System

Abstract

An object of the system according to the embodiment is to generate and realize an original nail design according to a user's desire.SOLUTION: A system according to an embodiment includes an image generator, a 3D model generator, and a nail printer. The image generation unit generates an image of a nail design desired by a user. The 3D model generation unit generates a 3D model based on the image of the nail design generated by the image generation unit. The nail printer performs coating on a nail based on the 3D model generated by the 3D model generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have limited the variety of nail designs available, making it difficult to create original designs that meet the user's needs.

[0005] The system according to the embodiment aims to generate and realize an original nail design according to the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation unit, a 3D model generation unit, and a nail printer. The image generation unit generates an image of a nail design desired by a user. The 3D model generation unit generates a 3D model based on the image of the nail design generated by the image generation unit. The nail printer applies a paint to a nail based on the 3D model generated by the 3D model generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and realize an original nail design according to the user's desire. [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 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 nail design system according to an embodiment of the present invention is a system that allows users to paint images created using image generation AI with a 3D nail printer. This system allows users to input their desired nail design into the image generation AI, which then generates a 3D model based on the image, creating an original nail design. This allows the nail design system to quickly create the original nail design desired by the user.

[0029] A nail design system according to an embodiment includes an image generation unit, a 3D model generation unit, and a nail printer. The image generation unit generates an image of a nail design desired by a user. For example, if a user requests a "floral nail design," the image generation unit generates an image of the floral nail design based on the user's request. The image generation unit can also generate images using technologies such as deep learning and GAN (generative artificial network). The 3D model generation unit generates a 3D model based on the image of the nail design generated by the image generation unit. For example, a 3D model of the floral nail design is created based on the generated image of the floral nail design. The 3D model generation unit can also generate the 3D model using technologies such as mesh generation and texture mapping. The nail printer applies the paint to the nails based on the 3D model generated by the 3D model generation unit. For example, the nail printer can apply the paint to one finger in approximately one minute. The nail printer can also apply the paint using technologies such as inkjet printing and UV curing. As a result, the nail design system according to an embodiment can quickly create an original nail design desired by a user.

[0030] The image generation unit can propose individually customized designs based on the user's past nail design history. For example, the image generation unit uses a generation AI to analyze the user's past nail design history and learn their preferences and trends. For example, it proposes the most suitable design for the user based on colors and patterns selected in the past. The image generation unit also references nail design images previously saved by the user and generates new designs based on them. For example, it proposes new variations similar to previous designs. The image generation unit also proposes customized designs tailored to specific events or seasons based on the user's past nail design history. For example, it generates a new Christmas design based on past Christmas designs. This makes it possible to propose designs based on the user's preferences and trends.

[0031] The image generation unit can automatically reflect themes according to the season or event. For example, the image generation unit's generation AI automatically recognizes themes according to the season or event and generates nail designs based on that. For example, in spring, it proposes a design with a cherry blossom motif. Also, when the user specifies a specific event, the image generation unit's generation AI generates a design that matches that event. For example, for Halloween, it proposes designs with pumpkin or ghost motifs. Also, the image generation unit's generation AI references calendar information and automatically generates designs according to the season or event. For example, in summer, it proposes designs with an ocean or beach theme. This makes it possible to automatically generate designs according to the season or event.

[0032] The image generation unit can suggest coordinating the generated nail design image with other fashion items. For example, when the generation AI generates a nail design image, the image generation unit references images of the user's bag or shoes and suggests a design that matches them. For example, it generates a nail design that matches the color and design of the bag. In addition, in order to coordinate with a fashion item specified by the user, the image generation unit analyzes the characteristics of the item and generates a nail design based on that. For example, it suggests a nail design that matches the design of the shoes. In addition, the image generation unit allows the generation AI to learn the user's fashion style and generate a nail design based on that. For example, it suggests a simple design for a casual style and an elegant design for a formal style. This makes it possible to suggest coordinating the nail design with other fashion items.

[0033] The image generation unit can fuse traditional designs from different cultures or regions. For example, the image generation unit allows the generation AI to learn traditional designs from different cultures and regions and incorporate them into nail designs. For example, it generates a nail design that fuses Japanese patterns with Indian mehendi designs. The image generation unit also generates nail designs based on design elements from a culture or region specified by the user. For example, it proposes nail designs that incorporate traditional African patterns or classical European designs. The image generation unit also allows the generation AI to combine designs from different cultures and regions to generate new nail designs. For example, it proposes a design that fuses traditional Chinese floral patterns with American pop art. This makes it possible to generate nail designs that incorporate designs from different cultures and regions.

[0034] The 3D model generation unit can automatically adapt to the shape or size of a user's fingers. For example, the generation AI of the 3D model generation unit scans the shape and size of a user's fingers and generates a 3D model based on that. For example, it creates a nail design that matches the length and width of the fingers. When the user inputs the shape and size of the fingers, the generation AI automatically adjusts the 3D model based on that data. For example, it adjusts the width of the design to match the thickness of the fingers. The 3D model generation unit also learns the shape and size of the user's fingers and generates an optimal 3D model based on that. For example, it creates a design that matches the curve of the fingers. This makes it possible to generate a 3D model that is adapted to the shape and size of the user's fingers.

[0035] The 3D model generation unit can simulate light reflection or shadow effects in real time. For example, the 3D model generation unit adds a function that allows the generation AI to simulate light reflection and shadow effects to the 3D model in real time. For example, it realistically reproduces the gloss and shading of a nail design. The 3D model generation unit also adds a function that allows the user to adjust the angle and intensity of light when viewing the 3D model. For example, it simulates how the design appears under different lighting conditions. The 3D model generation unit also applies light reflection and shadow effects to the 3D model in real time using the generation AI, allowing the user to check the details of the design. For example, it simulates color changes due to light reflection. This allows light reflection and shadow effects to be simulated in real time.

[0036] The 3D model generation unit can add a function that allows a user to virtually try on the generated 3D model using augmented reality technology. The 3D model generation unit adds a function that allows a user to virtually try on a 3D model generated by the generation AI using augmented reality (AR) technology. For example, a user can try on a nail design using a smartphone camera. The 3D model generation unit also provides a function that allows a user to check how the design looks in real time when trying on the 3D model using AR technology. For example, the design can be displayed superimposed on a finger. The 3D model generation unit also allows a user to try on the 3D model generated by the generation AI using AR technology, allowing the user to check the details of the design. For example, the user can check the design from different angles. This allows the generated 3D model to be virtually tried on using augmented reality technology.

[0037] Nail printers can be equipped with sensors that track the movements of the user's fingers, allowing them to respond to even the slightest movements during application. Nail printers can be equipped with sensors that track the movements of the user's fingers, allowing them to respond to even the slightest movements during application. For example, they can adjust the design so that it does not shift even if the finger moves slightly. Nail printers can also track the movements of the user's fingers in real time, allowing the design to be applied accurately even if there is movement during application. For example, they can constantly monitor the position of the finger and adjust the operation of the printer according to the movement. Nail printers can also develop systems that use sensors that track the movements of the fingers to allow them to respond to even the slightest movements during application. For example, they can add a function that automatically corrects the application position when the finger moves. This allows them to respond to even the slightest movements during application.

[0038] Nail printers can be equipped with a function to automatically mix and apply different materials. For example, a nail printer can be equipped with a function to automatically mix and apply different materials to combine glitter or holograms with a design selected by the user. For example, glitter can be added to part of the design. Nail printers can also automatically mix and apply different materials to a design selected by the user. For example, holograms can be used to add three-dimensionality to the design. Nail printers can also be equipped with a function to automatically mix and apply different materials to add a variety of textures to a design selected by the user. For example, a design combining glitter and holograms can be applied. This allows different materials to be automatically mixed and applied.

[0039] Nail printers can be linked with other beauty devices to provide comprehensive beauty services. For example, nail printers can be linked with other beauty devices to build a system that provides comprehensive beauty services. For example, they can be linked with a face mask printer to provide nail and face care at the same time. Nail printers can also be linked with other beauty devices so that users can use them in conjunction with other beauty devices. For example, they can be linked with a hair care device to provide total beauty care. Nail printers can also be linked with other beauty devices to develop a system that allows users to receive multiple beauty services at once. For example, they can be linked with a skin care device to provide nail and skin care at the same time. This allows them to be linked with other beauty devices to provide comprehensive beauty services.

[0040] Nail printers can be equipped with a function that allows multiple different designs to be applied simultaneously. For example, a different design can be applied to each finger. Furthermore, nail printers can be developed that allow multiple designs selected by the user to be applied at once. For example, different designs can be applied to each hand at the same time. Furthermore, nail printers can be provided that have a function that allows multiple different designs to be applied at once, allowing users to enjoy a variety of designs at once. For example, designs with different themes can be applied to each finger. This allows multiple different designs to be applied at once.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The image generation unit can predict and suggest nail design trends based on the user's preferences and past selections. For example, it can analyze past design history and learn the user's preferred colors and patterns. The image generation unit can also refer to external data such as social media and fashion magazines to suggest designs incorporating the latest trends. Furthermore, if the user desires a design tailored to a specific event or season, the image generation unit can generate a design based on that trend. This allows the user to always enjoy nail designs that are in line with the latest trends.

[0043] The image generation unit can suggest appropriate nail designs based on the user's lifestyle and occupation. For example, it can suggest simple and elegant designs to office workers and colorful and original designs to artists. The image generation unit can also learn the user's occupation and daily activities to generate designs that suit the user's lifestyle. Furthermore, if the user is participating in a specific event or activity, the image generation unit can suggest designs that suit that situation. This allows the user to enjoy nail designs that suit their lifestyle and occupation.

[0044] The image generation unit can suggest nail designs based on the user's health condition. For example, if the user has allergies, it can suggest designs that take the allergies into consideration. The image generation unit can also analyze the user's health condition and generate health-conscious designs based on the results. Furthermore, if the user has a specific health condition, the image generation unit can suggest designs that match that condition and provide a design that the user feels is healthiest. This allows the user to enjoy nail designs that suit their health condition.

[0045] The image generation unit can suggest nail designs based on the user's hobbies and interests. For example, if the user likes music, it can suggest designs related to music. The image generation unit can also analyze the user's hobbies and interests and generate designs that match the hobbies based on the results. Furthermore, if the user has a specific hobby or interest, the image generation unit can suggest designs that match the hobby or interest, providing the user with a design that they will enjoy most. This allows the user to enjoy nail designs that match their hobbies and interests.

[0046] The 3D model generation unit can track the user's finger movements in real time and automatically adjust the 3D model according to those movements. For example, when the user moves their finger, the shape of the 3D model is adjusted to match that movement. The 3D model generation unit can also analyze the user's finger movements and generate an optimal 3D model based on the results. Furthermore, when the user moves their finger, the 3D model generation unit automatically adjusts the design of the 3D model according to that movement, providing the user with a design that is most satisfying. This allows the user to enjoy a 3D model that responds to their finger movements.

[0047] The 3D model generation unit can propose optimal nail designs based on the shape and size of the user's fingers. For example, it can generate designs tailored to the length and width of the fingers. The 3D model generation unit can also analyze the shape and size of the user's fingers and generate optimal designs based on the results. Furthermore, if the user requests a design tailored to the shape and size of their fingers, the 3D model generation unit can automatically adjust the design and provide the user with the design that best satisfies them. This allows the user to enjoy nail designs tailored to the shape and size of their fingers.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The image generation unit generates an image of the nail design desired by the user. For example, if the user requests a "floral nail design," the image generation unit generates an image of a floral nail design based on that instruction. The image generation unit can also generate images using technologies such as deep learning and GAN (generative artificial network). Step 2: The 3D model generation unit generates a 3D model based on the nail design image generated by the image generation unit. For example, a 3D model of a floral pattern is created based on the generated image of a floral nail design. The 3D model generation unit can also generate the 3D model using techniques such as mesh generation and texture mapping. Step 3: The nail printer applies the design to the nail based on the 3D model generated by the 3D model generator. For example, the nail printer can apply the design to one finger in about one minute. The nail printer can also apply the design using inkjet or UV curing techniques.

[0050] (Example 2) The nail design system according to an embodiment of the present invention is a system that allows users to paint images created using image generation AI with a 3D nail printer. This system allows users to input their desired nail design into the image generation AI, which then generates a 3D model based on the image, creating an original nail design. This allows the nail design system to quickly create the original nail design desired by the user.

[0051] A nail design system according to an embodiment includes an image generation unit, a 3D model generation unit, and a nail printer. The image generation unit generates an image of a nail design desired by a user. For example, if a user requests a "floral nail design," the image generation unit generates an image of the floral nail design based on the user's request. The image generation unit can also generate images using technologies such as deep learning and GAN (generative artificial network). The 3D model generation unit generates a 3D model based on the image of the nail design generated by the image generation unit. For example, a 3D model of the floral nail design is created based on the generated image of the floral nail design. The 3D model generation unit can also generate the 3D model using technologies such as mesh generation and texture mapping. The nail printer applies the paint to the nails based on the 3D model generated by the 3D model generation unit. For example, the nail printer can apply the paint to one finger in approximately one minute. The nail printer can also apply the paint using technologies such as inkjet printing and UV curing. As a result, the nail design system according to an embodiment can quickly create an original nail design desired by a user.

[0052] The image generation unit can propose individually customized designs based on the user's past nail design history. For example, the image generation unit uses a generation AI to analyze the user's past nail design history and learn their preferences and trends. For example, it proposes the most suitable design for the user based on colors and patterns selected in the past. The image generation unit also references nail design images previously saved by the user and generates new designs based on them. For example, it proposes new variations similar to previous designs. The image generation unit also proposes customized designs tailored to specific events or seasons based on the user's past nail design history. For example, it generates a new Christmas design based on past Christmas designs. This makes it possible to propose designs based on the user's preferences and trends.

[0053] The image generation unit can automatically reflect themes according to the season or event. For example, the image generation unit's generation AI automatically recognizes themes according to the season or event and generates nail designs based on that. For example, in spring, it proposes a design with a cherry blossom motif. Also, when the user specifies a specific event, the image generation unit's generation AI generates a design that matches that event. For example, for Halloween, it proposes designs with pumpkin or ghost motifs. Also, the image generation unit's generation AI references calendar information and automatically generates designs according to the season or event. For example, in summer, it proposes designs with an ocean or beach theme. This makes it possible to automatically generate designs according to the season or event.

[0054] The image generation unit can use the emotion estimation function to generate a nail design based on the user's current emotional state. For example, the image generation unit uses the emotion estimation function to analyze the user's current emotional state and generate a nail design based on that. For example, if the user is relaxed, a design with gentle colors is suggested. The image generation unit also analyzes the user's emotional state in real time and generates a design based on the results. For example, if the user is excited, a bright color or bold pattern is suggested. The image generation unit also uses the emotion estimation function to generate a design that matches the user's emotions and suggests a design that will most satisfy the user. For example, if the user is sad, an uplifting and cheerful design is suggested. In this way, a design can be generated that matches the user's emotions.

[0055] The image generation unit can suggest coordinating the generated nail design image with other fashion items. For example, when the generation AI generates a nail design image, the image generation unit references images of the user's bag or shoes and suggests a design that matches them. For example, it generates a nail design that matches the color and design of the bag. In addition, in order to coordinate with a fashion item specified by the user, the image generation unit analyzes the characteristics of the item and generates a nail design based on that. For example, it suggests a nail design that matches the design of the shoes. In addition, the image generation unit allows the generation AI to learn the user's fashion style and generate a nail design based on that. For example, it suggests a simple design for a casual style and an elegant design for a formal style. This makes it possible to suggest coordinating the nail design with other fashion items.

[0056] The image generation unit can fuse traditional designs from different cultures or regions. For example, the image generation unit allows the generation AI to learn traditional designs from different cultures and regions and incorporate them into nail designs. For example, it generates a nail design that fuses Japanese patterns with Indian mehendi designs. The image generation unit also generates nail designs based on design elements from a culture or region specified by the user. For example, it proposes nail designs that incorporate traditional African patterns or classical European designs. The image generation unit also allows the generation AI to combine designs from different cultures and regions to generate new nail designs. For example, it proposes a design that fuses traditional Chinese floral patterns with American pop art. This makes it possible to generate nail designs that incorporate designs from different cultures and regions.

[0057] The image generation unit can add a function to use the emotion estimation function to generate a design that evokes the most positive emotion in the user and share it with other users. For example, the image generation unit adds a function to use the emotion estimation function to generate a design that evokes the most positive emotion in the user and share that design with other users. For example, designs with high emotion scores are shared on social media. The image generation unit also provides a platform where the generative AI generates optimal designs based on the user's emotional response and shares those designs. For example, designs that evoke positive emotions are shared with a community. The image generation unit also develops a function to generate a design that the user is most satisfied with based on the emotion estimation data and share that design with other users. For example, designs with high emotion scores are displayed in a ranking format. This allows the user to generate and share designs that evoke the most positive emotion.

[0058] The 3D model generation unit can automatically adapt to the shape or size of a user's fingers. For example, the generation AI of the 3D model generation unit scans the shape and size of a user's fingers and generates a 3D model based on that. For example, it creates a nail design that matches the length and width of the fingers. When the user inputs the shape and size of the fingers, the generation AI automatically adjusts the 3D model based on that data. For example, it adjusts the width of the design to match the thickness of the fingers. The 3D model generation unit also learns the shape and size of the user's fingers and generates an optimal 3D model based on that. For example, it creates a design that matches the curve of the fingers. This makes it possible to generate a 3D model that is adapted to the shape and size of the user's fingers.

[0059] The 3D model generation unit can simulate light reflection or shadow effects in real time. For example, the 3D model generation unit adds a function that allows the generation AI to simulate light reflection and shadow effects to the 3D model in real time. For example, it realistically reproduces the gloss and shading of a nail design. The 3D model generation unit also adds a function that allows the user to adjust the angle and intensity of light when viewing the 3D model. For example, it simulates how the design appears under different lighting conditions. The 3D model generation unit also applies light reflection and shadow effects to the 3D model in real time using the generation AI, allowing the user to check the details of the design. For example, it simulates color changes due to light reflection. This allows light reflection and shadow effects to be simulated in real time.

[0060] The 3D model generation unit can use the emotion estimation function to adjust the color tone and texture of the 3D model based on the user's emotion. For example, the 3D model generation unit uses the emotion estimation function to adjust the color tone and texture of the 3D model based on the user's emotion. For example, if the user is relaxed, soft colors and textures are suggested. The 3D model generation unit also analyzes the user's emotional state in real time and adjusts the color tone and texture of the 3D model based on the results. For example, if the user is excited, bright colors and glossy textures are suggested. The 3D model generation unit also uses the emotion estimation function to generate color tone and texture of the 3D model that matches the user's emotion and proposes a design that will most satisfy the user. For example, if the user is sad, bright colors that will cheer the user up are suggested. This makes it possible to adjust the color tone and texture according to the user's emotion.

[0061] The 3D model generation unit can add a function that allows a user to virtually try on the generated 3D model using augmented reality technology. The 3D model generation unit adds a function that allows a user to virtually try on a 3D model generated by the generation AI using augmented reality (AR) technology. For example, a user can try on a nail design using a smartphone camera. The 3D model generation unit also provides a function that allows a user to check how the design looks in real time when trying on the 3D model using AR technology. For example, the design can be displayed superimposed on a finger. The 3D model generation unit also allows a user to try on the 3D model generated by the generation AI using AR technology, allowing the user to check the details of the design. For example, the user can check the design from different angles. This allows the generated 3D model to be virtually tried on using augmented reality technology.

[0062] The 3D model generation unit can add a function to use the emotion estimation function to generate a 3D model that evokes the most positive emotion in the user and share it with other users. For example, the 3D model generation unit adds a function to use the emotion estimation function to generate a 3D model that evokes the most positive emotion in the user and share the model with other users. For example, models with high emotion scores are shared on social media. The 3D model generation unit also provides a platform in which the generation AI generates optimal 3D models based on the user's emotional response and shares the models. For example, models that evoke positive emotions are shared with the community. The 3D model generation unit also develops a function to generate a 3D model that evokes the most positive emotion in the user based on emotion estimation data and share the model with other users. For example, models with high emotion scores are displayed in a ranking format. This allows the generation and sharing of 3D models that evoke the most positive emotion in the user.

[0063] Nail printers can be equipped with sensors that track the movements of the user's fingers, allowing them to respond to even the slightest movements during application. Nail printers can be equipped with sensors that track the movements of the user's fingers, allowing them to respond to even the slightest movements during application. For example, they can adjust the design so that it does not shift even if the finger moves slightly. Nail printers can also track the movements of the user's fingers in real time, allowing the design to be applied accurately even if there is movement during application. For example, they can constantly monitor the position of the finger and adjust the operation of the printer according to the movement. Nail printers can also develop systems that use sensors that track the movements of the fingers to allow them to respond to even the slightest movements during application. For example, they can add a function that automatically corrects the application position when the finger moves. This allows them to respond to even the slightest movements during application.

[0064] Nail printers can be equipped with a function to automatically mix and apply different materials. For example, a nail printer can be equipped with a function to automatically mix and apply different materials to combine glitter or holograms with a design selected by the user. For example, glitter can be added to part of the design. Nail printers can also automatically mix and apply different materials to a design selected by the user. For example, holograms can be used to add three-dimensionality to the design. Nail printers can also be equipped with a function to automatically mix and apply different materials to add a variety of textures to a design selected by the user. For example, a design combining glitter and holograms can be applied. This allows different materials to be automatically mixed and applied.

[0065] A nail printer can use an emotion estimation function to adjust the application speed and pressure based on the user's emotions. For example, a nail printer adds a function to adjust the application speed and pressure based on the user's emotions using the emotion estimation function. For example, if the user is relaxed, a slow application speed is set. The nail printer also analyzes the user's emotional state in real time and adjusts the application speed and pressure based on the results. For example, if the user is nervous, the application pressure is reduced. The nail printer also uses the emotion estimation function to set the application speed and pressure according to the user's emotions, providing the user with the most comfortable application experience. For example, if the user is excited, a fast application speed is set. This makes it possible to adjust the application speed and pressure based on the user's emotions.

[0066] Nail printers can be linked with other beauty devices to provide comprehensive beauty services. For example, nail printers can be linked with other beauty devices to build a system that provides comprehensive beauty services. For example, they can be linked with a face mask printer to provide nail and face care at the same time. Nail printers can also be linked with other beauty devices so that users can use them in conjunction with other beauty devices. For example, they can be linked with a hair care device to provide total beauty care. Nail printers can also be linked with other beauty devices to develop a system that allows users to receive multiple beauty services at once. For example, they can be linked with a skin care device to provide nail and skin care at the same time. This allows them to be linked with other beauty devices to provide comprehensive beauty services.

[0067] Nail printers can be equipped with a function that allows multiple different designs to be applied simultaneously. For example, a different design can be applied to each finger. Furthermore, nail printers can be developed that allow multiple designs selected by the user to be applied at once. For example, different designs can be applied to each hand at the same time. Furthermore, nail printers can be provided that have a function that allows multiple different designs to be applied at once, allowing users to enjoy a variety of designs at once. For example, designs with different themes can be applied to each finger. This allows multiple different designs to be applied at once.

[0068] The nail printer can add a function that uses the emotion estimation function to apply the design that evokes the most positive emotion for the user and share the result with other users. For example, the nail printer can add a function that uses the emotion estimation function to apply the design that evokes the most positive emotion for the user and share the result with other users. For example, designs with high emotion scores can be shared on social media. The nail printer can also apply the optimal design based on the user's emotional response and provide a platform for sharing the result. For example, designs that evoke positive emotions can be shared with a community. The nail printer can also develop a function that uses emotion estimation data to apply the design that the user is most satisfied with and share the result with other users. For example, designs with high emotion scores can be displayed in a ranking format. This allows the user to apply and share the design that evokes the most positive emotion.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The image generation unit can predict and suggest nail design trends based on the user's preferences and past selections. For example, it can analyze past design history and learn the user's preferred colors and patterns. The image generation unit can also refer to external data such as social media and fashion magazines to suggest designs incorporating the latest trends. Furthermore, if the user desires a design tailored to a specific event or season, the image generation unit can generate a design based on that trend. This allows the user to always enjoy nail designs that are in line with the latest trends.

[0071] The image generation unit can estimate the user's emotions and suggest designs with a relaxing effect based on those emotions. For example, if the user is feeling stressed, it can suggest designs that incorporate calming colors and natural motifs. The image generation unit can also analyze the user's emotional state in real time and generate designs with a relaxing effect based on the results. Furthermore, if the user feels like relaxing, the image generation unit can suggest designs that match that emotion and provide the user with a design that will help them relax the most. This allows users to enjoy nail designs with a relaxing effect that match their emotions.

[0072] The image generation unit can suggest appropriate nail designs based on the user's lifestyle and occupation. For example, it can suggest simple and elegant designs to office workers and colorful and original designs to artists. The image generation unit can also learn the user's occupation and daily activities to generate designs that suit the user's lifestyle. Furthermore, if the user is participating in a specific event or activity, the image generation unit can suggest designs that suit that situation. This allows the user to enjoy nail designs that suit their lifestyle and occupation.

[0073] The image generation unit can use the emotion estimation function to adjust the complexity of the design based on the user's emotions. For example, if the user is relaxed, it proposes a simple and calm design, and if the user is excited, it proposes a complex and colorful design. The image generation unit can also analyze the user's emotional state in real time and adjust the complexity of the design based on the results. Furthermore, if the user is feeling a specific emotion, the image generation unit can propose a design complexity that matches that emotion and provide the user with a design that is most satisfying. This allows the user to enjoy the complexity of the design according to their emotions.

[0074] The image generation unit can suggest nail designs based on the user's health condition. For example, if the user has allergies, it can suggest designs that take the allergies into consideration. The image generation unit can also analyze the user's health condition and generate health-conscious designs based on the results. Furthermore, if the user has a specific health condition, the image generation unit can suggest designs that match that condition and provide a design that the user feels is healthiest. This allows the user to enjoy nail designs that suit their health condition.

[0075] The image generation unit can use the emotion estimation function to adjust the color tone of the design based on the user's emotions. For example, if the user is relaxed, it will suggest calm colors, and if the user is excited, it will suggest vibrant colors. The image generation unit can also analyze the user's emotional state in real time and adjust the color tone of the design based on the results. Furthermore, if the user is feeling a specific emotion, the image generation unit can suggest colors that match that emotion and provide a design that will satisfy the user most. This allows the user to enjoy colors that match their emotions.

[0076] The image generation unit can suggest nail designs based on the user's hobbies and interests. For example, if the user likes music, it can suggest designs related to music. The image generation unit can also analyze the user's hobbies and interests and generate designs that match the hobbies based on the results. Furthermore, if the user has a specific hobby or interest, the image generation unit can suggest designs that match the hobby or interest, providing the user with a design that they will enjoy most. This allows the user to enjoy nail designs that match their hobbies and interests.

[0077] The 3D model generation unit can track the user's finger movements in real time and automatically adjust the 3D model according to those movements. For example, when the user moves their finger, the shape of the 3D model is adjusted to match that movement. The 3D model generation unit can also analyze the user's finger movements and generate an optimal 3D model based on the results. Furthermore, when the user moves their finger, the 3D model generation unit automatically adjusts the design of the 3D model according to that movement, providing the user with a design that is most satisfying. This allows the user to enjoy a 3D model that responds to their finger movements.

[0078] The 3D model generation unit can use the emotion estimation function to adjust the shape of the 3D model based on the user's emotions. For example, if the user is relaxed, it will suggest a softer shape, and if the user is excited, it will suggest a sharper shape. The 3D model generation unit can also analyze the user's emotional state in real time and adjust the shape of the 3D model based on the results. Furthermore, if the user is feeling a specific emotion, the 3D model generation unit can suggest a shape that matches that emotion, providing the user with a design that satisfies them most. This allows the user to enjoy shapes that correspond to their emotions.

[0079] The 3D model generation unit can propose optimal nail designs based on the shape and size of the user's fingers. For example, it can generate designs tailored to the length and width of the fingers. The 3D model generation unit can also analyze the shape and size of the user's fingers and generate optimal designs based on the results. Furthermore, if the user requests a design tailored to the shape and size of their fingers, the 3D model generation unit can automatically adjust the design and provide the user with the design that best satisfies them. This allows the user to enjoy nail designs tailored to the shape and size of their fingers.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The image generation unit generates an image of the nail design desired by the user. For example, if the user requests a "floral nail design," the image generation unit generates an image of a floral nail design based on that instruction. The image generation unit can also generate images using technologies such as deep learning and GAN (generative artificial network). Step 2: The 3D model generation unit generates a 3D model based on the nail design image generated by the image generation unit. For example, a 3D model of a floral pattern is created based on the generated image of a floral nail design. The 3D model generation unit can also generate the 3D model using techniques such as mesh generation and texture mapping. Step 3: The nail printer applies the design to the nail based on the 3D model generated by the 3D model generator. For example, the nail printer can apply the design to one finger in about one minute. The nail printer can also apply the design using inkjet or UV curing techniques.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image generation unit that generates an image of a nail design desired by a user; a 3D model generation unit that generates a 3D model based on the image of the nail design generated by the image generation unit; a nail printer that applies paint to nails based on the 3D model generated by the 3D model generation unit. A system characterized by:

2. The image generation unit Automatically reflect seasonal or event themes 2. The system of claim 1.

3. The image generation unit Blending traditional designs from different cultures or regions 2. The system of claim 1.

4. The 3D model generation unit Simulate light reflection or shadow effects in real time 2. The system of claim 1.

5. The 3D model generation unit Add a function that allows users to virtually try on the generated 3D models using augmented reality technology.

2. The system of claim 1.

6. The nail printer is Equipped with a sensor that tracks the user's finger movements, it can respond to even the smallest movements during application.

2. The system of claim 1.

7. The nail printer is Links with other beauty devices to provide comprehensive beauty services 2. The system of claim 1.

8. The image generation unit Generate nail designs based on the user's current emotional state 2. The system of claim 1.

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