System

Abstract

An object of the system according to the embodiment is to comprehensively analyze voices, expressions, and actions of users and generate a realistic 3D avatar and an interactive AI.SOLUTION: A system according to an embodiment includes a sound analyzer, a facial expression analyzer, a behavior analyzer, a 3D avatar generator, and an interactive AI generator. The voice analysis unit analyzes the user's voice. The facial expression analysis unit analyzes a facial expression of the user. The behavior analysis unit analyzes a behavior of a user. The 3D avatar generator generates a 3D avatar based on the results analyzed by the voice analyzer, the facial expression analyzer, and the behavior analyzer. The interactive AI generator generates interactive AI based on the analyzed results of the voice analyzer, the facial expression analyzer, and the behavior analyzer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to fully analyze a user's voice, facial expressions, and behavior to generate realistic 3D avatars and conversational AI.

[0005] The system of the embodiment aims to comprehensively analyze the user's voice, facial expressions, and behavior to generate a realistic 3D avatar and interactive AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice analysis unit, a facial expression analysis unit, a behavior analysis unit, a 3D avatar generation unit, and an interactive AI generation unit. The voice analysis unit analyzes the user's voice. The facial expression analysis unit analyzes the user's facial expressions. The behavior analysis unit analyzes the user's behavior. The 3D avatar generation unit generates a 3D avatar based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. The interactive AI generation unit generates an interactive AI based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. [Effects of the Invention]

[0007] The system of the embodiment can comprehensively analyze a user's voice, facial expressions, and behavior to generate a realistic 3D avatar and interactive AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A service according to an embodiment of the present invention utilizes AI technology to create an alternate version of a user's self. This service runs on a device called Looking Glass Go and generates a realistic 3D avatar and interactive AI by analyzing the user's voice, facial expressions, and behavior. This enables the service to provide a 3D avatar and interactive AI that faithfully reproduces the user's characteristics.

[0029] The service according to the embodiment includes a voice analysis unit, a facial expression analysis unit, a behavior analysis unit, a 3D avatar generation unit, and an interactive AI generation unit. The voice analysis unit analyzes the user's voice. For example, the voice analysis unit analyzes the tone, pitch, rhythm, etc. of the user's voice and generates a voice for a 3D avatar based on the analysis. The voice analysis unit can also extract vocal characteristics from recorded data of the user's voice and generate a voice for the avatar. The voice analysis unit can also estimate the emotion of the user's voice in real time and dynamically adjust the tone and pitch of the avatar's voice according to the emotion. The facial expression analysis unit analyzes the user's facial expression. For example, the facial expression analysis unit captures the user's facial expression with a camera, recognizes facial expressions such as smile, anger, and surprise, and generates a facial expression for the 3D avatar based on the emotion. The facial expression analysis unit can also extract facial characteristics based on image data capturing the user's facial expression and generate a facial expression for the avatar. The facial expression analysis unit can also estimate the emotion of the user's facial expression in real time and dynamically change the avatar's facial expression according to the emotion. The behavior analysis unit analyzes user behavior. For example, the behavior analysis unit captures the user's movements and gestures with a camera, recognizes movements such as waving, walking, and sitting, and generates 3D avatar movements based on these. The behavior analysis unit can also extract movement characteristics from video data capturing the user's movements and generate avatar movements. The behavior analysis unit can also estimate the user's emotions in real time and dynamically change the avatar's movements based on those emotions. The 3D avatar generation unit generates a 3D avatar based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. For example, the 3D avatar generation unit generates a realistic 3D avatar based on characteristic data of the user's voice, facial expression, and behavior. The 3D avatar generation unit can also estimate the user's emotions in real time and dynamically change the avatar's appearance and clothing based on those emotions. The conversational AI generation unit generates conversational AI based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. For example, the conversational AI generation unit can generate conversational AI based on data on the user's voice and behavior, and can engage in natural conversations with the user.The conversational AI generation unit can also estimate the user's emotions in real time and dynamically change the conversational AI's responses depending on those emotions. This allows the service according to the embodiment to provide a 3D avatar and conversational AI that faithfully reproduces the user's characteristics. For example, a user can enjoy conversations with their 3D avatar or use the avatar to give a presentation. Furthermore, by utilizing conversational AI, a user can have natural conversations with their avatar and perform various simulations and training.

[0030] When analyzing the characteristics of a user's voice, the voice analysis unit can track changes in the user's voice based on the conversation history and reflect them in the avatar's voice. For example, the voice analysis unit stores the user's past conversation history in a database, and the AI ​​references that data to track changes in the voice. For example, if the user's voice tends to gradually get higher, the avatar's voice can be changed to match. The voice analysis unit can also analyze the characteristics of the user's voice and adjust the avatar's voice based on that data. For example, if the user's voice tends to get lower, the avatar's voice can be changed to match. This allows changes in the user's voice to be tracked and reflected in the avatar's voice.

[0031] The voice analysis unit uses the analysis results to generate avatar voices in different languages, thereby providing a multilingual avatar. The voice analysis unit, for example, analyzes the characteristics of the user's voice and generates avatar voices in different languages ​​based on that data. For example, it supports multiple languages, such as English, Japanese, and French. The voice analysis unit can also analyze the emotions in the user's voice and generate avatar voices in different languages ​​based on the results. For example, if the user speaks in English, the avatar will respond in English. This allows a multilingual avatar to be provided.

[0032] The voice analysis unit can add a function for the avatar to sing based on the characteristics, enabling development for entertainment applications. For example, the voice analysis unit analyzes the characteristics of the user's voice and adds a function for the avatar to sing based on that data. For example, the avatar sings in accordance with the tone and rhythm of the user's voice. The voice analysis unit can also analyze the emotions in the user's voice and have the avatar sing based on the results. For example, if the user is having fun, the avatar will sing an upbeat song. This allows the avatar to provide a singing function.

[0033] When analyzing the user's facial features, the facial expression analysis unit can track changes in the user's facial expressions based on the data and reflect them in the avatar's facial expression. For example, the facial expression analysis unit stores the user's past facial expression data in a database, and the AI ​​references that data to track changes in facial expressions. For example, if the user smiles frequently, the avatar will smile more accordingly. The facial expression analysis unit can also analyze the user's facial features and adjust the avatar's facial expression based on that data. For example, if the user frequently looks serious, the avatar will also look more serious accordingly. This allows changes in the user's facial expression to be tracked and reflected in the avatar's facial expression.

[0034] The facial expression analysis unit can provide a game in which an avatar uses facial expressions based on the characteristics, and can be used for entertainment purposes. For example, the facial expression analysis unit analyzes the characteristics of a user's facial expression and provides a game in which an avatar uses facial expressions based on that data. For example, a game in which an avatar earns points when a user smiles. The facial expression analysis unit can also analyze the emotions in a user's facial expression and provide a game in which an avatar uses a surprised expression based on the results. For example, if a user has a surprised expression, a game in which an avatar uses a surprised expression can be provided. This makes it possible to provide a game in which an avatar uses facial expressions.

[0035] When analyzing the characteristics of a user's behavior, the behavior analysis unit can track changes in the user's behavior based on the data and reflect them in the avatar's behavior. For example, the behavior analysis unit stores the user's past behavioral data in a database, and the AI ​​references that data to track changes in behavior. For example, if the user waves their hands frequently, the avatar will wave their hands accordingly. The behavior analysis unit can also analyze the characteristics of the user's behavior and adjust the avatar's behavior based on that data. For example, if the user's walking speed changes, the avatar will also change its walking speed accordingly. This allows changes in the user's behavior to be tracked and reflected in the avatar's behavior.

[0036] The behavior analysis unit uses the analysis results to allow the avatar to reproduce movements of different sports, which can be used for training purposes. The behavior analysis unit, for example, analyzes the characteristics of the user's behavior and, based on that data, allows the avatar to reproduce movements of different sports. For example, when the user plays tennis, the avatar will make the same tennis movements. The behavior analysis unit can also analyze the emotions of the user's behavior and, based on the results, allow the avatar to reproduce movements of sports. For example, when the user plays soccer, the avatar will make the same soccer movements. This allows movements of different sports to be reproduced, which can be used for training purposes.

[0037] The behavior analysis unit can add a function that causes the avatar to dance based on the characteristics, enabling development for entertainment applications. For example, the behavior analysis unit analyzes the characteristics of the user's behavior and adds a function that causes the avatar to dance based on that data. For example, when the user moves to a specific rhythm, the avatar also dances to the same rhythm. The behavior analysis unit can also analyze the emotions of the user's behavior and cause the avatar to dance based on the results. For example, if the user is having fun, the avatar will dance happily. This makes it possible to provide a function that causes the avatar to dance.

[0038] When analyzing a user's features, the 3D avatar generation unit can track changes in the user's appearance based on the data and reflect them in the avatar. For example, the 3D avatar generation unit stores the user's past appearance data in a database, and the AI ​​references that data to track changes in appearance. For example, if the user changes their hairstyle, the avatar's hairstyle will also change to match. The 3D avatar generation unit can also analyze the user's appearance features and adjust the avatar's appearance based on that data. For example, if the user changes their body shape, the avatar's body shape will also change to match. This allows changes in the user's appearance to be tracked and reflected in the avatar.

[0039] The 3D avatar generation unit uses the analysis results to have the avatar recreate different fashion styles, which can be used for fashion applications. The 3D avatar generation unit, for example, analyzes the user's characteristics and recreates different fashion styles for the avatar based on that data. For example, if the user prefers casual style, the avatar will also wear casual clothes. The 3D avatar generation unit can also analyze the user's emotions and recreate fashion styles for the avatar based on the results. For example, if the user prefers formal style, the avatar will also wear formal clothes. This allows different fashion styles to be recreated and can be used for fashion applications.

[0040] The 3D avatar generation unit can add a function for the avatar to cosplay based on the user's characteristics, enabling it to be used for entertainment purposes. For example, the 3D avatar generation unit can analyze the user's characteristics and add a function for the avatar to cosplay based on that data. For example, if the user likes a particular character, the avatar can cosplay as that character. The 3D avatar generation unit can also analyze the user's emotions and have the avatar cosplay based on the results. For example, if the user is having fun, the avatar will cosplay in a joyful manner. This allows the avatar to provide a cosplay function.

[0041] The conversational AI generation unit can analyze the user's dialogue history and optimize the conversational AI's responses by referring to the content of past conversations. The conversational AI generation unit, for example, stores the user's dialogue history in a database, and the AI ​​refers to that data to optimize the response. For example, the conversational AI generates an appropriate response based on what the user has said in the past. The conversational AI generation unit can also analyze the user's dialogue history and adjust the conversational AI's responses based on that data. For example, if the user frequently talks about a particular topic, the conversational AI generates a response related to that topic. This allows the conversational AI's responses to be optimized by referring to the content of past conversations.

[0042] The dialogue AI generation unit can use the dialogue history to enable the dialogue AI to provide dialogue in different languages, thereby realizing multilingual dialogue. The dialogue AI generation unit, for example, analyzes the user's dialogue history, and based on that data, the dialogue AI provides dialogue in different languages. For example, if the user speaks in English, the dialogue AI responds in English. The dialogue AI generation unit can also enable the dialogue AI to provide multilingual dialogue based on the user's dialogue history. For example, if the user speaks in Japanese, the dialogue AI responds in Japanese. This allows multilingual dialogue to be realized.

[0043] The conversational AI generation unit can recommend content that matches the user's hobbies and interests based on the conversation history. The conversational AI generation unit, for example, analyzes the user's conversation history, and the conversational AI recommends appropriate content based on that data. For example, if the user talks about movies, the conversational AI will recommend movies. The conversational AI generation unit can also recommend content that matches the user's hobbies and interests based on the user's conversation history. For example, if the user talks about music, the conversational AI will recommend music. This makes it possible to recommend content that matches the user's hobbies and interests.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] In analyzing the user's vocal characteristics, the voice analyzer can also monitor the user's vocal health. For example, it can track changes in vocal tone and pitch and issue a warning if the user may have a cold. It can also assess the user's stress level based on the voice characteristics and suggest relaxation techniques, if necessary. Furthermore, the voice analysis results can provide the user with a vocal training program to improve the quality of their voice.

[0046] When analyzing the characteristics of a user's voice, the voice analysis unit can track changes in the user's voice based on the conversation history and reflect them in the avatar's voice. For example, the user's past conversation history can be stored in a database, and the AI ​​can refer to that data to track changes in the voice. For example, if the user's voice tends to gradually get higher, the avatar's voice can be changed to match. The voice analysis unit can also analyze the characteristics of the user's voice and adjust the avatar's voice based on that data. For example, if the user's voice tends to get lower, the avatar's voice can be changed to match. This allows changes in the user's voice to be tracked and reflected in the avatar's voice.

[0047] The voice analysis unit can use the analysis results to generate avatar voices in different languages, providing a multilingual avatar. For example, it can analyze the characteristics of the user's voice and generate avatar voices in different languages ​​based on that data. For example, it can support multiple languages ​​such as English, Japanese, and French. The voice analysis unit can also analyze the emotions in the user's voice and generate avatar voices in different languages ​​based on the results. For example, if the user speaks in English, the avatar will respond in English. This allows a multilingual avatar to be provided.

[0048] When analyzing the user's facial features, the facial expression analysis unit can track changes in the user's facial expressions based on the data and reflect them in the avatar's facial expression. For example, the user's past facial expression data can be stored in a database, and the AI ​​can refer to that data to track changes in their facial expressions. For example, if the user smiles frequently, the avatar will smile more accordingly. The facial expression analysis unit can also analyze the user's facial features and adjust the avatar's facial expression based on that data. For example, if the user frequently looks serious, the avatar will also look more serious accordingly. This allows changes in the user's facial expressions to be tracked and reflected in the avatar's facial expression.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The voice analysis unit analyzes the user's voice. For example, it analyzes the tone, pitch, rhythm, etc. of the voice and generates the voice of the 3D avatar based on that. It can also extract voice characteristics from the user's voice recording data and generate the avatar's voice. It can also estimate the emotion of the user's voice in real time and dynamically adjust the tone and pitch of the avatar's voice according to that emotion. Step 2: The facial expression analysis unit analyzes the user's facial expression. For example, it captures the user's facial expression with a camera, recognizes expressions such as smile, anger, and surprise, and generates the 3D avatar's facial expression based on that. It can also extract facial features based on image data capturing the user's facial expression and generate the avatar's facial expression. It can also estimate the user's facial emotion in real time and dynamically change the avatar's facial expression according to that emotion. Step 3: The behavior analysis unit analyzes the user's behavior. For example, it captures the user's movements and gestures with a camera, recognizes actions such as waving, walking, and sitting, and generates the movements of a 3D avatar based on these. It can also extract movement characteristics from video data capturing the user's movements and generate the avatar's movements. It can also estimate the user's emotions in real time and dynamically change the avatar's movements according to those emotions. Step 4: The 3D avatar generation unit generates a 3D avatar based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. For example, a realistic 3D avatar can be generated based on the characteristic data of the user's voice, facial expression, and behavior. It can also estimate the user's emotions in real time and dynamically change the avatar's appearance and clothing according to those emotions. Step 5: The conversational AI generation unit generates a conversational AI based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. For example, a conversational AI can be generated based on the user's voice and behavior data, allowing it to have a natural conversation with the user. It can also estimate the user's emotions in real time and dynamically change the conversational AI's responses depending on those emotions.

[0051] (Example 2) A service according to an embodiment of the present invention utilizes AI technology to create an alternate version of a user's self. This service runs on a device called Looking Glass Go and generates a realistic 3D avatar and interactive AI by analyzing the user's voice, facial expressions, and behavior. This enables the service to provide a 3D avatar and interactive AI that faithfully reproduces the user's characteristics.

[0052] The service according to the embodiment includes a voice analysis unit, a facial expression analysis unit, a behavior analysis unit, a 3D avatar generation unit, and an interactive AI generation unit. The voice analysis unit analyzes the user's voice. For example, the voice analysis unit analyzes the tone, pitch, rhythm, etc. of the user's voice and generates a voice for a 3D avatar based on the analysis. The voice analysis unit can also extract vocal characteristics from recorded data of the user's voice and generate a voice for the avatar. The voice analysis unit can also estimate the emotion of the user's voice in real time and dynamically adjust the tone and pitch of the avatar's voice according to the emotion. The facial expression analysis unit analyzes the user's facial expression. For example, the facial expression analysis unit captures the user's facial expression with a camera, recognizes facial expressions such as smile, anger, and surprise, and generates a facial expression for the 3D avatar based on the emotion. The facial expression analysis unit can also extract facial characteristics based on image data capturing the user's facial expression and generate a facial expression for the avatar. The facial expression analysis unit can also estimate the emotion of the user's facial expression in real time and dynamically change the avatar's facial expression according to the emotion. The behavior analysis unit analyzes user behavior. For example, the behavior analysis unit captures the user's movements and gestures with a camera, recognizes movements such as waving, walking, and sitting, and generates 3D avatar movements based on these. The behavior analysis unit can also extract movement characteristics from video data capturing the user's movements and generate avatar movements. The behavior analysis unit can also estimate the user's emotions in real time and dynamically change the avatar's movements based on those emotions. The 3D avatar generation unit generates a 3D avatar based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. For example, the 3D avatar generation unit generates a realistic 3D avatar based on characteristic data of the user's voice, facial expression, and behavior. The 3D avatar generation unit can also estimate the user's emotions in real time and dynamically change the avatar's appearance and clothing based on those emotions. The conversational AI generation unit generates conversational AI based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. For example, the conversational AI generation unit can generate conversational AI based on data on the user's voice and behavior, and can engage in natural conversations with the user.The conversational AI generation unit can also estimate the user's emotions in real time and dynamically change the conversational AI's responses depending on those emotions. This allows the service according to the embodiment to provide a 3D avatar and conversational AI that faithfully reproduces the user's characteristics. For example, a user can enjoy conversations with their 3D avatar or use the avatar to give a presentation. Furthermore, by utilizing conversational AI, a user can have natural conversations with their avatar and perform various simulations and training.

[0053] The voice analysis unit can estimate the user's vocal emotion in real time and dynamically adjust the avatar's vocal tone or pitch according to that emotion. For example, when the user speaks, the voice analysis unit uses AI to analyze the vocal emotion in real time and instantly adjust the avatar's vocal tone and pitch. For example, if the user is excited, the avatar's voice will also change to sound more uplifted. The voice analysis unit can also analyze the user's vocal emotion and adjust the avatar's vocal tone and pitch based on the results. For example, if the user is calm, the avatar's voice will also change to a calmer tone. This allows the avatar's voice to be dynamically adjusted according to the user's emotions.

[0054] When analyzing the characteristics of a user's voice, the voice analysis unit can track changes in the user's voice based on the conversation history and reflect them in the avatar's voice. For example, the voice analysis unit stores the user's past conversation history in a database, and the AI ​​references that data to track changes in the voice. For example, if the user's voice tends to gradually get higher, the avatar's voice can be changed to match. The voice analysis unit can also analyze the characteristics of the user's voice and adjust the avatar's voice based on that data. For example, if the user's voice tends to get lower, the avatar's voice can be changed to match. This allows changes in the user's voice to be tracked and reflected in the avatar's voice.

[0055] The voice analysis unit enables the avatar to generate a response according to the emotion of the user based on the analysis results of the user's voice. The voice analysis unit, for example, analyzes the emotion of the user's voice, and the avatar generates an appropriate response based on the analysis results. For example, if the user is sad, the avatar will offer words of comfort. The voice analysis unit can also analyze the characteristics of the user's voice, and the avatar can generate a response based on that data. For example, if the user is excited, the avatar will offer words of encouragement. This allows the avatar to generate a response according to the user's emotion.

[0056] The voice analysis unit uses the analysis results to generate avatar voices in different languages, thereby providing a multilingual avatar. The voice analysis unit, for example, analyzes the characteristics of the user's voice and generates avatar voices in different languages ​​based on that data. For example, it supports multiple languages, such as English, Japanese, and French. The voice analysis unit can also analyze the emotions in the user's voice and generate avatar voices in different languages ​​based on the results. For example, if the user speaks in English, the avatar will respond in English. This allows a multilingual avatar to be provided.

[0057] The voice analysis unit can add a function for the avatar to sing based on the characteristics, enabling development for entertainment applications. For example, the voice analysis unit analyzes the characteristics of the user's voice and adds a function for the avatar to sing based on that data. For example, the avatar sings in accordance with the tone and rhythm of the user's voice. The voice analysis unit can also analyze the emotions in the user's voice and have the avatar sing based on the results. For example, if the user is having fun, the avatar will sing an upbeat song. This allows the avatar to provide a singing function.

[0058] The voice analysis unit uses the emotion estimation function to analyze the emotion in the user's voice, allowing the avatar to recommend music that matches the emotion. The voice analysis unit, for example, analyzes the emotion in the user's voice, and the avatar recommends appropriate music based on the results. For example, if the user wants to relax, the avatar recommends relaxing music. The voice analysis unit can also analyze the characteristics of the user's voice, allowing the avatar to recommend music based on that data. For example, if the user wants to feel energized, the avatar recommends uplifting music. This allows music to be recommended that matches the user's emotions.

[0059] The facial expression analysis unit can estimate the emotion of the user's facial expression in real time and dynamically change the avatar's facial expression according to that emotion. For example, the facial expression analysis unit captures the user's facial expression with a camera, and AI analyzes the emotion in real time. For example, if the user is smiling, the avatar's facial expression changes to make it smile as well. The facial expression analysis unit can also analyze the emotion of the user's facial expression and dynamically change the avatar's facial expression based on the results. For example, if the user is surprised, the avatar will also have a surprised expression. This makes it possible to dynamically change the avatar's facial expression according to the user's emotions.

[0060] When analyzing the user's facial features, the facial expression analysis unit can track changes in the user's facial expressions based on the data and reflect them in the avatar's facial expression. For example, the facial expression analysis unit stores the user's past facial expression data in a database, and the AI ​​references that data to track changes in facial expressions. For example, if the user smiles frequently, the avatar will smile more accordingly. The facial expression analysis unit can also analyze the user's facial features and adjust the avatar's facial expression based on that data. For example, if the user frequently looks serious, the avatar will also look more serious accordingly. This allows changes in the user's facial expression to be tracked and reflected in the avatar's facial expression.

[0061] The facial expression analysis unit allows the avatar to generate a gesture that corresponds to the emotion of the user based on the analysis result of the user's facial expression. The facial expression analysis unit, for example, analyzes the emotion of the user's facial expression, and the avatar generates an appropriate gesture based on the result. For example, if the user is happy, the avatar claps. The facial expression analysis unit can also analyze the characteristics of the user's facial expression, and the avatar can generate a gesture based on that data. For example, if the user is surprised, the avatar makes a surprised gesture. In this way, a gesture that corresponds to the user's emotion can be generated.

[0062] The facial expression analysis unit can provide a game in which an avatar uses facial expressions based on the characteristics, and can be used for entertainment purposes. For example, the facial expression analysis unit analyzes the characteristics of a user's facial expression and provides a game in which an avatar uses facial expressions based on that data. For example, a game in which an avatar earns points when a user smiles. The facial expression analysis unit can also analyze the emotions in a user's facial expression and provide a game in which an avatar uses a surprised expression based on the results. For example, if a user has a surprised expression, a game in which an avatar uses a surprised expression can be provided. This makes it possible to provide a game in which an avatar uses facial expressions.

[0063] The facial expression analysis unit uses the emotion estimation function to analyze the emotion in the user's facial expression, allowing the avatar to generate an artwork that corresponds to the emotion. The facial expression analysis unit, for example, analyzes the emotion in the user's facial expression, and the avatar generates an appropriate artwork based on the results. For example, if the user is relaxed, the avatar generates a relaxing artwork. The facial expression analysis unit can also analyze the characteristics of the user's facial expression, allowing the avatar to generate an artwork based on that data. For example, if the user is excited, the avatar generates an energetic artwork. In this way, an artwork that corresponds to the user's emotion can be generated.

[0064] The behavior analysis unit can estimate the emotions of a user's actions in real time and dynamically change the avatar's behavior according to those emotions. For example, the behavior analysis unit captures the user's actions with a camera and uses AI to analyze the emotions in real time. For example, when the user waves their hand, the avatar waves their hand in the same way. The behavior analysis unit can also analyze the emotions of a user's actions and dynamically change the avatar's behavior based on the results. For example, if the user is happy, the avatar will make a happy gesture. This makes it possible to dynamically change the avatar's behavior according to the user's emotions.

[0065] When analyzing the characteristics of a user's behavior, the behavior analysis unit can track changes in the user's behavior based on the data and reflect them in the avatar's behavior. For example, the behavior analysis unit stores the user's past behavioral data in a database, and the AI ​​references that data to track changes in behavior. For example, if the user waves their hands frequently, the avatar will wave their hands accordingly. The behavior analysis unit can also analyze the characteristics of the user's behavior and adjust the avatar's behavior based on that data. For example, if the user's walking speed changes, the avatar will also change its walking speed accordingly. This allows changes in the user's behavior to be tracked and reflected in the avatar's behavior.

[0066] The behavior analysis unit can generate an action for the avatar that corresponds to the emotion based on the analysis results of the user's behavior. The behavior analysis unit, for example, analyzes the emotion of the user's behavior, and the avatar generates an appropriate action based on the results. For example, if the user is happy, the avatar claps. The behavior analysis unit can also analyze the characteristics of the user's behavior, and the avatar can generate an action based on that data. For example, if the user is surprised, the avatar makes a surprised action. In this way, an action can be generated that corresponds to the user's emotion.

[0067] The behavior analysis unit uses the analysis results to allow the avatar to reproduce movements of different sports, which can be used for training purposes. The behavior analysis unit, for example, analyzes the characteristics of the user's behavior and, based on that data, allows the avatar to reproduce movements of different sports. For example, when the user plays tennis, the avatar will make the same tennis movements. The behavior analysis unit can also analyze the emotions of the user's behavior and, based on the results, allow the avatar to reproduce movements of sports. For example, when the user plays soccer, the avatar will make the same soccer movements. This allows movements of different sports to be reproduced, which can be used for training purposes.

[0068] The behavior analysis unit can add a function that causes the avatar to dance based on the characteristics, enabling development for entertainment applications. For example, the behavior analysis unit analyzes the characteristics of the user's behavior and adds a function that causes the avatar to dance based on that data. For example, when the user moves to a specific rhythm, the avatar also dances to the same rhythm. The behavior analysis unit can also analyze the emotions of the user's behavior and cause the avatar to dance based on the results. For example, if the user is having fun, the avatar will dance happily. This makes it possible to provide a function that causes the avatar to dance.

[0069] The behavior analysis unit uses the emotion estimation function to analyze the emotions of the user's behavior, and the avatar can provide a fitness program that matches the emotions. The behavior analysis unit, for example, analyzes the emotions of the user's behavior, and the avatar can provide an appropriate fitness program based on the results. For example, if the user wants to relax, the avatar can provide a relaxing fitness program. The behavior analysis unit can also analyze the characteristics of the user's behavior, and the avatar can provide a fitness program based on that data. For example, if the user wants to be energetic, the avatar can provide an energetic fitness program. In this way, a fitness program can be provided that matches the user's emotions.

[0070] The 3D avatar generation unit can estimate the user's emotions in real time and dynamically change the avatar's appearance or clothing according to those emotions. For example, the 3D avatar generation unit can analyze the user's emotions in real time and dynamically change the avatar's appearance and clothing based on the results. For example, if the user is happy, the avatar's clothing can be changed to a brighter color. The 3D avatar generation unit can also analyze the user's emotions and change the avatar's appearance based on the results. For example, if the user is calm, the avatar's appearance can be changed to a calmer expression. This allows the avatar's appearance and clothing to dynamically change according to the user's emotions.

[0071] When analyzing a user's features, the 3D avatar generation unit can track changes in the user's appearance based on the data and reflect them in the avatar. For example, the 3D avatar generation unit stores the user's past appearance data in a database, and the AI ​​references that data to track changes in appearance. For example, if the user changes their hairstyle, the avatar's hairstyle will also change to match. The 3D avatar generation unit can also analyze the user's appearance features and adjust the avatar's appearance based on that data. For example, if the user changes their body shape, the avatar's body shape will also change to match. This allows changes in the user's appearance to be tracked and reflected in the avatar.

[0072] The 3D avatar generation unit can select accessories that correspond to the avatar's emotions based on the results of the feature analysis. The 3D avatar generation unit, for example, analyzes the user's features, and the avatar selects appropriate accessories based on the results. For example, if the user is relaxed, the avatar selects accessories that will help them relax. The 3D avatar generation unit can also analyze the user's emotions, and the avatar selects accessories based on the results. For example, if the user wants to feel energized, the avatar selects accessories that will cheer them up. In this way, accessories can be selected that correspond to the user's emotions.

[0073] The 3D avatar generation unit uses the analysis results to have the avatar recreate different fashion styles, which can be used for fashion applications. The 3D avatar generation unit, for example, analyzes the user's characteristics and recreates different fashion styles for the avatar based on that data. For example, if the user prefers casual style, the avatar will also wear casual clothes. The 3D avatar generation unit can also analyze the user's emotions and recreate fashion styles for the avatar based on the results. For example, if the user prefers formal style, the avatar will also wear formal clothes. This allows different fashion styles to be recreated and can be used for fashion applications.

[0074] The 3D avatar generation unit can add a function for the avatar to cosplay based on the user's characteristics, enabling it to be used for entertainment purposes. For example, the 3D avatar generation unit can analyze the user's characteristics and add a function for the avatar to cosplay based on that data. For example, if the user likes a particular character, the avatar can cosplay as that character. The 3D avatar generation unit can also analyze the user's emotions and have the avatar cosplay based on the results. For example, if the user is having fun, the avatar will cosplay in a joyful manner. This allows the avatar to provide a cosplay function.

[0075] The 3D avatar generation unit can use the emotion estimation function to analyze the user's emotions and have the avatar provide makeup that matches the emotions. For example, the 3D avatar generation unit analyzes the user's emotions and has the avatar provide appropriate makeup based on the results. For example, if the user is relaxed, the avatar provides relaxing makeup. The 3D avatar generation unit can also analyze the user's characteristics and have the avatar provide makeup based on that data. For example, if the user wants to feel energized, the avatar provides energizing makeup. In this way, makeup that matches the user's emotions can be provided.

[0076] The conversational AI generation unit can analyze the user's dialogue history and optimize the conversational AI's responses by referring to the content of past conversations. The conversational AI generation unit, for example, stores the user's dialogue history in a database, and the AI ​​refers to that data to optimize the response. For example, the conversational AI generates an appropriate response based on what the user has said in the past. The conversational AI generation unit can also analyze the user's dialogue history and adjust the conversational AI's responses based on that data. For example, if the user frequently talks about a particular topic, the conversational AI generates a response related to that topic. This allows the conversational AI's responses to be optimized by referring to the content of past conversations.

[0077] The conversational AI generation unit can provide conversational AI advice according to the user's emotions based on the analysis results of the user's emotions. The conversational AI generation unit, for example, analyzes the user's emotions, and the conversational AI provides appropriate advice based on the results. For example, if the user is feeling stressed, the conversational AI will suggest ways to relax. The conversational AI generation unit can also analyze the characteristics of the user's emotions, and the conversational AI can provide advice based on that data. For example, if the user wants to feel better, the conversational AI will provide uplifting advice. This makes it possible to provide advice according to the user's emotions.

[0078] The dialogue AI generation unit can use the dialogue history to enable the dialogue AI to provide dialogue in different languages, thereby realizing multilingual dialogue. The dialogue AI generation unit, for example, analyzes the user's dialogue history, and based on that data, the dialogue AI provides dialogue in different languages. For example, if the user speaks in English, the dialogue AI responds in English. The dialogue AI generation unit can also enable the dialogue AI to provide multilingual dialogue based on the user's dialogue history. For example, if the user speaks in Japanese, the dialogue AI responds in Japanese. This allows multilingual dialogue to be realized.

[0079] The conversational AI generation unit can recommend content that matches the user's hobbies and interests based on the conversation history. The conversational AI generation unit, for example, analyzes the user's conversation history, and the conversational AI recommends appropriate content based on that data. For example, if the user talks about movies, the conversational AI will recommend movies. The conversational AI generation unit can also recommend content that matches the user's hobbies and interests based on the user's conversation history. For example, if the user talks about music, the conversational AI will recommend music. This makes it possible to recommend content that matches the user's hobbies and interests.

[0080] The conversational AI generation unit uses the emotion estimation function to analyze the user's emotions, and the conversational AI can suggest relaxation methods that correspond to the emotions. The conversational AI generation unit, for example, analyzes the user's emotions, and the conversational AI suggests appropriate relaxation methods based on the results. For example, if the user is feeling stressed, the conversational AI will suggest relaxation methods. The conversational AI generation unit can also analyze the characteristics of the user's emotions, and the conversational AI can suggest relaxation methods based on that data. For example, if the user wants to relax, the conversational AI will suggest ways to help them relax. This makes it possible to suggest relaxation methods that correspond to the user's emotions.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] In analyzing the user's vocal characteristics, the voice analyzer can also monitor the user's vocal health. For example, it can track changes in vocal tone and pitch and issue a warning if the user may have a cold. It can also assess the user's stress level based on the voice characteristics and suggest relaxation techniques, if necessary. Furthermore, the voice analysis results can provide the user with a vocal training program to improve the quality of their voice.

[0083] The voice analysis unit can estimate the user's vocal emotions in real time and dynamically adjust the avatar's vocal tone or pitch according to those emotions. For example, when the user speaks, the AI ​​analyzes the vocal emotions in real time and instantly adjusts the avatar's vocal tone and pitch. For example, if the user is excited, the avatar's voice will also change to sound more uplifted. The voice analysis unit can also analyze the user's vocal emotions and adjust the avatar's vocal tone and pitch based on the results. For example, if the user is calm, the avatar's voice will also change to a calmer tone. This allows the avatar's voice to be dynamically adjusted according to the user's emotions.

[0084] When analyzing the characteristics of a user's voice, the voice analysis unit can track changes in the user's voice based on the conversation history and reflect them in the avatar's voice. For example, the user's past conversation history can be stored in a database, and the AI ​​can refer to that data to track changes in the voice. For example, if the user's voice tends to gradually get higher, the avatar's voice can be changed to match. The voice analysis unit can also analyze the characteristics of the user's voice and adjust the avatar's voice based on that data. For example, if the user's voice tends to get lower, the avatar's voice can be changed to match. This allows changes in the user's voice to be tracked and reflected in the avatar's voice.

[0085] The voice analysis unit allows the avatar to generate a response according to the emotion of the user based on the analysis results of the user's voice. For example, the emotion of the user's voice is analyzed, and the avatar generates an appropriate response based on the analysis results. For example, if the user is sad, the avatar will offer words of comfort. The voice analysis unit can also analyze the characteristics of the user's voice, and the avatar can generate a response based on that data. For example, if the user is excited, the avatar will offer words of encouragement. This allows the avatar to generate a response according to the user's emotion.

[0086] The voice analysis unit can use the analysis results to generate avatar voices in different languages, providing a multilingual avatar. For example, it can analyze the characteristics of the user's voice and generate avatar voices in different languages ​​based on that data. For example, it can support multiple languages ​​such as English, Japanese, and French. The voice analysis unit can also analyze the emotions in the user's voice and generate avatar voices in different languages ​​based on the results. For example, if the user speaks in English, the avatar will respond in English. This allows a multilingual avatar to be provided.

[0087] The voice analysis unit can add a function that allows an avatar to sing based on the characteristics, which can be used for entertainment purposes. For example, the characteristics of the user's voice can be analyzed and a function that allows the avatar to sing based on that data can be added. For example, the avatar can sing in accordance with the tone and rhythm of the user's voice. The voice analysis unit can also analyze the emotions in the user's voice and have the avatar sing based on the results. For example, if the user is having fun, the avatar will sing an upbeat song. This allows the avatar to provide a singing function.

[0088] The voice analysis unit uses the emotion estimation function to analyze the emotion in the user's voice, allowing the avatar to recommend music that matches that emotion. For example, the emotion in the user's voice is analyzed, and the avatar recommends appropriate music based on the results. For example, if the user wants to relax, the avatar will recommend relaxing music. The voice analysis unit can also analyze the characteristics of the user's voice, allowing the avatar to recommend music based on that data. For example, if the user wants to feel energized, the avatar will recommend uplifting music. This allows music to be recommended that matches the user's emotions.

[0089] The facial expression analysis unit can estimate the emotion of the user's facial expression in real time and dynamically change the avatar's facial expression according to that emotion. For example, the user's facial expression is captured by a camera, and AI analyzes the emotion in real time. For example, if the user is smiling, the avatar's facial expression changes to make it smile as well. The facial expression analysis unit can also analyze the emotion of the user's facial expression and dynamically change the avatar's facial expression based on the results. For example, if the user is surprised, the avatar will also have a surprised expression. This makes it possible to dynamically change the avatar's facial expression according to the user's emotions.

[0090] When analyzing the user's facial features, the facial expression analysis unit can track changes in the user's facial expressions based on the data and reflect them in the avatar's facial expression. For example, the user's past facial expression data can be stored in a database, and the AI ​​can refer to that data to track changes in their facial expressions. For example, if the user smiles frequently, the avatar will smile more accordingly. The facial expression analysis unit can also analyze the user's facial features and adjust the avatar's facial expression based on that data. For example, if the user frequently looks serious, the avatar will also look more serious accordingly. This allows changes in the user's facial expressions to be tracked and reflected in the avatar's facial expression.

[0091] The facial expression analysis unit can generate gestures that correspond to the emotions of the avatar based on the analysis results of the user's facial expression. For example, the facial expression analysis unit can analyze the emotions of the user's facial expression and generate appropriate gestures based on the results. For example, if the user is happy, the avatar will clap. The facial expression analysis unit can also analyze the characteristics of the user's facial expression and generate gestures based on that data. For example, if the user is surprised, the avatar will make a surprised gesture. This allows the avatar to generate gestures that correspond to the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The voice analysis unit analyzes the user's voice. For example, it analyzes the tone, pitch, rhythm, etc. of the voice and generates the voice of the 3D avatar based on that. It can also extract voice characteristics from the user's voice recording data and generate the avatar's voice. It can also estimate the emotion of the user's voice in real time and dynamically adjust the tone and pitch of the avatar's voice according to that emotion. Step 2: The facial expression analysis unit analyzes the user's facial expression. For example, it captures the user's facial expression with a camera, recognizes expressions such as smile, anger, and surprise, and generates the 3D avatar's facial expression based on that. It can also extract facial features based on image data capturing the user's facial expression and generate the avatar's facial expression. It can also estimate the user's facial emotion in real time and dynamically change the avatar's facial expression according to that emotion. Step 3: The behavior analysis unit analyzes the user's behavior. For example, it captures the user's movements and gestures with a camera, recognizes actions such as waving, walking, and sitting, and generates the movements of a 3D avatar based on these. It can also extract movement characteristics from video data capturing the user's movements and generate the avatar's movements. It can also estimate the user's emotions in real time and dynamically change the avatar's movements according to those emotions. Step 4: The 3D avatar generation unit generates a 3D avatar based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. For example, a realistic 3D avatar can be generated based on the characteristic data of the user's voice, facial expression, and behavior. It can also estimate the user's emotions in real time and dynamically change the avatar's appearance and clothing according to those emotions. Step 5: The conversational AI generation unit generates a conversational AI based on the analysis results of the voice analysis unit, facial expression analysis unit, and behavior analysis unit. For example, a conversational AI can be generated based on the user's voice and behavior data, allowing it to have a natural conversation with the user. It can also estimate the user's emotions in real time and dynamically change the conversational AI's responses depending on those emotions.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 voice analysis unit that analyzes the user's voice; an expression analysis unit that analyzes a user's facial expression; a behavior analysis unit that analyzes user behavior; a 3D avatar generation unit that generates a 3D avatar based on the analysis results of the voice analysis unit, the facial expression analysis unit, and the behavior analysis unit; an interactive AI generation unit that generates an interactive AI based on the analysis results of the voice analysis unit, the facial expression analysis unit, and the behavior analysis unit; A system characterized by:

2. The voice analysis unit Estimating the user's vocal emotion in real time and dynamically adjusting the tone or pitch of the avatar's voice according to the emotion.

2. The system of claim 1.

3. The facial expression analysis unit Estimating the user's facial emotions in real time and dynamically changing the avatar's facial expressions in response to those emotions.

2. The system of claim 1.

4. The behavior analysis unit Estimating the emotions of a user's actions in real time and dynamically changing the behavior of the avatar in response to those emotions.

2. The system of claim 1.

5. The 3D avatar generation unit The user's emotions are estimated in real time, and the appearance or clothing of the avatar is dynamically changed according to the emotions.

2. The system of claim 1.

6. The interactive AI generation unit Estimating the user's emotions in real time and dynamically changing the response of the conversational AI according to those emotions.

2. The system of claim 1.

7. The voice analysis unit Based on the above features, we will add a function that allows the avatar to sing, and develop it for entertainment purposes.

2. The system of claim 1.

8. The facial expression analysis unit Based on the above features, the avatar will provide a game using facial expressions, and be used for entertainment purposes.

2. The system of claim 1.

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