System

Abstract

An object of the system according to the embodiment is to integrally perform generation of an avatar, transition to a Metaverse, storage of a record of conversation with a user, visual processing of application information, and payment of an incentive to a voice actor.SOLUTION: A system includes a generation part, a recording part, a visual processing part, a transition part, and an incentive payment part. The creator immediately creates the Live2D based avatar. The recording unit stores the conversation with the user for a long time. The visual processing portion visually processes the active application information. The transitioner causes the avatar to transition to the Metaverse upon deactivation. The incentive payment unit pays an incentive to the voice actor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not integrate avatar generation, storing conversation records with users, visual processing of app information, migration of avatars to the metaverse, or payment of incentives to voice actors, leaving room for improvement.

[0005] The system of the embodiment aims to comprehensively perform processes from avatar generation to transition to the metaverse, storage of conversation records with users, visual processing of application information, and incentive payments to voice actors. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a recording unit, a visual processing unit, a transition unit, and an incentive payment unit. The generation unit instantly generates a Live2D-based avatar. The recording unit stores conversations with the user for a long period of time. The visual processing unit visually processes information about running apps. The transition unit transitions the avatar to the metaverse when the app is not running. The incentive payment unit pays incentives to voice actors. [Effects of the Invention]

[0007] The system according to the embodiment can perform an integrated process from generating an avatar to transitioning to the metaverse, storing conversation records with users, visually processing application information, and paying incentives to voice actors. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention instantly generates a Live2D-based generative AI and stores long-term records of user conversations. This system visually processes information from running apps, such as videos and games, and provides a flow for users to enjoy the content together. When the app is not running, the avatar transitions to the metaverse and performs autonomous behavior. Furthermore, regarding voices, a business model is adopted in which incentives are paid to voice actors and other providers based on the number of active users. This allows the system to provide a richer experience for users. For example, users can enjoy conversations with avatars generated in real time, or play games and watch videos together. Furthermore, autonomous behavior within the metaverse ensures that the avatars are always active. Furthermore, incentive payments to voice actors can increase the motivation of voice providers.

[0029] The system according to the embodiment includes a generation unit, a recording unit, a visual processing unit, a transition unit, and an incentive payment unit. The generation unit instantly generates a Live2D-based avatar. For example, the generation AI sets the avatar's appearance and personality based on prompts entered by the user and generates the avatar in real time. The generation AI can also customize the avatar according to the user's requests. The recording unit stores conversations with the user for a long period of time. For example, the generation AI records the content of conversations with the user in text format and saves it in cloud storage. The recording unit can also search the saved conversation records so that the user can refer to them later. The visual processing unit visually processes information about running apps. For example, the generation AI analyzes the screen of a video or game being played by the user and provides appropriate comments or advice. The visual processing unit can also react in real time based on the content of the video. The transition unit transitions the avatar to the metaverse when the app is not running. For example, the generation AI transitions the avatar to the metaverse when the user is not using the app and allows it to interact with other avatars. The transition unit can also enable the avatar to perform specific tasks within the metaverse. The incentive payment unit pays incentives to voice actors. For example, each time the generation AI is used, a reward is paid to the voice actor who provided the voice. The incentive payment unit can also increase the reward according to the number of active users. This allows the system to provide a richer experience to users. For example, users can enjoy conversations with avatars generated in real time, or play games or watch videos together. Furthermore, the avatars can be autonomously behave within the metaverse, ensuring that they are always active. Furthermore, incentive payments to voice actors can increase the motivation of voice providers.

[0030] The generation unit can analyze the user's past behavioral history and automatically generate an avatar optimized to the user's preferences. For example, the generation unit uses a generation AI to analyze the user's past behavioral history and extract the characteristics of characters the user prefers. For example, it generates a new avatar based on the appearance and personality of avatars previously selected. The generation unit also stores the user's past actions and choices in a database and generates an avatar optimized to the user's preferences based on that data. For example, it reflects the user's preferred colors and styles. The generation unit also uses a generation AI to analyze the user's past behavioral history in real time and instantly generate an avatar tailored to the user's preferences. For example, it incorporates the characteristics of anime characters the user previously liked. This makes it possible to provide an avatar optimized to the user's preferences.

[0031] The generation unit can analyze the user's real-time facial expressions and tone of voice and dynamically adjust the avatar's facial expressions and movements based on the results. For example, the generation AI captures the user's facial expressions with a camera and adjusts the avatar's facial expressions in real time based on those expressions. For example, when the user smiles, the avatar also smiles. The generation unit also analyzes the user's tone of voice with a microphone and adjusts the avatar's movements to match that tone. For example, when the user is excited, the avatar also moves more actively. The generation AI also simultaneously analyzes the user's real-time facial expressions and tone of voice and dynamically adjusts the avatar's facial expressions and movements based on the results. For example, when the user is surprised, the avatar also makes a surprised expression. This makes it possible to dynamically adjust the avatar according to the user's real-time facial expressions and tone of voice.

[0032] The recording unit can analyze conversation records and automatically tag the user's interests and concerns, improving searchability. The recording unit, for example, analyzes conversation records using natural language processing technology and automatically tags the user's interests and concerns. For example, specific keywords and phrases are extracted and tagged. The recording unit also analyzes the content of conversations with users and builds a system that generates tags based on their interests and concerns. For example, tags related to hobbies and interests are automatically added. The recording unit also analyzes conversation records and tags the user's interests and concerns, improving searchability. For example, when a user searches for past conversations, results are displayed based on related tags. This improves searchability by tagging based on the user's interests and concerns.

[0033] The recording unit can summarize conversation records and extract and display only the important points. For example, the recording unit develops an algorithm to summarize conversation records and extract and display only the important points. For example, it automatically summarizes the main points and conclusions of the conversation. The recording unit also builds a system that analyzes the content of conversations with users and extracts and summarizes important points. For example, it highlights particularly important information in the conversation. The recording unit also adds a function to summarize conversation records and display only the important points so that users can easily refer to them later. For example, it displays the conversation summary on a dashboard. This allows users to efficiently check important points by summarizing the conversation record.

[0034] The visual processing unit can analyze the content of a video or game in real time and provide appropriate advice or comments to the user. For example, the visual processing unit uses a generation AI to analyze the content of a video or game in real time and provide appropriate advice to the user. For example, it can teach game strategies or highlights of the video. The visual processing unit also builds a system that analyzes the content of the video or game the user is watching or playing and provides comments based on that content. For example, it answers questions about the content of the video. The visual processing unit also uses a generation AI to analyze the content of a video or game in real time and provide appropriate advice or comments to the user. For example, it can provide hints based on the progress of the game. This makes it possible to provide advice and comments in real time based on the content of the video or game.

[0035] The visual processing unit can track the user's gaze and prioritize analyzing and displaying information in front of the gaze. For example, the visual processing unit uses a generation AI to track the user's gaze with a camera and prioritize analyzing information in front of the gaze. For example, it displays detailed information about the part the user is focusing on. The visual processing unit also builds a system that tracks the user's gaze in real time and prioritizes analyzing and displaying information in front of the gaze. For example, it displays an explanation of the object in front of the gaze. The visual processing unit also uses a generation AI to track the user's gaze and prioritize analyzing and displaying information in front of the gaze. For example, it provides related information about the part the user is looking at. This allows information to be prioritized and analyzed and displayed based on the user's gaze.

[0036] The transition unit can record the avatar's behavioral history within the metaverse so that the user can check it later. For example, the transition unit creates a system in which the generation AI records the avatar's behavioral history within the metaverse so that the user can check it later. For example, it stores information about events the avatar attended and users with whom the avatar interacted. The transition unit also records the avatar's behavior within the metaverse in real time so that the user can check that history later. For example, it displays the tasks the avatar performed and the goals it achieved. The transition unit also provides a function in which the generation AI records the avatar's behavioral history within the metaverse so that the user can check it later. For example, it stores information about places the avatar visited and items acquired. This allows the avatar's behavioral history within the metaverse to be recorded and checked later.

[0037] The transition unit can analyze interactions with other avatars in the metaverse and provide useful information to the user. For example, the transition unit constructs a system in which the generation AI analyzes interactions with other avatars in the metaverse and provides useful information to the user. For example, it displays the profiles and interests of users with whom the avatar has interacted. The transition unit also analyzes the avatar's interactions in the metaverse and provides useful information to the user based on the results. For example, it displays details of events the avatar attended and ratings of other avatars. The transition unit also adds a function in which the generation AI analyzes interactions with other avatars in the metaverse and provides useful information to the user. For example, it reports the knowledge and skills the avatar has acquired to the user. This allows the generation AI to analyze interactions with other avatars in the metaverse and provide useful information.

[0038] The incentive payment unit can analyze the voice actor's performance and dynamically adjust incentives based on user reactions. For example, the incentive payment unit constructs a system in which a generation AI analyzes the voice actor's performance and dynamically adjusts incentives based on user reactions. For example, it increases rewards for voice actors who are highly rated by users. The incentive payment unit also analyzes the voice actor's performance in real time and adjusts incentives based on user reactions. For example, it changes rewards according to user feedback. The incentive payment unit also analyzes the voice actor's performance using a generation AI and dynamically adjusts incentives based on user reaction data. For example, it increases rewards when the user's emotional reaction is positive. This makes it possible to dynamically adjust incentives based on the voice actor's performance.

[0039] The incentive payment unit can evaluate the quality of a voice actor's voice and provide additional incentives for high-quality voices. For example, the incentive payment unit builds a system in which a generation AI evaluates the quality of a voice actor's voice and provides additional incentives for high-quality voices. For example, it evaluates the clarity of the voice and the richness of emotional expression. The incentive payment unit also analyzes the quality of the voice actor's voice in real time and provides additional incentives based on the results. For example, it increases the reward when the voice quality is high. The incentive payment unit also evaluates the quality of a voice actor's voice and provides additional incentives for high-quality voices. For example, it increases the reward for voice actors who are highly rated by users. This makes it possible to provide additional incentives based on voice quality.

[0040] The incentive payment unit can accommodate voice actors for different languages ​​and provide incentives to international users. For example, the incentive payment unit builds a system in which the generation AI accommodates voice actors for different languages ​​and provides incentives to international users. For example, it accommodates multiple languages ​​such as English, Japanese, and Chinese. In addition, in order to provide incentives to voice actors for different languages, the generation AI evaluates the voice quality of the language. For example, it sets rewards for the voice actors for each language. In addition, the incentive payment unit can accommodate voice actors for different languages ​​and provide incentives to international users. For example, it adjusts rewards based on the evaluations of users of each language. This makes it possible to accommodate voice actors for different languages ​​and provide incentives to international users.

[0041] The incentive payment unit can analyze the voice actor's performance in real time and provide instant feedback based on the user's reaction. For example, the incentive payment unit builds a system in which a generation AI analyzes the voice actor's performance in real time and provides instant feedback based on the user's reaction. For example, if the user's rating is low, it points out areas for improvement. The incentive payment unit also analyzes the voice actor's performance in real time and provides instant feedback based on the results. For example, it gives advice based on the user's emotional reaction. The incentive payment unit also analyzes the voice actor's performance in real time and provides instant feedback based on the user's reaction data. For example, it sends compliments if the user's rating is high. This makes it possible to provide real-time feedback based on the voice actor's performance.

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

[0043] The recording unit can summarize conversation records and extract and display only the important points. For example, an algorithm will be developed to summarize conversation records and extract and display only the important points. The main points and conclusions of the conversation will be automatically summarized. The recording unit will also build a system that analyzes the content of conversations with users and extracts and summarizes the important points. This will highlight particularly important information in the conversation. Furthermore, the recording unit will add a function to summarize conversation records and display only the important points so that users can easily refer to them later. The conversation summary will be displayed on a dashboard. This will allow users to efficiently check the important points by summarizing the conversation record.

[0044] The visual processing unit can analyze the content of videos or games in real time and provide appropriate advice and comments to users. For example, the generation AI can analyze the content of videos or games in real time and provide appropriate advice to users, such as teaching them how to beat a game or highlights of the video. The visual processing unit can also build a system that analyzes the content of videos and games that users are watching or playing and provides comments based on that content. It can also answer questions about the content of the video. Furthermore, the visual processing unit can use the generation AI to analyze the content of videos or games in real time and provide appropriate advice and comments to users, such as giving hints based on the progress of the game. This makes it possible to provide advice and comments in real time based on the content of videos and games.

[0045] The transition unit can record the avatar's behavioral history within the metaverse, allowing the user to check it later. For example, a system can be built in which the generation AI records the avatar's behavioral history within the metaverse, allowing the user to check it later. Information on events the avatar attended and users with whom the avatar interacted is saved. The transition unit also records the avatar's behavior within the metaverse in real time, allowing the user to check that history later. It displays the tasks the avatar performed and goals it achieved. Furthermore, the transition unit provides a function in which the generation AI records the avatar's behavioral history within the metaverse, allowing the user to check it later. Information on places the avatar visited and items acquired is saved. This allows the avatar's behavioral history within the metaverse to be recorded and checked later.

[0046] The generation unit can analyze a user's past behavioral history and automatically generate an avatar optimized to the user's preferences. For example, the generation AI analyzes the user's past behavioral history and extracts the characteristics of the user's favorite characters. A new avatar is generated based on the appearance and personality of previously selected avatars. The generation unit also stores the user's past actions and choices in a database and generates an avatar optimized to the user's preferences based on that data. It reflects the user's preferred colors and styles. Furthermore, the generation unit uses the generation AI to analyze the user's past behavioral history in real time and instantly generate an avatar tailored to the user's preferences. It incorporates the characteristics of anime characters that the user has previously liked. This makes it possible to provide an avatar optimized to the user's preferences.

[0047] The recording unit can analyze conversation records and automatically tag the user's interests and concerns, improving searchability. For example, the conversation records can be analyzed using natural language processing technology to automatically tag the user's interests and concerns. Specific keywords and phrases are extracted and tags are attached. The recording unit can also analyze the content of conversations with users and build a system that generates tags based on their interests and concerns. Tags related to hobbies and interests are automatically attached. Furthermore, the recording unit analyzes conversation records and tags the user's interests and concerns, improving searchability. When a user searches for past conversations, results are displayed based on related tags. This improves searchability by tagging based on the user's interests and concerns.

[0048] The visual processing unit can track the user's gaze and prioritize analyzing and displaying information in front of the gaze. For example, the generation AI tracks the user's gaze with a camera and prioritizes analyzing information in front of the gaze. Detailed information about the part the user is focusing on is displayed. The visual processing unit also builds a system that tracks the user's gaze in real time and prioritizes analyzing and displaying information in front of the gaze. An explanation of the object in front of the gaze is displayed. Furthermore, the visual processing unit uses the generation AI to track the user's gaze and prioritize analyzing and displaying information in front of the gaze. It provides related information about the part the user is looking at. This allows information to be analyzed and displayed preferentially based on the user's gaze.

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

[0050] Step 1: The generator instantly generates a Live2D-based avatar. For example, the generator AI can set the avatar's appearance and personality based on prompts entered by the user and generate it in real time. The generator AI can also customize the avatar according to the user's requests. Step 2: The recording unit stores the conversation with the user for a long period of time. For example, the generation AI records the conversation with the user in text format and saves it in cloud storage. The recording unit can also search the saved conversation records so that the user can refer to them later. Step 3: The visual processing unit visually processes the information from the running app. For example, the generative AI analyzes the video or game screen the user is playing and provides appropriate comments and advice. The visual processing unit can also react in real time based on the content of the video. Step 4: The transition unit transitions the avatar to the metaverse when the app is not running. For example, the generation AI transitions the avatar to the metaverse when the user is not using the app, allowing it to interact with other avatars. The transition unit also allows the avatar to perform specific tasks within the metaverse. Step 5: The incentive payment unit pays an incentive to the voice actor. For example, each time the generation AI is used, the voice actor who provided the voice is paid a fee. The incentive payment unit can also increase the fee depending on the number of active users.

[0051] (Example 2) A system according to an embodiment of the present invention instantly generates a Live2D-based generative AI and stores long-term records of user conversations. This system visually processes information from running apps, such as videos and games, and provides a flow for users to enjoy the content together. When the app is not running, the avatar transitions to the metaverse and performs autonomous behavior. Furthermore, regarding voices, a business model is adopted in which incentives are paid to voice actors and other providers based on the number of active users. This allows the system to provide a richer experience for users. For example, users can enjoy conversations with avatars generated in real time, or play games and watch videos together. Furthermore, autonomous behavior within the metaverse ensures that the avatars are always active. Furthermore, incentive payments to voice actors can increase the motivation of voice providers.

[0052] The system according to the embodiment includes a generation unit, a recording unit, a visual processing unit, a transition unit, and an incentive payment unit. The generation unit instantly generates a Live2D-based avatar. For example, the generation AI sets the avatar's appearance and personality based on prompts entered by the user and generates the avatar in real time. The generation AI can also customize the avatar according to the user's requests. The recording unit stores conversations with the user for a long period of time. For example, the generation AI records the content of conversations with the user in text format and saves it in cloud storage. The recording unit can also search the saved conversation records so that the user can refer to them later. The visual processing unit visually processes information about running apps. For example, the generation AI analyzes the screen of a video or game being played by the user and provides appropriate comments or advice. The visual processing unit can also react in real time based on the content of the video. The transition unit transitions the avatar to the metaverse when the app is not running. For example, the generation AI transitions the avatar to the metaverse when the user is not using the app and allows it to interact with other avatars. The transition unit can also enable the avatar to perform specific tasks within the metaverse. The incentive payment unit pays incentives to voice actors. For example, each time the generation AI is used, a reward is paid to the voice actor who provided the voice. The incentive payment unit can also increase the reward according to the number of active users. This allows the system to provide a richer experience to users. For example, users can enjoy conversations with avatars generated in real time, or play games or watch videos together. Furthermore, the avatars can be autonomously behave within the metaverse, ensuring that they are always active. Furthermore, incentive payments to voice actors can increase the motivation of voice providers.

[0053] The generation unit can analyze the user's past behavioral history and automatically generate an avatar optimized to the user's preferences. For example, the generation unit uses a generation AI to analyze the user's past behavioral history and extract the characteristics of characters the user prefers. For example, it generates a new avatar based on the appearance and personality of avatars previously selected. The generation unit also stores the user's past actions and choices in a database and generates an avatar optimized to the user's preferences based on that data. For example, it reflects the user's preferred colors and styles. The generation unit also uses a generation AI to analyze the user's past behavioral history in real time and instantly generate an avatar tailored to the user's preferences. For example, it incorporates the characteristics of anime characters the user previously liked. This makes it possible to provide an avatar optimized to the user's preferences.

[0054] The generation unit can analyze the user's real-time facial expressions and tone of voice and dynamically adjust the avatar's facial expressions and movements based on the results. For example, the generation AI captures the user's facial expressions with a camera and adjusts the avatar's facial expressions in real time based on those expressions. For example, when the user smiles, the avatar also smiles. The generation unit also analyzes the user's tone of voice with a microphone and adjusts the avatar's movements to match that tone. For example, when the user is excited, the avatar also moves more actively. The generation AI also simultaneously analyzes the user's real-time facial expressions and tone of voice and dynamically adjusts the avatar's facial expressions and movements based on the results. For example, when the user is surprised, the avatar also makes a surprised expression. This makes it possible to dynamically adjust the avatar according to the user's real-time facial expressions and tone of voice.

[0055] The generation unit uses the emotion estimation function to generate an avatar according to the user's emotional state, thereby providing a character that matches the user's mood. The generation unit, for example, uses the emotion estimation function to analyze the user's emotional state and generate an avatar according to that emotion. For example, when the user is sad, a comforting character is provided. The generation unit also monitors the user's emotional state in real time and dynamically changes the avatar's appearance and personality to match the emotion. For example, when the user is happy, a cheerful character is generated. The generation unit also uses the emotion estimation function to accumulate user emotion data and generate an avatar that best suits the user's mood based on that data. For example, when the user is feeling stressed, a relaxing character is provided. This makes it possible to provide an avatar that matches the user's emotional state.

[0056] The recording unit can analyze conversation records and automatically tag the user's interests and concerns, improving searchability. The recording unit, for example, analyzes conversation records using natural language processing technology and automatically tags the user's interests and concerns. For example, specific keywords and phrases are extracted and tagged. The recording unit also analyzes the content of conversations with users and builds a system that generates tags based on their interests and concerns. For example, tags related to hobbies and interests are automatically added. The recording unit also analyzes conversation records and tags the user's interests and concerns, improving searchability. For example, when a user searches for past conversations, results are displayed based on related tags. This improves searchability by tagging based on the user's interests and concerns.

[0057] The recording unit can summarize conversation records and extract and display only the important points. For example, the recording unit develops an algorithm to summarize conversation records and extract and display only the important points. For example, it automatically summarizes the main points and conclusions of the conversation. The recording unit also builds a system that analyzes the content of conversations with users and extracts and summarizes important points. For example, it highlights particularly important information in the conversation. The recording unit also adds a function to summarize conversation records and display only the important points so that users can easily refer to them later. For example, it displays the conversation summary on a dashboard. This allows users to efficiently check important points by summarizing the conversation record.

[0058] The recording unit can use the emotion estimation function to record changes in emotions during a recorded conversation, allowing the user to later check what emotions the user was feeling during the conversation. The recording unit, for example, uses the emotion estimation function to record changes in the user's emotions during a conversation in real time. For example, it analyzes what emotions the user felt during the conversation. The recording unit also adds emotion data to the conversation record, allowing the user to later check what emotions the user was feeling during the conversation. For example, it assigns an emotion score to each part of the conversation. The recording unit also uses the emotion estimation function to record changes in emotions during a conversation, building a system that allows the user to later check the changes in emotions. For example, it displays changes in emotions on a conversation timeline. This allows changes in emotions during a conversation to be recorded and checked later.

[0059] The visual processing unit can analyze the content of a video or game in real time and provide appropriate advice or comments to the user. For example, the visual processing unit uses a generation AI to analyze the content of a video or game in real time and provide appropriate advice to the user. For example, it can teach game strategies or highlights of the video. The visual processing unit also builds a system that analyzes the content of the video or game the user is watching or playing and provides comments based on that content. For example, it answers questions about the content of the video. The visual processing unit also uses a generation AI to analyze the content of a video or game in real time and provide appropriate advice or comments to the user. For example, it can provide hints based on the progress of the game. This makes it possible to provide advice and comments in real time based on the content of the video or game.

[0060] The visual processing unit can track the user's gaze and prioritize analyzing and displaying information in front of the gaze. For example, the visual processing unit uses a generation AI to track the user's gaze with a camera and prioritize analyzing information in front of the gaze. For example, it displays detailed information about the part the user is focusing on. The visual processing unit also builds a system that tracks the user's gaze in real time and prioritizes analyzing and displaying information in front of the gaze. For example, it displays an explanation of the object in front of the gaze. The visual processing unit also uses a generation AI to track the user's gaze and prioritize analyzing and displaying information in front of the gaze. For example, it provides related information about the part the user is looking at. This allows information to be prioritized and analyzed and displayed based on the user's gaze.

[0061] The visual processing unit uses the emotion estimation function to analyze whether the user is enjoying the content, and if so, can suggest further related content. For example, the visual processing unit uses the emotion estimation function to analyze whether the user is enjoying the content, and if so, suggests related content. For example, when the user is smiling, it suggests a similar video. The visual processing unit also monitors the user's emotional state in real time, and builds a system that suggests related content if the user is enjoying the content. For example, when the user is excited, it suggests an action game. The visual processing unit also analyzes whether the user is enjoying the content based on the emotion estimation data, and if so, suggests further related content. For example, when the user is moved, it suggests an emotional movie. In this way, it is possible to suggest related content when the user is enjoying the content.

[0062] The transition unit can record the avatar's behavioral history within the metaverse so that the user can check it later. For example, the transition unit creates a system in which the generation AI records the avatar's behavioral history within the metaverse so that the user can check it later. For example, it stores information about events the avatar attended and users with whom the avatar interacted. The transition unit also records the avatar's behavior within the metaverse in real time so that the user can check that history later. For example, it displays the tasks the avatar performed and the goals it achieved. The transition unit also provides a function in which the generation AI records the avatar's behavioral history within the metaverse so that the user can check it later. For example, it stores information about places the avatar visited and items acquired. This allows the avatar's behavioral history within the metaverse to be recorded and checked later.

[0063] The transition unit can analyze interactions with other avatars in the metaverse and provide useful information to the user. For example, the transition unit constructs a system in which the generation AI analyzes interactions with other avatars in the metaverse and provides useful information to the user. For example, it displays the profiles and interests of users with whom the avatar has interacted. The transition unit also analyzes the avatar's interactions in the metaverse and provides useful information to the user based on the results. For example, it displays details of events the avatar attended and ratings of other avatars. The transition unit also adds a function in which the generation AI analyzes interactions with other avatars in the metaverse and provides useful information to the user. For example, it reports the knowledge and skills the avatar has acquired to the user. This allows the generation AI to analyze interactions with other avatars in the metaverse and provide useful information.

[0064] The transition unit can use the emotion estimation function to analyze the impact of the avatar's actions in the metaverse on the user's emotions and prioritize actions that have a positive impact. For example, the transition unit can use the emotion estimation function to analyze the impact of the avatar's actions in the metaverse on the user's emotions and prioritize actions that have a positive impact. For example, the avatar takes an action that entertains the user. The transition unit also analyzes the avatar's actions in the metaverse and builds a system that evaluates the impact of the actions on the user's emotions. For example, the avatar takes an action that relaxes the user. The transition unit also analyzes the impact of the avatar's actions in the metaverse on the user's emotions based on the emotion estimation data and prioritizes actions that have a positive impact. For example, the avatar takes an action that encourages the user. This makes it possible to analyze the impact of the avatar's actions in the metaverse on the user's emotions and prioritize actions that have a positive impact.

[0065] The incentive payment unit can analyze the voice actor's performance and dynamically adjust incentives based on user reactions. For example, the incentive payment unit constructs a system in which a generation AI analyzes the voice actor's performance and dynamically adjusts incentives based on user reactions. For example, it increases rewards for voice actors who are highly rated by users. The incentive payment unit also analyzes the voice actor's performance in real time and adjusts incentives based on user reactions. For example, it changes rewards according to user feedback. The incentive payment unit also analyzes the voice actor's performance using a generation AI and dynamically adjusts incentives based on user reaction data. For example, it increases rewards when the user's emotional reaction is positive. This makes it possible to dynamically adjust incentives based on the voice actor's performance.

[0066] The incentive payment unit can evaluate the quality of a voice actor's voice and provide additional incentives for high-quality voices. For example, the incentive payment unit builds a system in which a generation AI evaluates the quality of a voice actor's voice and provides additional incentives for high-quality voices. For example, it evaluates the clarity of the voice and the richness of emotional expression. The incentive payment unit also analyzes the quality of the voice actor's voice in real time and provides additional incentives based on the results. For example, it increases the reward when the voice quality is high. The incentive payment unit also evaluates the quality of a voice actor's voice and provides additional incentives for high-quality voices. For example, it increases the reward for voice actors who are highly rated by users. This makes it possible to provide additional incentives based on voice quality.

[0067] The incentive payment unit uses the emotion estimation function to analyze what emotions the user feels toward the voice of a voice actor, and can increase incentives for voice actors who elicit positive emotions. The incentive payment unit, for example, uses the emotion estimation function to analyze what emotions the user feels toward the voice of a voice actor, and builds a system that increases incentives for voice actors who elicit positive emotions. For example, the incentive payment unit increases rewards when the user is enjoying themselves. The incentive payment unit also analyzes the user's emotional reactions in real time, and increases incentives for voice actors who elicit positive emotions. For example, the incentive payment unit increases rewards when the user is moved. The incentive payment unit also analyzes what emotions the user feels toward the voice of a voice actor, based on the emotion estimation data, and increases incentives for voice actors who elicit positive emotions. For example, the incentive payment unit increases rewards when the user is relaxed. In this way, incentives can be increased based on the user's emotions.

[0068] The incentive payment unit can accommodate voice actors for different languages ​​and provide incentives to international users. For example, the incentive payment unit builds a system in which the generation AI accommodates voice actors for different languages ​​and provides incentives to international users. For example, it accommodates multiple languages ​​such as English, Japanese, and Chinese. In addition, in order to provide incentives to voice actors for different languages, the generation AI evaluates the voice quality of the language. For example, it sets rewards for the voice actors for each language. In addition, the incentive payment unit can accommodate voice actors for different languages ​​and provide incentives to international users. For example, it adjusts rewards based on the evaluations of users of each language. This makes it possible to accommodate voice actors for different languages ​​and provide incentives to international users.

[0069] The incentive payment unit can analyze the voice actor's performance in real time and provide instant feedback based on the user's reaction. For example, the incentive payment unit builds a system in which a generation AI analyzes the voice actor's performance in real time and provides instant feedback based on the user's reaction. For example, if the user's rating is low, it points out areas for improvement. The incentive payment unit also analyzes the voice actor's performance in real time and provides instant feedback based on the results. For example, it gives advice based on the user's emotional reaction. The incentive payment unit also analyzes the voice actor's performance in real time and provides instant feedback based on the user's reaction data. For example, it sends compliments if the user's rating is high. This makes it possible to provide real-time feedback based on the voice actor's performance.

[0070] The incentive payment unit uses the emotion estimation function to analyze what emotions the user feels toward the voice of a voice actor, and can increase incentives for voice actors who elicit positive emotions. The incentive payment unit, for example, uses the emotion estimation function to analyze what emotions the user feels toward the voice of a voice actor, and builds a system that increases incentives for voice actors who elicit positive emotions. For example, the incentive payment unit increases rewards when the user is enjoying themselves. The incentive payment unit also analyzes the user's emotional reactions in real time, and increases incentives for voice actors who elicit positive emotions. For example, the incentive payment unit increases rewards when the user is moved. The incentive payment unit also analyzes what emotions the user feels toward the voice of a voice actor, based on the emotion estimation data, and increases incentives for voice actors who elicit positive emotions. For example, the incentive payment unit increases rewards when the user is relaxed. In this way, incentives can be increased based on the user's emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The generation unit can analyze the user's real-time facial expressions and tone of voice and dynamically adjust the avatar's facial expressions and movements based on that. For example, the generation AI captures the user's facial expressions with a camera and adjusts the avatar's facial expressions in real time based on those expressions. When the user smiles, the avatar also smiles. The generation unit also analyzes the user's tone of voice with a microphone and adjusts the avatar's movements to match that tone. When the user is excited, the avatar also moves more actively. Furthermore, the generation AI simultaneously analyzes the user's real-time facial expressions and tone of voice and dynamically adjusts the avatar's facial expressions and movements based on that. When the user is surprised, the avatar also makes a surprised expression. This makes it possible to dynamically adjust the avatar according to the user's real-time facial expressions and tone of voice.

[0073] The recording unit can summarize conversation records and extract and display only the important points. For example, an algorithm will be developed to summarize conversation records and extract and display only the important points. The main points and conclusions of the conversation will be automatically summarized. The recording unit will also build a system that analyzes the content of conversations with users and extracts and summarizes the important points. This will highlight particularly important information in the conversation. Furthermore, the recording unit will add a function to summarize conversation records and display only the important points so that users can easily refer to them later. The conversation summary will be displayed on a dashboard. This will allow users to efficiently check the important points by summarizing the conversation record.

[0074] The visual processing unit can analyze the content of videos or games in real time and provide appropriate advice and comments to users. For example, the generation AI can analyze the content of videos or games in real time and provide appropriate advice to users, such as teaching them how to beat a game or highlights of the video. The visual processing unit can also build a system that analyzes the content of videos and games that users are watching or playing and provides comments based on that content. It can also answer questions about the content of the video. Furthermore, the visual processing unit can use the generation AI to analyze the content of videos or games in real time and provide appropriate advice and comments to users, such as giving hints based on the progress of the game. This makes it possible to provide advice and comments in real time based on the content of videos and games.

[0075] The transition unit can record the avatar's behavioral history within the metaverse, allowing the user to check it later. For example, a system can be built in which the generation AI records the avatar's behavioral history within the metaverse, allowing the user to check it later. Information on events the avatar attended and users with whom the avatar interacted is saved. The transition unit also records the avatar's behavior within the metaverse in real time, allowing the user to check that history later. It displays the tasks the avatar performed and goals it achieved. Furthermore, the transition unit provides a function in which the generation AI records the avatar's behavioral history within the metaverse, allowing the user to check it later. Information on places the avatar visited and items acquired is saved. This allows the avatar's behavioral history within the metaverse to be recorded and checked later.

[0076] The incentive payment unit can analyze the voice actor's performance and dynamically adjust incentives based on user reactions. For example, a system can be constructed in which the generation AI analyzes the voice actor's performance and dynamically adjusts incentives based on user reactions. Rewards are increased for voice actors who receive high user ratings. The incentive payment unit also analyzes the voice actor's performance in real time and adjusts incentives based on user reactions. Rewards are changed according to user feedback. Furthermore, the incentive payment unit uses the generation AI to analyze the voice actor's performance and dynamically adjust incentives based on user reaction data. Rewards are increased when the user's emotional reaction is positive. This makes it possible to dynamically adjust incentives based on the voice actor's performance.

[0077] The generation unit can analyze a user's past behavioral history and automatically generate an avatar optimized to the user's preferences. For example, the generation AI analyzes the user's past behavioral history and extracts the characteristics of the user's favorite characters. A new avatar is generated based on the appearance and personality of previously selected avatars. The generation unit also stores the user's past actions and choices in a database and generates an avatar optimized to the user's preferences based on that data. It reflects the user's preferred colors and styles. Furthermore, the generation unit uses the generation AI to analyze the user's past behavioral history in real time and instantly generate an avatar tailored to the user's preferences. It incorporates the characteristics of anime characters that the user has previously liked. This makes it possible to provide an avatar optimized to the user's preferences.

[0078] The recording unit can analyze conversation records and automatically tag the user's interests and concerns, improving searchability. For example, the conversation records can be analyzed using natural language processing technology to automatically tag the user's interests and concerns. Specific keywords and phrases are extracted and tags are attached. The recording unit can also analyze the content of conversations with users and build a system that generates tags based on their interests and concerns. Tags related to hobbies and interests are automatically attached. Furthermore, the recording unit analyzes conversation records and tags the user's interests and concerns, improving searchability. When a user searches for past conversations, results are displayed based on related tags. This improves searchability by tagging based on the user's interests and concerns.

[0079] The visual processing unit can track the user's gaze and prioritize analyzing and displaying information in front of the gaze. For example, the generation AI tracks the user's gaze with a camera and prioritizes analyzing information in front of the gaze. Detailed information about the part the user is focusing on is displayed. The visual processing unit also builds a system that tracks the user's gaze in real time and prioritizes analyzing and displaying information in front of the gaze. An explanation of the object in front of the gaze is displayed. Furthermore, the visual processing unit uses the generation AI to track the user's gaze and prioritize analyzing and displaying information in front of the gaze. It provides related information about the part the user is looking at. This allows information to be analyzed and displayed preferentially based on the user's gaze.

[0080] The visual processing unit uses the emotion estimation function to analyze whether the user is enjoying the content, and if so, can suggest further related content. For example, the emotion estimation function can be used to analyze whether the user is enjoying the content, and if so, suggest related content. When the user is smiling, similar videos can be suggested. The visual processing unit can also monitor the user's emotional state in real time, and build a system that suggests related content if the user is enjoying it. When the user is excited, an action game can be suggested. Furthermore, the visual processing unit can analyze whether the user is enjoying the content based on the emotion estimation data, and if so, suggest further related content. When the user is moved, an emotional movie can be suggested. In this way, related content can be suggested when the user is enjoying it.

[0081] The incentive payment unit can use the emotion estimation function to analyze what emotions the user feels toward the voice of a voice actor, and increase incentives for voice actors who elicit positive emotions. For example, a system can be constructed in which the emotion estimation function is used to analyze what emotions the user feels toward the voice of a voice actor, and increase incentives for voice actors who elicit positive emotions. Rewards are increased when the user is enjoying themselves. The incentive payment unit also analyzes the user's emotional response in real time, and increases incentives for voice actors who elicit positive emotions. Rewards are increased when the user is moved. Furthermore, the incentive payment unit analyzes what emotions the user feels toward the voice of a voice actor, based on the emotion estimation data, and increases incentives for voice actors who elicit positive emotions. Rewards are increased when the user is relaxed. In this way, incentives can be increased based on the user's emotions.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The generator instantly generates a Live2D-based avatar. For example, the generator AI can set the avatar's appearance and personality based on prompts entered by the user and generate it in real time. The generator AI can also customize the avatar according to the user's requests. Step 2: The recording unit stores the conversation with the user for a long period of time. For example, the generation AI records the conversation with the user in text format and saves it in cloud storage. The recording unit can also search the saved conversation records so that the user can refer to them later. Step 3: The visual processing unit visually processes the information from the running app. For example, the generative AI analyzes the video or game screen the user is playing and provides appropriate comments and advice. The visual processing unit can also react in real time based on the content of the video. Step 4: The transition unit transitions the avatar to the metaverse when the app is not running. For example, the generation AI transitions the avatar to the metaverse when the user is not using the app, allowing it to interact with other avatars. The transition unit also allows the avatar to perform specific tasks within the metaverse. Step 5: The incentive payment unit pays an incentive to the voice actor. For example, each time the generation AI is used, the voice actor who provided the voice is paid a fee. The incentive payment unit can also increase the fee depending on the number of active users.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 generator that instantly generates Live2D-based avatars, a recording unit that stores conversations with users for a long period of time; a visual processing unit that visually processes information about running applications; a transition unit that transitions the avatar to the metaverse when it is not activated; an incentive payment unit that pays incentives to voice actors; A system characterized by:

2. The generation unit Analyzing the user's past behavior history and automatically generating the avatar optimized to the user's preferences 2. The system of claim 1.

3. The generation unit Analyzing the user's real-time facial expressions and tone of voice and dynamically adjusting the avatar's facial expressions and movements based on the analysis.

2. The system of claim 1.

4. The generation unit The avatar is generated according to the emotional state of the user, and a character that matches the mood of the user is provided.

2. The system of claim 1.

5. The recording unit Analyze conversation records and automatically tag the user's interests to improve searchability 2. The system of claim 1.

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