System

Abstract

To provide a system for enabling a user to easily create and customize a unique virtual avatar.SOLUTION: The system comprises a facial feature generator, a customizer, a clothes generator, an accessory generator, a real-time customizer, and a digital identity generator. The facial feature generation unit generates a facial feature using the generation AI. The clothing generation unit generates clothing using the generation AI. The accessory generating unit generates an accessory using the generation AI. The customization unit customizes facial features, clothes, and accessories. The real-time customizing unit combines and customizes the facial features, clothes and accessories in real time. The digital identity generation unit combines the facial features, clothes and accessories customized by the real-time customization unit to generate a unique digital identity.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 technologies have had the problem of making it difficult for users to easily create and customize their own virtual avatars.

[0005] The system according to the embodiment aims to enable users to easily create and customize their own virtual avatars. [Means for solving the problem]

[0006] A system according to an embodiment includes a facial feature generation unit, a customization unit, an clothing generation unit, an accessory generation unit, a real-time customization unit, and a digital identity generation unit. The facial feature generation unit generates facial features using a generation AI. The customization unit customizes the facial features generated by the facial feature generation unit. The clothing generation unit generates clothing using the generation AI. The customization unit customizes the clothing generated by the clothing generation unit. The accessory generation unit generates accessories using the generation AI. The customization unit customizes the accessories generated by the accessory generation unit. The real-time customization unit customizes in real time by combining facial features, clothing, and accessories. The digital identity generation unit generates a unique digital identity by combining the facial features, clothing, and accessories customized by the real-time customization unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create and customize their own virtual avatars. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A virtual avatar generation system according to an embodiment of the present invention allows users to easily create and customize their own virtual avatar. The system uses generative AI to combine facial features, clothing, accessories, etc. to generate a unique digital identity in real time. This allows users to easily create and customize their own virtual avatar.

[0029] A virtual avatar generation system according to an embodiment includes a facial feature generation unit, an outfit generation unit, an accessory generation unit, a real-time customization unit, and a digital identity generation unit. The facial feature generation unit generates facial features using a generation AI. For example, the generation AI generates facial features using a text generation AI (e.g., LLM). The generation AI can also generate facial features using a multimodal generation AI. The generation AI also generates facial features based on prompts entered by a user. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information among facial features and generate facial features based on that information. The outfit generation unit generates outfits using the generation AI. For example, the generation AI generates outfits using a text generation AI (e.g., LLM). The generation AI can also generate outfits using a multimodal generation AI. The generation AI also generates outfits based on prompts entered by a user. For example, a text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. A multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information in an outfit and generate the outfit based on that information. The accessory generation unit generates accessories using the generation AI. For example, the generation AI generates accessories using a text generation AI (e.g., LLM). The generation AI can also generate accessories using a multimodal generation AI. The generation AI also generates accessories based on prompts entered by the user. For example, a text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. A multimodal generation AI can handle not only text but also multiple modalities, including images and audio. The generation AI uses keyword extraction technology to identify particularly important information in an accessory and generate the accessory based on that information.The real-time customization unit customizes the avatar in real time by combining facial features, clothing, and accessories. For example, the real-time customization unit instantly reflects the facial features, clothing, and accessories selected by the user on the avatar. The digital identity generation unit generates a unique digital identity by combining the facial features, clothing, and accessories customized by the real-time customization unit. For example, the digital identity generation unit generates a unique avatar that is different from other users based on the facial features, clothing, and accessories selected by the user. In this way, the virtual avatar generation system according to the embodiment allows users to easily create and customize their own virtual avatar. For example, when creating an avatar to use in games or on social media, a user can customize the avatar to their own preferences and have a unique digital identity that is different from other users.

[0030] The facial feature generation unit can analyze the user's past photos or videos, learn individual features, and generate more realistic facial features. For example, the facial feature generation unit analyzes past photos and videos uploaded by the user, and the generation AI learns facial features based on that data. For example, it learns the user's facial contours and eye shape. The facial feature generation unit also extracts the user's facial features from past photos and videos, and the generation AI generates a realistic face based on those features. For example, it reproduces the shape of the user's nose and mouth. The facial feature generation unit also analyzes the user's past photos and videos, and the generation AI customizes the facial features based on that data. For example, it reflects the user's hairstyle and skin color. This makes it possible to generate realistic facial features based on the user's past data.

[0031] The facial feature generation unit can provide customization options based on the user's cultural background or preferences. In the facial feature generation unit, for example, the generation AI customizes the facial features based on the user's cultural background. For example, it may reflect the traditional facial features of a particular region. In addition, the facial feature generation unit customizes the facial features based on the user's preferences. For example, it may incorporate the facial features of a celebrity the user likes. In addition, the facial feature generation unit provides customization options based on the user's cultural background and preferences, and the generation AI generates facial features based on those options. For example, it may allow the user to select traditional facial features of a particular ethnic group. This enables customization based on the user's cultural background and preferences.

[0032] The facial feature generation unit can provide an option to generate the face of an animal or fantasy character. For example, the facial feature generation unit trains the generation AI to learn the facial features of animals and provides an option for the user to select an animal face. For example, the facial feature generation unit generates the face of a cat or a dog. The facial feature generation unit also trains the generation AI to learn the facial features of fantasy characters and provides an option for the user to select a fantasy character face. For example, the facial feature generation unit generates the face of an elf or a dragon. The facial feature generation unit also trains the generation AI to learn the facial features of animals or fantasy characters and provides an option for the user to customize the face based on the features. For example, the facial feature generation unit generates the face of a unicorn or a phoenix. This provides an option to generate the face of an animal or fantasy character.

[0033] The facial feature generation unit can provide a function for exchanging facial features with other users and collaborating to generate a new face. For example, the facial feature generation unit allows a user to exchange facial features with other users, and the generation AI generates a new face based on those features. For example, friends exchange facial features to create a new face. The facial feature generation unit also provides a collaboration function, allowing multiple users to customize facial features together. For example, a new face is generated by combining facial features within a family or group. The facial feature generation unit also provides a function for exchanging facial features with other users, and the generation AI generates a new face based on those features. For example, a user creates a new face by incorporating facial features of a celebrity. This provides a function for exchanging facial features with other users and collaborating to generate a new face.

[0034] The clothing generation unit can suggest clothing according to the season or weather and reflect it on the avatar in real time. For example, the generation AI of the clothing generation unit analyzes seasonal and weather data and suggests clothing based on that data. For example, in winter, it suggests warm coats and sweaters. The clothing generation unit also suggests clothing in real time based on weather data of the user's current location. For example, it suggests raincoats and umbrellas on rainy days. The clothing generation unit also suggests clothing according to the season and weather, and the generation AI reflects the suggestions on the avatar in real time. For example, it suggests light clothing and sunglasses in summer. This makes it possible to suggest clothing in real time according to the season and weather.

[0035] The clothing generation unit can provide an option to select historical costumes or futuristic designs. For example, the clothing generation unit trains the generation AI with historical costume data, providing an option for the user to select historical costumes. For example, it generates medieval dresses and armor. The clothing generation unit also trains the generation AI with futuristic clothing designs, providing an option for the user to select futuristic designs. For example, it generates cyberpunk-style clothing. The clothing generation unit also trains the generation AI with historical costumes and futuristic designs, providing an option for the user to customize clothing based on the characteristics of the designs. For example, it generates Victorian dresses and space suits. This provides an option to select historical costumes or futuristic designs.

[0036] The clothing generation unit can provide a function for exchanging clothing with other users and collaborating to generate a new style. For example, the clothing generation unit allows a user to exchange clothing with other users, and the generation AI generates a new style based on that clothing. For example, friends exchange clothing to create a new style. The clothing generation unit also provides a collaboration function, allowing multiple users to customize clothing together. For example, a family or group can combine clothing to generate a new style. The clothing generation unit also provides a function for exchanging clothing with other users, and the generation AI generates a new style based on that clothing. For example, a user creates a new style by incorporating clothing worn by a celebrity. This provides a function for exchanging clothing with other users and collaborating to generate a new style.

[0037] The accessory generation unit can analyze the user's past accessory history and generate accessories based on individual preferences. For example, the accessory generation unit analyzes data on accessories selected by the user in the past, and the generation AI generates accessories that suit individual preferences based on that data. For example, the user's preferred colors and designs are reflected. The accessory generation unit also suggests accessories that suit the user's preferences based on the user's past accessory history. For example, the generation AI reflects the user's frequently selected brands and styles. The accessory generation unit also analyzes the user's past accessory history, and the generation AI customizes accessories based on that data. For example, the generation AI reflects the accessories the user prefers for specific events. This makes it possible to generate accessories that suit the user's preferences based on the user's past data.

[0038] The accessory generation unit can suggest accessories that match an event or season and reflect them on the avatar in real time. For example, the generation AI of the accessory generation unit analyzes specific event or season data and suggests accessories based on that data. For example, at Christmas, it would suggest Christmas tree earrings and necklaces. The accessory generation unit also suggests accessories in real time based on event data of the user's current location. For example, it would suggest Halloween-themed accessories for Halloween. The accessory generation unit also suggests accessories that match a specific event or season, and the generation AI reflects the suggestions on the avatar in real time. For example, it would suggest beach-themed accessories in summer. This makes it possible to suggest accessories in real time according to specific events or seasons.

[0039] The accessory generation unit can provide an option to generate accessories based on custom designs or images uploaded by the user. For example, the accessory generation unit provides an option for the user to generate accessories based on custom designs by having the generation AI learn custom design data. For example, an accessory is generated based on a design drawn by the user. The accessory generation unit also provides an option for the generation AI to generate accessories based on images uploaded by the user. For example, an accessory is generated based on a photo taken by the user. The accessory generation unit also provides an option for the generation AI to customize accessories based on custom designs or uploaded images. For example, an accessory is generated based on a work of art that the user likes. This makes it possible to generate accessories based on custom designs or uploaded images.

[0040] The accessory generation unit can provide a function for exchanging accessories with other users and collaborating to generate new designs. For example, the accessory generation unit allows a user to exchange accessories with other users, and the generation AI generates a new design based on those accessories. For example, friends exchange accessories to create a new design. The accessory generation unit also provides a collaboration function, allowing multiple users to customize accessories together. For example, a family or group can combine accessories to generate a new design. The accessory generation unit also provides a function for exchanging accessories with other users, and the generation AI generates a new design based on those accessories. For example, a user creates a new design incorporating accessories from a celebrity. This provides a function for exchanging accessories with other users and collaborating to generate a new design.

[0041] The real-time customization unit performs real-time customization in response to the user's movements or gestures, enabling the generation AI to provide a more interactive avatar. For example, the real-time customization unit analyzes the user's movements and gestures, and the generation AI performs real-time customization in response to those movements. For example, when the user waves, the avatar also waves. The real-time customization unit also uses movement recognition technology to reflect changes in the avatar in response to the user's gestures in real time. For example, when the user smiles, the avatar also smiles. The real-time customization unit also collects user movement data, and the generation AI performs real-time customization based on that data. For example, when the user walks, the avatar also walks in the same way. This makes it possible to provide an interactive avatar in response to the user's movements and gestures.

[0042] The real-time customization unit can provide a collaboration function that allows multiple users to customize avatars simultaneously. For example, the real-time customization unit provides a collaboration function that allows multiple users to customize avatars simultaneously, and the generation AI reflects the customization in real time. For example, friends customize an avatar together. The real-time customization unit also provides a collaboration function that allows multiple users to customize avatars together. For example, families or groups customize avatars. The real-time customization unit also provides a collaboration function that allows multiple users to customize avatars simultaneously, and the generation AI reflects the customization in real time. For example, users customize a celebrity's avatar together. This provides a collaboration function that allows multiple users to customize avatars simultaneously.

[0043] The real-time customization unit can provide a preset function that saves the results of real-time customization and allows for later reuse. The real-time customization unit, for example, saves the results of real-time customization performed by the user and allows for later reuse. For example, it saves customization of an avatar created by the user. The real-time customization unit also provides a preset function that allows the user to easily reuse the customization results saved by the user. For example, it reuses combinations of clothing and accessories saved by the user. The real-time customization unit also saves the results of real-time customization, and the generation AI proposes new customization based on the results. For example, it proposes a new style based on the customization results saved by the user. This provides a preset function that saves the results of real-time customization and allows for later reuse.

[0044] The digital identity generation unit can analyze a user's past digital identity history and generate a unique identity based on their individual preferences. For example, the digital identity generation unit analyzes digital identity data selected by the user in the past, and the generation AI generates a unique identity that suits their individual preferences based on that data. For example, it may reflect the user's favorite colors and styles. The digital identity generation unit also suggests a unique identity that suits the user's preferences based on the user's past digital identity history. For example, it may reflect the user's frequently selected designs and themes. The digital identity generation unit also analyzes the user's past digital identity history, and the generation AI customizes the unique identity based on that data. For example, it may reflect the user's preferred identity for a particular event. This allows a unique identity to be generated based on the user's past digital identity history.

[0045] The digital identity generation unit can propose a unique digital identity based on the user's social background or interests. In the digital identity generation unit, for example, the generation AI proposes a unique digital identity based on the user's social background. For example, it may reflect a design related to a particular region or culture. In addition, the digital identity generation unit proposes a unique digital identity based on the user's interests and hobbies. For example, it may reflect a design related to the user's favorite sports or music. In addition, the digital identity generation unit provides customization options based on the user's social background and interests, and the generation AI generates a unique digital identity based on those options. For example, it may allow the user to select a design related to a particular occupation or lifestyle. This makes it possible to propose a unique digital identity based on the user's social background and interests.

[0046] The digital identity generation unit can provide an option to generate an identity that incorporates elements from different cultures or regions. For example, the digital identity generation unit can train the generation AI on design data from different cultures or regions, allowing users to generate an identity that incorporates those elements. For example, a design that reflects African prints or traditional Asian patterns. The digital identity generation unit can also train the generation AI to create an identity that incorporates elements from different cultures or regions, allowing users to customize the identity based on those features. For example, a European medieval-style design or a South American carnival-style design can be generated. The digital identity generation unit can also train the generation AI on elements from different cultures or regions, allowing users to generate a unique digital identity that incorporates those elements. For example, a design that reflects traditional Indian designs or minimalist Scandinavian designs. This provides an option to generate an identity that incorporates elements from different cultures or regions.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The virtual avatar generation system may further include a voice generation unit. The voice generation unit generates the user's voice using a generation AI. For example, the generation AI generates natural-sounding voices based on text entered by the user. The voice generation unit may also learn the characteristics of the user's voice and imitate the user's voice in real time. For example, when the user speaks, the voice may be captured in real time, and the generation AI may generate voices based on the captured voices. The voice generation unit may also adjust the tone and pitch of the voice according to the user's emotional state. For example, if the user is excited, the generation AI may generate an excited voice. This allows the virtual avatar to reflect the user's voice in real time, providing a more interactive experience.

[0049] The virtual avatar generation system can further include a gesture recognition unit. The gesture recognition unit captures the user's movements and gestures in real time, and the generation AI generates avatar movements based on those movements. For example, when the user waves their hand, the avatar waves in the same way. The gesture recognition unit can also analyze the user's facial expressions and body movements, and the generation AI can reflect those movements in real time. For example, when the user smiles, the avatar also smiles. The gesture recognition unit can also collect user movement data, and the generation AI can customize the avatar's movements based on that data. For example, when the user dances, the avatar dances in the same way. This makes it possible to generate avatars in real time according to the user's movements and gestures.

[0050] The virtual avatar generation system may further include an environment recognition unit. The environment recognition unit captures the user's surrounding environment, and the generation AI generates the avatar's background and settings based on that environment. For example, if the user is outdoors, the generation AI sets the background to a natural landscape. The environment recognition unit may also analyze the user's location information, and the generation AI may customize the avatar's background based on that location. For example, if the user is in a city, the generation AI sets the background to a city landscape. The environment recognition unit may also collect information about the sound and light around the user, and the generation AI may adjust the avatar's settings based on that information. For example, if the user is at night, the generation AI sets the background to a night landscape. This enables real-time avatar generation according to the user's environment.

[0051] The virtual avatar generation system can further include a health condition recognition unit. The health condition recognition unit collects the user's health data, and the generation AI adjusts the avatar's appearance and behavior based on that data. For example, if the user is tired, the generation AI makes the avatar's facial expression look tired. The health condition recognition unit can also analyze the user's vital data, such as heart rate and body temperature, and the generation AI can adjust the avatar's behavior based on that data. For example, if the user is relaxed, the generation AI can slow down the avatar's movements. The health condition recognition unit can also allow the generation AI to customize the avatar's appearance based on the user's health data. For example, if the user is healthy, the generation AI will make the avatar's appearance look healthy. This enables real-time avatar generation according to the user's health condition.

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

[0053] Step 1: The facial feature generation unit generates facial features using a generation AI. For example, the generation AI may use a text generation AI (e.g., LLM) or a multimodal generation AI to generate facial features based on prompts entered by the user. The generation AI may use keyword extraction technology to identify particularly important information among facial features and generate facial features based on that information. Step 2: The customization unit customizes the facial features generated by the facial feature generation unit, allowing the user to adjust the generated facial features to suit their preferences. Step 3: The outfit generation unit generates outfits using a generation AI. For example, the generation AI may use a text generation AI (e.g., LLM) or a multimodal generation AI to generate outfits based on prompts entered by the user. The generation AI may use keyword extraction technology to identify particularly important information in the outfits and generate outfits based on that information. Step 4: The customization unit customizes the outfit generated by the outfit generation unit, allowing the user to customize the outfit to suit their own preferences. Step 5: The accessory generation unit generates the accessory using a generation AI. For example, the generation AI may use a text generation AI (e.g., LLM) or a multimodal generation AI to generate the accessory based on the prompt entered by the user. The generation AI may use keyword extraction technology to identify particularly important information in the accessory and generate the accessory based on that information. Step 6: The customization unit customizes the accessory generated by the accessory generation unit, allowing the user to adjust the generated accessory to suit their preferences. Step 7: The real-time customization unit customizes the avatar in real time by combining facial features, clothing, and accessories. For example, the real-time customization unit instantly reflects the facial features, clothing, and accessories selected by the user on the avatar. Step 8: The digital identity generator generates a unique digital identity by combining the facial features, clothing, and accessories customized by the real-time customization unit. For example, the digital identity generator generates a unique avatar that is different from other users based on the facial features, clothing, and accessories selected by the user.

[0054] (Example 2) A virtual avatar generation system according to an embodiment of the present invention allows users to easily create and customize their own virtual avatar. The system uses generative AI to combine facial features, clothing, accessories, etc. to generate a unique digital identity in real time. This allows users to easily create and customize their own virtual avatar.

[0055] A virtual avatar generation system according to an embodiment includes a facial feature generation unit, an outfit generation unit, an accessory generation unit, a real-time customization unit, and a digital identity generation unit. The facial feature generation unit generates facial features using a generation AI. For example, the generation AI generates facial features using a text generation AI (e.g., LLM). The generation AI can also generate facial features using a multimodal generation AI. The generation AI also generates facial features based on prompts entered by a user. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information among facial features and generate facial features based on that information. The outfit generation unit generates outfits using the generation AI. For example, the generation AI generates outfits using a text generation AI (e.g., LLM). The generation AI can also generate outfits using a multimodal generation AI. The generation AI also generates outfits based on prompts entered by a user. For example, a text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. A multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to identify particularly important information in an outfit and generate the outfit based on that information. The accessory generation unit generates accessories using the generation AI. For example, the generation AI generates accessories using a text generation AI (e.g., LLM). The generation AI can also generate accessories using a multimodal generation AI. The generation AI also generates accessories based on prompts entered by the user. For example, a text generation AI has trained on large amounts of text data and has advanced natural language processing capabilities. A multimodal generation AI can handle not only text but also multiple modalities, including images and audio. The generation AI uses keyword extraction technology to identify particularly important information in an accessory and generate the accessory based on that information.The real-time customization unit customizes the avatar in real time by combining facial features, clothing, and accessories. For example, the real-time customization unit instantly reflects the facial features, clothing, and accessories selected by the user on the avatar. The digital identity generation unit generates a unique digital identity by combining the facial features, clothing, and accessories customized by the real-time customization unit. For example, the digital identity generation unit generates a unique avatar that is different from other users based on the facial features, clothing, and accessories selected by the user. In this way, the virtual avatar generation system according to the embodiment allows users to easily create and customize their own virtual avatar. For example, when creating an avatar to use in games or on social media, a user can customize the avatar to their own preferences and have a unique digital identity that is different from other users.

[0056] The facial feature generation unit automatically generates facial features according to the user's emotional state, and can reflect facial expressions that match the emotional state in real time. For example, the facial feature generation unit captures the user's facial expressions in real time through a camera, and the generation AI automatically generates facial features based on that emotional state. For example, if the user is smiling, the generation AI generates a smiling face. The facial feature generation unit also analyzes the user's emotional state, and the generation AI reflects facial features that match that emotion in real time. For example, if the user is surprised, the generation AI generates a surprised expression. The facial feature generation unit also collects the user's emotional data, and the generation AI automatically generates facial features based on that data. For example, if the user is sad, the generation AI generates a sad expression. This makes it possible to generate facial features in real time according to the user's emotions.

[0057] The facial feature generation unit can analyze the user's past photos or videos, learn individual features, and generate more realistic facial features. For example, the facial feature generation unit analyzes past photos and videos uploaded by the user, and the generation AI learns facial features based on that data. For example, it learns the user's facial contours and eye shape. The facial feature generation unit also extracts the user's facial features from past photos and videos, and the generation AI generates a realistic face based on those features. For example, it reproduces the shape of the user's nose and mouth. The facial feature generation unit also analyzes the user's past photos and videos, and the generation AI customizes the facial features based on that data. For example, it reflects the user's hairstyle and skin color. This makes it possible to generate realistic facial features based on the user's past data.

[0058] The facial feature generation unit can provide customization options based on the user's cultural background or preferences. In the facial feature generation unit, for example, the generation AI customizes the facial features based on the user's cultural background. For example, it may reflect the traditional facial features of a particular region. In addition, the facial feature generation unit customizes the facial features based on the user's preferences. For example, it may incorporate the facial features of a celebrity the user likes. In addition, the facial feature generation unit provides customization options based on the user's cultural background and preferences, and the generation AI generates facial features based on those options. For example, it may allow the user to select traditional facial features of a particular ethnic group. This enables customization based on the user's cultural background and preferences.

[0059] The facial feature generation unit can provide an option to generate the face of an animal or fantasy character. For example, the facial feature generation unit trains the generation AI to learn the facial features of animals and provides an option for the user to select an animal face. For example, the facial feature generation unit generates the face of a cat or a dog. The facial feature generation unit also trains the generation AI to learn the facial features of fantasy characters and provides an option for the user to select a fantasy character face. For example, the facial feature generation unit generates the face of an elf or a dragon. The facial feature generation unit also trains the generation AI to learn the facial features of animals or fantasy characters and provides an option for the user to customize the face based on the features. For example, the facial feature generation unit generates the face of a unicorn or a phoenix. This provides an option to generate the face of an animal or fantasy character.

[0060] The facial feature generation unit can provide a function for exchanging facial features with other users and collaborating to generate a new face. For example, the facial feature generation unit allows a user to exchange facial features with other users, and the generation AI generates a new face based on those features. For example, friends exchange facial features to create a new face. The facial feature generation unit also provides a collaboration function, allowing multiple users to customize facial features together. For example, a new face is generated by combining facial features within a family or group. The facial feature generation unit also provides a function for exchanging facial features with other users, and the generation AI generates a new face based on those features. For example, a user creates a new face by incorporating facial features of a celebrity. This provides a function for exchanging facial features with other users and collaborating to generate a new face.

[0061] The facial feature generation unit can use the emotion estimation function to analyze the emotional response to the facial features selected by the user and suggest optimal facial features. The facial feature generation unit, for example, uses the emotion estimation function to analyze the emotional response to the facial features selected by the user in real time. For example, it analyzes the emotional response when the user selects a smiling face. The facial feature generation unit also uses the generation AI to suggest optimal facial features based on the user's emotional response data. For example, it preferentially suggests facial features that indicate positive emotions. The facial feature generation unit also uses the emotion estimation function to analyze the emotional response to the facial features selected by the user and suggests optimal facial features based on the results. For example, it makes suggestions based on the emotion score for the facial features selected by the user. This makes it possible to suggest optimal facial features based on the user's emotional response.

[0062] The clothing generation unit can automatically generate clothing according to the user's emotional state and reflect a style that matches the emotional state in real time. For example, the clothing generation unit analyzes the user's emotional state, and the generation AI automatically generates clothing that matches that emotion. For example, if the user is in a happy mood, it generates bright-colored clothing. The clothing generation unit also uses an emotion estimation function to reflect clothing that matches the user's emotional state in real time. For example, if the user is relaxed, it generates casual clothing. The clothing generation unit also collects the user's emotional data, and the generation AI automatically generates clothing based on that data. For example, if the user is nervous, it generates formal clothing. This makes it possible to generate clothing in real time according to the user's emotions.

[0063] The clothing generation unit can suggest clothing according to the season or weather and reflect it on the avatar in real time. For example, the generation AI of the clothing generation unit analyzes seasonal and weather data and suggests clothing based on that data. For example, in winter, it suggests warm coats and sweaters. The clothing generation unit also suggests clothing in real time based on weather data of the user's current location. For example, it suggests raincoats and umbrellas on rainy days. The clothing generation unit also suggests clothing according to the season and weather, and the generation AI reflects the suggestions on the avatar in real time. For example, it suggests light clothing and sunglasses in summer. This makes it possible to suggest clothing in real time according to the season and weather.

[0064] The clothing generation unit can provide an option to select historical costumes or futuristic designs. For example, the clothing generation unit trains the generation AI with historical costume data, providing an option for the user to select historical costumes. For example, it generates medieval dresses and armor. The clothing generation unit also trains the generation AI with futuristic clothing designs, providing an option for the user to select futuristic designs. For example, it generates cyberpunk-style clothing. The clothing generation unit also trains the generation AI with historical costumes and futuristic designs, providing an option for the user to customize clothing based on the characteristics of the designs. For example, it generates Victorian dresses and space suits. This provides an option to select historical costumes or futuristic designs.

[0065] The clothing generation unit can provide a function for exchanging clothing with other users and collaborating to generate a new style. For example, the clothing generation unit allows a user to exchange clothing with other users, and the generation AI generates a new style based on that clothing. For example, friends exchange clothing to create a new style. The clothing generation unit also provides a collaboration function, allowing multiple users to customize clothing together. For example, a family or group can combine clothing to generate a new style. The clothing generation unit also provides a function for exchanging clothing with other users, and the generation AI generates a new style based on that clothing. For example, a user creates a new style by incorporating clothing worn by a celebrity. This provides a function for exchanging clothing with other users and collaborating to generate a new style.

[0066] The clothing generation unit can use the emotion estimation function to analyze the emotional response to the clothing selected by the user and suggest optimal clothing. The clothing generation unit, for example, uses the emotion estimation function to analyze the emotional response to the clothing selected by the user in real time. For example, it analyzes the emotional response to the clothing selected by the user. The clothing generation unit also uses the generation AI to suggest optimal clothing based on the user's emotional response data. For example, it prioritizes suggesting clothing for which the user expressed positive emotions. The clothing generation unit also uses the emotion estimation function to analyze the emotional response to the clothing selected by the user and suggests optimal clothing based on the results. For example, it makes suggestions based on the emotion score for the clothing selected by the user. This makes it possible to suggest optimal clothing based on the user's emotional response.

[0067] The accessory generation unit can automatically generate accessories according to the user's emotional state and reflect designs that match the emotional state in real time. For example, the accessory generation unit analyzes the user's emotional state, and the generation AI automatically generates accessories that match that emotion. For example, if the user is in a happy mood, it generates brightly colored accessories. The accessory generation unit also uses an emotion estimation function to reflect accessories that match the user's emotional state in real time. For example, if the user is relaxed, it generates casual accessories. The accessory generation unit also collects the user's emotional data, and the generation AI automatically generates accessories based on that data. For example, if the user is nervous, it generates formal accessories. This makes it possible to generate accessories in real time according to the user's emotions.

[0068] The accessory generation unit can analyze the user's past accessory history and generate accessories based on individual preferences. For example, the accessory generation unit analyzes data on accessories selected by the user in the past, and the generation AI generates accessories that suit individual preferences based on that data. For example, the user's preferred colors and designs are reflected. The accessory generation unit also suggests accessories that suit the user's preferences based on the user's past accessory history. For example, the generation AI reflects the user's frequently selected brands and styles. The accessory generation unit also analyzes the user's past accessory history, and the generation AI customizes accessories based on that data. For example, the generation AI reflects the accessories the user prefers for specific events. This makes it possible to generate accessories that suit the user's preferences based on the user's past data.

[0069] The accessory generation unit can suggest accessories that match an event or season and reflect them on the avatar in real time. For example, the generation AI of the accessory generation unit analyzes specific event or season data and suggests accessories based on that data. For example, at Christmas, it would suggest Christmas tree earrings and necklaces. The accessory generation unit also suggests accessories in real time based on event data of the user's current location. For example, it would suggest Halloween-themed accessories for Halloween. The accessory generation unit also suggests accessories that match a specific event or season, and the generation AI reflects the suggestions on the avatar in real time. For example, it would suggest beach-themed accessories in summer. This makes it possible to suggest accessories in real time according to specific events or seasons.

[0070] The accessory generation unit can provide an option to generate accessories based on custom designs or images uploaded by the user. For example, the accessory generation unit provides an option for the user to generate accessories based on custom designs by having the generation AI learn custom design data. For example, an accessory is generated based on a design drawn by the user. The accessory generation unit also provides an option for the generation AI to generate accessories based on images uploaded by the user. For example, an accessory is generated based on a photo taken by the user. The accessory generation unit also provides an option for the generation AI to customize accessories based on custom designs or uploaded images. For example, an accessory is generated based on a work of art that the user likes. This makes it possible to generate accessories based on custom designs or uploaded images.

[0071] The accessory generation unit can provide a function for exchanging accessories with other users and collaborating to generate new designs. For example, the accessory generation unit allows a user to exchange accessories with other users, and the generation AI generates a new design based on those accessories. For example, friends exchange accessories to create a new design. The accessory generation unit also provides a collaboration function, allowing multiple users to customize accessories together. For example, a family or group can combine accessories to generate a new design. The accessory generation unit also provides a function for exchanging accessories with other users, and the generation AI generates a new design based on those accessories. For example, a user creates a new design incorporating accessories from a celebrity. This provides a function for exchanging accessories with other users and collaborating to generate a new design.

[0072] The accessory generation unit can use the emotion estimation function to analyze the emotional response to the accessory selected by the user and suggest the most suitable accessory. For example, the accessory generation unit uses the emotion estimation function to analyze the emotional response to the accessory selected by the user in real time. For example, it analyzes the emotional response to the accessory selected by the user. The accessory generation unit also uses the generation AI to suggest the most suitable accessory based on the user's emotional response data. For example, it preferentially suggests accessories for which the user expressed positive emotions. The accessory generation unit also uses the emotion estimation function to analyze the emotional response to the accessory selected by the user and suggests the most suitable accessory based on the results. For example, it makes suggestions based on the emotion score for the accessory selected by the user. This makes it possible to suggest the most suitable accessory based on the user's emotional response.

[0073] The real-time customization unit performs real-time customization according to the user's emotional state, allowing the generation AI to instantly reflect changes in the avatar to match that emotional state. For example, the real-time customization unit analyzes the user's emotional state, and the generation AI performs real-time customization to match that emotion. For example, if the user is in a happy mood, the avatar's facial expression and clothing are brightened. The real-time customization unit also uses an emotion estimation function to reflect changes in the avatar in real time according to the user's emotional state. For example, if the user is relaxed, the avatar's posture and movements are made gentler. The real-time customization unit also collects user emotional data, and the generation AI performs real-time customization based on that data. For example, if the user is nervous, the avatar's facial expression and clothing are made more formal. This enables the avatar to change in real time according to the user's emotions.

[0074] The real-time customization unit performs real-time customization in response to the user's movements or gestures, enabling the generation AI to provide a more interactive avatar. For example, the real-time customization unit analyzes the user's movements and gestures, and the generation AI performs real-time customization in response to those movements. For example, when the user waves, the avatar also waves. The real-time customization unit also uses movement recognition technology to reflect changes in the avatar in response to the user's gestures in real time. For example, when the user smiles, the avatar also smiles. The real-time customization unit also collects user movement data, and the generation AI performs real-time customization based on that data. For example, when the user walks, the avatar also walks in the same way. This makes it possible to provide an interactive avatar in response to the user's movements and gestures.

[0075] The real-time customization unit can provide a collaboration function that allows multiple users to customize avatars simultaneously. For example, the real-time customization unit provides a collaboration function that allows multiple users to customize avatars simultaneously, and the generation AI reflects the customization in real time. For example, friends customize an avatar together. The real-time customization unit also provides a collaboration function that allows multiple users to customize avatars together. For example, families or groups customize avatars. The real-time customization unit also provides a collaboration function that allows multiple users to customize avatars simultaneously, and the generation AI reflects the customization in real time. For example, users customize a celebrity's avatar together. This provides a collaboration function that allows multiple users to customize avatars simultaneously.

[0076] The real-time customization unit can provide a preset function that saves the results of real-time customization and allows for later reuse. The real-time customization unit, for example, saves the results of real-time customization performed by the user and allows for later reuse. For example, it saves customization of an avatar created by the user. The real-time customization unit also provides a preset function that allows the user to easily reuse the customization results saved by the user. For example, it reuses combinations of clothing and accessories saved by the user. The real-time customization unit also saves the results of real-time customization, and the generation AI proposes new customization based on the results. For example, it proposes a new style based on the customization results saved by the user. This provides a preset function that saves the results of real-time customization and allows for later reuse.

[0077] The real-time customization unit can use the emotion estimation function to analyze the emotional response to the customization performed by the user in real time and suggest optimal customization. The real-time customization unit, for example, uses the emotion estimation function to analyze the emotional response to the customization performed by the user in real time. For example, it analyzes the emotional response to the customization selected by the user. The real-time customization unit also uses the generation AI to suggest optimal customization based on the user's emotional response data. For example, it prioritizes suggesting customizations for which the user expressed positive emotions. The real-time customization unit also uses the emotion estimation function to analyze the emotional response to the customization performed by the user in real time and suggests optimal customization based on the results. For example, it makes suggestions based on the emotional score for the customization selected by the user. This makes it possible to suggest optimal real-time customization based on the user's emotional response.

[0078] The digital identity generation unit automatically generates a unique digital identity according to the user's emotional state, reflecting the user's personality in real time. For example, the digital identity generation unit analyzes the user's emotional state, and the generation AI automatically generates a unique digital identity that matches that emotion. For example, if the user is in a happy mood, a bright-colored identity is generated. The digital identity generation unit also uses an emotion estimation function to reflect the digital identity in real time according to the user's emotional state. For example, if the user is relaxed, a casual identity is generated. The digital identity generation unit also collects the user's emotional data, and the generation AI automatically generates a unique digital identity based on that data. For example, if the user is nervous, a formal identity is generated. This makes it possible to generate a unique digital identity in real time according to the user's emotions.

[0079] The digital identity generation unit can analyze a user's past digital identity history and generate a unique identity based on their individual preferences. For example, the digital identity generation unit analyzes digital identity data selected by the user in the past, and the generation AI generates a unique identity that suits their individual preferences based on that data. For example, it may reflect the user's favorite colors and styles. The digital identity generation unit also suggests a unique identity that suits the user's preferences based on the user's past digital identity history. For example, it may reflect the user's frequently selected designs and themes. The digital identity generation unit also analyzes the user's past digital identity history, and the generation AI customizes the unique identity based on that data. For example, it may reflect the user's preferred identity for a particular event. This allows a unique identity to be generated based on the user's past digital identity history.

[0080] The digital identity generation unit can propose a unique digital identity based on the user's social background or interests. In the digital identity generation unit, for example, the generation AI proposes a unique digital identity based on the user's social background. For example, it may reflect a design related to a particular region or culture. In addition, the digital identity generation unit proposes a unique digital identity based on the user's interests and hobbies. For example, it may reflect a design related to the user's favorite sports or music. In addition, the digital identity generation unit provides customization options based on the user's social background and interests, and the generation AI generates a unique digital identity based on those options. For example, it may allow the user to select a design related to a particular occupation or lifestyle. This makes it possible to propose a unique digital identity based on the user's social background and interests.

[0081] The digital identity generation unit can provide an option to generate an identity that incorporates elements from different cultures or regions. For example, the digital identity generation unit can train the generation AI on design data from different cultures or regions, allowing users to generate an identity that incorporates those elements. For example, a design that reflects African prints or traditional Asian patterns. The digital identity generation unit can also train the generation AI to create an identity that incorporates elements from different cultures or regions, allowing users to customize the identity based on those features. For example, a European medieval-style design or a South American carnival-style design can be generated. The digital identity generation unit can also train the generation AI on elements from different cultures or regions, allowing users to generate a unique digital identity that incorporates those elements. For example, a design that reflects traditional Indian designs or minimalist Scandinavian designs. This provides an option to generate an identity that incorporates elements from different cultures or regions.

[0082] The digital identity generation unit can use the emotion estimation function to analyze the emotional reactions to the digital identity generated by the user and suggest an optimal identity. The digital identity generation unit, for example, uses the emotion estimation function to analyze the emotional reactions to the digital identity generated by the user in real time. For example, it analyzes the emotional reactions to the identity selected by the user. The digital identity generation unit also uses the generation AI to suggest an optimal digital identity based on the user's emotional reaction data. For example, it preferentially suggests identities for which the user has expressed positive emotions. The digital identity generation unit also uses the emotion estimation function to analyze the emotional reactions to the digital identity generated by the user and suggests an optimal identity based on the results. For example, it makes a suggestion based on the emotion score for the identity selected by the user. This makes it possible to suggest an optimal digital identity based on the user's emotional reaction.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The virtual avatar generation system may further include a voice generation unit. The voice generation unit generates the user's voice using a generation AI. For example, the generation AI generates natural-sounding voices based on text entered by the user. The voice generation unit may also learn the characteristics of the user's voice and imitate the user's voice in real time. For example, when the user speaks, the voice may be captured in real time, and the generation AI may generate voices based on the captured voices. The voice generation unit may also adjust the tone and pitch of the voice according to the user's emotional state. For example, if the user is excited, the generation AI may generate an excited voice. This allows the virtual avatar to reflect the user's voice in real time, providing a more interactive experience.

[0085] The virtual avatar generation system can further include a gesture recognition unit. The gesture recognition unit captures the user's movements and gestures in real time, and the generation AI generates avatar movements based on those movements. For example, when the user waves their hand, the avatar waves in the same way. The gesture recognition unit can also analyze the user's facial expressions and body movements, and the generation AI can reflect those movements in real time. For example, when the user smiles, the avatar also smiles. The gesture recognition unit can also collect user movement data, and the generation AI can customize the avatar's movements based on that data. For example, when the user dances, the avatar dances in the same way. This makes it possible to generate avatars in real time according to the user's movements and gestures.

[0086] The virtual avatar generation system may further include an environment recognition unit. The environment recognition unit captures the user's surrounding environment, and the generation AI generates the avatar's background and settings based on that environment. For example, if the user is outdoors, the generation AI sets the background to a natural landscape. The environment recognition unit may also analyze the user's location information, and the generation AI may customize the avatar's background based on that location. For example, if the user is in a city, the generation AI sets the background to a city landscape. The environment recognition unit may also collect information about the sound and light around the user, and the generation AI may adjust the avatar's settings based on that information. For example, if the user is at night, the generation AI sets the background to a night landscape. This enables real-time avatar generation according to the user's environment.

[0087] The virtual avatar generation system can further include a health condition recognition unit. The health condition recognition unit collects the user's health data, and the generation AI adjusts the avatar's appearance and behavior based on that data. For example, if the user is tired, the generation AI makes the avatar's facial expression look tired. The health condition recognition unit can also analyze the user's vital data, such as heart rate and body temperature, and the generation AI can adjust the avatar's behavior based on that data. For example, if the user is relaxed, the generation AI can slow down the avatar's movements. The health condition recognition unit can also allow the generation AI to customize the avatar's appearance based on the user's health data. For example, if the user is healthy, the generation AI will make the avatar's appearance look healthy. This enables real-time avatar generation according to the user's health condition.

[0088] The virtual avatar generation system may further include a music generation unit. The music generation unit uses a generation AI to generate music tailored to the user's preferences. For example, the generation AI generates music based on keywords entered by the user. The music generation unit may also analyze the user's emotional state and generate music tailored to that emotion. For example, if the user is relaxed, the generation AI may generate relaxing music. The music generation unit may also analyze the user's past music history, and the generation AI may suggest music based on that history. For example, music in a genre that the user often listens to may be generated. This allows music tailored to the user's preferences and emotions to be generated in real time.

[0089] The virtual avatar generation system can further include a story generation unit. The story generation unit uses a generation AI to generate a story based on user input. For example, the generation AI generates a story based on keywords or themes entered by the user. The story generation unit can also analyze the user's emotional state and generate a story that matches that emotion. For example, if the user is in a happy mood, the generation AI will generate a fun story. The story generation unit can also analyze the user's past story history, and the generation AI can suggest stories based on that history. For example, it can generate a story in a genre that the user prefers. This makes it possible to generate stories in real time that match the user's preferences and emotions.

[0090] The virtual avatar generation system may further include a feedback collection unit. The feedback collection unit collects feedback from users and the generation AI improves the generation of the avatar based on the feedback. For example, when a user provides feedback on the appearance or behavior of an avatar, the generation AI learns the feedback and reflects it in the next generation. The feedback collection unit may also analyze the user's emotional state and collect feedback based on the emotion. For example, if the user expresses positive emotions toward the avatar, the emotion may be collected as feedback. The feedback collection unit may also analyze the user's past feedback history and allow the generation AI to customize the generation of the avatar based on the history. This enables real-time avatar generation based on user feedback.

[0091] The virtual avatar generation system may further include an education support unit. The education support unit uses a generation AI to support the user's learning. For example, the generation AI generates learning content based on the topic the user wants to learn. The education support unit may also analyze the user's learning progress and provide learning content tailored to that progress. For example, if the user shows progress in a specific field, the generation AI may provide advanced content related to that field. The education support unit may also analyze the user's emotional state and provide learning content tailored to that emotion. For example, if the user is concentrating, the generation AI may provide content that will help them concentrate. This allows for real-time support of the user's learning.

[0092] The virtual avatar generation system can further include a relaxation support unit. The relaxation support unit uses the generation AI to support the user's relaxation. For example, when the user feels like relaxing, the generation AI generates relaxation content. The relaxation support unit can also analyze the user's emotional state and provide relaxation content tailored to that emotion. For example, if the user is feeling stressed, the generation AI can provide content to reduce stress. The relaxation support unit can also analyze the user's past relaxation history, and the generation AI can suggest relaxation content based on that history. This allows for real-time support of the user's relaxation.

[0093] The virtual avatar generation system may further include an entertainment generation unit. The entertainment generation unit uses a generation AI to support the user's entertainment. For example, when the user feels like having fun, the generation AI generates entertainment content. The entertainment generation unit may also analyze the user's emotional state and provide entertainment content that matches that emotion. For example, if the user is bored, the generation AI may provide interesting content. The entertainment generation unit may also analyze the user's past entertainment history, and the generation AI may suggest entertainment content based on that history. This allows for real-time support of the user's entertainment.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The facial feature generation unit generates facial features using a generation AI. For example, the generation AI may use a text generation AI (e.g., LLM) or a multimodal generation AI to generate facial features based on prompts entered by the user. The generation AI may use keyword extraction technology to identify particularly important information among facial features and generate facial features based on that information. Step 2: The customization unit customizes the facial features generated by the facial feature generation unit, allowing the user to adjust the generated facial features to suit their preferences. Step 3: The outfit generation unit generates outfits using a generation AI. For example, the generation AI may use a text generation AI (e.g., LLM) or a multimodal generation AI to generate outfits based on prompts entered by the user. The generation AI may use keyword extraction technology to identify particularly important information in the outfits and generate outfits based on that information. Step 4: The customization unit customizes the outfit generated by the outfit generation unit, allowing the user to customize the outfit to suit their own preferences. Step 5: The accessory generation unit generates the accessory using a generation AI. For example, the generation AI may use a text generation AI (e.g., LLM) or a multimodal generation AI to generate the accessory based on the prompt entered by the user. The generation AI may use keyword extraction technology to identify particularly important information in the accessory and generate the accessory based on that information. Step 6: The customization unit customizes the accessory generated by the accessory generation unit, allowing the user to adjust the generated accessory to suit their preferences. Step 7: The real-time customization unit customizes the avatar in real time by combining facial features, clothing, and accessories. For example, the real-time customization unit instantly reflects the facial features, clothing, and accessories selected by the user on the avatar. Step 8: The digital identity generator generates a unique digital identity by combining the facial features, clothing, and accessories customized by the real-time customization unit. For example, the digital identity generator generates a unique avatar that is different from other users based on the facial features, clothing, and accessories selected by the user.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a facial feature generation unit that generates facial features using a generative AI; a customization unit that customizes the facial features generated by the facial feature generation unit; a clothing generation unit that generates clothing using a generation AI; a customization unit that customizes the clothing generated by the clothing generation unit; an accessory generation unit that generates accessories using a generation AI; a customization unit that customizes the accessory generated by the accessory generation unit; a real-time customization unit that customizes the facial features, the clothing, and the accessories in real time by combining them; a digital identity generation unit that generates a unique digital identity by combining the facial features, the clothing, and the accessories customized by the real-time customization unit. A system characterized by:

2. The facial feature generation unit The facial features corresponding to the emotional state are automatically generated, and facial expressions corresponding to the emotional state are reflected in real time.

2. The system of claim 1.

3. The facial feature generation unit Analyzes the user's past photos or videos and learns individual features to generate more realistic facial features.

2. The system of claim 1.

4. The facial feature generation unit Providing customization options based on a user's cultural background or preferences 2. The system of claim 1.

5. The facial feature generation unit Providing an option to generate said face for an animal or fantasy character 2. The system of claim 1.

6. The facial feature generation unit Provides the ability to exchange facial features with other users and collaborate to create new faces 2. The system of claim 1.

Images