyable designs. This makes it possible to propose designs that elicit positive emotions in users.

[0087] The custom-made tool uses an emotion estimation function to provide real-time feedback on the user's emotions as they try on 3D clothing, making suggestions that elicit positive emotions. For example, as the user tries on 3D clothing, the generative AI provides real-time feedback on their emotions. If the user is enjoying themselves, the tool will provide an even more enjoyable fitting experience. This makes it possible to make suggestions that elicit positive emotions from the user.

[0088] The custom-made tool uses emotion estimation functionality to analyze the emotions a user feels when placing an order, and can provide an ordering experience that elicits positive emotions. For example, when a user places an order, the generative AI analyzes their emotions and provides an ordering experience that elicits positive emotions. If the user is enjoying themselves, an even more enjoyable ordering experience is provided. This makes it possible to provide an ordering experience that elicits positive emotions in the user.

[0089] The custom-made tool uses emotion estimation functionality to provide real-time feedback on the emotions users feel when placing an order, making suggestions that elicit positive emotions. For example, when a user places an order, the generative AI provides real-time feedback on their emotions. If the user is enjoying themselves, the AI ​​can provide an even more enjoyable ordering experience. This makes it possible to make suggestions that elicit positive emotions from the user.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The body data input unit inputs the user's body data, such as height, weight, chest circumference, waist circumference, and hip circumference. The body data input unit can also use generative AI to predict changes in body shape in real time and suggest clothing that will fit the user's future body shape. Step 2: The design customization unit customizes the clothing design based on the body data entered by the body data input unit. For example, the user can select the color, pattern, material, style, etc. The design customization unit can also use generation AI to analyze the user's past fashion history and suggest designs based on their preferences. Step 3: The 3D fitting unit generates a 3D model based on the clothing design customized by the design customization unit and allows the user to try it on. For example, the user can rotate the 3D model on the screen and zoom in and out to check how the clothing will look from various angles. The 3D fitting unit can also use generative AI to provide a real-time 3D fitting simulation that responds to the user's movements, allowing the user to check the fit according to their movements.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0159] 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. a body type data input unit for inputting body type data of a user; a design customization unit that customizes a design of clothing based on the body type data input by the body type data input unit; a 3D fitting unit that generates a 3D model based on the clothing design customized by the design customization unit and allows the user to try on the clothing. A system characterized by:

2. The body type data input unit When the user inputs their body shape data, the generative AI is used to predict changes in body shape in real time and suggest clothes that fit their future body shape. The system of claim 1 .

3. The design customization unit The generative AI is used to analyze the user's past fashion history and suggest designs based on their preferences. The system of claim 1 .

4. The 3D fitting section includes: Providing a real-time 3D fitting simulation that responds to the user's movements, allowing the user to check the fit according to their movements The system of claim 1 .

5. The custom-made ordering department The generation AI is used to analyze the user's past order history and suggest repeat orders and related products. The system of claim 1 .

6. The body type data input unit Using an emotion estimation function, the emotion of the user when entering body type data is analyzed, and an interface for reducing stress is provided. The system of claim 1 .

7. The design customization unit Using emotion estimation function, the emotions of the user when customizing the design are analyzed, and a design proposal that elicits positive emotions is made. The system of claim 1 .

8. The 3D fitting section includes: Using the emotion estimation function, the emotions of the user when trying on 3D clothing are analyzed, and a fitting experience that elicits positive emotions is provided. The system of claim 1 .

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