System

Abstract

An object of the system according to the embodiment is to create a digital clone including thoughts, voices, and appearances of a user and to cope with different languages and generate a 3D avatar.SOLUTION: A system according to an embodiment includes a data-analyzing unit, a digital clone generation unit, a language conversion unit, a 3D avatar generation unit, a storage unit, and a dialogue unit. The data analyzer creates a digital clone containing the user's thoughts, voice, and appearance. The digital clone generation unit generates a digital clone based on the data analyzed by the data analysis unit. The language conversion unit converts the digital clone generated by the digital clone generation unit into a different language. The 3D avatar generation unit generates the 3D avatar based on the appearance of the digital clone generated by the digital clone generation unit. The storage unit stores the digital clone. The interaction unit performs interaction with the digital clone.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] Previous technologies have had issues with creating digital clones that include a user's thoughts, voice, and appearance, and not being able to respond to different languages ​​or generate 3D avatars.

[0005] The system according to the embodiment aims to create a digital clone of a user, including their thoughts, voice, and appearance, and to be able to speak different languages ​​and generate a 3D avatar. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, a digital clone generation unit, a language conversion unit, a 3D avatar generation unit, a storage unit, and an interaction unit. The data analysis unit creates a digital clone including the user's thoughts, voice, and appearance. The digital clone generation unit generates the digital clone based on data analyzed by the data analysis unit. The language conversion unit converts the digital clone generated by the digital clone generation unit into a different language. The 3D avatar generation unit generates a 3D avatar based on the appearance of the digital clone generated by the digital clone generation unit. The storage unit stores the digital clone. The interaction unit interacts with the digital clone. [Effects of the Invention]

[0007] The system according to the embodiment can create a digital clone of a user, including their thoughts, voice, and appearance, and can also speak different languages ​​and generate 3D avatars. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The digital clone platform according to an embodiment of the present invention is a system that creates a digital clone of a user, including their thoughts, voice, and appearance, and features the ability to speak different languages ​​and generate 3D avatars, thereby forming a digital legacy of the user and advancing the cloning process through interaction.

[0029] A digital clone platform according to an embodiment includes a data analysis unit, a digital clone generation unit, a language conversion unit, a 3D avatar generation unit, a storage unit, and a dialogue unit. The data analysis unit analyzes data including a user's thoughts, voice, and appearance. For example, the data analysis unit analyzes the user's voice data and extracts voice characteristics. The data analysis unit can also analyze the user's image data and extract appearance characteristics. The data analysis unit can also analyze the user's text data and extract thoughts and personality. The digital clone generation unit generates a digital clone based on the data analyzed by the data analysis unit. For example, the digital clone generation unit generates a digital clone that reproduces the user's voice. The digital clone generation unit can also generate a digital clone that reproduces the user's appearance. The digital clone generation unit can also generate a digital clone that reflects the user's thoughts and personality. The language conversion unit converts the digital clone generated by the digital clone generation unit into a different language. For example, the language conversion unit converts a Japanese digital clone of a user into English. The language conversion unit may also convert a French digital clone of a user into Spanish. The language conversion unit may also convert a German digital clone of a user into Chinese. The 3D avatar generation unit generates a 3D avatar based on the appearance of the digital clone generated by the digital clone generation unit. For example, the 3D avatar generation unit may generate a 3D avatar based on a photo of the user's face. The 3D avatar generation unit may also generate a 3D avatar based on a full-body photo of the user. The 3D avatar generation unit may also generate a 3D avatar based on a video of the user. The storage unit stores the digital clone. For example, the storage unit may store the digital clone in cloud storage. The storage unit may also store the digital clone in local storage. The storage unit may also store the digital clone in external storage. The dialogue unit interacts with the digital clone. For example, the dialogue unit may enable the user to interact with the digital clone via voice. The dialogue unit may also enable the user to interact with the digital clone via text.The dialogue unit may also enable a user to interact with the digital clone through video, allowing the digital clone platform according to embodiments to create a digital clone that includes the user's thoughts, voice, and appearance, and allows for interaction in different languages ​​and the generation of a 3D avatar.

[0030] The data analysis unit can analyze the user's brainwave data and generate a digital clone that reflects their thought patterns. For example, the data analysis unit has the user wear a dedicated brainwave measurement device and collect data on their daily thoughts and emotions. The generation AI analyzes this brainwave data and generates a digital clone that reflects the user's thought patterns. The data analysis unit also collects brainwave data when the user is working on a specific task, and the generation AI analyzes their thought patterns based on that data. This makes it possible to create a digital clone that reflects the user's problem-solving ability and creativity. The data analysis unit also analyzes brainwave data in real time and generates a digital clone that reflects the user's current thought state. For example, it can compare brainwave data when the user is relaxed and when they are concentrating and create clones that reflect each state. This makes it possible to generate a digital clone that reflects the user's thought patterns.

[0031] The data analysis unit can analyze a user's past social media posts and email history to generate a digital clone that reflects the user's personality in more detail. For example, the data analysis unit collects the user's social media posts, and the generation AI analyzes their content. For example, the data analysis unit generates a digital clone that reflects the user's personality based on the frequency and content of posts and the language used. The data analysis unit also analyzes the user's email history to extract communication style and interests. Based on this data, the generation AI can create a digital clone that reflects the user's personality in more detail. The data analysis unit can also analyze emotional fluctuations from social media posts and email history to generate a digital clone that reflects the user's emotional patterns. For example, it can compare periods with many positive posts with periods with many negative posts and create clones that reflect each state. This allows the generation of a digital clone that reflects the user's personality in more detail.

[0032] The digital clone generation unit can generate a digital clone of a pet or animal, enabling it to interact with its owner. For example, an owner can upload photos and videos of their pet, and the generation AI analyzes the data to generate a digital clone of the pet. This allows the owner to virtually interact with their pet. The digital clone generation unit also collects behavioral data of the pet (for example, walking patterns and cries), and the generation AI analyzes the data to generate a digital clone of the pet. This creates a clone that reflects the pet's personality. The digital clone generation unit also allows the owner to input memories and episodes with their pet, and the generation AI generates a digital clone of the pet based on that information. This allows the owner to virtually recreate memories with their pet. This allows the generation of a digital clone of a pet or animal, enabling it to interact with the owner.

[0033] The digital clone generation unit can generate digital clones of historical figures and provide dialogue for educational purposes. For example, the digital clone generation unit uploads a photograph or portrait of the historical figure, and the generation AI analyzes the data to generate a digital clone. This allows students to virtually interact with the historical figure. The digital clone generation unit can also analyze the historical figure's writings and letters, and the generation AI generates a digital clone based on the content. This allows students to learn about the historical figure's thoughts and opinions. The digital clone generation unit can also create a database of the historical figure's life and achievements, and the generation AI generates a digital clone based on that information. This allows students to virtually experience the historical figure's life. This allows digital clones of historical figures to be generated and provide dialogue for educational purposes.

[0034] The language conversion unit can generate a digital clone that reflects the user's dialect and regional expressions. For example, the language conversion unit collects the dialect and regional expressions spoken by the user, and the generation AI analyzes the data to generate a digital clone. This creates a clone that reflects the user's regional characteristics. The language conversion unit also references a regional language database to learn the user's dialect and expressions, and the generation AI generates a digital clone based on that information. This reflects regional expressions. The language conversion unit also allows the user to input dialects and regional expressions, and the generation AI generates a digital clone based on that data. This creates a clone that reflects the user's individuality in more detail. This makes it possible to generate a digital clone that reflects the user's dialect and regional expressions.

[0035] The language conversion unit can analyze the user's language learning history and generate a digital clone that supports conversations in the language being learned. For example, the language conversion unit collects data from the user's language learning app, and the generation AI analyzes the data to generate a digital clone that supports conversations in the language being learned. This allows the user to converse with the clone in the language being learned. The language conversion unit can also analyze the user's language learning history, and the generation AI can use that information to generate a digital clone that supports conversations in the language being learned. For example, the clone can use phrases and words that the user is learning. The language conversion unit can also analyze data entered by the user in the language being learned, and the generation AI can use that data to generate a digital clone that supports conversations in the language being learned. This allows the user to practice with the clone in the language being learned. This allows the digital clone to be generated that supports conversations in the language being learned.

[0036] The language conversion unit can generate a digital clone that is compatible with sign language or visual language, enabling dialogue with the hearing impaired. For example, the language conversion unit collects sign language movement data, and the generation AI analyzes the data to generate a digital clone that is compatible with sign language. This allows the hearing impaired to interact with the clone in sign language. The language conversion unit also collects visual language data, and the generation AI analyzes the data to generate a digital clone that is compatible with visual language. This allows users who use visual language to interact with the clone. The language conversion unit also references a database of sign language or visual language, and the generation AI generates a digital clone that is compatible with sign language or visual language based on that information. This allows the hearing impaired to interact smoothly with the clone. This allows the generation of a digital clone that is compatible with sign language or visual language, enabling dialogue with the hearing impaired.

[0037] The language conversion unit can generate a digital clone that reflects culturally specific gestures and customs to promote intercultural communication. For example, the language conversion unit collects data on gestures and customs of different cultures, and the generation AI analyzes the data to generate a digital clone that reflects culturally specific expressions. This facilitates intercultural communication. The language conversion unit also allows a user to input culturally specific gestures and customs, and the generation AI generates a digital clone based on that data. This creates a clone that reflects the user's cultural background. The language conversion unit also references a database of different cultures, and the generation AI generates a digital clone that reflects culturally specific gestures and customs based on that information. This deepens intercultural understanding. This allows a digital clone that reflects culturally specific gestures and customs to promote intercultural communication.

[0038] The 3D avatar generation unit can analyze a user's movements and gestures using motion capture technology and generate a 3D avatar that reproduces realistic movements. For example, the 3D avatar generation unit has the user wear a motion capture suit and collect daily movements and gestures. The generation AI analyzes this data and generates a 3D avatar that reproduces realistic movements. The 3D avatar generation unit can also upload videos of the user performing specific movements or gestures, and the generation AI can analyze the data and generate a 3D avatar that reproduces realistic movements. The 3D avatar generation unit can also analyze a user's movements and gestures in real time using motion capture technology and generate a 3D avatar based on that data. This creates an avatar that reflects the user's movements in real time. This makes it possible to generate a 3D avatar that realistically reproduces the user's movements and gestures.

[0039] The 3D avatar generation unit can generate a 3D avatar that allows the user to customize their clothing and accessories. For example, the 3D avatar generation unit allows the user to upload photos of their clothing and accessories, and the generation AI analyzes the data to generate a customizable 3D avatar. This allows the user to create an avatar that reflects their own style. The 3D avatar generation unit also allows the generation AI to generate a customizable 3D avatar based on data of the clothing and accessories selected by the user. For example, the clothing and accessories selected by the user can be reflected in the avatar. The 3D avatar generation unit also provides an interface for the user to customize their clothing and accessories, and the generation AI generates the 3D avatar based on that data. This allows the user to customize the avatar to suit their preferences. This allows the generation of a 3D avatar that allows the user to customize their clothing and accessories.

[0040] The 3D avatar generation unit generates a 3D avatar of the user's pet or animal, allowing the owner to interact with the pet as a virtual pet. For example, the owner uploads photos and videos of their pet, and the generation AI analyzes the data to generate a 3D avatar of the pet. This allows the owner to virtually interact with their pet. The 3D avatar generation unit also collects behavioral data of the pet (e.g., walking patterns and cries), and the generation AI analyzes the data to generate a 3D avatar of the pet. This creates an avatar that reflects the pet's personality. The 3D avatar generation unit also allows the owner to input memories and episodes with their pet, and the generation AI generates a 3D avatar of the pet based on that information. This allows the owner to virtually recreate memories with their pet. This allows the user to virtually interact with their pet as a virtual pet. The 3D avatar generation unit generates a 3D avatar of the user's pet or animal, allowing the owner to interact with the pet as a virtual pet.

[0041] The 3D avatar generator generates 3D avatars of historical figures and fictional characters, which can be used for education and entertainment. For example, the 3D avatar generator uploads a photo or portrait of a historical figure, and the generation AI analyzes the data to generate a 3D avatar. This allows students to virtually interact with historical figures. The 3D avatar generator also uploads design data for fictional characters, and the generation AI analyzes the data to generate a 3D avatar. This allows characters to be used in entertainment content. The 3D avatar generator also analyzes the writings and letters of historical figures, and the generation AI generates a 3D avatar based on the content. This allows students to learn about the person's thoughts and opinions. This allows the generation of 3D avatars of historical figures and fictional characters, which can be used for education and entertainment.

[0042] The storage unit can analyze the user's life log data and generate a digital legacy that reflects important events in their life. For example, the storage unit collects life log data (e.g., diaries and photos) that the user records daily, and the generation AI analyzes that data to generate a digital legacy that reflects important events in their life. The storage unit also analyzes data in which the user records specific events and occurrences, and the generation AI generates a digital legacy based on that information. This reflects the highlights of the user's life. The storage unit also analyzes the user's life log data in real time and automatically extracts important events to generate a digital legacy. For example, it reflects important events such as trips and weddings. This allows the storage unit to analyze the user's life log data and generate a digital legacy that reflects important events in their life.

[0043] The storage unit can link digital clones of the user's family and friends to integrate multiple digital legacies. For example, the storage unit creates digital clones of the user's family and friends and links them to integrate multiple digital legacies. This brings together the digital legacies of the people involved in the user's life. The storage unit also uses the digital clones of family and friends as a generation AI to analyze the data and generate an integrated digital legacy, which reflects the important people in the user's life. The storage unit also allows the user to input memories and episodes with family and friends, and the generation AI uses that information to integrate multiple digital legacies. This brings together important moments in the user's life. This makes it possible to link digital clones of the user's family and friends to integrate multiple digital legacies.

[0044] The preservation unit can create a digital legacy that reflects historical events and cultural background and can be used for educational purposes. For example, the preservation unit collects data on historical events and cultural background, and the generation AI analyzes the data to generate a digital legacy. This allows students to learn about history and culture. The preservation unit also allows users to input information about historical events and cultural background, and the generation AI generates a digital legacy based on that data. This creates a legacy that reflects the user's knowledge. The preservation unit also references a database of historical events and cultural background, and the generation AI generates a digital legacy based on that information. This creates a legacy that can be used for educational purposes. This allows students to create a digital legacy that reflects historical events and cultural background and can be used for educational purposes.

[0045] The storage unit can create a digital legacy that reflects the user's hobbies and interests and share it within the community. For example, the storage unit collects data on the user's hobbies and interests, and the generation AI analyzes the data to generate a digital legacy. This creates a legacy that reflects the user's individuality. The storage unit also allows the user to input information on hobbies and interests, and the generation AI generates a digital legacy based on that data. This creates a legacy that reflects the user's hobbies and interests. The storage unit also references a database on the user's hobbies and interests, and the generation AI generates a digital legacy based on that information. This creates a legacy that reflects the user's hobbies and interests. This creates a digital legacy that reflects the user's hobbies and interests, and can be shared within the community.

[0046] The dialogue unit can analyze the user's dialogue history and learn to realize more natural dialogue. For example, the dialogue unit collects the dialogue history between the user and the generation AI, analyzes that data, and learns to realize more natural dialogue. In this way, the generation AI learns the user's dialogue style. The dialogue unit also analyzes the dialogue patterns and trends of the generation AI based on the user's dialogue history, and develops algorithms to realize more natural dialogue. The dialogue unit also analyzes the content of the dialogue between the user and the generation AI, and the generation AI learns to improve the quality of the dialogue based on that data. This makes the dialogue with the user smoother. In this way, the dialogue unit can analyze the user's dialogue history and learn to realize more natural dialogue.

[0047] The dialogue unit can support group dialogue and enable simultaneous dialogue with multiple users. For example, the dialogue unit builds a system that allows multiple users to simultaneously dialogue with the generation AI. This enables group dialogue and promotes communication between users. The dialogue unit also analyzes the history of group dialogue, and the generation AI develops an algorithm that optimizes dialogue with multiple users based on that data. This allows the generation AI to improve the quality of group dialogue. The dialogue unit also collects emotional data when multiple users simultaneously dialogue, and the generation AI generates responses based on that data according to the emotions. This makes group dialogue smoother. This supports group dialogue and enables simultaneous dialogue with multiple users.

[0048] The dialogue unit can automatically summarize the content of the dialogue and provide feedback to the user. For example, the dialogue unit will build a system that automatically summarizes the content of the dialogue a user has with the generation AI and provides feedback on that summary to the user. This allows the user to easily grasp the main points of the dialogue. The dialogue unit will also develop an algorithm that analyzes the content of the dialogue in real time and allows the generation AI to generate a summary based on that data. This ensures that the user does not miss important points in the dialogue. The dialogue unit will also build a system that analyzes the history of the dialogue a user has with the generation AI and automatically generates a summary based on that data. This allows the user to easily look back on past dialogue. This allows the dialogue content to be automatically summarized and feedback to be provided to the user.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The digital clone generation unit can analyze the user's health data and generate a digital clone that reflects the user's health condition. For example, the generation AI can analyze data collected from the user's fitness tracker or smartwatch and generate a digital clone that reflects the user's health condition based on that data. The digital clone generation unit can also analyze the user's medical records and generate a digital clone that reflects the user's health condition based on that information. This allows the generation of a digital clone that reflects the user's health condition in real time.

[0051] The data analysis unit can analyze a user's hobbies and interests and generate a digital clone based on that information. For example, the generation AI can analyze posts about hobbies shared by the user on social media, and use that data to generate a digital clone that reflects those hobbies. The data analysis unit can also analyze the history of content the user views online, and the generation AI can use that information to generate a digital clone that reflects the user's interests. This makes it possible to generate a digital clone that reflects the user's hobbies and interests.

[0052] The data analysis unit can analyze the user's sleep data and generate a digital clone that reflects their sleep patterns. For example, the data analysis unit can analyze the sleep data recorded by the user using a smartwatch or fitness tracker, and the generation AI can use that data to generate a digital clone that reflects their sleep patterns. The data analysis unit can also analyze the sleep diary kept by the user, and the generation AI can use that information to generate a digital clone that reflects their sleep patterns. This allows the generation of a digital clone that reflects the user's sleep patterns.

[0053] The digital clone generation unit can analyze the user's travel history and generate a digital clone that reflects information about the travel destinations. For example, the user can upload photos and videos of travel destinations they have visited in the past, and the generation AI can analyze that data to generate a digital clone that reflects information about the travel destinations. The digital clone generation unit can also analyze the user's travel blogs and reviews, and the generation AI can use that information to generate a digital clone that reflects information about the travel destinations. This makes it possible to generate a digital clone that reflects the user's travel history.

[0054] The digital clone generation unit can generate digital clones of historical figures and provide dialogue for educational purposes. For example, a photo or portrait of a historical figure can be uploaded, and the generation AI can analyze the data to generate a digital clone. This allows students to virtually interact with the historical figure. The digital clone generation unit can also analyze the historical figure's writings and letters, and the generation AI can generate a digital clone based on the content. This allows students to learn about the historical figure's thoughts and opinions. The digital clone generation unit can also create a database of the historical figure's life and achievements, and the generation AI can generate a digital clone based on that information. This allows students to virtually experience the historical figure's life. This allows digital clones of historical figures to be generated and provide dialogue for educational purposes.

[0055] The language conversion unit can generate a digital clone that reflects the user's dialect and regional expressions. For example, the dialect and regional expressions spoken by the user are collected, and the generation AI analyzes the data to generate a digital clone. This creates a clone that reflects the user's regional characteristics. The language conversion unit also references a regional language database to learn the user's dialect and expressions, and the generation AI generates a digital clone based on that information. This reflects regional expressions. The language conversion unit also allows the user to input dialects and regional expressions, and the generation AI generates a digital clone based on that data. This creates a clone that reflects the user's individuality in more detail. This makes it possible to generate a digital clone that reflects the user's dialect and regional expressions.

[0056] The storage unit can analyze the user's life log data and generate a digital legacy that reflects important events in their life. For example, the storage unit collects life log data (e.g., diaries and photos) that the user records daily, and the generation AI analyzes that data to generate a digital legacy that reflects important events in their life. The storage unit can also analyze data in which the user records specific events and occurrences, and the generation AI can generate a digital legacy based on that information. This reflects the highlights of the user's life. The storage unit can also analyze the user's life log data in real time and automatically extract important events to generate a digital legacy. For example, it can reflect important events such as trips and weddings. This allows the storage unit to analyze the user's life log data and generate a digital legacy that reflects important events in their life.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data analysis unit analyzes data including the user's thoughts, voice, and appearance. For example, the data analysis unit analyzes the user's voice data and extracts voice characteristics. The data analysis unit can also analyze the user's image data and extract appearance characteristics. The data analysis unit can also analyze the user's text data and extract thoughts and personality traits. Step 2: The digital clone generation unit generates a digital clone based on the data analyzed by the data analysis unit. For example, the digital clone generation unit generates a digital clone that reproduces the user's voice. The digital clone generation unit can also generate a digital clone that reproduces the user's appearance. The digital clone generation unit can also generate a digital clone that reflects the user's thoughts and personality. Step 3: The language conversion unit converts the digital clone generated by the digital clone generation unit into a different language. For example, the language conversion unit converts the user's Japanese digital clone into English. The language conversion unit can also convert the user's French digital clone into Spanish. The language conversion unit can also convert the user's German digital clone into Chinese. Step 4: The 3D avatar generation unit generates a 3D avatar based on the appearance of the digital clone generated by the digital clone generation unit. For example, the 3D avatar generation unit generates a 3D avatar based on a photo of the user's face. The 3D avatar generation unit can also generate a 3D avatar based on a photo of the user's entire body. The 3D avatar generation unit can also generate a 3D avatar based on a video of the user. Step 5: The storage unit stores the digital clone. For example, the storage unit stores the digital clone in cloud storage. The storage unit can also store the digital clone in local storage. The storage unit can also store the digital clone in external storage. Step 6: The interaction unit interacts with the digital clone. For example, the interaction unit may enable the user to interact with the digital clone via voice. The interaction unit may also enable the user to interact with the digital clone via text. The interaction unit may also enable the user to interact with the digital clone via video.

[0059] (Example 2) The digital clone platform according to an embodiment of the present invention is a system that creates a digital clone of a user, including their thoughts, voice, and appearance, and features the ability to speak different languages ​​and generate 3D avatars, thereby forming a digital legacy of the user and advancing the cloning process through interaction.

[0060] A digital clone platform according to an embodiment includes a data analysis unit, a digital clone generation unit, a language conversion unit, a 3D avatar generation unit, a storage unit, and a dialogue unit. The data analysis unit analyzes data including a user's thoughts, voice, and appearance. For example, the data analysis unit analyzes the user's voice data and extracts voice characteristics. The data analysis unit can also analyze the user's image data and extract appearance characteristics. The data analysis unit can also analyze the user's text data and extract thoughts and personality. The digital clone generation unit generates a digital clone based on the data analyzed by the data analysis unit. For example, the digital clone generation unit generates a digital clone that reproduces the user's voice. The digital clone generation unit can also generate a digital clone that reproduces the user's appearance. The digital clone generation unit can also generate a digital clone that reflects the user's thoughts and personality. The language conversion unit converts the digital clone generated by the digital clone generation unit into a different language. For example, the language conversion unit converts a Japanese digital clone of a user into English. The language conversion unit may also convert a French digital clone of a user into Spanish. The language conversion unit may also convert a German digital clone of a user into Chinese. The 3D avatar generation unit generates a 3D avatar based on the appearance of the digital clone generated by the digital clone generation unit. For example, the 3D avatar generation unit may generate a 3D avatar based on a photo of the user's face. The 3D avatar generation unit may also generate a 3D avatar based on a full-body photo of the user. The 3D avatar generation unit may also generate a 3D avatar based on a video of the user. The storage unit stores the digital clone. For example, the storage unit may store the digital clone in cloud storage. The storage unit may also store the digital clone in local storage. The storage unit may also store the digital clone in external storage. The dialogue unit interacts with the digital clone. For example, the dialogue unit may enable the user to interact with the digital clone via voice. The dialogue unit may also enable the user to interact with the digital clone via text.The dialogue unit may also enable a user to interact with the digital clone through video, allowing the digital clone platform according to embodiments to create a digital clone that includes the user's thoughts, voice, and appearance, and allows for interaction in different languages ​​and the generation of a 3D avatar.

[0061] The data analysis unit can analyze the user's brainwave data and generate a digital clone that reflects their thought patterns. For example, the data analysis unit has the user wear a dedicated brainwave measurement device and collect data on their daily thoughts and emotions. The generation AI analyzes this brainwave data and generates a digital clone that reflects the user's thought patterns. The data analysis unit also collects brainwave data when the user is working on a specific task, and the generation AI analyzes their thought patterns based on that data. This makes it possible to create a digital clone that reflects the user's problem-solving ability and creativity. The data analysis unit also analyzes brainwave data in real time and generates a digital clone that reflects the user's current thought state. For example, it can compare brainwave data when the user is relaxed and when they are concentrating and create clones that reflect each state. This makes it possible to generate a digital clone that reflects the user's thought patterns.

[0062] The data analysis unit can analyze a user's past social media posts and email history to generate a digital clone that reflects the user's personality in more detail. For example, the data analysis unit collects the user's social media posts, and the generation AI analyzes their content. For example, the data analysis unit generates a digital clone that reflects the user's personality based on the frequency and content of posts and the language used. The data analysis unit also analyzes the user's email history to extract communication style and interests. Based on this data, the generation AI can create a digital clone that reflects the user's personality in more detail. The data analysis unit can also analyze emotional fluctuations from social media posts and email history to generate a digital clone that reflects the user's emotional patterns. For example, it can compare periods with many positive posts with periods with many negative posts and create clones that reflect each state. This allows the generation of a digital clone that reflects the user's personality in more detail.

[0063] The data analysis unit can use the emotion estimation function to generate a digital clone that reflects the user's emotional state in real time. The data analysis unit, for example, analyzes the user's facial expressions and voice in real time and uses the emotion estimation function to generate a digital clone that reflects the user's emotional state. For example, when the user is smiling, the clone also smiles. The data analysis unit also collects the user's biometric data (heart rate, galvanic skin response, etc.) in real time and analyzes the data using the emotion estimation function. This makes it possible to generate a digital clone that reflects the user's emotional state. The data analysis unit also analyzes text data entered by the user in real time and uses the emotion estimation function to generate a digital clone that reflects the user's emotional state. For example, if the text contains a lot of positive words, the clone can also show a positive expression. This makes it possible to generate a digital clone that reflects the user's emotional state in real time.

[0064] The digital clone generation unit can generate a digital clone of a pet or animal, enabling it to interact with its owner. For example, an owner can upload photos and videos of their pet, and the generation AI analyzes the data to generate a digital clone of the pet. This allows the owner to virtually interact with their pet. The digital clone generation unit also collects behavioral data of the pet (for example, walking patterns and cries), and the generation AI analyzes the data to generate a digital clone of the pet. This creates a clone that reflects the pet's personality. The digital clone generation unit also allows the owner to input memories and episodes with their pet, and the generation AI generates a digital clone of the pet based on that information. This allows the owner to virtually recreate memories with their pet. This allows the generation of a digital clone of a pet or animal, enabling it to interact with the owner.

[0065] The digital clone generation unit can generate digital clones of historical figures and provide dialogue for educational purposes. For example, the digital clone generation unit uploads a photograph or portrait of the historical figure, and the generation AI analyzes the data to generate a digital clone. This allows students to virtually interact with the historical figure. The digital clone generation unit can also analyze the historical figure's writings and letters, and the generation AI generates a digital clone based on the content. This allows students to learn about the historical figure's thoughts and opinions. The digital clone generation unit can also create a database of the historical figure's life and achievements, and the generation AI generates a digital clone based on that information. This allows students to virtually experience the historical figure's life. This allows digital clones of historical figures to be generated and provide dialogue for educational purposes.

[0066] The digital clone generation unit can use the emotion estimation function to generate a digital clone that generates music and videos according to the user's emotions. The digital clone generation unit, for example, analyzes the user's emotional state in real time and creates a digital clone that generates music according to the emotion. For example, when the user is relaxed, relaxing music is played. The digital clone generation unit also creates a digital clone that generates videos according to the user's emotions based on the user's emotional state. For example, comforting videos can be played when the user is sad. The digital clone generation unit also analyzes the user's emotional data and builds a system that automatically generates music and videos according to the user's emotions. For example, positive videos can be played when the emotion score is high. This makes it possible to generate a digital clone that generates music and videos according to the user's emotions.

[0067] The language conversion unit can generate a digital clone that reflects the user's dialect and regional expressions. For example, the language conversion unit collects the dialect and regional expressions spoken by the user, and the generation AI analyzes the data to generate a digital clone. This creates a clone that reflects the user's regional characteristics. The language conversion unit also references a regional language database to learn the user's dialect and expressions, and the generation AI generates a digital clone based on that information. This reflects regional expressions. The language conversion unit also allows the user to input dialects and regional expressions, and the generation AI generates a digital clone based on that data. This creates a clone that reflects the user's individuality in more detail. This makes it possible to generate a digital clone that reflects the user's dialect and regional expressions.

[0068] The language conversion unit can analyze the user's language learning history and generate a digital clone that supports conversations in the language being learned. For example, the language conversion unit collects data from the user's language learning app, and the generation AI analyzes the data to generate a digital clone that supports conversations in the language being learned. This allows the user to converse with the clone in the language being learned. The language conversion unit can also analyze the user's language learning history, and the generation AI can use that information to generate a digital clone that supports conversations in the language being learned. For example, the clone can use phrases and words that the user is learning. The language conversion unit can also analyze data entered by the user in the language being learned, and the generation AI can use that data to generate a digital clone that supports conversations in the language being learned. This allows the user to practice with the clone in the language being learned. This allows the digital clone to be generated that supports conversations in the language being learned.

[0069] The language conversion unit can use the emotion estimation function to generate a digital clone that optimizes emotional expression in different languages. The language conversion unit, for example, analyzes the user's emotional state in real time and generates a digital clone that optimally expresses those emotions in different languages. For example, the user uses the optimal phrase to express joy in English. The language conversion unit also uses the emotion estimation function to learn emotional expressions in different languages, and the generation AI generates a digital clone based on that information. This creates a clone that accurately reflects the user's emotions. The language conversion unit also analyzes emotional expressions input by the user in different languages, and the generation AI generates a digital clone that optimally expresses emotions based on that data. This allows the user's emotions to be accurately conveyed in different languages. This makes it possible to generate a digital clone that optimizes emotional expression in different languages.

[0070] The language conversion unit can generate a digital clone that is compatible with sign language or visual language, enabling dialogue with the hearing impaired. For example, the language conversion unit collects sign language movement data, and the generation AI analyzes the data to generate a digital clone that is compatible with sign language. This allows the hearing impaired to interact with the clone in sign language. The language conversion unit also collects visual language data, and the generation AI analyzes the data to generate a digital clone that is compatible with visual language. This allows users who use visual language to interact with the clone. The language conversion unit also references a database of sign language or visual language, and the generation AI generates a digital clone that is compatible with sign language or visual language based on that information. This allows the hearing impaired to interact smoothly with the clone. This allows the generation of a digital clone that is compatible with sign language or visual language, enabling dialogue with the hearing impaired.

[0071] The language conversion unit can generate a digital clone that reflects culturally specific gestures and customs to promote intercultural communication. For example, the language conversion unit collects data on gestures and customs of different cultures, and the generation AI analyzes the data to generate a digital clone that reflects culturally specific expressions. This facilitates intercultural communication. The language conversion unit also allows a user to input culturally specific gestures and customs, and the generation AI generates a digital clone based on that data. This creates a clone that reflects the user's cultural background. The language conversion unit also references a database of different cultures, and the generation AI generates a digital clone that reflects culturally specific gestures and customs based on that information. This deepens intercultural understanding. This allows a digital clone that reflects culturally specific gestures and customs to promote intercultural communication.

[0072] The language conversion unit can use the emotion estimation function to generate a digital clone that accurately conveys emotional nuances in different languages. The language conversion unit, for example, analyzes the user's emotional state in real time and generates a digital clone that accurately conveys those emotions in different languages. For example, the language conversion unit uses the optimal phrase when the user expresses gratitude in French. The language conversion unit also uses the emotion estimation function to learn emotional nuances in different languages, and the generation AI generates a digital clone based on that information. This allows the user's emotions to be accurately conveyed in different languages. The language conversion unit also analyzes emotional expressions input by the user in different languages, and the generation AI generates a digital clone that accurately conveys emotional nuances based on that data. This allows the user's emotions to be accurately conveyed in different languages. This allows the digital clone to be generated that accurately conveys emotional nuances in different languages.

[0073] The 3D avatar generation unit can analyze a user's movements and gestures using motion capture technology and generate a 3D avatar that reproduces realistic movements. For example, the 3D avatar generation unit has the user wear a motion capture suit and collect daily movements and gestures. The generation AI analyzes this data and generates a 3D avatar that reproduces realistic movements. The 3D avatar generation unit can also upload videos of the user performing specific movements or gestures, and the generation AI can analyze the data and generate a 3D avatar that reproduces realistic movements. The 3D avatar generation unit can also analyze a user's movements and gestures in real time using motion capture technology and generate a 3D avatar based on that data. This creates an avatar that reflects the user's movements in real time. This makes it possible to generate a 3D avatar that realistically reproduces the user's movements and gestures.

[0074] The 3D avatar generation unit can generate a 3D avatar that allows the user to customize their clothing and accessories. For example, the 3D avatar generation unit allows the user to upload photos of their clothing and accessories, and the generation AI analyzes the data to generate a customizable 3D avatar. This allows the user to create an avatar that reflects their own style. The 3D avatar generation unit also allows the generation AI to generate a customizable 3D avatar based on data of the clothing and accessories selected by the user. For example, the clothing and accessories selected by the user can be reflected in the avatar. The 3D avatar generation unit also provides an interface for the user to customize their clothing and accessories, and the generation AI generates the 3D avatar based on that data. This allows the user to customize the avatar to suit their preferences. This allows the generation of a 3D avatar that allows the user to customize their clothing and accessories.

[0075] The 3D avatar generation unit can use an emotion estimation function to generate a 3D avatar that reflects facial expressions and postures corresponding to the user's emotions in real time. The 3D avatar generation unit, for example, analyzes the user's facial expressions and voice in real time and uses the emotion estimation function to generate a 3D avatar that reflects the user's emotional state. For example, when the user is smiling, the avatar also smiles. The 3D avatar generation unit also collects the user's biometric data (such as heart rate and galvanic skin response) in real time and analyzes the data using the emotion estimation function. This allows the generation of a 3D avatar that reflects the user's emotional state. The 3D avatar generation unit also analyzes text data entered by the user in real time and uses the emotion estimation function to generate a 3D avatar that reflects the user's emotional state. For example, if the user's voice contains a lot of positive words, the avatar can also show a positive expression. This allows the generation of a 3D avatar that reflects facial expressions and postures corresponding to the user's emotions in real time.

[0076] The 3D avatar generation unit generates a 3D avatar of the user's pet or animal, allowing the owner to interact with the pet as a virtual pet. For example, the owner uploads photos and videos of their pet, and the generation AI analyzes the data to generate a 3D avatar of the pet. This allows the owner to virtually interact with their pet. The 3D avatar generation unit also collects behavioral data of the pet (e.g., walking patterns and cries), and the generation AI analyzes the data to generate a 3D avatar of the pet. This creates an avatar that reflects the pet's personality. The 3D avatar generation unit also allows the owner to input memories and episodes with their pet, and the generation AI generates a 3D avatar of the pet based on that information. This allows the owner to virtually recreate memories with their pet. This allows the user to virtually interact with their pet as a virtual pet. The 3D avatar generation unit generates a 3D avatar of the user's pet or animal, allowing the owner to interact with the pet as a virtual pet.

[0077] The 3D avatar generator generates 3D avatars of historical figures and fictional characters, which can be used for education and entertainment. For example, the 3D avatar generator uploads a photo or portrait of a historical figure, and the generation AI analyzes the data to generate a 3D avatar. This allows students to virtually interact with historical figures. The 3D avatar generator also uploads design data for fictional characters, and the generation AI analyzes the data to generate a 3D avatar. This allows characters to be used in entertainment content. The 3D avatar generator also analyzes the writings and letters of historical figures, and the generation AI generates a 3D avatar based on the content. This allows students to learn about the person's thoughts and opinions. This allows the generation of 3D avatars of historical figures and fictional characters, which can be used for education and entertainment.

[0078] The 3D avatar generation unit can use the emotion estimation function to generate a 3D avatar that changes its background and environment according to the user's emotions. For example, the 3D avatar generation unit analyzes the user's emotional state in real time and generates a 3D avatar that changes its background and environment according to the user's emotions. For example, when the user is relaxed, a relaxing background is displayed. The 3D avatar generation unit also uses the emotion estimation function to build a system that automatically generates backgrounds and environments based on the user's emotional state. This provides an optimal environment according to the user's emotions. The 3D avatar generation unit also analyzes text data entered by the user in real time and uses the emotion estimation function to generate backgrounds and environments that reflect the user's emotional state. For example, if there are a lot of positive words, a bright background is displayed. This makes it possible to generate a 3D avatar that changes its background and environment according to the user's emotions.

[0079] The storage unit can analyze the user's life log data and generate a digital legacy that reflects important events in their life. For example, the storage unit collects life log data (e.g., diaries and photos) that the user records daily, and the generation AI analyzes that data to generate a digital legacy that reflects important events in their life. The storage unit also analyzes data in which the user records specific events and occurrences, and the generation AI generates a digital legacy based on that information. This reflects the highlights of the user's life. The storage unit also analyzes the user's life log data in real time and automatically extracts important events to generate a digital legacy. For example, it reflects important events such as trips and weddings. This allows the storage unit to analyze the user's life log data and generate a digital legacy that reflects important events in their life.

[0080] The storage unit can link digital clones of the user's family and friends to integrate multiple digital legacies. For example, the storage unit creates digital clones of the user's family and friends and links them to integrate multiple digital legacies. This brings together the digital legacies of the people involved in the user's life. The storage unit also uses the digital clones of family and friends as a generation AI to analyze the data and generate an integrated digital legacy, which reflects the important people in the user's life. The storage unit also allows the user to input memories and episodes with family and friends, and the generation AI uses that information to integrate multiple digital legacies. This brings together important moments in the user's life. This makes it possible to link digital clones of the user's family and friends to integrate multiple digital legacies.

[0081] The storage unit can use an emotion estimation function to generate a digital legacy that emphasizes the user's emotional moments. The storage unit, for example, analyzes the user's emotional state in real time and generates a digital legacy that emphasizes emotional moments using the emotion estimation function. For example, it emphasizes moments that moved the user. The storage unit also generates a digital legacy by using a generation AI to extract emotional moments based on the user's emotion data, thereby reflecting the emotional highlights of the user's life. The storage unit also analyzes text data entered by the user in real time and generates a digital legacy that emphasizes emotional moments using the emotion estimation function. For example, it emphasizes moments with strong positive emotions. This makes it possible to generate a digital legacy that emphasizes the user's emotional moments.

[0082] The preservation unit can create a digital legacy that reflects historical events and cultural background and can be used for educational purposes. For example, the preservation unit collects data on historical events and cultural background, and the generation AI analyzes the data to generate a digital legacy. This allows students to learn about history and culture. The preservation unit also allows users to input information about historical events and cultural background, and the generation AI generates a digital legacy based on that data. This creates a legacy that reflects the user's knowledge. The preservation unit also references a database of historical events and cultural background, and the generation AI generates a digital legacy based on that information. This creates a legacy that can be used for educational purposes. This allows students to create a digital legacy that reflects historical events and cultural background and can be used for educational purposes.

[0083] The storage unit can create a digital legacy that reflects the user's hobbies and interests and share it within the community. For example, the storage unit collects data on the user's hobbies and interests, and the generation AI analyzes the data to generate a digital legacy. This creates a legacy that reflects the user's individuality. The storage unit also allows the user to input information on hobbies and interests, and the generation AI generates a digital legacy based on that data. This creates a legacy that reflects the user's hobbies and interests. The storage unit also references a database on the user's hobbies and interests, and the generation AI generates a digital legacy based on that information. This creates a legacy that reflects the user's hobbies and interests. This creates a digital legacy that reflects the user's hobbies and interests, and can be shared within the community.

[0084] The storage unit can use the emotion estimation function to generate memorial videos and albums based on the user's emotions. The storage unit, for example, analyzes the user's emotional state in real time and generates memorial videos based on those emotions. For example, it creates a video that emphasizes moments that moved the user. The storage unit also uses a generation AI to generate an album based on the user's emotion data. This creates an album that reflects the user's emotional moments. The storage unit also analyzes text data entered by the user in real time and uses the emotion estimation function to generate memorial videos and albums based on emotions. For example, it creates an album that emphasizes moments of strong positive emotions. This makes it possible to generate memorial videos and albums based on the user's emotions.

[0085] The dialogue unit can analyze the user's dialogue history and learn to realize more natural dialogue. For example, the dialogue unit collects the dialogue history between the user and the generation AI, analyzes that data, and learns to realize more natural dialogue. In this way, the generation AI learns the user's dialogue style. The dialogue unit also analyzes the dialogue patterns and trends of the generation AI based on the user's dialogue history, and develops algorithms to realize more natural dialogue. The dialogue unit also analyzes the content of the dialogue between the user and the generation AI, and the generation AI learns to improve the quality of the dialogue based on that data. This makes the dialogue with the user smoother. In this way, the dialogue unit can analyze the user's dialogue history and learn to realize more natural dialogue.

[0086] The dialogue unit can analyze the user's facial expressions and tone of voice during a conversation and generate a response that corresponds to their emotions. For example, the dialogue unit can analyze the user's facial expressions during a conversation in real time and generate a response that corresponds to their emotions. For example, when the user is laughing, the generation AI will also respond positively. The dialogue unit can also analyze the user's tone of voice in real time and generate a response that corresponds to their emotions. For example, when the user is angry, the generation AI will respond calmly. The dialogue unit can also simultaneously analyze the user's facial expressions and tone of voice and generate a response that corresponds to their emotions based on that data. This allows the generation AI to conduct a conversation that matches the user's emotions. This allows the dialogue unit to analyze the user's facial expressions and tone of voice during a conversation and generate a response that corresponds to their emotions.

[0087] The dialogue unit can use the emotion estimation function to optimize the dialogue content according to the user's emotional state. For example, the dialogue unit analyzes the user's emotional state in real time and optimizes the dialogue content according to that emotion. For example, when the user is sad, it generates dialogue content that comforts them. The dialogue unit also uses the emotion estimation function to develop an algorithm that optimizes the dialogue content based on the user's emotional state. This allows the generation AI to conduct dialogue that matches the user's emotions. The dialogue unit also analyzes text data entered by the user in real time and uses the emotion estimation function to optimize the dialogue content according to that emotional state. For example, when the user is feeling strongly positive, it generates encouraging dialogue content. This makes it possible to optimize the dialogue content according to the user's emotional state.

[0088] The dialogue unit can support group dialogue and enable simultaneous dialogue with multiple users. For example, the dialogue unit builds a system that allows multiple users to simultaneously dialogue with the generation AI. This enables group dialogue and promotes communication between users. The dialogue unit also analyzes the history of group dialogue, and the generation AI develops an algorithm that optimizes dialogue with multiple users based on that data. This allows the generation AI to improve the quality of group dialogue. The dialogue unit also collects emotional data when multiple users simultaneously dialogue, and the generation AI generates responses based on that data according to the emotions. This makes group dialogue smoother. This supports group dialogue and enables simultaneous dialogue with multiple users.

[0089] The dialogue unit can automatically summarize the content of the dialogue and provide feedback to the user. For example, the dialogue unit will build a system that automatically summarizes the content of the dialogue a user has with the generation AI and provides feedback on that summary to the user. This allows the user to easily grasp the main points of the dialogue. The dialogue unit will also develop an algorithm that analyzes the content of the dialogue in real time and allows the generation AI to generate a summary based on that data. This ensures that the user does not miss important points in the dialogue. The dialogue unit will also build a system that analyzes the history of the dialogue a user has with the generation AI and automatically generates a summary based on that data. This allows the user to easily look back on past dialogue. This allows the dialogue content to be automatically summarized and feedback to be provided to the user.

[0090] The dialogue unit uses the emotion estimation function to monitor emotional changes during a dialogue in real time, thereby improving the quality of the dialogue. For example, the dialogue unit analyzes the user's emotional state in real time and builds a system that monitors those emotional changes. This allows the generation AI to provide feedback to improve the quality of the dialogue. The dialogue unit also uses the emotion estimation function to monitor emotional changes during a dialogue in real time, and develops an algorithm that optimizes the dialogue content based on that data. This allows the generation AI to conduct a dialogue that is tailored to the user's emotions. The dialogue unit also analyzes text data entered by the user in real time, and builds a system that monitors those emotional changes using the emotion estimation function. This allows the generation AI to provide feedback to improve the quality of the dialogue. This allows the dialogue unit to monitor emotional changes during a dialogue in real time, thereby improving the quality of the dialogue.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The digital clone generation unit can analyze the user's health data and generate a digital clone that reflects the user's health condition. For example, the generation AI can analyze data collected from the user's fitness tracker or smartwatch and generate a digital clone that reflects the user's health condition based on that data. The digital clone generation unit can also analyze the user's medical records and generate a digital clone that reflects the user's health condition based on that information. This allows the generation of a digital clone that reflects the user's health condition in real time.

[0093] The data analysis unit can analyze a user's hobbies and interests and generate a digital clone based on that information. For example, the generation AI can analyze posts about hobbies shared by the user on social media, and use that data to generate a digital clone that reflects those hobbies. The data analysis unit can also analyze the history of content the user views online, and the generation AI can use that information to generate a digital clone that reflects the user's interests. This makes it possible to generate a digital clone that reflects the user's hobbies and interests.

[0094] The data analysis unit can analyze the user's sleep data and generate a digital clone that reflects their sleep patterns. For example, the data analysis unit can analyze the sleep data recorded by the user using a smartwatch or fitness tracker, and the generation AI can use that data to generate a digital clone that reflects their sleep patterns. The data analysis unit can also analyze the sleep diary kept by the user, and the generation AI can use that information to generate a digital clone that reflects their sleep patterns. This allows the generation of a digital clone that reflects the user's sleep patterns.

[0095] The data analysis unit can use the emotion estimation function to generate personalized advertisements based on the user's emotions. For example, it can analyze the user's emotional state in real time and display advertisements that correspond to those emotions. For example, when the user is relaxed, it can introduce products that will help them relax. The data analysis unit also builds a system in which the generation AI automatically generates advertisements based on emotions based on the user's emotional data. This allows the optimal advertisement to be provided according to the user's emotions. This makes it possible to generate personalized advertisements based on the user's emotions.

[0096] The digital clone generation unit can analyze the user's travel history and generate a digital clone that reflects information about the travel destinations. For example, the user can upload photos and videos of travel destinations they have visited in the past, and the generation AI can analyze that data to generate a digital clone that reflects information about the travel destinations. The digital clone generation unit can also analyze the user's travel blogs and reviews, and the generation AI can use that information to generate a digital clone that reflects information about the travel destinations. This makes it possible to generate a digital clone that reflects the user's travel history.

[0097] The digital clone generation unit can generate digital clones of historical figures and provide dialogue for educational purposes. For example, a photo or portrait of a historical figure can be uploaded, and the generation AI can analyze the data to generate a digital clone. This allows students to virtually interact with the historical figure. The digital clone generation unit can also analyze the historical figure's writings and letters, and the generation AI can generate a digital clone based on the content. This allows students to learn about the historical figure's thoughts and opinions. The digital clone generation unit can also create a database of the historical figure's life and achievements, and the generation AI can generate a digital clone based on that information. This allows students to virtually experience the historical figure's life. This allows digital clones of historical figures to be generated and provide dialogue for educational purposes.

[0098] The digital clone generation unit can use the emotion estimation function to generate a digital clone that generates music and videos according to the user's emotions. For example, it can analyze the user's emotional state in real time and create a digital clone that generates music according to that emotion. For example, it can play relaxing music when the user is relaxed. The digital clone generation unit also creates a digital clone that generates videos according to the user's emotions based on the user's emotional state. For example, it can play comforting videos when the user is sad. The digital clone generation unit also analyzes the user's emotional data and builds a system that automatically generates music and videos according to the user's emotions. For example, it can play positive videos when the emotion score is high. This makes it possible to generate a digital clone that generates music and videos according to the user's emotions.

[0099] The language conversion unit can generate a digital clone that reflects the user's dialect and regional expressions. For example, the dialect and regional expressions spoken by the user are collected, and the generation AI analyzes the data to generate a digital clone. This creates a clone that reflects the user's regional characteristics. The language conversion unit also references a regional language database to learn the user's dialect and expressions, and the generation AI generates a digital clone based on that information. This reflects regional expressions. The language conversion unit also allows the user to input dialects and regional expressions, and the generation AI generates a digital clone based on that data. This creates a clone that reflects the user's individuality in more detail. This makes it possible to generate a digital clone that reflects the user's dialect and regional expressions.

[0100] The language conversion unit can use the emotion estimation function to generate a digital clone that optimizes emotional expression in different languages. For example, it can analyze the user's emotional state in real time and generate a digital clone that optimally expresses those emotions in different languages. For example, it can use the optimal phrase for the user to express joy in English. The language conversion unit also uses the emotion estimation function to learn emotional expressions in different languages, and the generation AI generates a digital clone based on that information. This creates a clone that accurately reflects the user's emotions. The language conversion unit also analyzes emotional expressions input by the user in different languages, and the generation AI generates a digital clone that optimally expresses emotions based on that data. This allows the user's emotions to be accurately conveyed in different languages. This makes it possible to generate a digital clone that optimizes emotional expression in different languages.

[0101] The storage unit can analyze the user's life log data and generate a digital legacy that reflects important events in their life. For example, the storage unit collects life log data (e.g., diaries and photos) that the user records daily, and the generation AI analyzes that data to generate a digital legacy that reflects important events in their life. The storage unit can also analyze data in which the user records specific events and occurrences, and the generation AI can generate a digital legacy based on that information. This reflects the highlights of the user's life. The storage unit can also analyze the user's life log data in real time and automatically extract important events to generate a digital legacy. For example, it can reflect important events such as trips and weddings. This allows the storage unit to analyze the user's life log data and generate a digital legacy that reflects important events in their life.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The data analysis unit analyzes data including the user's thoughts, voice, and appearance. For example, the data analysis unit analyzes the user's voice data and extracts voice characteristics. The data analysis unit can also analyze the user's image data and extract appearance characteristics. The data analysis unit can also analyze the user's text data and extract thoughts and personality traits. Step 2: The digital clone generation unit generates a digital clone based on the data analyzed by the data analysis unit. For example, the digital clone generation unit generates a digital clone that reproduces the user's voice. The digital clone generation unit can also generate a digital clone that reproduces the user's appearance. The digital clone generation unit can also generate a digital clone that reflects the user's thoughts and personality. Step 3: The language conversion unit converts the digital clone generated by the digital clone generation unit into a different language. For example, the language conversion unit converts the user's Japanese digital clone into English. The language conversion unit can also convert the user's French digital clone into Spanish. The language conversion unit can also convert the user's German digital clone into Chinese. Step 4: The 3D avatar generation unit generates a 3D avatar based on the appearance of the digital clone generated by the digital clone generation unit. For example, the 3D avatar generation unit generates a 3D avatar based on a photo of the user's face. The 3D avatar generation unit can also generate a 3D avatar based on a photo of the user's entire body. The 3D avatar generation unit can also generate a 3D avatar based on a video of the user. Step 5: The storage unit stores the digital clone. For example, the storage unit stores the digital clone in cloud storage. The storage unit can also store the digital clone in local storage. The storage unit can also store the digital clone in external storage. Step 6: The interaction unit interacts with the digital clone. For example, the interaction unit may enable the user to interact with the digital clone via voice. The interaction unit may also enable the user to interact with the digital clone via text. The interaction unit may also enable the user to interact with the digital clone via video.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data analysis unit that creates a digital clone of the user, including their thoughts, voice, and appearance; a digital clone generation unit that generates a digital clone based on the data analyzed by the data analysis unit; a language conversion unit that converts the digital clone generated by the digital clone generation unit into a different language; a 3D avatar generation unit that generates a 3D avatar based on the appearance of the digital clone generated by the digital clone generation unit; a storage unit for storing the digital clone; and a dialogue unit that dialogues with the digital clone. A system characterized by:

2. The data analysis unit Analyzing the user's brainwave data and generating the digital clone that reflects their thought patterns 2. The system of claim 1.

3. The data analysis unit Analyzing the user's past social media posts and email history to generate a digital clone that reflects their personality in more detail 2. The system of claim 1.

4. The data analysis unit Creating a digital clone that reflects the user's emotional state in real time 2. The system of claim 1.

5. The digital clone generation unit Creating digital clones of pets and animals that can interact with their owners 2. The system of claim 1.

6. The digital clone generation unit Creating digital clones of historical figures and providing dialogue for educational purposes 2. The system of claim 1.

7. The digital clone generation unit Generate music and images according to the user's emotions.

2. The system of claim 1.

Images