System

Abstract

An object of a system according to an embodiment is to provide an interactive experience with a past family.SOLUTION: A system according to an embodiment includes an image generation unit, a dialogue generation unit, and a space generation unit. The image generation unit generates a 3D avatar from a family photograph or a moving image using the image generation AI. The dialogue generation unit generates a dialogue for the 3D avatar and the user to converse in natural languages. The space generation unit generates a Metaverse space from a material provided by a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of being one-way in preserving memories using photos and videos, and not being able to provide an interactive experience.

[0005] The system according to the embodiment aims to provide an interactive experience with past family members. [Means for solving the problem]

[0006] The system according to the embodiment includes an image generation unit, a dialogue generation unit, and a space generation unit. The image generation unit generates a 3D avatar from family photos and videos using image generation AI. The dialogue generation unit generates dialogue for the 3D avatar to converse in natural language with the user. The space generation unit generates a metaverse space from materials provided by the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide an interactive experience with past family members. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention not only preserves memories through photos and videos, but also allows users to converse with past family members using generative AI and avatars, and recreate past spaces in the metaverse. This allows users to experience memories with their past family members more deeply and feel a connection with family members who live far away or have passed away.

[0029] The system according to the embodiment includes an image generation unit, a dialogue generation unit, and a space generation unit. The image generation unit uses an image generation AI to generate a 3D avatar from family photos and videos. For example, when a user provides a family photo as input, a realistic 3D avatar is generated based on the photo. The image generation unit generates a 3D avatar by having the generation AI receive prompts containing user instructions as input information. For example, the generation AI receives a prompt such as "Please create a 3D avatar based on this photo" and generates an avatar. The dialogue generation unit generates a dialogue program that allows the user to converse with the generated 3D avatar in natural language. For example, when a user asks, "Mom, how was your day?", the generation AI generates a response such as "It was a great day today." The dialogue generation unit analyzes the user's natural language commands and questions and generates an appropriate response based on the content. The space generation unit generates a metaverse space from materials provided by the user. For example, when a photo of a family gathering is input, a realistic 3D space based on the photo is generated. In addition, the space generation unit generates a 3D space based on the user's instructions, and the AI ​​receives the prompt "Please create a metaverse space based on this photo" as input. This allows the system to recreate past family memories and interact with them in the metaverse space.

[0030] The image generation unit can receive inputs such as family photos and videos, as well as handwritten notes and drawings, and generate a 3D avatar based on them. For example, the image generation unit scans handwritten notes and drawings provided by the user, and the image generation AI generates a 3D avatar based on them. For example, if a child draws a family picture, a 3D avatar based on that drawing is generated. The image generation unit also receives handwritten notes and drawings as input, and the image generation AI analyzes their contents to extract the characteristics of the 3D avatar. For example, an avatar can be generated that reflects the distinctive hairstyle or clothing written in the note. The image generation unit also generates a prototype of the 3D avatar based on the handwritten notes and drawings provided by the user, and the image generation AI allows the user to fine-tune the prototype. For example, the avatar's facial expressions can be set based on the facial expressions drawn in the drawing. This makes it possible to generate 3D avatars based on handwritten notes and drawings.

[0031] The image generation unit can combine the generated 3D avatar with voice synthesis technology based on recordings of family members' voices provided by the user. For example, the image generation unit analyzes recordings of family members' voices provided by the user and uses voice synthesis technology to have the 3D avatar reproduce those voices. For example, a mother's voice can be recorded and an avatar based on that voice can converse. The image generation unit also uses voice synthesis technology to extract features of the family members' voices provided by the user and incorporate those voices into the 3D avatar. For example, an avatar that reflects the tone and accent of the voice can be generated. The image generation unit also constructs a system in which voice synthesis technology generates the voices of the 3D avatar in real time based on recordings of the family members' voices provided by the user. For example, when the user asks a question, the avatar responds in that voice. This generates 3D avatars that reproduce the family members' voices, providing a more realistic experience.

[0032] The image generation unit can generate a 3D avatar of a family pet based on photos and videos of the pet, allowing the pet to interact with the family in the metaverse space. The image generation unit uses image generation AI to generate a 3D avatar of the pet based on photos and videos of the pet provided by the user. For example, when a photo of a dog or cat is input, a 3D avatar of that pet is generated. The image generation unit also analyzes the photos and videos of the pet, and the image generation AI extracts the pet's characteristics to generate a 3D avatar. For example, an avatar that reflects the pet's coat color and body shape is generated. The image generation unit also builds a system that allows the generated 3D avatar of the pet to interact with 3D avatars of family members in the metaverse space. For example, it recreates a scene of playing with the pet. This generates a 3D avatar of the pet, allowing the pet to interact with the family in the metaverse space.

[0033] The image generation unit can make the generated 3D avatar importable into other metaverse platforms and games. For example, the image generation unit provides a format conversion function for importing the generated 3D avatar into other metaverse platforms and games. For example, it converts the avatar into FBX or OBJ format. The image generation unit also works with other metaverse platforms and games to develop an API that allows the generated 3D avatar to be easily imported. For example, it is compatible with Unity and Unreal Engine. The image generation unit also provides a compatibility check function for using the generated 3D avatar on different metaverse platforms and games. For example, it checks whether the avatar's movements and facial expressions are correctly reproduced. This allows the generated 3D avatar to be used on other metaverse platforms and games.

[0034] The dialogue generation unit can change the facial expressions and gestures of the 3D avatar in real time according to the content of the conversation. For example, the dialogue generation unit builds a system that analyzes the content of the conversation and changes the facial expressions and gestures of the 3D avatar in real time. For example, a smile is displayed when expressing joy. The dialogue generation unit also uses natural language processing technology to generate the 3D avatar's movements according to what the user says. For example, the eyes are widened when expressing surprise. The dialogue generation unit also understands the context of the conversation and causes the 3D avatar to use appropriate facial expressions and gestures. For example, a sad expression is displayed when telling a sad story. This allows for more natural dialogue.

[0035] The dialogue generation unit can save a conversation history and generate a response that understands the context based on the content of the past conversation. The dialogue generation unit, for example, builds a system that saves the conversation history in a database and generates a response that understands the context based on the content of the past conversation. For example, the response is generated by referring to the content of the previous conversation. The dialogue generation unit also uses natural language processing technology to analyze the past conversation history and generate a response that understands the context. For example, it performs follow-up on previous questions. The dialogue generation unit also develops a system that understands the user's preferences and interests based on the conversation history and generates a response accordingly. For example, it prioritizes topics that the user likes. In this way, a response that understands the context based on the content of the past conversation is generated.

[0036] The dialogue generation unit can support conversations in different languages ​​and accommodate international users. The dialogue generation unit uses, for example, natural language processing technology to build a system that supports conversations in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The dialogue generation unit also provides a function in which a 3D avatar converses in a language selected by the user. For example, if the user selects Japanese, the avatar responds in Japanese. The dialogue generation unit also develops a system that translates conversations in different languages ​​in real time, enabling users to communicate smoothly. For example, if the user speaks in English, the avatar responds in Japanese. This supports conversations in different languages ​​and accommodates international users.

[0037] The dialogue generation unit can save the content of a conversation not only as text but also as an audio or video message so that it can be played back later. For example, the dialogue generation unit builds a system that saves the content of a conversation not only as text but also as an audio or video message. For example, it allows a user to record a conversation and play it back later. The dialogue generation unit also provides a function that saves audio data of a conversation and allows a user to play the audio later. For example, an important conversation can be saved and checked later. The dialogue generation unit also develops a system that saves the content of a conversation as a video message and allows a user to play the video. For example, it saves a video message that includes the movements and facial expressions of a 3D avatar. This allows the content of a conversation to be saved as text, audio, or a video message and played back later.

[0038] The space generation unit can generate a metaverse space that reflects the season and time of day based on materials provided by the user. The space generation unit, for example, analyzes the shooting date and time of photos and videos provided by the user and generates a metaverse space that reflects the season and time of day. For example, a space based on summer photos recreates summer scenery and lighting conditions. The space generation unit also sets the environment according to the season and time of day and builds a system that generates a metaverse space based on materials provided by the user. For example, a space based on winter photos recreates a snowy landscape and a cold atmosphere. The space generation unit also generates a metaverse space in real time that reflects the season and time of day based on the shooting date and time of the materials provided by the user. For example, a space based on evening photos recreates a sunset landscape. This generates a metaverse space that reflects the season and time of day.

[0039] The space generation unit can track the user's actions and movements within the metaverse space and add interactive elements accordingly. For example, the space generation unit tracks the user's actions and movements within the metaverse space and builds a system that adds interactive elements based on that data. For example, when a user approaches a specific location, a special event occurs. The space generation unit also tracks the user's movements in real time and generates interactive elements according to those movements. For example, when a user waves their hand, an avatar responds. The space generation unit also analyzes the user's behavioral data within the metaverse space and develops a system that adds interactive elements based on that behavior. For example, when a user touches a specific object, an animation is played. This adds interactive elements according to the user's actions and movements.

[0040] The space generation unit can make the metaverse space available as a place for education and training, and provide scenarios for learning and skill improvement. For example, the space generation unit can build a system that makes the metaverse space available as an educational space and provides learning scenarios. For example, learning can be done in a space that recreates historical events. The space generation unit can also use the metaverse space as a training space and provide scenarios for skill improvement. For example, it can perform surgical simulations for medical training. The space generation unit can also provide scenarios for learning and skill improvement within the metaverse space, and develop a system that allows users to learn interactively. For example, it can provide conversation scenarios for language learning. This allows the metaverse space to be used as a place for education and training.

[0041] The space generation unit can automatically generate events and activities within the metaverse space, allowing users to participate. For example, the space generation unit builds a system that automatically generates events and activities within the metaverse space, allowing users to participate. For example, a virtual concert or sporting event is held. The space generation unit also automatically generates events and activities within the metaverse space based on the user's interests and concerns. For example, a live event of a user's favorite artist is held. The space generation unit also develops a system that generates events and activities within the metaverse space in real time, allowing users to participate. For example, a virtual workshop is held in which users can participate. This allows events and activities within the metaverse space to be automatically generated, allowing users to participate.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The system not only generates 3D avatars of family members based on photos and videos of the family provided by the user, but also combines voice synthesis technology based on recordings of the family members' voices provided by the user. For example, the system analyzes recordings of the family members' voices provided by the user and uses voice synthesis technology to reproduce the voices in the 3D avatars. For example, a mother's voice can be recorded and an avatar based on that voice can converse. The system also uses voice synthesis technology to extract characteristics of the family members' voices provided by the user and incorporate them into the 3D avatars. For example, an avatar can be generated that reflects the tone and accent of the voice. Furthermore, a system is built in which voice synthesis technology generates the voices of the 3D avatars in real time based on recordings of the family members' voices provided by the user. For example, when a user asks a question, the avatar responds in that voice. This generates 3D avatars that reproduce the family members' voices, providing a more realistic experience.

[0044] The system can take inputs such as handwritten notes and drawings, as well as family photos and videos provided by the user, and generate 3D avatars based on them. For example, the system scans handwritten notes and drawings provided by the user and uses the image generation AI to generate a 3D avatar. For example, if a child draws a family, a 3D avatar based on that drawing is generated. The system also receives handwritten notes and drawings as input, analyzes their contents, and extracts the characteristics of the 3D avatar. For example, it generates an avatar that reflects the distinctive hairstyle or clothing written in the notes. The image generation AI also generates a prototype of the 3D avatar based on the handwritten notes and drawings provided by the user, allowing the user to fine-tune the prototype. For example, the avatar's facial expressions can be set based on the facial expressions drawn in the drawings. This makes it possible to generate 3D avatars based on handwritten notes and drawings.

[0045] The system can combine the generated 3D avatar with speech synthesis technology based on recordings of family members' voices provided by the user. For example, the system analyzes recordings of family members' voices provided by the user and uses speech synthesis technology to reproduce those voices in a 3D avatar. For example, a mother's voice can be recorded and an avatar based on that voice can converse. The system can also use speech synthesis technology to extract features of the family members' voices provided by the user and incorporate those voices into the 3D avatar. For example, an avatar can be generated that reflects the tone and accent of the voice. Furthermore, a system can be constructed in which speech synthesis technology generates the voices of 3D avatars in real time based on recordings of family members' voices provided by the user. For example, when a user asks a question, the avatar responds in that voice. This generates 3D avatars that reproduce the family members' voices, providing a more realistic experience.

[0046] The system can generate a 3D avatar of a family pet based on photos and videos of the pet, allowing it to interact with the family in the metaverse space. For example, image generation AI generates a 3D avatar of the pet based on photos and videos of the pet provided by the user. For example, if a photo of a dog or cat is input, a 3D avatar of that pet is generated. The image generation AI can also analyze the photos and videos of the pet, extracting the pet's characteristics and generating a 3D avatar. For example, an avatar that reflects the pet's coat color and body shape can be generated. A system will also be built that allows the generated 3D avatar of the pet to interact with 3D avatars of family members in the metaverse space. For example, a scene of playing with the pet can be recreated. This generates a 3D avatar of the pet, allowing it to interact with the family in the metaverse space.

[0047] The system can make it possible to import the generated 3D avatar into other metaverse platforms and games. For example, it can provide a format conversion function for importing the generated 3D avatar into other metaverse platforms and games. For example, it can convert the avatar into FBX or OBJ format. It can also develop an API that can work with other metaverse platforms and games to easily import the generated 3D avatar. For example, it can support Unity and Unreal Engine. It can also provide a compatibility check function for using the generated 3D avatar on different metaverse platforms and games. For example, it can check whether the avatar's movements and facial expressions are correctly reproduced. This allows the generated 3D avatar to be used on other metaverse platforms and games.

[0048] The system can change the facial expressions and gestures of a 3D avatar in real time depending on the content of the conversation. For example, we will build a system that analyzes the content of the conversation and changes the facial expressions and gestures of a 3D avatar in real time. For example, it will display a smile when expressing joy. In addition, natural language processing technology will be used to generate the 3D avatar's movements according to what the user says. For example, it will open its eyes wide when expressing surprise. In addition, it will understand the context of the conversation and make the 3D avatar use appropriate facial expressions and gestures. For example, it will display a sad expression when talking about sad things. This will enable more natural dialogue.

[0049] The system can store conversation history and generate responses that understand the context based on the content of past conversations. For example, we will build a system that stores conversation history in a database and generates responses that understand the context based on the content of past conversations. For example, we will respond by referring to the content of the previous conversation. We will also use natural language processing technology to analyze past conversation history and generate responses that understand the context. For example, we will follow up on previous questions. We will also develop a system that understands the user's preferences and interests based on the conversation history and generates responses accordingly. For example, we will prioritize topics that the user likes. This will generate responses that understand the context based on the content of past conversations.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The image generation unit uses image generation AI to generate a 3D avatar from family photos and videos. For example, when a user provides a family photo as input, a realistic 3D avatar is generated based on that photo. The image generation unit also generates a 3D avatar using the generation AI, which receives prompts containing user instructions as input information. For example, the generation AI receives a prompt such as "Please create a 3D avatar based on this photo," and generates an avatar. Step 2: The dialogue generation unit generates a dialogue program that allows the user to converse in natural language with the generated 3D avatar. For example, if the user asks, "Mom, how was your day?", the generation AI generates a response such as, "Today was a very good day." The dialogue generation unit also uses the generation AI to analyze the user's natural language commands and questions and generate appropriate responses based on their content. Step 3: The space generation unit generates a Metaverse space from materials provided by the user. For example, if a photo of a family gathering is input, a realistic 3D space based on that photo is generated. The space generation unit also generates a 3D space by receiving prompts containing user instructions as input information. For example, the generation AI receives a prompt such as "Please create a Metaverse space based on this photo" and generates the space.

[0052] (Example 2) The system according to the embodiment of the present invention not only preserves memories through photos and videos, but also allows users to converse with past family members using generative AI and avatars, and recreate past spaces in the metaverse. This allows users to experience memories with their past family members more deeply and feel a connection with family members who live far away or have passed away.

[0053] The system according to the embodiment includes an image generation unit, a dialogue generation unit, and a space generation unit. The image generation unit uses an image generation AI to generate a 3D avatar from family photos and videos. For example, when a user provides a family photo as input, a realistic 3D avatar is generated based on the photo. The image generation unit generates a 3D avatar by having the generation AI receive prompts containing user instructions as input information. For example, the generation AI receives a prompt such as "Please create a 3D avatar based on this photo" and generates an avatar. The dialogue generation unit generates a dialogue program that allows the user to converse with the generated 3D avatar in natural language. For example, when a user asks, "Mom, how was your day?", the generation AI generates a response such as "It was a great day today." The dialogue generation unit analyzes the user's natural language commands and questions and generates an appropriate response based on the content. The space generation unit generates a metaverse space from materials provided by the user. For example, when a photo of a family gathering is input, a realistic 3D space based on the photo is generated. In addition, the space generation unit generates a 3D space based on the user's instructions, and the AI ​​receives the prompt "Please create a metaverse space based on this photo" as input. This allows the system to recreate past family memories and interact with them in the metaverse space.

[0054] The image generation unit can receive inputs such as family photos and videos, as well as handwritten notes and drawings, and generate a 3D avatar based on them. For example, the image generation unit scans handwritten notes and drawings provided by the user, and the image generation AI generates a 3D avatar based on them. For example, if a child draws a family picture, a 3D avatar based on that drawing is generated. The image generation unit also receives handwritten notes and drawings as input, and the image generation AI analyzes their contents to extract the characteristics of the 3D avatar. For example, an avatar can be generated that reflects the distinctive hairstyle or clothing written in the note. The image generation unit also generates a prototype of the 3D avatar based on the handwritten notes and drawings provided by the user, and the image generation AI allows the user to fine-tune the prototype. For example, the avatar's facial expressions can be set based on the facial expressions drawn in the drawing. This makes it possible to generate 3D avatars based on handwritten notes and drawings.

[0055] The image generation unit can combine the generated 3D avatar with voice synthesis technology based on recordings of family members' voices provided by the user. For example, the image generation unit analyzes recordings of family members' voices provided by the user and uses voice synthesis technology to have the 3D avatar reproduce those voices. For example, a mother's voice can be recorded and an avatar based on that voice can converse. The image generation unit also uses voice synthesis technology to extract features of the family members' voices provided by the user and incorporate those voices into the 3D avatar. For example, an avatar that reflects the tone and accent of the voice can be generated. The image generation unit also constructs a system in which voice synthesis technology generates the voices of the 3D avatar in real time based on recordings of the family members' voices provided by the user. For example, when the user asks a question, the avatar responds in that voice. This generates 3D avatars that reproduce the family members' voices, providing a more realistic experience.

[0056] The image generation unit can use the emotion estimation function to generate 3D avatars that reflect the emotions of family members estimated from photos and videos. For example, the image generation unit uses the emotion estimation function to analyze the emotions of family members from photos and videos and generate 3D avatars that reflect those emotions. For example, a smiling avatar is generated based on a photo of a smiling person. The image generation unit also builds a system that sets the facial expressions and movements of the 3D avatar based on emotion data estimated from photos and videos. For example, an avatar based on a photo of a sad expression will have a sad expression. The image generation unit also uses the emotion estimation function to analyze the emotions of family members from photos and videos in real time and generate 3D avatars that reflect those emotions. For example, an avatar will smile based on a scene in a video where they are laughing. This generates 3D avatars that are rich in emotion, enabling more natural conversations.

[0057] The image generation unit can generate a 3D avatar of a family pet based on photos and videos of the pet, allowing the pet to interact with the family in the metaverse space. The image generation unit uses image generation AI to generate a 3D avatar of the pet based on photos and videos of the pet provided by the user. For example, when a photo of a dog or cat is input, a 3D avatar of that pet is generated. The image generation unit also analyzes the photos and videos of the pet, and the image generation AI extracts the pet's characteristics to generate a 3D avatar. For example, an avatar that reflects the pet's coat color and body shape is generated. The image generation unit also builds a system that allows the generated 3D avatar of the pet to interact with 3D avatars of family members in the metaverse space. For example, it recreates a scene of playing with the pet. This generates a 3D avatar of the pet, allowing the pet to interact with the family in the metaverse space.

[0058] The image generation unit can make the generated 3D avatar importable into other metaverse platforms and games. For example, the image generation unit provides a format conversion function for importing the generated 3D avatar into other metaverse platforms and games. For example, it converts the avatar into FBX or OBJ format. The image generation unit also works with other metaverse platforms and games to develop an API that allows the generated 3D avatar to be easily imported. For example, it is compatible with Unity and Unreal Engine. The image generation unit also provides a compatibility check function for using the generated 3D avatar on different metaverse platforms and games. For example, it checks whether the avatar's movements and facial expressions are correctly reproduced. This allows the generated 3D avatar to be used on other metaverse platforms and games.

[0059] The image generation unit can use the emotion estimation function to analyze the emotions a user feels when creating an avatar and provide an avatar generation process that elicits positive emotions. For example, the image generation unit can use the emotion estimation function to analyze the emotions a user feels when creating an avatar in real time and provide feedback to elicit positive emotions. For example, it can analyze whether the user is enjoying themselves. The image generation unit can also adjust the avatar generation process based on the user's emotion data and provide an interface to elicit positive emotions. For example, it can suggest an avatar that makes the user smile. The image generation unit can also use the emotion estimation function to monitor the emotions a user feels when creating an avatar and provide advice in real time to elicit positive emotions. For example, it can generate an avatar that satisfies the user. This allows the user to create an avatar while feeling positive emotions.

[0060] The dialogue generation unit can change the facial expressions and gestures of the 3D avatar in real time according to the content of the conversation. For example, the dialogue generation unit builds a system that analyzes the content of the conversation and changes the facial expressions and gestures of the 3D avatar in real time. For example, a smile is displayed when expressing joy. The dialogue generation unit also uses natural language processing technology to generate the 3D avatar's movements according to what the user says. For example, the eyes are widened when expressing surprise. The dialogue generation unit also understands the context of the conversation and causes the 3D avatar to use appropriate facial expressions and gestures. For example, a sad expression is displayed when telling a sad story. This allows for more natural dialogue.

[0061] The dialogue generation unit can save a conversation history and generate a response that understands the context based on the content of the past conversation. The dialogue generation unit, for example, builds a system that saves the conversation history in a database and generates a response that understands the context based on the content of the past conversation. For example, the response is generated by referring to the content of the previous conversation. The dialogue generation unit also uses natural language processing technology to analyze the past conversation history and generate a response that understands the context. For example, it performs follow-up on previous questions. The dialogue generation unit also develops a system that understands the user's preferences and interests based on the conversation history and generates a response accordingly. For example, it prioritizes topics that the user likes. In this way, a response that understands the context based on the content of the past conversation is generated.

[0062] The dialogue generation unit can use the emotion estimation function to analyze the user's emotions in real time and generate a response accordingly. The dialogue generation unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions in real time and generates a response according to those emotions. For example, if the user is sad, the dialogue generation unit may offer comforting words. The dialogue generation unit may also analyze the user's facial expressions and voice and generate a response based on the emotion data. For example, if the user is laughing, the dialogue generation unit may make a joke. The dialogue generation unit may also use the emotion estimation function to develop a system that monitors the user's emotional changes in real time and generates a response accordingly. For example, if the user is angry, the dialogue generation unit may respond calmly. In this way, a response according to the user's emotions is generated.

[0063] The dialogue generation unit can support conversations in different languages ​​and accommodate international users. The dialogue generation unit uses, for example, natural language processing technology to build a system that supports conversations in different languages. For example, it supports multiple languages ​​such as English, French, and Chinese. The dialogue generation unit also provides a function in which a 3D avatar converses in a language selected by the user. For example, if the user selects Japanese, the avatar responds in Japanese. The dialogue generation unit also develops a system that translates conversations in different languages ​​in real time, enabling users to communicate smoothly. For example, if the user speaks in English, the avatar responds in Japanese. This supports conversations in different languages ​​and accommodates international users.

[0064] The dialogue generation unit can save the content of a conversation not only as text but also as an audio or video message so that it can be played back later. For example, the dialogue generation unit builds a system that saves the content of a conversation not only as text but also as an audio or video message. For example, it allows a user to record a conversation and play it back later. The dialogue generation unit also provides a function that saves audio data of a conversation and allows a user to play the audio later. For example, an important conversation can be saved and checked later. The dialogue generation unit also develops a system that saves the content of a conversation as a video message and allows a user to play the video. For example, it saves a video message that includes the movements and facial expressions of a 3D avatar. This allows the content of a conversation to be saved as text, audio, or a video message and played back later.

[0065] The dialogue generation unit can use the emotion estimation function to analyze the emotions felt by the user during a conversation and provide a conversation scenario for eliciting positive emotions. The dialogue generation unit, for example, uses the emotion estimation function to analyze the emotions felt by the user during a conversation in real time and build a system for providing a conversation scenario for eliciting positive emotions. For example, it analyzes whether the user is enjoying themselves. The dialogue generation unit also generates a conversation scenario for eliciting positive emotions based on the user's emotion data. For example, it provides topics that will make the user smile. The dialogue generation unit also uses the emotion estimation function to monitor the emotions felt by the user during a conversation and provides advice in real time for eliciting positive emotions. For example, it advances a conversation that satisfies the user. This allows the user to have a conversation while maintaining positive emotions.

[0066] The space generation unit can generate a metaverse space that reflects the season and time of day based on materials provided by the user. The space generation unit, for example, analyzes the shooting date and time of photos and videos provided by the user and generates a metaverse space that reflects the season and time of day. For example, a space based on summer photos recreates summer scenery and lighting conditions. The space generation unit also sets the environment according to the season and time of day and builds a system that generates a metaverse space based on materials provided by the user. For example, a space based on winter photos recreates a snowy landscape and a cold atmosphere. The space generation unit also generates a metaverse space in real time that reflects the season and time of day based on the shooting date and time of the materials provided by the user. For example, a space based on evening photos recreates a sunset landscape. This generates a metaverse space that reflects the season and time of day.

[0067] The space generation unit can track the user's actions and movements within the metaverse space and add interactive elements accordingly. For example, the space generation unit tracks the user's actions and movements within the metaverse space and builds a system that adds interactive elements based on that data. For example, when a user approaches a specific location, a special event occurs. The space generation unit also tracks the user's movements in real time and generates interactive elements according to those movements. For example, when a user waves their hand, an avatar responds. The space generation unit also analyzes the user's behavioral data within the metaverse space and develops a system that adds interactive elements based on that behavior. For example, when a user touches a specific object, an animation is played. This adds interactive elements according to the user's actions and movements.

[0068] The space generation unit can use the emotion estimation function to analyze the emotions felt by the user in the metaverse space and provide changes to the environment according to the emotions. For example, the space generation unit uses the emotion estimation function to analyze the emotions felt by the user in the metaverse space in real time and build a system that provides changes to the environment according to the emotions. For example, if the user is relaxed, calm music is played. The space generation unit also dynamically changes the environment in the metaverse space based on the user's emotion data. For example, if the user is excited, a colorful landscape is displayed. The space generation unit also uses the emotion estimation function to monitor the emotions felt by the user in the metaverse space and develop a system that provides changes to the environment according to the emotions in real time. For example, if the user is sad, a calm landscape is displayed. This provides changes to the environment according to the user's emotions.

[0069] The space generation unit can make the metaverse space available as a place for education and training, and provide scenarios for learning and skill improvement. For example, the space generation unit can build a system that makes the metaverse space available as an educational space and provides learning scenarios. For example, learning can be done in a space that recreates historical events. The space generation unit can also use the metaverse space as a training space and provide scenarios for skill improvement. For example, it can perform surgical simulations for medical training. The space generation unit can also provide scenarios for learning and skill improvement within the metaverse space, and develop a system that allows users to learn interactively. For example, it can provide conversation scenarios for language learning. This allows the metaverse space to be used as a place for education and training.

[0070] The space generation unit can automatically generate events and activities within the metaverse space, allowing users to participate. For example, the space generation unit builds a system that automatically generates events and activities within the metaverse space, allowing users to participate. For example, a virtual concert or sporting event is held. The space generation unit also automatically generates events and activities within the metaverse space based on the user's interests and concerns. For example, a live event of a user's favorite artist is held. The space generation unit also develops a system that generates events and activities within the metaverse space in real time, allowing users to participate. For example, a virtual workshop is held in which users can participate. This allows events and activities within the metaverse space to be automatically generated, allowing users to participate.

[0071] The space generation unit can use the emotion estimation function to identify the most relaxing environment for the user within the metaverse space and provide that environment. The space generation unit, for example, uses the emotion estimation function to build a system that identifies the most relaxing environment for the user within the metaverse space. For example, it provides relaxing scenery and music based on the user's emotion data. The space generation unit also analyzes the user's emotion data and dynamically generates the most relaxing environment. For example, it displays a calm seaside scene when the user is relaxing. The space generation unit also uses the emotion estimation function to develop a system that provides the most relaxing environment for the user within the metaverse space in real time. For example, when the user is feeling stressed, it automatically generates a relaxing environment. This provides the user with the most relaxing environment.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The system not only generates 3D avatars of family members based on photos and videos of the family provided by the user, but also combines voice synthesis technology based on recordings of the family members' voices provided by the user. For example, the system analyzes recordings of the family members' voices provided by the user and uses voice synthesis technology to reproduce the voices in the 3D avatars. For example, a mother's voice can be recorded and an avatar based on that voice can converse. The system also uses voice synthesis technology to extract characteristics of the family members' voices provided by the user and incorporate them into the 3D avatars. For example, an avatar can be generated that reflects the tone and accent of the voice. Furthermore, a system is built in which voice synthesis technology generates the voices of the 3D avatars in real time based on recordings of the family members' voices provided by the user. For example, when a user asks a question, the avatar responds in that voice. This generates 3D avatars that reproduce the family members' voices, providing a more realistic experience.

[0074] The system can take inputs such as handwritten notes and drawings, as well as family photos and videos provided by the user, and generate 3D avatars based on them. For example, the system scans handwritten notes and drawings provided by the user and uses the image generation AI to generate a 3D avatar. For example, if a child draws a family, a 3D avatar based on that drawing is generated. The system also receives handwritten notes and drawings as input, analyzes their contents, and extracts the characteristics of the 3D avatar. For example, it generates an avatar that reflects the distinctive hairstyle or clothing written in the notes. The image generation AI also generates a prototype of the 3D avatar based on the handwritten notes and drawings provided by the user, allowing the user to fine-tune the prototype. For example, the avatar's facial expressions can be set based on the facial expressions drawn in the drawings. This makes it possible to generate 3D avatars based on handwritten notes and drawings.

[0075] The system can combine the generated 3D avatar with speech synthesis technology based on recordings of family members' voices provided by the user. For example, the system analyzes recordings of family members' voices provided by the user and uses speech synthesis technology to reproduce those voices in a 3D avatar. For example, a mother's voice can be recorded and an avatar based on that voice can converse. The system can also use speech synthesis technology to extract features of the family members' voices provided by the user and incorporate those voices into the 3D avatar. For example, an avatar can be generated that reflects the tone and accent of the voice. Furthermore, a system can be constructed in which speech synthesis technology generates the voices of 3D avatars in real time based on recordings of family members' voices provided by the user. For example, when a user asks a question, the avatar responds in that voice. This generates 3D avatars that reproduce the family members' voices, providing a more realistic experience.

[0076] The system can use the emotion estimation function to generate 3D avatars that reflect the emotions of family members estimated from photos and videos. For example, the emotion estimation function can be used to analyze the emotions of family members from photos and videos, and generate 3D avatars that reflect those emotions. For example, a smiling avatar can be generated based on a photo of a smiling person. We can also build a system that sets the facial expressions and movements of 3D avatars based on emotional data estimated from photos and videos. For example, an avatar based on a photo of a sad expression can have a sad expression. We can also use the emotion estimation function to analyze the emotions of family members from photos and videos in real time, and generate 3D avatars that reflect those emotions. For example, an avatar can smile based on scenes in a video where they are laughing. This allows for the generation of 3D avatars that are rich in emotion, enabling more natural conversations.

[0077] The system can generate a 3D avatar of a family pet based on photos and videos of the pet, allowing it to interact with the family in the metaverse space. For example, image generation AI generates a 3D avatar of the pet based on photos and videos of the pet provided by the user. For example, if a photo of a dog or cat is input, a 3D avatar of that pet is generated. The image generation AI can also analyze the photos and videos of the pet, extracting the pet's characteristics and generating a 3D avatar. For example, an avatar that reflects the pet's coat color and body shape can be generated. A system will also be built that allows the generated 3D avatar of the pet to interact with 3D avatars of family members in the metaverse space. For example, a scene of playing with the pet can be recreated. This generates a 3D avatar of the pet, allowing it to interact with the family in the metaverse space.

[0078] The system can make it possible to import the generated 3D avatar into other metaverse platforms and games. For example, it can provide a format conversion function for importing the generated 3D avatar into other metaverse platforms and games. For example, it can convert the avatar into FBX or OBJ format. It can also develop an API that can work with other metaverse platforms and games to easily import the generated 3D avatar. For example, it can support Unity and Unreal Engine. It can also provide a compatibility check function for using the generated 3D avatar on different metaverse platforms and games. For example, it can check whether the avatar's movements and facial expressions are correctly reproduced. This allows the generated 3D avatar to be used on other metaverse platforms and games.

[0079] The system can use the emotion estimation function to analyze the emotions a user feels when creating an avatar and provide an avatar generation process that elicits positive emotions. For example, the emotion estimation function can be used to analyze the emotions a user feels when creating an avatar in real time and provide feedback to elicit positive emotions. For example, it can analyze whether the user is enjoying themselves. Furthermore, based on the user's emotion data, the system can adjust the avatar generation process and provide an interface to elicit positive emotions. For example, it can suggest an avatar that will make the user smile. Furthermore, the emotion estimation function can be used to monitor the emotions a user feels when creating an avatar and provide advice in real time to elicit positive emotions. For example, it can generate an avatar that makes the user satisfied. This allows the user to create an avatar while feeling positive emotions.

[0080] The system can change the facial expressions and gestures of a 3D avatar in real time depending on the content of the conversation. For example, we will build a system that analyzes the content of the conversation and changes the facial expressions and gestures of a 3D avatar in real time. For example, it will display a smile when expressing joy. In addition, natural language processing technology will be used to generate the 3D avatar's movements according to what the user says. For example, it will open its eyes wide when expressing surprise. In addition, it will understand the context of the conversation and make the 3D avatar use appropriate facial expressions and gestures. For example, it will display a sad expression when talking about sad things. This will enable more natural dialogue.

[0081] The system can store conversation history and generate responses that understand the context based on the content of past conversations. For example, we will build a system that stores conversation history in a database and generates responses that understand the context based on the content of past conversations. For example, we will respond by referring to the content of the previous conversation. We will also use natural language processing technology to analyze past conversation history and generate responses that understand the context. For example, we will follow up on previous questions. We will also develop a system that understands the user's preferences and interests based on the conversation history and generates responses accordingly. For example, we will prioritize topics that the user likes. This will generate responses that understand the context based on the content of past conversations.

[0082] The system can use the emotion estimation function to analyze the user's emotions in real time and generate a response accordingly. For example, we will build a system that uses the emotion estimation function to analyze the user's emotions in real time and generate a response according to those emotions. For example, if the user is sad, we will say something comforting. We will also analyze the user's facial expressions and voice and generate a response based on the emotional data. For example, we will tell a joke if the user is laughing. We will also develop a system that uses the emotion estimation function to monitor changes in the user's emotions in real time and generate a response according to them. For example, we will respond calmly if the user is angry. In this way, a response according to the user's emotions will be generated.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The image generation unit uses image generation AI to generate a 3D avatar from family photos and videos. For example, when a user provides a family photo as input, a realistic 3D avatar is generated based on that photo. The image generation unit also generates a 3D avatar using the generation AI, which receives prompts containing user instructions as input information. For example, the generation AI receives a prompt such as "Please create a 3D avatar based on this photo," and generates an avatar. Step 2: The dialogue generation unit generates a dialogue program that allows the user to converse in natural language with the generated 3D avatar. For example, if the user asks, "Mom, how was your day?", the generation AI generates a response such as, "Today was a very good day." The dialogue generation unit also uses the generation AI to analyze the user's natural language commands and questions and generate appropriate responses based on their content. Step 3: The space generation unit generates a Metaverse space from materials provided by the user. For example, if a photo of a family gathering is input, a realistic 3D space based on that photo is generated. The space generation unit also generates a 3D space by receiving prompts containing user instructions as input information. For example, the generation AI receives a prompt such as "Please create a Metaverse space based on this photo" and generates the space.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An image generation part that uses image generation AI to generate 3D avatars from family photos and videos, a dialogue generation unit for allowing the 3D avatar and the user to converse in natural language; A space generation unit that generates a metaverse space from materials provided by a user. A system characterized by:

2. The image generation unit It takes inputs such as family photos and videos, as well as handwritten notes and drawings, and generates the 3D avatar based on them.

2. The system of claim 1.

3. The image generation unit The generated 3D avatar is then combined with voice synthesis technology based on recordings of family members' voices provided by the user.

2. The system of claim 1.

4. The image generation unit Generate the 3D avatars that reflect the emotions of the family members estimated from photos and videos.

2. The system of claim 1.

5. The image generation unit Based on photos and videos of family pets, a 3D avatar of the pet will be generated, allowing the pet to interact with the family in the metaverse space.

2. The system of claim 1.

Images