system

Abstract

An object of a system according to an embodiment is to enable a user to easily generate, manage, and interact with an AItuber.SOLUTION: A system includes a generation part, a communication part, an expansion part, and an NFT part. The generation unit generates AItuber using the generation AI. The communication unit allows the user to communicate with the AItuber generated by the generation unit. The deployment unit deploys the AItuber on the platform and interacts with the fan. The NFT part turns the AItuber into an NFT to enable exchange or trade.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult for users to easily participate as producers in the creation and management of AI characters.

[0005] The system according to the embodiment aims to enable users to easily create, manage, and interact with AItubers. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation unit, a communication unit, a deployment unit, and an NFT unit. The generation unit generates an AItuber using a generation AI. The communication unit allows users to communicate with the AItuber generated by the generation unit. The deployment unit deploys the AItuber on a platform and allows it to interact with fans. The NFT unit converts the AItuber into an NFT, enabling it to be exchanged or bought and sold. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create, manage, and interact with AItubers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AItuber production system according to an embodiment of the present invention is a system that creates AItubers and provides various new experiences as their producer. This system allows users to communicate with and develop AItubers, interact with fans, live stream, and even convert them into NFTs for exchange and trading. As a result, the AItuber production system allows users to consistently create, communicate with, deploy, and convert AItubers into NFTs.

[0029] The AItuber production system according to the embodiment includes a generation unit, a communication unit, a deployment unit, and an NFT conversion unit. The generation unit generates an AItuber using a generation AI. For example, the generation AI receives prompts containing user instructions as input and generates the AItuber's character, personality, appearance, etc. The generation AI uses technologies such as deep learning and GAN (generative artificial network). The communication unit allows the user to communicate with the AItuber generated by the generation unit. For example, the user can issue various instructions to the AItuber and watch its growth. The generation AI causes the AItuber to perform specific actions based on the user's instructions. The deployment unit deploys the AItuber on a platform and allows it to interact with fans. For example, the generation AI determines the AItuber's activities based on the user's instructions and supports communication with fans. The NFT conversion unit converts the AItuber into an NFT, enabling it to be exchanged or bought and sold. For example, the generation AI generates an NFT based on the AItuber's data, which users can exchange or buy with other users. This allows the AItuber production system to allow users to consistently create, communicate, deploy, and convert AItubers into NFTs.

[0030] The generation unit can generate an AItuber based on the user's prompt. For example, the generation unit incorporates an emotion estimation function into the generation AI and analyzes the user's emotions when entering a prompt. For example, if the user is feeling happy, the generation AI generates an AItuber with a bright and cheerful personality. The generation AI also dynamically adjusts the AItuber's appearance based on the user's emotions. For example, if the user is relaxed, the generation AI generates an AItuber with a calm expression. Furthermore, the emotion estimation function is used to dynamically change the AItuber's personality according to the user's emotions. For example, if the user is feeling stressed, the generation AI generates an AItuber with a soothing personality. This allows the generation of an AItuber based on the user's instructions.

[0031] The communication unit can make the AItuber take specific actions based on the user's instructions. In the communication unit, for example, the generation AI analyzes the user's past prompt history and learns the user's preferences. For example, it extracts the characteristics of AItubers that the user prefers based on the trends of prompts entered in the past. The generation AI also suggests the most suitable AItuber based on the user's prompt history. For example, it prioritizes the generation of AItubers with characteristics that have been entered frequently in the past. Furthermore, the generation AI learns the prompt history in real time to suggest AItubers optimized for the user's preferences. For example, it immediately reflects newly entered prompts and updates the suggested content. This makes it possible to make the AItuber take specific actions based on the user's instructions.

[0032] The development unit can produce the AItuber's live streaming and promote interaction with fans. For example, the development unit allows the generation AI to receive user feedback in real time and instantly modify the AItuber's appearance. For example, if a user provides feedback such as "I want my hair to be a lighter color," the generation AI instantly changes the hair color. The generation AI also instantly modifies the AItuber's personality based on user feedback. For example, if a user provides feedback such as "I want my personality to be more lively," the generation AI instantly changes the personality. Furthermore, the generation AI receives user feedback in real time and dynamically adjusts the AItuber's appearance and personality. For example, if a user provides feedback such as "I want my AItuber to smile more," the generation AI instantly increases the number of smiles. This allows the development unit to produce the AItuber's live streaming and promote interaction with fans.

[0033] The NFT conversion unit generates NFTs based on AItuber data, allowing users to exchange or buy and sell them. For example, the generation AI evaluates the AItuber's emotional value and automatically sets the price of the NFT based on that value. For example, the NFT conversion unit calculates the AItuber's emotional value based on fans' emotional response data and sets a price according to that value. The generation AI also evaluates the AItuber's emotional value in real time, dynamically adjusting the price of the NFT. For example, the price increases as fans' emotional response increases. Furthermore, the generation AI analyzes the AItuber's emotional value and automatically sets the price of the NFT based on that data. For example, NFTs of AItubers with high emotional value are set at a high price. This allows NFTs to be generated based on AItuber data, allowing users to exchange or buy and sell them.

[0034] The generation unit learns the user's past prompt history and can suggest an AItuber optimized to the user's preferences. In the generation unit, for example, the generation AI analyzes the user's past prompt history and learns the user's preferences. For example, it extracts the characteristics of AItubers preferred by the user based on trends in prompts entered in the past. The generation AI also suggests the optimal AItuber based on the user's prompt history. For example, it prioritizes the generation of AItubers with characteristics that have been entered frequently in the past. Furthermore, the generation AI learns the prompt history in real time to suggest AItubers optimized to the user's preferences. For example, it immediately reflects newly entered prompts and updates the suggested content. This allows the generation AI to learn the user's past prompt history and suggest AItubers optimized to the user's preferences.

[0035] The generation unit receives user feedback in real time and can instantly modify the AItuber's appearance and personality. For example, the generation AI receives user feedback in real time and instantly modifies the AItuber's appearance. For example, if a user provides feedback such as "I want my hair to be a lighter color," the generation AI instantly changes the hair color. The generation AI also instantly modifies the AItuber's personality based on user feedback. For example, if a user provides feedback such as "I want my personality to be more cheerful," the generation AI instantly changes the personality. Furthermore, the generation AI receives user feedback in real time and dynamically adjusts the AItuber's appearance and personality. For example, if a user provides feedback such as "I want my AItuber to smile more," the generation AI instantly increases the number of smiles. This allows the generation AI to receive user feedback in real time and instantly modify the AItuber's appearance and personality.

[0036] The generation unit can generate AItubers that incorporate characteristics of different cultures and regions. For example, the generation AI learns the characteristics of different cultures and regions and generates AItubers that reflect them. For example, it generates an AItuber wearing traditional Japanese clothing or an AItuber that incorporates American pop culture. The generation AI also generates AItubers based on the characteristics of cultures and regions specified by the user. For example, if a user inputs a prompt saying, "I want to create a French-style AItuber," the generation AI will generate an AItuber that reflects French culture. Furthermore, the generation AI generates AItubers that incorporate characteristics of different cultures and regions, supporting global expansion. For example, it can generate AItubers that correspond to the language and culture of each country, thereby attracting an international fan base. This makes it possible to generate AItubers that incorporate characteristics of different cultures and regions.

[0037] The generation unit can analyze the user's voice and facial expressions and customize the AItuber's facial expressions and voice based on that. For example, the generation AI analyzes the user's voice and customizes the AItuber's voice based on that. For example, the AItuber's voice is adjusted based on the tone and pitch of the user's voice. The generation unit also analyzes the user's facial expressions and customizes the AItuber's facial expressions based on that. For example, if the user is smiling when speaking, the generation AI changes the AItuber's facial expression to a smiling one. Furthermore, the generation AI analyzes the user's voice and facial expressions in real time and dynamically customizes the AItuber's facial expressions and voice based on that. For example, if the user makes a surprised expression, the generation AI changes the AItuber's facial expression to a surprised one. This allows the AItuber's facial expressions and voice to be customized based on the user's voice and facial expressions.

[0038] The communication unit can learn the user's communication patterns and realize more natural conversations. For example, the generation AI in the communication unit learns the user's communication patterns and adjusts the AItuber's conversations based on those patterns. For example, it learns the user's frequently used phrases and expressions, and the AItuber uses them. The generation AI also analyzes the user's communication patterns and instructs the AItuber to have natural conversations. For example, if the user asks many questions, the AItuber will prepare appropriate answers. Furthermore, the generation AI learns the user's communication patterns in real time and dynamically adjusts the AItuber's conversations. For example, if the user often tells jokes, the AItuber will engage in conversations with humor. This allows the system to learn the user's communication patterns and realize more natural conversations.

[0039] The communication unit can record the AItuber's growth process and visualize the progress of growth for the user. For example, the generation AI records the AItuber's growth process and visualizes the progress of growth for the user based on that data. For example, the generation AI displays the skills the AItuber has acquired and the goals they have achieved in a graph. The generation AI also records the AItuber's growth process and provides feedback to the user based on that data. For example, it shows the degree to which the AItuber has grown numerically. Furthermore, the generation AI records the AItuber's growth process in real time and visualizes that progress for the user. For example, it sends a notification when the AItuber acquires a new skill. This allows the AItuber's growth process to be recorded and the progress of growth to be visualized for the user.

[0040] The communication unit can support communication in different languages, enabling interaction with international users. For example, the communication unit allows the generation AI to learn different languages, enabling the AItuber to communicate in multiple languages. For example, it supports dialogue in multiple languages, such as English, French, and Chinese. The generation AI also translates in real time so that the AItuber communicates in the language specified by the user. For example, if a user speaks in Japanese, the AItuber will reply in English. Furthermore, the generation AI supports communication in different languages, promoting interaction with international users. For example, the AItuber will respond to comments in multiple languages ​​during live streaming. This supports communication in different languages ​​and enables interaction with international users.

[0041] The communication unit can automatically adjust the AItuber's activity schedule to match the user's lifestyle rhythm. For example, the generation AI analyzes the user's lifestyle rhythm and automatically adjusts the AItuber's activity schedule based on that. For example, if the user is a night owl, the AItuber's activity time is set to evening. The generation AI also adjusts the AItuber's activity schedule based on the user's calendar and schedule data. For example, the AItuber will avoid activity times when the user is busy. Furthermore, the generation AI adjusts the AItuber's activity schedule in real time to match the user's lifestyle rhythm. For example, if the user makes a sudden change to their plans, the AItuber's schedule will also be changed immediately. This allows the AItuber's activity schedule to be automatically adjusted to match the user's lifestyle rhythm.

[0042] The development section can analyze fan feedback in real time and optimize the AItuber's performance. For example, the generation AI analyzes fan feedback in real time and optimizes the AItuber's performance based on that data. For example, it analyzes fan comments and adjusts the AItuber's talk content. The generation AI also dynamically changes the AItuber's performance based on fan feedback. For example, if fans request to hear more songs, the AItuber will perform additional songs. Furthermore, the generation AI receives fan feedback in real time and instantly optimizes the AItuber's performance. For example, if fans respond positively to a performance, it will strengthen those elements. This allows the generation AI to analyze fan feedback in real time and optimize the AItuber's performance.

[0043] The development unit can analyze data on interactions with fans and suggest the most effective ways to interact. For example, the generation AI analyzes data on interactions with fans and suggests the most effective ways to interact. For example, it identifies the topics and activities that fans respond to most and determines the content of the AItuber's activities based on that. Furthermore, based on the data on interactions with fans, the generation AI instructs the AItuber on the optimal way to interact. For example, it identifies the time of day when fans leave the most comments and conducts live broadcasts during that time. Furthermore, the generation AI analyzes data on interactions with fans in real time and dynamically suggests the most effective ways to interact. For example, if fans show high interest in specific content, it increases the amount of that content. This allows the development unit to analyze data on interactions with fans and suggest the most effective ways to interact.

[0044] The development unit integrates and manages the AItuber's activities across different platforms, enabling seamless fan interaction. For example, the development unit uses a generation AI to integrate and manage the AItuber's activities across different platforms, enabling seamless fan interaction. For example, activities on YouTube and Twitch are centrally managed, allowing fans to have the same experience on both platforms. The generation AI also analyzes fan reactions on different platforms and adjusts the AItuber's activities. For example, it changes the content of YouTube live broadcasts based on reactions on Twitter (registered trademark). Furthermore, the generation AI integrates data across different platforms to optimize the AItuber's activities. For example, it determines the content for YouTube based on the number of followers on Instagram (registered trademark). This allows the AItuber's activities to be integrated and managed across different platforms, enabling seamless fan interaction.

[0045] The development unit can analyze fans' interests and customize the AItuber's content based on that. For example, the generation AI analyzes fans' interests and customizes the AItuber's content based on that. For example, if a fan is interested in a particular game, the AItuber will create a video of them playing that game. The generation AI also dynamically changes the AItuber's content based on fan interest data. For example, if a fan is interested in cooking, the AItuber will post cooking videos. Furthermore, the generation AI analyzes fans' interests in real time and customizes the AItuber's content based on that. For example, if a fan is interested in a new movie, the AItuber will review that movie. This allows the generation AI to analyze fans' interests and customize the AItuber's content based on that.

[0046] The NFT conversion unit can evaluate the emotional value of an AItuber and automatically set the price of the NFT based on that value. For example, the generation AI in the NFT conversion unit evaluates the emotional value of an AItuber and automatically sets the price of the NFT based on that value. For example, the emotional value of an AItuber is calculated based on emotional reaction data from fans and a price is set according to that value. The emotional value of an AItuber is also evaluated in real time, and the generation AI dynamically adjusts the price of the NFT. For example, the price increases as the emotional reaction of fans increases. Furthermore, the generation AI analyzes the emotional value of an AItuber and automatically sets the price of the NFT based on that data. For example, an NFT of an AItuber with high emotional value is set at a high price. This allows the emotional value of an AItuber to be evaluated and the price of the NFT to be automatically set based on that value.

[0047] The NFT conversion unit can learn the user's transaction history and suggest the most suitable trading partner. For example, the generation AI in the NFT conversion unit analyzes the user's transaction history and suggests the most suitable trading partner. For example, it identifies trading partners that the user is likely to be interested in based on past transaction data. Furthermore, the generation AI suggests the most suitable trading partner in real time based on the user's transaction history. For example, if a user likes AItubers in a particular genre, it will suggest trading partners who are knowledgeable in that genre. Furthermore, the generation AI learns the user's transaction history and dynamically suggests the most suitable trading partner. For example, it suggests similar trading partners based on the user's past successful transaction patterns. This makes it possible to learn the user's transaction history and suggest the most suitable trading partner.

[0048] The NFTization unit can analyze NFT transaction data and predict market trends. In the NFTization unit, for example, the generation AI analyzes NFT transaction data and predicts market trends. For example, it predicts future price fluctuations based on past transaction data. The generation AI also predicts market trends in real time based on NFT transaction data. For example, it predicts that the popularity of a particular AItuber will increase based on an increase in transaction volume. Furthermore, the generation AI analyzes NFT transaction data and predicts market trends based on that data. For example, it predicts that AItubers of a particular genre will become popular in the future. In this way, NFT transaction data can be analyzed and market trends can be predicted.

[0049] The NFT generation unit supports NFT transactions across different blockchain platforms, increasing transaction diversity. For example, the generation AI of the NFT generation unit supports NFT transactions across different blockchain platforms. For example, it generates NFTs that can be traded on both Ethereum and Binance Smart Chain. The generation AI also integrates and manages transactions across different blockchain platforms, increasing transaction diversity. For example, it centrally manages transaction data across multiple platforms. Furthermore, the generation AI supports NFT transactions across different blockchain platforms in real time. For example, it automatically coordinates transactions on the user's preferred platform. This allows the unit to support NFT transactions across different blockchain platforms, increasing transaction diversity.

[0050] The NFTization unit manages the user's portfolio and can suggest optimal NFT combinations. For example, the generation AI in the NFTization unit analyzes the user's NFT portfolio and suggests optimal combinations. For example, it may suggest a portfolio that takes risk diversification into consideration. The generation AI also suggests optimal NFT combinations in real time based on the user's portfolio data. For example, it may suggest combinations that meet the user's investment goals. Furthermore, the generation AI manages the user's portfolio and dynamically suggests optimal NFT combinations. For example, it adjusts the portfolio in response to market fluctuations. This allows the unit to manage the user's portfolio and suggest optimal NFT combinations.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The generation unit can analyze the user's voice and facial expressions and customize the AItuber's facial expressions and voice based on that. For example, the generation AI analyzes the user's voice and customizes the AItuber's voice based on that. For example, it adjusts the AItuber's voice based on the tone and pitch of the user's voice. It also analyzes the user's facial expressions and customizes the AItuber's facial expressions based on that. For example, if the user is smiling while speaking, the generation AI changes the AItuber's facial expression to a smiling one. Furthermore, the generation AI analyzes the user's voice and facial expressions in real time and dynamically customizes the AItuber's facial expressions and voice based on that. For example, if the user makes a surprised expression, the generation AI changes the AItuber's facial expression to a surprised one. This allows the AItuber's facial expressions and voice to be customized based on the user's voice and facial expressions.

[0053] The generation unit can generate AItubers that incorporate characteristics of different cultures and regions. For example, the generation AI can learn the characteristics of different cultures and regions and generate AItubers that reflect them. For example, it can generate an AItuber wearing traditional Japanese clothing or one that incorporates American pop culture. The generation AI can also generate AItubers based on the characteristics of cultures and regions specified by the user. For example, if a user inputs a prompt saying, "I want to create a French-style AItuber," the generation AI will generate an AItuber that reflects French culture. Furthermore, the generation AI can generate AItubers that incorporate characteristics of different cultures and regions, supporting global expansion. For example, it can generate AItubers that correspond to the language and culture of each country, thereby attracting an international fan base. This makes it possible to generate AItubers that incorporate characteristics of different cultures and regions.

[0054] The communication unit can learn the user's communication patterns and realize more natural conversations. For example, the generation AI learns the user's communication patterns and adjusts the AItuber's conversations based on those patterns. For example, it learns the user's frequently used phrases and expressions, and the AItuber uses them. The generation AI also analyzes the user's communication patterns and instructs the AItuber to have natural conversations. For example, if the user asks many questions, the AItuber will prepare appropriate answers. Furthermore, the generation AI learns the user's communication patterns in real time and dynamically adjusts the AItuber's conversations. For example, if the user often tells jokes, the AItuber will engage in conversations with humor. This allows the system to learn the user's communication patterns and realize more natural conversations.

[0055] The development section can analyze fan feedback in real time and optimize the AItuber's performance. For example, the generation AI analyzes fan feedback in real time and optimizes the AItuber's performance based on that data. For example, it analyzes fan comments and adjusts the AItuber's talk content. The generation AI also dynamically changes the AItuber's performance based on fan feedback. For example, if fans request to hear more songs, the AItuber will perform additional songs. Furthermore, the generation AI receives fan feedback in real time and instantly optimizes the AItuber's performance. For example, if fans respond positively to a performance, it will strengthen those elements. This allows the generation AI to analyze fan feedback in real time and optimize the AItuber's performance.

[0056] The NFT conversion unit can learn the user's transaction history and suggest the most suitable trading partner. For example, the generation AI analyzes the user's transaction history and suggests the most suitable trading partner. For example, it identifies trading partners that the user is likely to be interested in based on past transaction data. The generation AI also suggests the most suitable trading partner in real time based on the user's transaction history. For example, if a user likes AItubers in a particular genre, it will suggest trading partners who are knowledgeable in that genre. Furthermore, the generation AI learns the user's transaction history and dynamically suggests the most suitable trading partner. For example, it suggests similar trading partners based on the patterns of the user's successful transactions in the past. This makes it possible to learn the user's transaction history and suggest the most suitable trading partner.

[0057] The development unit can integrate and manage the AItuber's activities across different platforms, enabling seamless fan interaction. For example, the generation AI can integrate and manage the AItuber's activities across different platforms, enabling seamless fan interaction. For example, activities on YouTube and Twitch can be centrally managed, allowing fans to have the same experience on both platforms. The generation AI can also analyze fan reactions on different platforms and adjust the AItuber's activities. For example, it can change the content of YouTube live broadcasts based on reactions on Twitter (registered trademark). Furthermore, the generation AI can integrate data across different platforms to optimize the AItuber's activities. For example, it can determine the content for YouTube based on the number of followers on Instagram (registered trademark). This allows the AItuber's activities to be integrated and managed across different platforms, enabling seamless fan interaction.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The generation unit uses a generation AI to generate an AItuber. For example, the generation AI receives prompts containing user instructions as input and generates the AItuber's character, personality, appearance, etc. The generation AI uses technologies such as deep learning and GAN (generative artificial network). Step 2: In the communication section, the user communicates with the AItuber generated by the generation section. For example, the user can give various instructions to the AItuber and watch its growth. The generation AI then makes the AItuber take specific actions based on the user's instructions. Step 3: The development department deploys the AItuber on the platform and allows it to interact with fans. For example, the generation AI determines the AItuber's activities based on user instructions and supports communication with fans. Step 4: The NFT conversion unit converts the AItuber into an NFT, enabling it to be traded or bought and sold. For example, the generation AI generates an NFT based on the AItuber's data, which users can then trade with other users or buy and sell.

[0060] (Example 2) The AItuber production system according to an embodiment of the present invention is a system that creates AItubers and provides various new experiences as their producer. This system allows users to communicate with and develop AItubers, interact with fans, live stream, and even convert them into NFTs for exchange and trading. As a result, the AItuber production system allows users to consistently create, communicate with, deploy, and convert AItubers into NFTs.

[0061] The AItuber production system according to the embodiment includes a generation unit, a communication unit, a deployment unit, and an NFT conversion unit. The generation unit generates an AItuber using a generation AI. For example, the generation AI receives prompts containing user instructions as input and generates the AItuber's character, personality, appearance, etc. The generation AI uses technologies such as deep learning and GAN (generative artificial network). The communication unit allows the user to communicate with the AItuber generated by the generation unit. For example, the user can issue various instructions to the AItuber and watch its growth. The generation AI causes the AItuber to perform specific actions based on the user's instructions. The deployment unit deploys the AItuber on a platform and allows it to interact with fans. For example, the generation AI determines the AItuber's activities based on the user's instructions and supports communication with fans. The NFT conversion unit converts the AItuber into an NFT, enabling it to be exchanged or bought and sold. For example, the generation AI generates an NFT based on the AItuber's data, which users can exchange or buy with other users. This allows the AItuber production system to allow users to consistently create, communicate, deploy, and convert AItubers into NFTs.

[0062] The generation unit can generate an AItuber based on the user's prompt. For example, the generation unit incorporates an emotion estimation function into the generation AI and analyzes the user's emotions when entering a prompt. For example, if the user is feeling happy, the generation AI generates an AItuber with a bright and cheerful personality. The generation AI also dynamically adjusts the AItuber's appearance based on the user's emotions. For example, if the user is relaxed, the generation AI generates an AItuber with a calm expression. Furthermore, the emotion estimation function is used to dynamically change the AItuber's personality according to the user's emotions. For example, if the user is feeling stressed, the generation AI generates an AItuber with a soothing personality. This allows the generation of an AItuber based on the user's instructions.

[0063] The communication unit can make the AItuber take specific actions based on the user's instructions. In the communication unit, for example, the generation AI analyzes the user's past prompt history and learns the user's preferences. For example, it extracts the characteristics of AItubers that the user prefers based on the trends of prompts entered in the past. The generation AI also suggests the most suitable AItuber based on the user's prompt history. For example, it prioritizes the generation of AItubers with characteristics that have been entered frequently in the past. Furthermore, the generation AI learns the prompt history in real time to suggest AItubers optimized for the user's preferences. For example, it immediately reflects newly entered prompts and updates the suggested content. This makes it possible to make the AItuber take specific actions based on the user's instructions.

[0064] The development unit can produce the AItuber's live streaming and promote interaction with fans. For example, the development unit allows the generation AI to receive user feedback in real time and instantly modify the AItuber's appearance. For example, if a user provides feedback such as "I want my hair to be a lighter color," the generation AI instantly changes the hair color. The generation AI also instantly modifies the AItuber's personality based on user feedback. For example, if a user provides feedback such as "I want my personality to be more lively," the generation AI instantly changes the personality. Furthermore, the generation AI receives user feedback in real time and dynamically adjusts the AItuber's appearance and personality. For example, if a user provides feedback such as "I want my AItuber to smile more," the generation AI instantly increases the number of smiles. This allows the development unit to produce the AItuber's live streaming and promote interaction with fans.

[0065] The NFT conversion unit generates NFTs based on AItuber data, allowing users to exchange or buy and sell them. For example, the generation AI evaluates the AItuber's emotional value and automatically sets the price of the NFT based on that value. For example, the NFT conversion unit calculates the AItuber's emotional value based on fans' emotional response data and sets a price according to that value. The generation AI also evaluates the AItuber's emotional value in real time, dynamically adjusting the price of the NFT. For example, the price increases as fans' emotional response increases. Furthermore, the generation AI analyzes the AItuber's emotional value and automatically sets the price of the NFT based on that data. For example, NFTs of AItubers with high emotional value are set at a high price. This allows NFTs to be generated based on AItuber data, allowing users to exchange or buy and sell them.

[0066] The generation unit can dynamically adjust the AItuber's personality and appearance based on the user's emotions. For example, the generation unit incorporates an emotion estimation function into the generation AI and analyzes the user's emotions when entering prompts. For example, if the user is feeling happy, the generation AI generates an AItuber with a bright and cheerful personality. The generation unit also dynamically adjusts the AItuber's appearance based on the user's emotions. For example, if the user is relaxed, the generation AI generates an AItuber with a calm expression. Furthermore, the emotion estimation function is used to dynamically change the AItuber's personality according to the user's emotions. For example, if the user is feeling stressed, the generation AI generates an AItuber with a soothing personality. This makes it possible to dynamically adjust the AItuber's personality and appearance based on the user's emotions.

[0067] The generation unit learns the user's past prompt history and can suggest an AItuber optimized to the user's preferences. In the generation unit, for example, the generation AI analyzes the user's past prompt history and learns the user's preferences. For example, it extracts the characteristics of AItubers preferred by the user based on trends in prompts entered in the past. The generation AI also suggests the optimal AItuber based on the user's prompt history. For example, it prioritizes the generation of AItubers with characteristics that have been entered frequently in the past. Furthermore, the generation AI learns the prompt history in real time to suggest AItubers optimized to the user's preferences. For example, it immediately reflects newly entered prompts and updates the suggested content. This allows the generation AI to learn the user's past prompt history and suggest AItubers optimized to the user's preferences.

[0068] The generation unit receives user feedback in real time and can instantly modify the AItuber's appearance and personality. For example, the generation AI receives user feedback in real time and instantly modifies the AItuber's appearance. For example, if a user provides feedback such as "I want my hair to be a lighter color," the generation AI instantly changes the hair color. The generation AI also instantly modifies the AItuber's personality based on user feedback. For example, if a user provides feedback such as "I want my personality to be more cheerful," the generation AI instantly changes the personality. Furthermore, the generation AI receives user feedback in real time and dynamically adjusts the AItuber's appearance and personality. For example, if a user provides feedback such as "I want my AItuber to smile more," the generation AI instantly increases the number of smiles. This allows the generation AI to receive user feedback in real time and instantly modify the AItuber's appearance and personality.

[0069] The generation unit can generate AItubers that incorporate characteristics of different cultures and regions. For example, the generation AI learns the characteristics of different cultures and regions and generates AItubers that reflect them. For example, it generates an AItuber wearing traditional Japanese clothing or an AItuber that incorporates American pop culture. The generation AI also generates AItubers based on the characteristics of cultures and regions specified by the user. For example, if a user inputs a prompt saying, "I want to create a French-style AItuber," the generation AI will generate an AItuber that reflects French culture. Furthermore, the generation AI generates AItubers that incorporate characteristics of different cultures and regions, supporting global expansion. For example, it can generate AItubers that correspond to the language and culture of each country, thereby attracting an international fan base. This makes it possible to generate AItubers that incorporate characteristics of different cultures and regions.

[0070] The generation unit can analyze the user's voice and facial expressions and customize the AItuber's facial expressions and voice based on that. For example, the generation AI analyzes the user's voice and customizes the AItuber's voice based on that. For example, the AItuber's voice is adjusted based on the tone and pitch of the user's voice. The generation unit also analyzes the user's facial expressions and customizes the AItuber's facial expressions based on that. For example, if the user is smiling when speaking, the generation AI changes the AItuber's facial expression to a smiling one. Furthermore, the generation AI analyzes the user's voice and facial expressions in real time and dynamically customizes the AItuber's facial expressions and voice based on that. For example, if the user makes a surprised expression, the generation AI changes the AItuber's facial expression to a surprised one. This allows the AItuber's facial expressions and voice to be customized based on the user's voice and facial expressions.

[0071] The generation unit can use the emotion estimation function to extract the features of an AItuber that the user most emotionally empathizes with and generate an AItuber with those features. The generation unit, for example, uses the emotion estimation function to extract the features of an AItuber that the user most emotionally empathizes with. For example, the generation unit analyzes the features of AItubers that the user has previously empathized with and generates a new AItuber based on those features. Furthermore, based on the user's emotional reaction, the generation AI extracts the features of an AItuber that are most likely to be empathized with and generates an AItuber with those features. For example, if the user empathizes with smiles, the generation AI generates an AItuber that smiles a lot. Furthermore, the emotion estimation function can be used to extract the features of an AItuber that the user most emotionally empathizes with in real time and generate an AItuber with those features. For example, if the user shows an emotional expression, the generation AI generates an AItuber with an emotional expression. This allows the generation unit to extract the features of an AItuber that the user most emotionally empathizes with and generate an AItuber with those features.

[0072] The communication unit can estimate the user's emotions and instruct the AItuber to communicate appropriately according to those emotions. For example, the generation AI analyzes the user's emotions in real time and instructs the AItuber to communicate according to those emotions. For example, if the user is sad, the AItuber will offer words of comfort. Furthermore, based on the user's emotions, the generation AI instructs the AItuber to communicate appropriately. For example, if the user is excited, the AItuber will offer words of empathy. Furthermore, the generation AI estimates the user's emotions and instructs the AItuber to communicate according to those emotions in real time. For example, if the user is tired, the AItuber will offer words to relax. This allows the AItuber to communicate appropriately according to the user's emotions.

[0073] The communication unit can learn the user's communication patterns and realize more natural conversations. For example, the generation AI in the communication unit learns the user's communication patterns and adjusts the AItuber's conversations based on those patterns. For example, it learns the user's frequently used phrases and expressions, and the AItuber uses them. The generation AI also analyzes the user's communication patterns and instructs the AItuber to have natural conversations. For example, if the user asks many questions, the AItuber will prepare appropriate answers. Furthermore, the generation AI learns the user's communication patterns in real time and dynamically adjusts the AItuber's conversations. For example, if the user often tells jokes, the AItuber will engage in conversations with humor. This allows the system to learn the user's communication patterns and realize more natural conversations.

[0074] The communication unit can record the AItuber's growth process and visualize the progress of growth for the user. For example, the generation AI records the AItuber's growth process and visualizes the progress of growth for the user based on that data. For example, the generation AI displays the skills the AItuber has acquired and the goals they have achieved in a graph. The generation AI also records the AItuber's growth process and provides feedback to the user based on that data. For example, it shows the degree to which the AItuber has grown numerically. Furthermore, the generation AI records the AItuber's growth process in real time and visualizes that progress for the user. For example, it sends a notification when the AItuber acquires a new skill. This allows the AItuber's growth process to be recorded and the progress of growth to be visualized for the user.

[0075] The communication unit can support communication in different languages, enabling interaction with international users. For example, the communication unit allows the generation AI to learn different languages, enabling the AItuber to communicate in multiple languages. For example, it supports dialogue in multiple languages, such as English, French, and Chinese. The generation AI also translates in real time so that the AItuber communicates in the language specified by the user. For example, if a user speaks in Japanese, the AItuber will reply in English. Furthermore, the generation AI supports communication in different languages, promoting interaction with international users. For example, the AItuber will respond to comments in multiple languages ​​during live streaming. This supports communication in different languages ​​and enables interaction with international users.

[0076] The communication unit can automatically adjust the AItuber's activity schedule to match the user's lifestyle rhythm. For example, the generation AI analyzes the user's lifestyle rhythm and automatically adjusts the AItuber's activity schedule based on that. For example, if the user is a night owl, the AItuber's activity time is set to evening. The generation AI also adjusts the AItuber's activity schedule based on the user's calendar and schedule data. For example, the AItuber will avoid activity times when the user is busy. Furthermore, the generation AI adjusts the AItuber's activity schedule in real time to match the user's lifestyle rhythm. For example, if the user makes a sudden change to their plans, the AItuber's schedule will also be changed immediately. This allows the AItuber's activity schedule to be automatically adjusted to match the user's lifestyle rhythm.

[0077] The communication unit can use the emotion estimation function to propose a development plan that will provide the user with the most emotional satisfaction. For example, the communication unit uses the emotion estimation function to have the generation AI propose a development plan that will provide the user with the most emotional satisfaction. For example, the development plan is constructed around activities that the user feels are enjoyable. The generation AI also proposes an optimal development plan based on the user's emotional response. For example, activities that the user feels positive about are prioritized. Furthermore, the emotion estimation function is used to propose a development plan that will provide the user with the most emotional satisfaction in real time. For example, the development plan is dynamically adjusted every time the user's emotions change. This makes it possible to propose a development plan that will provide the user with the most emotional satisfaction.

[0078] The development unit can estimate the fan's emotions and adjust the AItuber's activities based on those emotions. In the development unit, for example, the generation AI analyzes the fan's emotions in real time and adjusts the AItuber's activities based on those emotions. For example, if the fan is excited, the AItuber will perform an energetic performance. Furthermore, the generation AI dynamically changes the AItuber's activities based on the fan's emotions. For example, if the fan wants to relax, the AItuber will talk calmly. Furthermore, the generation AI estimates the fan's emotions and instructs the AItuber on activities based on those emotions. For example, if the fan is moved, the AItuber will send an inspiring message. This makes it possible to adjust the AItuber's activities based on the fan's emotions.

[0079] The development section can analyze fan feedback in real time and optimize the AItuber's performance. For example, the generation AI analyzes fan feedback in real time and optimizes the AItuber's performance based on that data. For example, it analyzes fan comments and adjusts the AItuber's talk content. The generation AI also dynamically changes the AItuber's performance based on fan feedback. For example, if fans request to hear more songs, the AItuber will perform additional songs. Furthermore, the generation AI receives fan feedback in real time and instantly optimizes the AItuber's performance. For example, if fans respond positively to a performance, it will strengthen those elements. This allows the generation AI to analyze fan feedback in real time and optimize the AItuber's performance.

[0080] The development unit can analyze data on interactions with fans and suggest the most effective ways to interact. For example, the generation AI analyzes data on interactions with fans and suggests the most effective ways to interact. For example, it identifies the topics and activities that fans respond to most and determines the content of the AItuber's activities based on that. Furthermore, based on the data on interactions with fans, the generation AI instructs the AItuber on the optimal way to interact. For example, it identifies the time of day when fans leave the most comments and conducts live broadcasts during that time. Furthermore, the generation AI analyzes data on interactions with fans in real time and dynamically suggests the most effective ways to interact. For example, if fans show high interest in specific content, it increases the amount of that content. This allows the development unit to analyze data on interactions with fans and suggest the most effective ways to interact.

[0081] The development unit integrates and manages the AItuber's activities across different platforms, enabling seamless fan interaction. For example, the development unit uses a generation AI to integrate and manage the AItuber's activities across different platforms, enabling seamless fan interaction. For example, activities on YouTube and Twitch are centrally managed, allowing fans to have the same experience on both platforms. The generation AI also analyzes fan reactions on different platforms and adjusts the AItuber's activities. For example, it changes the content of YouTube live broadcasts based on reactions on Twitter (registered trademark). Furthermore, the generation AI integrates data across different platforms to optimize the AItuber's activities. For example, it determines the content for YouTube based on the number of followers on Instagram (registered trademark). This allows the AItuber's activities to be integrated and managed across different platforms, enabling seamless fan interaction.

[0082] The development unit can analyze fans' interests and customize the AItuber's content based on that. For example, the generation AI analyzes fans' interests and customizes the AItuber's content based on that. For example, if a fan is interested in a particular game, the AItuber will create a video of them playing that game. The generation AI also dynamically changes the AItuber's content based on fan interest data. For example, if a fan is interested in cooking, the AItuber will post cooking videos. Furthermore, the generation AI analyzes fans' interests in real time and customizes the AItuber's content based on that. For example, if a fan is interested in a new movie, the AItuber will review that movie. This allows the generation AI to analyze fans' interests and customize the AItuber's content based on that.

[0083] The development unit uses the emotion estimation function to generate content that fans most emotionally empathize with, thereby increasing the popularity of the AItuber. The development unit, for example, uses the emotion estimation function to generate content that fans most emotionally empathize with. For example, the AItuber creates a video based on a story that moves fans. Furthermore, based on the emotional reactions of fans, the generation AI optimizes the AItuber's content. For example, it increases the amount of content that makes fans smile. Furthermore, it uses the emotion estimation function to generate content that fans most emotionally empathize with in real time. For example, it creates a video that captures a moment that excites fans. This generates content that fans most emotionally empathize with, thereby increasing the popularity of the AItuber.

[0084] The NFT conversion unit can evaluate the emotional value of an AItuber and automatically set the price of the NFT based on that value. For example, the generation AI in the NFT conversion unit evaluates the emotional value of an AItuber and automatically sets the price of the NFT based on that value. For example, the emotional value of an AItuber is calculated based on emotional reaction data from fans and a price is set according to that value. The emotional value of an AItuber is also evaluated in real time, and the generation AI dynamically adjusts the price of the NFT. For example, the price increases as the emotional reaction of fans increases. Furthermore, the generation AI analyzes the emotional value of an AItuber and automatically sets the price of the NFT based on that data. For example, an NFT of an AItuber with high emotional value is set at a high price. This allows the emotional value of an AItuber to be evaluated and the price of the NFT to be automatically set based on that value.

[0085] The NFT conversion unit can learn the user's transaction history and suggest the most suitable trading partner. For example, the generation AI in the NFT conversion unit analyzes the user's transaction history and suggests the most suitable trading partner. For example, it identifies trading partners that the user is likely to be interested in based on past transaction data. Furthermore, the generation AI suggests the most suitable trading partner in real time based on the user's transaction history. For example, if a user likes AItubers in a particular genre, it will suggest trading partners who are knowledgeable in that genre. Furthermore, the generation AI learns the user's transaction history and dynamically suggests the most suitable trading partner. For example, it suggests similar trading partners based on the user's past successful transaction patterns. This makes it possible to learn the user's transaction history and suggest the most suitable trading partner.

[0086] The NFTization unit can analyze NFT transaction data and predict market trends. In the NFTization unit, for example, the generation AI analyzes NFT transaction data and predicts market trends. For example, it predicts future price fluctuations based on past transaction data. The generation AI also predicts market trends in real time based on NFT transaction data. For example, it predicts that the popularity of a particular AItuber will increase based on an increase in transaction volume. Furthermore, the generation AI analyzes NFT transaction data and predicts market trends based on that data. For example, it predicts that AItubers of a particular genre will become popular in the future. In this way, NFT transaction data can be analyzed and market trends can be predicted.

[0087] The NFT generation unit supports NFT transactions across different blockchain platforms, increasing transaction diversity. For example, the generation AI of the NFT generation unit supports NFT transactions across different blockchain platforms. For example, it generates NFTs that can be traded on both Ethereum and Binance Smart Chain. The generation AI also integrates and manages transactions across different blockchain platforms, increasing transaction diversity. For example, it centrally manages transaction data across multiple platforms. Furthermore, the generation AI supports NFT transactions across different blockchain platforms in real time. For example, it automatically coordinates transactions on the user's preferred platform. This allows the unit to support NFT transactions across different blockchain platforms, increasing transaction diversity.

[0088] The NFTization unit manages the user's portfolio and can suggest optimal NFT combinations. For example, the generation AI in the NFTization unit analyzes the user's NFT portfolio and suggests optimal combinations. For example, it may suggest a portfolio that takes risk diversification into consideration. The generation AI also suggests optimal NFT combinations in real time based on the user's portfolio data. For example, it may suggest combinations that meet the user's investment goals. Furthermore, the generation AI manages the user's portfolio and dynamically suggests optimal NFT combinations. For example, it adjusts the portfolio in response to market fluctuations. This allows the unit to manage the user's portfolio and suggest optimal NFT combinations.

[0089] The NFTization unit can use the emotion estimation function to support NFT transactions that are most emotionally satisfying for users. The NFTization unit, for example, uses the emotion estimation function to support NFT transactions that are most emotionally satisfying for users. For example, it prioritizes suggesting transactions that excite the user. Furthermore, based on the user's emotional response, the generation AI suggests optimal NFT transactions. For example, it recommends transactions in which the user shows positive emotions. Furthermore, it uses the emotion estimation function to support NFT transactions that are most emotionally satisfying for users in real time. For example, it prioritizes executing transactions that increase the user's emotions. This makes it possible to support NFT transactions that are most emotionally satisfying for users.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The generation unit can analyze the user's voice and facial expressions and customize the AItuber's facial expressions and voice based on that. For example, the generation AI analyzes the user's voice and customizes the AItuber's voice based on that. For example, it adjusts the AItuber's voice based on the tone and pitch of the user's voice. It also analyzes the user's facial expressions and customizes the AItuber's facial expressions based on that. For example, if the user is smiling while speaking, the generation AI changes the AItuber's facial expression to a smiling one. Furthermore, the generation AI analyzes the user's voice and facial expressions in real time and dynamically customizes the AItuber's facial expressions and voice based on that. For example, if the user makes a surprised expression, the generation AI changes the AItuber's facial expression to a surprised one. This allows the AItuber's facial expressions and voice to be customized based on the user's voice and facial expressions.

[0092] The communication unit can estimate the user's emotions and instruct the AItuber to communicate appropriately based on those emotions. For example, the generation AI analyzes the user's emotions in real time and instructs the AItuber to communicate according to those emotions. For example, if the user is sad, the AItuber will offer words of comfort. The generation AI also instructs the AItuber to communicate appropriately based on the user's emotions. For example, if the user is excited, the AItuber will offer words of empathy. Furthermore, the generation AI can estimate the user's emotions and instruct the AItuber to communicate according to those emotions in real time. For example, if the user is tired, the AItuber will offer words to relax. This allows the AItuber to communicate appropriately based on the user's emotions.

[0093] The development section can estimate the fan's emotions and adjust the AItuber's activities based on those emotions. For example, the generation AI analyzes the fan's emotions in real time and adjusts the AItuber's activities based on those emotions. For example, if the fan is excited, the AItuber will perform an energetic performance. The generation AI also dynamically changes the AItuber's activities based on the fan's emotions. For example, if the fan wants to relax, the AItuber will talk calmly. Furthermore, the generation AI estimates the fan's emotions and instructs the AItuber on activities based on those emotions. For example, if the fan is moved, the AItuber will send an inspiring message. This allows the AItuber's activities to be adjusted based on the fan's emotions.

[0094] The NFTization unit can use the emotion estimation function to support NFT transactions that are most emotionally satisfying for users. For example, the emotion estimation function can be used to support NFT transactions that are most emotionally satisfying for users. For example, transactions that excite users can be prioritized. Furthermore, based on the user's emotional response, the generation AI can suggest optimal NFT transactions. For example, transactions that show positive emotions for the user can be recommended. Furthermore, the emotion estimation function can be used to support NFT transactions that are most emotionally satisfying for users in real time. For example, transactions that increase the user's emotions can be prioritized. This can support NFT transactions that are most emotionally satisfying for users.

[0095] The generation unit can use the emotion estimation function to extract the features of an AItuber that a user most emotionally empathizes with and generate an AItuber with those features. For example, the emotion estimation function can be used to extract the features of an AItuber that a user most emotionally empathizes with. For example, the emotion estimation function can be used to analyze the features of AItubers that a user has previously empathized with and generate a new AItuber based on those features. Furthermore, based on the user's emotional reaction, the generation AI can extract the features of an AItuber that are most likely to be empathized with and generate an AItuber with those features. For example, if a user empathizes with smiling, the generation AI can generate an AItuber that smiles a lot. Furthermore, the emotion estimation function can be used to extract the features of an AItuber that a user most emotionally empathizes with in real time and generate an AItuber with those features. For example, if a user shows an emotional expression, the generation AI can generate an AItuber with an emotional expression. This allows the generation unit to extract the features of an AItuber that a user most emotionally empathizes with and generate an AItuber with those features.

[0096] The generation unit can generate AItubers that incorporate characteristics of different cultures and regions. For example, the generation AI can learn the characteristics of different cultures and regions and generate AItubers that reflect them. For example, it can generate an AItuber wearing traditional Japanese clothing or one that incorporates American pop culture. The generation AI can also generate AItubers based on the characteristics of cultures and regions specified by the user. For example, if a user inputs a prompt saying, "I want to create a French-style AItuber," the generation AI will generate an AItuber that reflects French culture. Furthermore, the generation AI can generate AItubers that incorporate characteristics of different cultures and regions, supporting global expansion. For example, it can generate AItubers that correspond to the language and culture of each country, thereby attracting an international fan base. This makes it possible to generate AItubers that incorporate characteristics of different cultures and regions.

[0097] The communication unit can learn the user's communication patterns and realize more natural conversations. For example, the generation AI learns the user's communication patterns and adjusts the AItuber's conversations based on those patterns. For example, it learns the user's frequently used phrases and expressions, and the AItuber uses them. The generation AI also analyzes the user's communication patterns and instructs the AItuber to have natural conversations. For example, if the user asks many questions, the AItuber will prepare appropriate answers. Furthermore, the generation AI learns the user's communication patterns in real time and dynamically adjusts the AItuber's conversations. For example, if the user often tells jokes, the AItuber will engage in conversations with humor. This allows the system to learn the user's communication patterns and realize more natural conversations.

[0098] The development section can analyze fan feedback in real time and optimize the AItuber's performance. For example, the generation AI analyzes fan feedback in real time and optimizes the AItuber's performance based on that data. For example, it analyzes fan comments and adjusts the AItuber's talk content. The generation AI also dynamically changes the AItuber's performance based on fan feedback. For example, if fans request to hear more songs, the AItuber will perform additional songs. Furthermore, the generation AI receives fan feedback in real time and instantly optimizes the AItuber's performance. For example, if fans respond positively to a performance, it will strengthen those elements. This allows the generation AI to analyze fan feedback in real time and optimize the AItuber's performance.

[0099] The NFT conversion unit can learn the user's transaction history and suggest the most suitable trading partner. For example, the generation AI analyzes the user's transaction history and suggests the most suitable trading partner. For example, it identifies trading partners that the user is likely to be interested in based on past transaction data. The generation AI also suggests the most suitable trading partner in real time based on the user's transaction history. For example, if a user likes AItubers in a particular genre, it will suggest trading partners who are knowledgeable in that genre. Furthermore, the generation AI learns the user's transaction history and dynamically suggests the most suitable trading partner. For example, it suggests similar trading partners based on the patterns of the user's successful transactions in the past. This makes it possible to learn the user's transaction history and suggest the most suitable trading partner.

[0100] The development unit can integrate and manage the AItuber's activities across different platforms, enabling seamless fan interaction. For example, the generation AI can integrate and manage the AItuber's activities across different platforms, enabling seamless fan interaction. For example, activities on YouTube and Twitch can be centrally managed, allowing fans to have the same experience on both platforms. The generation AI can also analyze fan reactions on different platforms and adjust the AItuber's activities. For example, it can change the content of YouTube live broadcasts based on reactions on Twitter (registered trademark). Furthermore, the generation AI can integrate data across different platforms to optimize the AItuber's activities. For example, it can determine the content for YouTube based on the number of followers on Instagram (registered trademark). This allows the AItuber's activities to be integrated and managed across different platforms, enabling seamless fan interaction.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The generation unit uses a generation AI to generate an AItuber. For example, the generation AI receives prompts containing user instructions as input and generates the AItuber's character, personality, appearance, etc. The generation AI uses technologies such as deep learning and GAN (generative artificial network). Step 2: In the communication section, the user communicates with the AItuber generated by the generation section. For example, the user can give various instructions to the AItuber and watch its growth. The generation AI then makes the AItuber take specific actions based on the user's instructions. Step 3: The development department deploys the AItuber on the platform and allows it to interact with fans. For example, the generation AI determines the AItuber's activities based on user instructions and supports communication with fans. Step 4: The NFT conversion unit converts the AItuber into an NFT, enabling it to be traded or bought and sold. For example, the generation AI generates an NFT based on the AItuber's data, which users can then trade with other users or buy and sell.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 AI 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 AI 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 AI 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 generation unit that generates an AItuber using a generation AI; A communication unit through which a user communicates with the AItuber generated by the generation unit; A development department that develops the AItuber on a platform and allows it to interact with fans; An NFT conversion unit that converts the AItuber into an NFT and enables exchange or trading. A system characterized by:

2. The generation unit Generate the AItuber based on the user's prompts 2. The system of claim 1.

3. The communication unit Making the AItuber take a specific action based on the user's instructions 2. The system of claim 1.

4. The expansion section Produce live broadcasts for the AItubers and promote interaction with their fans 2. The system of claim 1.

5. The NFT unit is Generate the NFT based on the AItuber data and allow the user to exchange or buy / sell it.

2. The system of claim 1.

6. The generation unit Dynamically adjust the AItuber's personality and appearance based on the user's emotions 2. The system of claim 1.

7. The generation unit The AItuber is optimized to the user's preferences by learning the user's past prompt history.

2. The system of claim 1.

8. The generation unit Receive real-time feedback from the user and instantly modify the AItuber's appearance and personality.

2. The system of claim 1.

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