system

Abstract

An object of the system according to the embodiment is to consistently perform generation, training, development, and NFT of a AI character.SOLUTION: A system includes a generation part, a communication part, a raising part, an expansion part, and an NFT part. The generating unit generates AItuber based on an instruction from the producer. The communication unit performs communication between the AItuber generated by the generation unit and the producer. The fostering unit fosters the AItuber based on the communication with the producer performed by the communication unit. The deployment unit deploys the AItuber raised by the raising unit on the platform. The NFT part turns the AItuber developed by the development part into an NFT.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] With conventional technology, it was difficult to consistently generate, train, deploy, and even convert AI characters into NFTs.

[0005] The system of the embodiment aims to consistently carry out the entire process from generating AI characters to training, deployment, and NFT conversion. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation unit, a communication unit, a training unit, a deployment unit, and an NFT unit. The generation unit generates an AItuber based on instructions from a producer. The communication unit communicates between the AItuber generated by the generation unit and the producer. The training unit trains the AItuber based on communication with the producer conducted by the communication unit. The deployment unit deploys the AItuber trained by the training unit on a platform. The NFT unit converts the AItuber deployed by the deployment unit into an NFT. [Effects of the Invention]

[0007] The system according to the embodiment can consistently perform the entire process from generating AI characters to training, deployment, and NFT conversion. [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 system according to an embodiment of the present invention is a system that generates AItubers and provides new experiences for producers. This system allows producers to communicate with and train AItubers, deploy them on the platform, allow them to interact with fans, and hold live performances. Furthermore, the trained AItubers can be converted into NFTs, which can be traded or bought and sold with other producers. This allows producers to enjoy new experiences with their AItubers. For example, they can deepen their bonds with their AItubers through communication, and help their AItubers grow through interactions with fans. Furthermore, converting AItubers into NFTs can increase their value, allowing them to be traded or bought and sold with other producers, creating new business opportunities.

[0029] An AItuber system according to an embodiment includes a generation unit, a communication unit, a training unit, a deployment unit, and an NFT conversion unit. The generation unit generates an AItuber based on instructions from a producer. For example, the generation AI customizes the AItuber's appearance, personality, voice, etc. based on the producer's instructions. The generation AI generates the AItuber using a text generation AI (e.g., LLM) or a multimodal generation AI. The communication unit communicates between the AItuber generated by the generation unit and the producer. For example, the generation AI generates answers for the AItuber to the producer's questions and communicates them to the producer. The training unit trains the AItuber based on communication with the producer conducted by the communication unit. For example, the generation AI teaches the AItuber specific skills and knowledge based on the producer's instructions. The deployment unit deploys the AItuber trained by the training unit on a platform. For example, the generation AI supports the AItuber in live streaming and communicating with fans in real time. The NFT conversion unit converts the AItuber deployed by the deployment unit into an NFT. For example, the generation AI will generate NFTs based on information such as the AItuber's growth, skills, popularity, etc., and evaluate their value. This allows the AItuber system to allow producers to generate AItubers, nurture them through communication, deploy them on the platform, and turn them into NFTs.

[0030] The generation unit can learn the producer's past instruction history and propose an AItuber optimized to the producer's preferences. For example, the generation AI analyzes the producer's past instruction history and learns the producer's preferred appearance and personality patterns. For example, if the producer has generated many blue-haired characters in the past, the generation AI will propose an AItuber with blue hair next time. Furthermore, based on the producer's instruction history, the generation AI proposes an AItuber that suits the producer's preferences. For example, if the producer has generated many characters with lively personalities in the past, the generation AI will propose an AItuber with a lively personality next time. Furthermore, the generation AI learns the producer's instruction history and automatically generates an AItuber optimized to the producer's preferences. For example, based on the past instruction history, the generation AI proposes an AItuber that combines the producer's preferred appearance and personality. In this way, the generation unit can learn the producer's past instruction history and propose an AItuber optimized to the producer's preferences.

[0031] The generation unit can generate a backstory and worldview in addition to the AItuber's appearance and personality. For example, the generation AI generates a detailed backstory in addition to the AItuber's appearance and personality. For example, it sets what kind of past the AItuber has and what goals they have. The generation AI also generates the AItuber's worldview and describes how the AItuber lives in it. For example, it sets the virtual city in which the AItuber lives and the culture of that city. The generation AI also generates a detailed backstory and worldview in addition to the AItuber's appearance and personality. For example, it sets what kind of adventures the AItuber has had and what kind of friends they have. This makes it possible to generate a backstory and worldview in addition to the AItuber's appearance and personality.

[0032] The generation unit can analyze the producer's social media accounts and customize the AItuber based on the producer's interests and concerns. For example, the generation AI analyzes the producer's social media accounts and customizes the AItuber based on the producer's topics of interest and hobbies. For example, if the producer is interested in music, it generates an AItuber that likes music. It also analyzes the producer's social media accounts and customizes the AItuber based on the accounts the producer follows and the content of their posts. For example, if the producer is interested in sports, it generates an AItuber that likes sports. It also analyzes the producer's social media accounts and customizes the AItuber based on the producer's interests and concerns. For example, if the producer is interested in anime, it generates an AItuber that likes anime. This allows the generation AI to analyze the producer's social media accounts and customize the AItuber based on the producer's interests and concerns.

[0033] The training department can learn the producer's past training history and propose the optimal training plan. For example, the generation AI analyzes the producer's past training history and learns what skills the producer prioritizes. For example, if the producer has practiced singing a lot in the past, it will suggest practicing singing again next time. The generation AI will also propose the optimal training plan based on the producer's training history. For example, if the producer has practiced dancing a lot in the past, it will suggest practicing dancing again next time. The generation AI will also learn the producer's training history and propose a training plan optimized to the producer's preferences. For example, it will propose a training plan that emphasizes the skills the producer prefers based on the past training history. In this way, the generation AI can learn the producer's past training history and propose the optimal training plan.

[0034] The communication unit can analyze the producer's lifestyle and suggest the optimal timing for communication. For example, the generation AI analyzes the producer's lifestyle and suggests communication with the AItuber during the producer's most relaxed time. For example, it suggests talking with the AItuber during the evening relaxation time. The generation AI also analyzes the producer's lifestyle and suggests the optimal timing for communication. For example, if the producer is active in the morning, it suggests communication with the AItuber in the morning. The generation AI also analyzes the producer's lifestyle and customizes the timing for communication with the AItuber based on that. For example, if the producer relaxes during their lunch break, it suggests talking with the AItuber during their lunch break. In this way, the generation AI can analyze the producer's lifestyle and suggest the optimal timing for communication.

[0035] The training department can analyze the producer's social media account and propose a training plan based on the producer's interests and concerns. For example, the generation AI analyzes the producer's social media account and proposes a training plan based on the producer's topics of interest and hobbies. For example, if the producer is interested in music, a training plan for music-related skills is proposed. The generation AI can also analyze the producer's social media account and propose a training plan based on the accounts the producer follows and the content of their posts. For example, if the producer is interested in sports, a training plan for sports-related skills is proposed. The generation AI can also analyze the producer's social media account and propose a training plan based on the producer's interests and concerns. For example, if the producer is interested in anime, a training plan for anime-related skills is proposed. This makes it possible to analyze the producer's social media account and propose a training plan based on the producer's interests and concerns.

[0036] The development unit can learn the fan's past comment history and suggest the optimal answer to the AItuber. For example, the generation AI analyzes the fan's past comment history and learns what types of questions and comments the fan frequently asks. For example, if many fans have asked the same question in the past, the development unit suggests the optimal answer to that question. The generation AI also suggests the optimal answer to the AItuber based on the fan's comment history. For example, it suggests a detailed answer to a question about a topic that many fans have been interested in in the past. The generation AI also learns the fan's comment history and suggests the AItuber an answer that is optimized for the fan's preferences. For example, it suggests answers that the fan prefers based on the past comment history. In this way, the generation AI can learn the fan's past comment history and suggest the optimal answer to the AItuber.

[0037] The development unit can analyze the fan's SNS account and suggest content to the AItuber based on the fan's interests and concerns. For example, the generation AI analyzes the fan's SNS account and suggests content to the AItuber based on the fan's topics of interest and hobbies. For example, if the fan is interested in music, it suggests music-related content. It also analyzes the fan's SNS account and suggests content to the AItuber based on the accounts the fan follows and the content of their posts. For example, if the fan is interested in sports, it suggests sports-related content. It also analyzes the fan's SNS account and suggests content to the AItuber based on the fan's interests and concerns. For example, if the fan is interested in anime, it suggests anime-related content. In this way, it is possible to analyze the fan's SNS account and suggest content to the AItuber based on the fan's interests and concerns.

[0038] The development unit can analyze the fan's lifestyle rhythm and suggest the optimal timing for live streaming. For example, the generation AI analyzes the fan's lifestyle rhythm and suggests live streaming during the time when the fan is most relaxed. For example, it suggests live streaming in the evening when they are relaxing. The development unit also analyzes the fan's lifestyle rhythm and suggests the optimal timing for live streaming. For example, if the fan is active in the morning, it suggests live streaming in the morning. The generation AI also analyzes the fan's lifestyle rhythm and customizes the timing of live streaming based on that. For example, if the fan relaxes during their lunch break, it suggests live streaming during their lunch break. In this way, the development unit can analyze the fan's lifestyle rhythm and suggest the optimal timing for live streaming.

[0039] The NFTization unit learns the producer's past trading history and can propose optimal exchange / trading plans. For example, the generation AI analyzes the producer's past trading history and learns the producer's trading preferences. For example, if the producer has traded many expensive NFTs in the past, it will propose a more expensive NFT next time. The generation AI also proposes optimal exchange / trading plans based on the producer's trading history. For example, if the producer has exchanged many NFTs in the past, it will propose an exchange next time as well. The generation AI also learns the producer's trading history and proposes exchange / trading plans optimized to the producer's preferences. For example, it proposes trading plans that the producer prefers based on past trading history. In this way, the generation AI can learn the producer's past trading history and propose optimal exchange / trading plans.

[0040] The NFTization unit can analyze the producer's social media account and suggest an NFT value based on the producer's interests. For example, the generation AI analyzes the producer's social media account and suggests an NFT value for the AItuber based on the producer's topics of interest and hobbies. For example, if the producer is interested in music, it suggests an NFT value related to music. It can also analyze the producer's social media account and suggest an NFT value for the AItuber based on the accounts the producer follows and the content of their posts. For example, if the producer is interested in sports, it suggests an NFT value related to sports. It can also analyze the producer's social media account and suggest an NFT value based on the producer's interests. For example, if the producer is interested in anime, it suggests an NFT value related to anime. This makes it possible to analyze the producer's social media account and suggest an NFT value based on their interests.

[0041] The NFT unit can analyze the producer's lifestyle and suggest the optimal timing for trading. For example, the generation AI analyzes the producer's lifestyle and suggests trading during the time when the producer is most relaxed. For example, it suggests trading during the evening relaxation time. The NFT unit also analyzes the producer's lifestyle and suggests the optimal timing for trading. For example, if the producer is active in the morning, it suggests trading in the morning. The generation AI also analyzes the producer's lifestyle and customizes the timing for trading based on that. For example, if the producer relaxes during their lunch break, it suggests trading during their lunch break. In this way, the producer's lifestyle can be analyzed and the optimal timing for trading can be suggested.

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

[0043] The generation unit generates an AItuber based on the producer's instructions. For example, the generation AI customizes the AItuber's appearance, personality, voice, etc. based on the producer's instructions. The generation AI generates an AItuber using a text generation AI (e.g., LLM) or a multimodal generation AI. The communication unit communicates between the AItuber generated by the generation unit and the producer. For example, the generation AI generates answers for the AItuber to the producer's questions and communicates them to the producer. The training unit trains the AItuber based on communication with the producer conducted by the communication unit. For example, the generation AI teaches the AItuber specific skills and knowledge based on the producer's instructions. The deployment unit deploys the AItuber trained by the training unit on the platform. For example, the generation AI supports the AItuber in live streaming and communicating with fans in real time. The NFT conversion unit converts the AItuber deployed by the deployment unit into an NFT. For example, the generation AI generates NFTs based on information such as the AItuber's growth, skills, and popularity, and evaluates their value. This allows the AItuber system to allow producers to generate AItubers, nurture them through communication, deploy them on the platform, and turn them into NFTs.

[0044] The generation unit can learn the producer's past instruction history and propose an AItuber optimized to the producer's preferences. For example, the generation AI analyzes the producer's past instruction history and learns the producer's preferred appearance and personality patterns. For example, if a producer has generated many blue-haired characters in the past, it will propose an AItuber with blue hair next time. Furthermore, based on the producer's instruction history, the generation AI proposes an AItuber that suits the producer's preferences. For example, if a producer has generated many characters with lively personalities in the past, it will propose an AItuber with a lively personality next time. Furthermore, the generation AI learns the producer's instruction history and automatically generates an AItuber optimized to the producer's preferences. For example, based on the past instruction history, it proposes an AItuber that combines the producer's preferred appearance and personality. In this way, it can learn the producer's past instruction history and propose an AItuber optimized to the producer's preferences.

[0045] The generation unit can generate a backstory and worldview in addition to the AItuber's appearance and personality. For example, the generation AI generates a detailed backstory in addition to the AItuber's appearance and personality. For example, it sets what kind of past the AItuber has and what goals they have. The generation AI also generates the AItuber's worldview and describes how the AItuber lives in it. For example, it sets the virtual city in which the AItuber lives and the culture of that city. The generation AI also generates a detailed backstory and worldview in addition to the AItuber's appearance and personality. For example, it sets what kind of adventures the AItuber has had and what kind of friends they have. This allows it to generate a backstory and worldview in addition to the AItuber's appearance and personality.

[0046] The generation unit can analyze the producer's social media accounts and customize the AItuber based on the producer's interests and concerns. For example, the generation AI analyzes the producer's social media accounts and customizes the AItuber based on the producer's topics of interest and hobbies. For example, if the producer is interested in music, it generates an AItuber that likes music. It also analyzes the producer's social media accounts and customizes the AItuber based on the accounts the producer follows and the content of their posts. For example, if the producer is interested in sports, it generates an AItuber that likes sports. It also analyzes the producer's social media accounts and customizes the AItuber based on the producer's interests and concerns. For example, if the producer is interested in anime, it generates an AItuber that likes anime. This makes it possible to analyze the producer's social media accounts and customize the AItuber based on the producer's interests and concerns.

[0047] The training department can learn a producer's past training history and propose the optimal training plan. For example, the generation AI analyzes a producer's past training history and learns what skills the producer prioritizes. For example, if the producer has practiced singing a lot in the past, it will suggest practicing singing again next time. The generation AI also proposes the optimal training plan based on the producer's training history. For example, if the producer has practiced dancing a lot in the past, it will suggest practicing dancing again next time. The generation AI also learns the producer's training history and proposes a training plan optimized to the producer's preferences. For example, it proposes a training plan that emphasizes the skills the producer prefers based on the past training history. In this way, the generation AI can learn a producer's past training history and propose the optimal training plan.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The generation unit generates an AItuber based on the producer's instructions. For example, the generation AI customizes the AItuber's appearance, personality, voice, etc. based on the producer's instructions. The generation AI generates the AItuber using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The communication unit communicates between the AItuber generated by the generation unit and the producer. For example, the generation AI generates an answer for the AItuber to the producer's question and communicates it to the producer. Step 3: The training department trains the AItuber based on the communication with the producer conducted by the communication department. For example, the generation AI teaches the AItuber specific skills and knowledge based on the producer's instructions. Step 4: The Development Department deploys the AItubers developed by the Development Department on the platform. For example, the Generation AI helps the AItubers to broadcast live and communicate with fans in real time. Step 5: The NFT conversion unit converts the AItuber deployed by the expansion unit into an NFT. For example, the generation AI generates an NFT based on information such as the AItuber's growth, skills, and popularity, and evaluates its value.

[0050] (Example 2) The AItuber system according to an embodiment of the present invention is a system that generates AItubers and provides new experiences for producers. This system allows producers to communicate with and train AItubers, deploy them on the platform, allow them to interact with fans, and hold live performances. Furthermore, the trained AItubers can be converted into NFTs, which can be traded or bought and sold with other producers. This allows producers to enjoy new experiences with their AItubers. For example, they can deepen their bonds with their AItubers through communication, and help their AItubers grow through interactions with fans. Furthermore, converting AItubers into NFTs can increase their value, allowing them to be traded or bought and sold with other producers, creating new business opportunities.

[0051] An AItuber system according to an embodiment includes a generation unit, a communication unit, a training unit, a deployment unit, and an NFT conversion unit. The generation unit generates an AItuber based on instructions from a producer. For example, the generation AI customizes the AItuber's appearance, personality, voice, etc. based on the producer's instructions. The generation AI generates the AItuber using a text generation AI (e.g., LLM) or a multimodal generation AI. The communication unit communicates between the AItuber generated by the generation unit and the producer. For example, the generation AI generates answers for the AItuber to the producer's questions and communicates them to the producer. The training unit trains the AItuber based on communication with the producer conducted by the communication unit. For example, the generation AI teaches the AItuber specific skills and knowledge based on the producer's instructions. The deployment unit deploys the AItuber trained by the training unit on a platform. For example, the generation AI supports the AItuber in live streaming and communicating with fans in real time. The NFT conversion unit converts the AItuber deployed by the deployment unit into an NFT. For example, the generation AI will generate NFTs based on information such as the AItuber's growth, skills, popularity, etc., and evaluate their value. This allows the AItuber system to allow producers to generate AItubers, nurture them through communication, deploy them on the platform, and turn them into NFTs.

[0052] The generation unit can dynamically adjust the AItuber's appearance and personality according to the producer's emotional state. For example, the generation unit incorporates an emotion estimation function into the generation AI, and when the producer is happy, the generation unit brightens the AItuber's appearance and makes the personality cheerful. For example, when the producer is smiling, the AItuber's hair color is changed to a bright color and the personality is set to cheerful. Also, when the producer is sad, the AItuber's appearance is changed to a subdued color and the personality is set to gentle. For example, when the producer is depressed, the AItuber's clothing is changed to a subdued color and the personality is set to gentle. Also, when the producer is angry, the AItuber's appearance is emphasized and the personality is set to calm. For example, when the producer is angry, the AItuber's eye color is made sharp and the personality is set to calm. This makes it possible to dynamically adjust the AItuber's appearance and personality according to the producer's emotional state.

[0053] The generation unit can learn the producer's past instruction history and propose an AItuber optimized to the producer's preferences. For example, the generation AI analyzes the producer's past instruction history and learns the producer's preferred appearance and personality patterns. For example, if the producer has generated many blue-haired characters in the past, the generation AI will propose an AItuber with blue hair next time. Furthermore, based on the producer's instruction history, the generation AI proposes an AItuber that suits the producer's preferences. For example, if the producer has generated many characters with lively personalities in the past, the generation AI will propose an AItuber with a lively personality next time. Furthermore, the generation AI learns the producer's instruction history and automatically generates an AItuber optimized to the producer's preferences. For example, based on the past instruction history, the generation AI proposes an AItuber that combines the producer's preferred appearance and personality. In this way, the generation unit can learn the producer's past instruction history and propose an AItuber optimized to the producer's preferences.

[0054] The generation unit can analyze the producer's voice and facial expressions in real time and customize the AItuber's reactions based on that. For example, the generation AI analyzes the producer's tone of voice and facial expressions in real time and customizes the AItuber's reactions. For example, if the producer is smiling when speaking, the AItuber will also respond with a smile. The generation AI also analyzes the producer's tone of voice and facial expressions and dynamically changes the AItuber's reactions. For example, if the producer is speaking with a serious expression, the AItuber will also respond with a serious expression. The generation AI also analyzes the producer's voice and facial expressions and customizes the AItuber's reactions based on that. For example, if the producer has a surprised expression, the AItuber will also respond with a surprised expression. This makes it possible to analyze the producer's voice and facial expressions in real time and customize the AItuber's reactions based on that.

[0055] The generation unit can generate a backstory and worldview in addition to the AItuber's appearance and personality. For example, the generation AI generates a detailed backstory in addition to the AItuber's appearance and personality. For example, it sets what kind of past the AItuber has and what goals they have. The generation AI also generates the AItuber's worldview and describes how the AItuber lives in it. For example, it sets the virtual city in which the AItuber lives and the culture of that city. The generation AI also generates a detailed backstory and worldview in addition to the AItuber's appearance and personality. For example, it sets what kind of adventures the AItuber has had and what kind of friends they have. This makes it possible to generate a backstory and worldview in addition to the AItuber's appearance and personality.

[0056] The generation unit can analyze the producer's social media accounts and customize the AItuber based on the producer's interests and concerns. For example, the generation AI analyzes the producer's social media accounts and customizes the AItuber based on the producer's topics of interest and hobbies. For example, if the producer is interested in music, it generates an AItuber that likes music. It also analyzes the producer's social media accounts and customizes the AItuber based on the accounts the producer follows and the content of their posts. For example, if the producer is interested in sports, it generates an AItuber that likes sports. It also analyzes the producer's social media accounts and customizes the AItuber based on the producer's interests and concerns. For example, if the producer is interested in anime, it generates an AItuber that likes anime. This allows the generation AI to analyze the producer's social media accounts and customize the AItuber based on the producer's interests and concerns.

[0057] The generation unit can analyze the producer's emotions and propose the optimal generation process. For example, the generation unit uses an emotion estimation function to analyze the producer's emotions in real time when generating an AItuber and propose the optimal generation process. For example, if the producer is having fun, the generation process can be made more creative. The generation unit can also analyze the producer's emotional state and customize the AItuber generation process based on that. For example, if the producer is relaxed, the generation process can be carried out at a leisurely pace. The generation unit can also use the emotion estimation function to analyze the producer's emotions and propose the optimal generation process. For example, if the producer is impatient, the generation process can be carried out quickly. This makes it possible to analyze the producer's emotions and propose the optimal generation process.

[0058] The communication unit can analyze the producer's emotional state in real time and instruct the AItuber how to communicate accordingly. For example, the generation AI analyzes the producer's emotional state in real time, and if the producer is happy, the AItuber will respond with a happy expression. For example, when the producer is smiling while speaking, the AItuber will also respond with a smile. The communication unit also analyzes the producer's emotional state and instructs the AItuber how to communicate accordingly. For example, if the producer is sad, the AItuber will say comforting words. The generation AI also analyzes the producer's emotional state in real time and customizes the AItuber's response based on that. For example, if the producer is angry, the AItuber will respond calmly. This allows the communication unit to instruct the AItuber how to communicate according to the producer's emotional state.

[0059] The training department can learn the producer's past training history and propose the optimal training plan. For example, the generation AI analyzes the producer's past training history and learns what skills the producer prioritizes. For example, if the producer has practiced singing a lot in the past, it will suggest practicing singing again next time. The generation AI will also propose the optimal training plan based on the producer's training history. For example, if the producer has practiced dancing a lot in the past, it will suggest practicing dancing again next time. The generation AI will also learn the producer's training history and propose a training plan optimized to the producer's preferences. For example, it will propose a training plan that emphasizes the skills the producer prefers based on the past training history. In this way, the generation AI can learn the producer's past training history and propose the optimal training plan.

[0060] The communication unit can analyze the producer's voice and facial expressions and customize the AItuber's reactions based on that. For example, the generation AI in the communication unit analyzes the producer's tone of voice and facial expressions and customizes the AItuber's reactions. For example, if the producer is smiling when speaking, the AItuber will also respond with a smile. The generation AI also analyzes the producer's tone of voice and facial expressions and dynamically changes the AItuber's reactions. For example, if the producer is speaking with a serious expression, the AItuber will also respond with a serious expression. The generation AI also analyzes the producer's voice and facial expressions and customizes the AItuber's reactions based on that. For example, if the producer has a surprised expression, the AItuber will also respond with a surprised expression. This makes it possible to analyze the producer's voice and facial expressions and customize the AItuber's reactions based on that.

[0061] The communication unit can analyze the producer's lifestyle and suggest the optimal timing for communication. For example, the generation AI analyzes the producer's lifestyle and suggests communication with the AItuber during the producer's most relaxed time. For example, it suggests talking with the AItuber during the evening relaxation time. The generation AI also analyzes the producer's lifestyle and suggests the optimal timing for communication. For example, if the producer is active in the morning, it suggests communication with the AItuber in the morning. The generation AI also analyzes the producer's lifestyle and customizes the timing for communication with the AItuber based on that. For example, if the producer relaxes during their lunch break, it suggests talking with the AItuber during their lunch break. In this way, the generation AI can analyze the producer's lifestyle and suggest the optimal timing for communication.

[0062] The training department can analyze the producer's social media account and propose a training plan based on the producer's interests and concerns. For example, the generation AI analyzes the producer's social media account and proposes a training plan based on the producer's topics of interest and hobbies. For example, if the producer is interested in music, a training plan for music-related skills is proposed. The generation AI can also analyze the producer's social media account and propose a training plan based on the accounts the producer follows and the content of their posts. For example, if the producer is interested in sports, a training plan for sports-related skills is proposed. The generation AI can also analyze the producer's social media account and propose a training plan based on the producer's interests and concerns. For example, if the producer is interested in anime, a training plan for anime-related skills is proposed. This makes it possible to analyze the producer's social media account and propose a training plan based on the producer's interests and concerns.

[0063] The communication unit can analyze the producer's emotions and suggest the optimal communication method. For example, the communication unit uses an emotion estimation function to analyze the emotions of the producer when communicating with the AItuber in real time and suggest the optimal communication method. For example, if the producer is relaxed, it suggests a relaxed conversation. It also analyzes the producer's emotional state and customizes the communication method with the AItuber based on that. For example, if the producer is feeling stressed, it suggests a conversation that will reduce stress. It also uses the emotion estimation function to analyze the producer's emotions and suggest the optimal communication method. For example, if the producer is excited, it suggests a conversation that shares the producer's excitement. In this way, it is possible to analyze the producer's emotions and suggest the optimal communication method.

[0064] The development unit can analyze the emotional state of fans in real time and instruct the AItuber to communicate accordingly. For example, the generation AI analyzes the emotional state of fans in real time, and if the fan is happy, the AItuber will respond with a happy expression. For example, if a fan comments with a smile, the AItuber will also respond with a smile. The development unit also analyzes the emotional state of fans and instructs the AItuber to communicate accordingly. For example, if a fan is sad, the AItuber will say comforting words. The generation AI also analyzes the emotional state of fans in real time and customizes the AItuber's response based on that. For example, if a fan is angry, the AItuber will respond calmly. This allows the AItuber to be instructed to communicate according to the fan's emotional state.

[0065] The development unit can learn the fan's past comment history and suggest the optimal answer to the AItuber. For example, the generation AI analyzes the fan's past comment history and learns what types of questions and comments the fan frequently asks. For example, if many fans have asked the same question in the past, the development unit suggests the optimal answer to that question. The generation AI also suggests the optimal answer to the AItuber based on the fan's comment history. For example, it suggests a detailed answer to a question about a topic that many fans have been interested in in the past. The generation AI also learns the fan's comment history and suggests the AItuber an answer that is optimized for the fan's preferences. For example, it suggests answers that the fan prefers based on the past comment history. In this way, the generation AI can learn the fan's past comment history and suggest the optimal answer to the AItuber.

[0066] The development section can analyze the fan's voice and facial expression and customize the AItuber's response based on that. For example, the generation AI analyzes the fan's tone of voice and facial expression and customizes the AItuber's response. For example, if the fan is smiling when speaking, the AItuber will also respond with a smile. The generation AI also analyzes the fan's tone of voice and facial expression and dynamically changes the AItuber's response. For example, if the fan is speaking with a serious expression, the AItuber will also respond with a serious expression. The generation AI also analyzes the fan's voice and facial expression and customizes the AItuber's response based on that. For example, if the fan has a surprised expression, the AItuber will also respond with a surprised expression. This allows the generation AI to analyze the fan's voice and facial expression and customize the AItuber's response based on that.

[0067] The development unit can analyze the fan's SNS account and suggest content to the AItuber based on the fan's interests and concerns. For example, the generation AI analyzes the fan's SNS account and suggests content to the AItuber based on the fan's topics of interest and hobbies. For example, if the fan is interested in music, it suggests music-related content. It also analyzes the fan's SNS account and suggests content to the AItuber based on the accounts the fan follows and the content of their posts. For example, if the fan is interested in sports, it suggests sports-related content. It also analyzes the fan's SNS account and suggests content to the AItuber based on the fan's interests and concerns. For example, if the fan is interested in anime, it suggests anime-related content. In this way, it is possible to analyze the fan's SNS account and suggest content to the AItuber based on the fan's interests and concerns.

[0068] The development unit can analyze the fan's lifestyle rhythm and suggest the optimal timing for live streaming. For example, the generation AI analyzes the fan's lifestyle rhythm and suggests live streaming during the time when the fan is most relaxed. For example, it suggests live streaming in the evening when they are relaxing. The development unit also analyzes the fan's lifestyle rhythm and suggests the optimal timing for live streaming. For example, if the fan is active in the morning, it suggests live streaming in the morning. The generation AI also analyzes the fan's lifestyle rhythm and customizes the timing of live streaming based on that. For example, if the fan relaxes during their lunch break, it suggests live streaming during their lunch break. In this way, the development unit can analyze the fan's lifestyle rhythm and suggest the optimal timing for live streaming.

[0069] The development unit can analyze the emotions of fans and suggest the optimal way to interact. For example, the development unit uses an emotion estimation function to analyze the emotions of fans when interacting with AItubers in real time and suggest the optimal way to interact. For example, if the fan is having fun, it suggests fun conversations. It also analyzes the emotional state of the fan and customizes the way to interact with the AItuber based on that. For example, if the fan is feeling stressed, it suggests conversations that will reduce stress. It also uses the emotion estimation function to analyze the emotions of fans and suggest the optimal way to interact. For example, if the fan is excited, it suggests conversations that will share the excitement. In this way, it is possible to analyze the emotions of fans and suggest the optimal way to interact.

[0070] The NFTization unit can analyze the producer's emotional state and dynamically evaluate the AItuber's NFT value based on that. For example, the generation AI analyzes the producer's emotional state, and if the producer is happy, it will evaluate the AItuber's NFT value higher. For example, if the producer is smiling, it will increase the AItuber's NFT value. The NFTization unit can also analyze the producer's emotional state and dynamically evaluate the AItuber's NFT value based on that. For example, if the producer is sad, it will lower the AItuber's NFT value. The generation AI can also analyze the producer's emotional state and dynamically evaluate the AItuber's NFT value based on that. For example, if the producer is angry, it will stabilize the AItuber's NFT value. This allows the NFTization unit to analyze the producer's emotional state and dynamically evaluate the AItuber's NFT value based on that.

[0071] The NFTization unit learns the producer's past trading history and can propose optimal exchange / trading plans. For example, the generation AI analyzes the producer's past trading history and learns the producer's trading preferences. For example, if the producer has traded many expensive NFTs in the past, it will propose a more expensive NFT next time. The generation AI also proposes optimal exchange / trading plans based on the producer's trading history. For example, if the producer has exchanged many NFTs in the past, it will propose an exchange next time as well. The generation AI also learns the producer's trading history and proposes exchange / trading plans optimized to the producer's preferences. For example, it proposes trading plans that the producer prefers based on past trading history. In this way, the generation AI can learn the producer's past trading history and propose optimal exchange / trading plans.

[0072] The NFT unit can analyze the producer's voice and facial expressions and customize the AItuber's NFT value based on that. For example, the generation AI analyzes the producer's tone of voice and facial expressions to customize the AItuber's NFT value. For example, if the producer is smiling when speaking, the AItuber's NFT value increases. The generation AI also analyzes the producer's tone of voice and facial expressions and dynamically changes the AItuber's NFT value based on that. For example, if the producer is speaking with a serious expression, the AItuber's NFT value stabilizes. The generation AI also analyzes the producer's voice and facial expressions and customizes the AItuber's NFT value based on that. For example, if the producer has a surprised expression, the AItuber's NFT value fluctuates. This allows the generation AI to analyze the producer's voice and facial expressions and customize the AItuber's NFT value based on that.

[0073] The NFTization unit can analyze the producer's social media account and suggest an NFT value based on the producer's interests. For example, the generation AI analyzes the producer's social media account and suggests an NFT value for the AItuber based on the producer's topics of interest and hobbies. For example, if the producer is interested in music, it suggests an NFT value related to music. It can also analyze the producer's social media account and suggest an NFT value for the AItuber based on the accounts the producer follows and the content of their posts. For example, if the producer is interested in sports, it suggests an NFT value related to sports. It can also analyze the producer's social media account and suggest an NFT value based on the producer's interests. For example, if the producer is interested in anime, it suggests an NFT value related to anime. This makes it possible to analyze the producer's social media account and suggest an NFT value based on their interests.

[0074] The NFT unit can analyze the producer's lifestyle and suggest the optimal timing for trading. For example, the generation AI analyzes the producer's lifestyle and suggests trading during the time when the producer is most relaxed. For example, it suggests trading during the evening relaxation time. The NFT unit also analyzes the producer's lifestyle and suggests the optimal timing for trading. For example, if the producer is active in the morning, it suggests trading in the morning. The generation AI also analyzes the producer's lifestyle and customizes the timing for trading based on that. For example, if the producer relaxes during their lunch break, it suggests trading during their lunch break. In this way, the producer's lifestyle can be analyzed and the optimal timing for trading can be suggested.

[0075] The NFTization unit can analyze the producer's emotions and suggest the optimal trading method. For example, the NFTization unit uses an emotion estimation function to analyze the producer's emotions in real time when trading an AItuber's NFTs and suggest the optimal trading method. For example, if the producer is relaxed, it will suggest a relaxed trading method. It also analyzes the producer's emotional state and customizes the AItuber's NFT trading method based on that. For example, if the producer is feeling stressed, it will suggest a trading method that reduces stress. It also uses the emotion estimation function to analyze the producer's emotions and suggest the optimal trading method. For example, if the producer is excited, it will suggest a trading method that shares the excitement. In this way, it is possible to analyze the producer's emotions and suggest the optimal trading method.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The generation unit generates an AItuber based on the producer's instructions. For example, the generation AI customizes the AItuber's appearance, personality, voice, etc. based on the producer's instructions. The generation AI generates an AItuber using a text generation AI (e.g., LLM) or a multimodal generation AI. The communication unit communicates between the AItuber generated by the generation unit and the producer. For example, the generation AI generates answers for the AItuber to the producer's questions and communicates them to the producer. The training unit trains the AItuber based on communication with the producer conducted by the communication unit. For example, the generation AI teaches the AItuber specific skills and knowledge based on the producer's instructions. The deployment unit deploys the AItuber trained by the training unit on the platform. For example, the generation AI supports the AItuber in live streaming and communicating with fans in real time. The NFT conversion unit converts the AItuber deployed by the deployment unit into an NFT. For example, the generation AI generates NFTs based on information such as the AItuber's growth, skills, and popularity, and evaluates their value. This allows the AItuber system to allow producers to generate AItubers, nurture them through communication, deploy them on the platform, and turn them into NFTs.

[0078] The generation unit can dynamically adjust the AItuber's appearance and personality according to the producer's emotional state. For example, by incorporating an emotion estimation function into the generation AI, if the producer is happy, the AItuber's appearance will be brighter and its personality will be more cheerful. For example, when the producer is smiling, the AItuber's hair color will be changed to a brighter color and its personality will be set to a more cheerful one. If the producer is sad, the AItuber's appearance will be changed to a more subdued color and its personality will be set to a more gentle one. For example, when the producer is depressed, the AItuber's clothing will be changed to a more subdued color and its personality will be set to a more gentle one. If the producer is angry, the AItuber's appearance will be emphasized and its personality will be set to a more calm one. For example, when the producer is angry, the AItuber's eye color will be sharper and its personality will be set to a more calm one. This allows the AItuber's appearance and personality to be dynamically adjusted according to the producer's emotional state.

[0079] The generation unit can learn the producer's past instruction history and propose an AItuber optimized to the producer's preferences. For example, the generation AI analyzes the producer's past instruction history and learns the producer's preferred appearance and personality patterns. For example, if a producer has generated many blue-haired characters in the past, it will propose an AItuber with blue hair next time. Furthermore, based on the producer's instruction history, the generation AI proposes an AItuber that suits the producer's preferences. For example, if a producer has generated many characters with lively personalities in the past, it will propose an AItuber with a lively personality next time. Furthermore, the generation AI learns the producer's instruction history and automatically generates an AItuber optimized to the producer's preferences. For example, based on the past instruction history, it proposes an AItuber that combines the producer's preferred appearance and personality. In this way, it can learn the producer's past instruction history and propose an AItuber optimized to the producer's preferences.

[0080] The generation unit can analyze the producer's voice and facial expressions in real time and customize the AItuber's reactions based on that. For example, the generation AI analyzes the producer's tone of voice and facial expressions in real time and customizes the AItuber's reactions. For example, if the producer is smiling while speaking, the AItuber will also respond with a smile. The generation AI also analyzes the producer's tone of voice and facial expressions and dynamically changes the AItuber's reactions. For example, if the producer is speaking with a serious expression, the AItuber will also respond with a serious expression. The generation AI also analyzes the producer's voice and facial expressions and customizes the AItuber's reactions based on that. For example, if the producer has a surprised expression, the AItuber will also respond with a surprised expression. This makes it possible to analyze the producer's voice and facial expressions in real time and customize the AItuber's reactions based on that.

[0081] The generation unit can generate a backstory and worldview in addition to the AItuber's appearance and personality. For example, the generation AI generates a detailed backstory in addition to the AItuber's appearance and personality. For example, it sets what kind of past the AItuber has and what goals they have. The generation AI also generates the AItuber's worldview and describes how the AItuber lives in it. For example, it sets the virtual city in which the AItuber lives and the culture of that city. The generation AI also generates a detailed backstory and worldview in addition to the AItuber's appearance and personality. For example, it sets what kind of adventures the AItuber has had and what kind of friends they have. This allows it to generate a backstory and worldview in addition to the AItuber's appearance and personality.

[0082] The generation unit can analyze the producer's social media accounts and customize the AItuber based on the producer's interests and concerns. For example, the generation AI analyzes the producer's social media accounts and customizes the AItuber based on the producer's topics of interest and hobbies. For example, if the producer is interested in music, it generates an AItuber that likes music. It also analyzes the producer's social media accounts and customizes the AItuber based on the accounts the producer follows and the content of their posts. For example, if the producer is interested in sports, it generates an AItuber that likes sports. It also analyzes the producer's social media accounts and customizes the AItuber based on the producer's interests and concerns. For example, if the producer is interested in anime, it generates an AItuber that likes anime. This makes it possible to analyze the producer's social media accounts and customize the AItuber based on the producer's interests and concerns.

[0083] The generation unit can analyze the producer's emotions and propose the optimal generation process. For example, it uses an emotion estimation function to analyze the producer's emotions in real time when generating an AItuber and propose the optimal generation process. For example, if the producer is having fun, it makes the generation process more creative. It also analyzes the producer's emotional state and customizes the AItuber generation process based on that. For example, if the producer is relaxed, it proceeds with the generation process at a leisurely pace. It also uses the emotion estimation function to analyze the producer's emotions and propose the optimal generation process. For example, if the producer is impatient, it proceeds with the generation process quickly. This makes it possible to analyze the producer's emotions and propose the optimal generation process.

[0084] The communication unit can analyze the producer's emotional state in real time and instruct the AItuber on how to communicate accordingly. For example, if the generation AI analyzes the producer's emotional state in real time, and the producer is happy, the AItuber will respond with a happy expression. For example, when the producer is smiling while speaking, the AItuber will also respond with a smile. The generation AI can also analyze the producer's emotional state and instruct the AItuber on how to communicate accordingly. For example, if the producer is sad, the AItuber will say comforting words. The generation AI can also analyze the producer's emotional state in real time and customize the AItuber's response based on that. For example, if the producer is angry, the AItuber will respond calmly. This allows the AItuber to communicate according to the producer's emotional state.

[0085] The training department can learn a producer's past training history and propose the optimal training plan. For example, the generation AI analyzes a producer's past training history and learns what skills the producer prioritizes. For example, if the producer has practiced singing a lot in the past, it will suggest practicing singing again next time. The generation AI also proposes the optimal training plan based on the producer's training history. For example, if the producer has practiced dancing a lot in the past, it will suggest practicing dancing again next time. The generation AI also learns the producer's training history and proposes a training plan optimized to the producer's preferences. For example, it proposes a training plan that emphasizes the skills the producer prefers based on the past training history. In this way, the generation AI can learn a producer's past training history and propose the optimal training plan.

[0086] The communication department can analyze the producer's voice and facial expressions and customize the AItuber's reactions based on that. For example, the generation AI analyzes the producer's tone of voice and facial expressions and customizes the AItuber's reactions. For example, if the producer is smiling when speaking, the AItuber will also respond with a smile. The generation AI can also analyze the producer's tone of voice and facial expressions and dynamically change the AItuber's reactions. For example, if the producer is speaking with a serious expression, the AItuber will also respond with a serious expression. The generation AI can also analyze the producer's voice and facial expressions and customize the AItuber's reactions based on that. For example, if the producer has a surprised expression, the AItuber will also respond with a surprised expression. This makes it possible to analyze the producer's voice and facial expressions and customize the AItuber's reactions based on that.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The generation unit generates an AItuber based on the producer's instructions. For example, the generation AI customizes the AItuber's appearance, personality, voice, etc. based on the producer's instructions. The generation AI generates the AItuber using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The communication unit communicates between the AItuber generated by the generation unit and the producer. For example, the generation AI generates an answer for the AItuber to the producer's question and communicates it to the producer. Step 3: The training department trains the AItuber based on the communication with the producer conducted by the communication department. For example, the generation AI teaches the AItuber specific skills and knowledge based on the producer's instructions. Step 4: The Development Department deploys the AItubers developed by the Development Department on the platform. For example, the Generation AI helps the AItubers to broadcast live and communicate with fans in real time. Step 5: The NFT conversion unit converts the AItuber deployed by the expansion unit into an NFT. For example, the generation AI generates an NFT based on information such as the AItuber's growth, skills, and popularity, and evaluates its value.

[0089] 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.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The 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.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The 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.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 AItubers based on instructions from a producer; A communication unit that communicates between the AItuber generated by the generation unit and the producer; A training department that trains the AItuber based on communication with the producer conducted by the communication department; A deployment unit that deploys the AItuber trained by the training unit on a platform; An NFT conversion unit that converts the AItuber expanded by the expansion unit into an NFT. A system characterized by:

2. The generation unit Dynamically adjust the appearance and personality of the AITuber according to the emotional state of the producer.

2. The system of claim 1.

3. The generation unit The AITuber is optimized to the producer's preferences by learning the producer's past instruction history.

2. The system of claim 1.

4. The generation unit Analyze the producer's voice and facial expressions in real time and customize the AItuber's responses based on that.

2. The system of claim 1.

5. The generation unit In addition to the appearance and personality of the AItuber, a backstory and worldview are also generated.

2. The system of claim 1.

6. The generation unit Analyzing the producer's social media accounts and customizing the AItuber based on the producer's interests 2. The system of claim 1.

7. The generation unit Analyze the producer's emotions and propose the optimal production process 2. The system of claim 1.

8. The communication unit Analyzing the emotional state of the producer in real time and instructing the AItuber to communicate accordingly.

2. The system of claim 1.

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