system

Abstract

An object of a system according to an embodiment is to make it possible to easily create an AITuber without advanced programming skills.SOLUTION: A system includes an avatar generation unit, a script generation unit, an speech synthesis unit, a motion synchronizing unit, and a subtitle generation unit. The avatar generation unit generates an avatar of 3D or Live2D. The script generation section generates a script of the content. The speech synthesis unit synthesizes a voice. The motion synchronization unit synchronizes the motion with the voice. The caption generation unit generates a caption.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, creating an AITuber requires advanced programming skills, making it difficult for ordinary users to create one easily.

[0005] The system according to the embodiment aims to make it possible to easily create an AITuber without advanced programming skills. [Means for solving the problem]

[0006] The system according to the embodiment includes an avatar generation unit, a script generation unit, a voice synthesis unit, a motion synchronization unit, and a subtitle generation unit. The avatar generation unit generates an avatar using 3D or Live2D. The script generation unit generates a script for the content. The voice synthesis unit synthesizes voice. The motion synchronization unit synchronizes motion with voice. The subtitle generation unit generates subtitles. [Effects of the Invention]

[0007] The system according to the embodiment can make it easy to create an AITuber without advanced programming skills. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AITuber production platform according to an embodiment of the present invention is a system that allows users to easily create AITubers without requiring advanced programming skills, thereby enabling users to easily create AITubers and expanding the domestic AITuber market.

[0029] An AITuber production platform according to an embodiment includes an avatar generation unit, a script generation unit, a voice synthesis unit, a motion synchronization unit, and a subtitle generation unit. The avatar generation unit generates 3D or Live2D avatars. For example, the generation AI generates an avatar design based on a prompt entered by a user. For example, when a prompt such as "Create a cute cat-eared girl character" is entered, the generation AI generates an avatar based on the instruction. The script generation unit generates a script for content. For example, the generation AI creates a script based on a prompt entered by a user. For example, when a prompt such as "Create a script for a game commentary" is entered, the generation AI generates a script based on the instruction. The voice synthesis unit synthesizes voice. For example, the generation AI synthesizes voice based on text entered by a user. For example, when text such as "Hello, today I'll introduce a new game" is entered, the generation AI generates voice based on the text. The motion synchronization unit synchronizes motion with voice. For example, the generation AI synchronizes the motion of the avatar with the synthesized voice. For example, the generation AI automatically moves the avatar's mouth and waves its hands in time with the audio. The subtitle generation unit generates subtitles. For example, the generation AI automatically generates subtitles based on synthesized audio. For example, it converts audio content into text and adds it to videos as subtitles. This allows the AITuber production platform according to the embodiment to easily create AITubers without requiring users to have advanced programming skills. For example, through the LINE VOOM app, users can consistently create avatars, write content scripts, synthesize voices, synchronize motions, and add subtitles. This will enable more people to debut as AITubers, and is expected to expand the domestic AITuber market.

[0030] The avatar generation unit automatically generates an avatar from a user's photos or videos, creating an avatar with a realistic appearance. For example, the avatar generation unit uses a generation AI to analyze facial features based on a photo uploaded by the user and automatically generate a realistic 3D avatar. For example, it reproduces the facial contours, eye position, hairstyle, and other details in detail. This allows for the automatic generation of an avatar with a realistic appearance from the user's photos or videos.

[0031] The avatar generation unit can learn the characteristics of the user's voice and speaking style and customize the avatar's facial expressions and movements based on that. For example, the avatar generation unit analyzes the user's voice data, and the generation AI realistically reproduces the avatar's mouth movements based on the tone and rhythm of the voice. For example, it adjusts the mouth movements according to the speaking speed and emphasis. This makes it possible to customize the avatar's facial expressions and movements based on the characteristics of the user's voice and speaking style.

[0032] The script generation unit can learn the user's past content and preferences and generate personalized scripts based on that. For example, the script generation unit analyzes content created by the user in the past, and the generation AI learns its style and theme. For example, it generates a new script based on the topics and speaking styles of past videos. This makes it possible to generate personalized scripts based on the user's past content and preferences.

[0033] The script generation unit can generate multiple script variations based on keywords or themes input by the user and provide options. For example, the script generation unit generates multiple script variations using a generation AI based on keywords input by the user. For example, if "horror" is input, different horror story scripts will be suggested. This allows multiple script variations to be generated based on keywords or themes input by the user and provide options.

[0034] The speech synthesis unit can learn the characteristics of the user's voice and synthesize natural-sounding speech based on that. For example, the speech synthesis unit collects the user's voice data, and the generation AI learns those characteristics. For example, it analyzes the tone, rhythm, and pronunciation habits of the voice and synthesizes natural-sounding speech based on that. This allows natural-sounding speech to be synthesized based on the characteristics of the user's voice.

[0035] The speech synthesis unit can synthesize speech with different emotions and tones, allowing the user to select from them. For example, the speech synthesis unit can provide speech that expresses emotions such as joy, anger, and sadness. This allows speech with different emotions and tones to be synthesized, allowing the user to select from them.

[0036] The motion synchronization unit can fine-tune the avatar's motion based on the rhythm and intonation of the voice. For example, the generation AI analyzes the rhythm of the voice and fine-tunes the avatar's movements based on that. For example, it adds a hand waving motion to match the emphasized parts of the voice. This allows the avatar's motion to be fine-tuned based on the rhythm and intonation of the voice.

[0037] The motion synchronization unit can learn the user's past motion data and generate natural movements based on that. For example, the motion synchronization unit collects the user's past motion data, and the generation AI learns it. For example, it analyzes walking and hand movements from past videos and generates natural movements based on that. This makes it possible to generate natural movements based on the user's past motion data.

[0038] The subtitle generation unit converts audio content into text in real time and can generate subtitles instantly. For example, the subtitle generation unit uses a generation AI to analyze audio content in real time, convert it into text, and generate subtitles instantly. For example, subtitles can be displayed in real time during live streaming. This allows audio content to be converted into text in real time and subtitles to be generated instantly.

[0039] The subtitle generation unit can automatically generate subtitles in different fonts and styles and allow the user to select from them. For example, the subtitle generation unit may automatically generate subtitles in different fonts using a generation AI and allow the user to select from them. For example, it may provide handwritten fonts and modern fonts. This allows subtitles in different fonts and styles to be automatically generated and selected by the user.

[0040] The subtitle generation unit can automatically translate subtitles into different languages ​​to accommodate international audiences. For example, the generation AI can automatically translate the content of the subtitles into different languages, allowing users to accommodate international audiences. For example, it can translate into English, Japanese, Spanish, etc. This allows subtitles to be automatically translated into different languages ​​to accommodate international audiences.

[0041] The subtitle generation unit can add effects and animations to subtitles to create visually appealing content. For example, the generation AI of the subtitle generation unit automatically adds effects to subtitles to create visually appealing content. For example, shadow and light effects are added to subtitles. The generation AI of the subtitle generation unit can also add animations to subtitles to create visually moving content. For example, animations are added so that subtitles move on the screen or fade in and out. This allows effects and animations to be added to subtitles to create visually appealing content.

[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 AITuber production platform may also have an export function that allows users to use the avatars they create on other platforms or applications. For example, the generated avatar can be used as a profile picture on a social networking site or imported as a character in a game. The export function also supports different file formats, allowing users to use the avatar for a variety of purposes. Furthermore, the export function may also enable the output of animation data, including the avatar's movements and facial expressions. This allows users to widely use the avatars they create on the AITuber production platform on other platforms and applications.

[0044] AITuber production platforms can also automatically analyze user-created content and suggest optimal posting times. For example, the generation AI can analyze past posting data to identify the most active times of day for viewers. This allows users to post content at the most effective times, increasing viewer engagement. The platform can also suggest posting schedules tailored to specific events or trends. For example, posting content related to specific holidays or popular topics in a timely manner can more easily attract viewers' attention.

[0045] The AITuber production platform can also be equipped with a function to automatically collect and analyze viewer feedback on user-created content. For example, the generation AI can analyze comments and reactions and classify viewer responses as positive, negative, or neutral. This allows users to understand viewer opinions and reflect them in their next content creation. The feedback analysis function can also identify viewer preferences and interests and suggest content improvements and new ideas to users. For example, if viewers show a high interest in a particular topic, it can suggest increasing content related to that topic.

[0046] The AITuber production platform can also include a feature that automatically translates user-created content and provides it in different languages. For example, generative AI can translate audio and subtitles in real time and provide them to viewers in multiple languages, such as English, Japanese, and Spanish. This allows users to distribute content to an international audience and increase global engagement. The translation feature can also customize content for different cultures and regions. For example, using localized expressions and examples for viewers in specific regions can more easily resonate with viewers.

[0047] AITuber production platforms can also be equipped with a function to automatically insert advertisements into content created by users. For example, the generation AI can select the most appropriate advertisement based on the content and viewer interests and insert it at the appropriate time. This allows users to earn revenue and provide viewers with highly relevant advertisements. The ad insertion function can also allow users to customize the type and timing of advertisements. For example, users can set it to prioritize the display of advertisements for specific brands or products, or adjust the frequency of advertisements so as not to disrupt the viewer's viewing experience.

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

[0049] Step 1: The avatar generator generates a 3D or Live2D avatar. For example, the generator AI generates an avatar design based on a prompt entered by the user. For example, if a user enters a prompt such as "Create a cute girl character with cat ears," the generator AI will generate an avatar based on that instruction. Step 2: The script generator generates a script for the content. For example, the generation AI creates a script based on a prompt entered by the user. For example, if a user enters a prompt such as "Please write a script for a game commentary," the generation AI generates a script based on that instruction. Step 3: The speech synthesis unit synthesizes speech. For example, the generation AI synthesizes speech based on the text entered by the user. For example, if you enter text such as "Hello, today I'll introduce a new game," the generation AI will generate speech based on that text. Step 4: The motion synchronization unit synchronizes the motion with the audio. For example, the generation AI synchronizes the avatar's motion with the synthesized audio. For example, the generation AI automatically moves the avatar's mouth or waves its hands in time with the audio. Step 5: The subtitle generator generates subtitles. For example, the AI ​​automatically generates subtitles based on the synthesized speech. For example, it converts the speech into text and adds it to the video as subtitles.

[0050] (Example 2) The AITuber production platform according to an embodiment of the present invention is a system that allows users to easily create AITubers without requiring advanced programming skills, thereby enabling users to easily create AITubers and expanding the domestic AITuber market.

[0051] An AITuber production platform according to an embodiment includes an avatar generation unit, a script generation unit, a voice synthesis unit, a motion synchronization unit, and a subtitle generation unit. The avatar generation unit generates 3D or Live2D avatars. For example, the generation AI generates an avatar design based on a prompt entered by a user. For example, when a prompt such as "Create a cute cat-eared girl character" is entered, the generation AI generates an avatar based on the instruction. The script generation unit generates a script for content. For example, the generation AI creates a script based on a prompt entered by a user. For example, when a prompt such as "Create a script for a game commentary" is entered, the generation AI generates a script based on the instruction. The voice synthesis unit synthesizes voice. For example, the generation AI synthesizes voice based on text entered by a user. For example, when text such as "Hello, today I'll introduce a new game" is entered, the generation AI generates voice based on the text. The motion synchronization unit synchronizes motion with voice. For example, the generation AI synchronizes the motion of the avatar with the synthesized voice. For example, the generation AI automatically moves the avatar's mouth and waves its hands in time with the audio. The subtitle generation unit generates subtitles. For example, the generation AI automatically generates subtitles based on synthesized audio. For example, it converts audio content into text and adds it to videos as subtitles. This allows the AITuber production platform according to the embodiment to easily create AITubers without requiring users to have advanced programming skills. For example, through the LINE VOOM app, users can consistently create avatars, write content scripts, synthesize voices, synchronize motions, and add subtitles. This will enable more people to debut as AITubers, and is expected to expand the domestic AITuber market.

[0052] The avatar generation unit automatically generates an avatar from a user's photos or videos, creating an avatar with a realistic appearance. For example, the avatar generation unit uses a generation AI to analyze facial features based on a photo uploaded by the user and automatically generate a realistic 3D avatar. For example, it reproduces the facial contours, eye position, hairstyle, and other details in detail. This allows for the automatic generation of an avatar with a realistic appearance from the user's photos or videos.

[0053] The avatar generation unit can learn the characteristics of the user's voice and speaking style and customize the avatar's facial expressions and movements based on that. For example, the avatar generation unit analyzes the user's voice data, and the generation AI realistically reproduces the avatar's mouth movements based on the tone and rhythm of the voice. For example, it adjusts the mouth movements according to the speaking speed and emphasis. This makes it possible to customize the avatar's facial expressions and movements based on the characteristics of the user's voice and speaking style.

[0054] The avatar generation unit uses an emotion estimation function to generate avatar expressions and movements in real time according to the user's emotions. For example, the avatar generation unit analyzes the user's facial expressions in real time, and the generation AI generates avatar expressions according to those emotions. For example, smiling or angry expressions are instantly reflected. This allows avatar expressions and movements to be generated in real time according to the user's emotions.

[0055] The script generation unit can learn the user's past content and preferences and generate personalized scripts based on that. For example, the script generation unit analyzes content created by the user in the past, and the generation AI learns its style and theme. For example, it generates a new script based on the topics and speaking styles of past videos. This makes it possible to generate personalized scripts based on the user's past content and preferences.

[0056] The script generation unit can generate multiple script variations based on keywords or themes input by the user and provide options. For example, the script generation unit generates multiple script variations using a generation AI based on keywords input by the user. For example, if "horror" is input, different horror story scripts will be suggested. This allows multiple script variations to be generated based on keywords or themes input by the user and provide options.

[0057] The script generation unit can use the emotion estimation function to adjust the tone and content of the script according to the user's emotions. For example, the script generation unit analyzes the user's emotions in real time, and the generation AI adjusts the tone of the script based on that. For example, if the user is excited, it generates a script with an energetic tone. This allows the tone and content of the script to be adjusted according to the user's emotions.

[0058] The speech synthesis unit can learn the characteristics of the user's voice and synthesize natural-sounding speech based on that. For example, the speech synthesis unit collects the user's voice data, and the generation AI learns those characteristics. For example, it analyzes the tone, rhythm, and pronunciation habits of the voice and synthesizes natural-sounding speech based on that. This allows natural-sounding speech to be synthesized based on the characteristics of the user's voice.

[0059] The speech synthesis unit can synthesize speech with different emotions and tones, allowing the user to select from them. For example, the speech synthesis unit can provide speech that expresses emotions such as joy, anger, and sadness. This allows speech with different emotions and tones to be synthesized, allowing the user to select from them.

[0060] The speech synthesis unit can use the emotion estimation function to adjust the tone and speed of the speech according to the user's emotions. For example, the speech synthesis unit analyzes the user's emotions in real time, and the generation AI adjusts the tone of the speech based on that. For example, if the user is excited, it generates a speech with an energetic tone. This allows the tone and speed of the speech to be adjusted according to the user's emotions.

[0061] The motion synchronization unit can fine-tune the avatar's motion based on the rhythm and intonation of the voice. For example, the generation AI analyzes the rhythm of the voice and fine-tunes the avatar's movements based on that. For example, it adds a hand waving motion to match the emphasized parts of the voice. This allows the avatar's motion to be fine-tuned based on the rhythm and intonation of the voice.

[0062] The motion synchronization unit can learn the user's past motion data and generate natural movements based on that. For example, the motion synchronization unit collects the user's past motion data, and the generation AI learns it. For example, it analyzes walking and hand movements from past videos and generates natural movements based on that. This makes it possible to generate natural movements based on the user's past motion data.

[0063] The motion synchronization unit uses the emotion estimation function to generate motions in real time that correspond to the user's emotions. For example, the motion synchronization unit analyzes the user's emotions in real time, and the generation AI generates avatar motions based on that. For example, if the user is happy, a smiling and waving motion is added. This allows motions to be generated in real time that correspond to the user's emotions.

[0064] The subtitle generation unit converts audio content into text in real time and can generate subtitles instantly. For example, the subtitle generation unit uses a generation AI to analyze audio content in real time, convert it into text, and generate subtitles instantly. For example, subtitles can be displayed in real time during live streaming. This allows audio content to be converted into text in real time and subtitles to be generated instantly.

[0065] The subtitle generation unit can automatically generate subtitles in different fonts and styles and allow the user to select from them. For example, the subtitle generation unit may automatically generate subtitles in different fonts using a generation AI and allow the user to select from them. For example, it may provide handwritten fonts and modern fonts. This allows subtitles in different fonts and styles to be automatically generated and selected by the user.

[0066] The subtitle generation unit can use the emotion estimation function to adjust the tone and content of subtitles according to the user's emotions. For example, the subtitle generation unit analyzes the user's emotions in real time, and the generation AI adjusts the tone of the subtitles based on that. For example, if the user is excited, it generates subtitles with an energetic tone. This allows the tone and content of the subtitles to be adjusted according to the user's emotions.

[0067] The subtitle generation unit can automatically translate subtitles into different languages ​​to accommodate international audiences. For example, the generation AI can automatically translate the content of the subtitles into different languages, allowing users to accommodate international audiences. For example, it can translate into English, Japanese, Spanish, etc. This allows subtitles to be automatically translated into different languages ​​to accommodate international audiences.

[0068] The subtitle generation unit can add effects and animations to subtitles to create visually appealing content. For example, the generation AI of the subtitle generation unit automatically adds effects to subtitles to create visually appealing content. For example, shadow and light effects are added to subtitles. The generation AI of the subtitle generation unit can also add animations to subtitles to create visually moving content. For example, animations are added so that subtitles move on the screen or fade in and out. This allows effects and animations to be added to subtitles to create visually appealing content.

[0069] The subtitle generation unit uses the emotion estimation function to analyze the emotions of the user when adding subtitles and can suggest subtitles that elicit positive emotions. For example, the subtitle generation unit analyzes the emotions of the user when adding subtitles in real time, and the generation AI suggests subtitles that elicit positive emotions. For example, it generates subtitles with a cheerful tone and bright content. The subtitle generation unit can also use the emotion estimation function to adjust the tone and content of subtitles according to the user's emotions. For example, if the user is feeling down, it can suggest subtitles that include an encouraging message. This makes it possible to analyze the emotions of the user when adding subtitles and suggest subtitles that elicit positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The AITuber production platform may also have an export function that allows users to use the avatars they create on other platforms or applications. For example, the generated avatar can be used as a profile picture on a social networking site or imported as a character in a game. The export function also supports different file formats, allowing users to use the avatar for a variety of purposes. Furthermore, the export function may also enable the output of animation data, including the avatar's movements and facial expressions. This allows users to widely use the avatars they create on the AITuber production platform on other platforms and applications.

[0072] AITuber production platforms can also automatically analyze user-created content and suggest optimal posting times. For example, the generation AI can analyze past posting data to identify the most active times of day for viewers. This allows users to post content at the most effective times, increasing viewer engagement. The platform can also suggest posting schedules tailored to specific events or trends. For example, posting content related to specific holidays or popular topics in a timely manner can more easily attract viewers' attention.

[0073] The AITuber production platform can also be equipped with a function to automatically collect and analyze viewer feedback on user-created content. For example, the generation AI can analyze comments and reactions and classify viewer responses as positive, negative, or neutral. This allows users to understand viewer opinions and reflect them in their next content creation. The feedback analysis function can also identify viewer preferences and interests and suggest content improvements and new ideas to users. For example, if viewers show a high interest in a particular topic, it can suggest increasing content related to that topic.

[0074] The AITuber production platform can also include a feature that automatically translates user-created content and provides it in different languages. For example, generative AI can translate audio and subtitles in real time and provide them to viewers in multiple languages, such as English, Japanese, and Spanish. This allows users to distribute content to an international audience and increase global engagement. The translation feature can also customize content for different cultures and regions. For example, using localized expressions and examples for viewers in specific regions can more easily resonate with viewers.

[0075] AITuber production platforms can also be equipped with a function to automatically insert advertisements into content created by users. For example, the generation AI can select the most appropriate advertisement based on the content and viewer interests and insert it at the appropriate time. This allows users to earn revenue and provide viewers with highly relevant advertisements. The ad insertion function can also allow users to customize the type and timing of advertisements. For example, users can set it to prioritize the display of advertisements for specific brands or products, or adjust the frequency of advertisements so as not to disrupt the viewer's viewing experience.

[0076] The AITuber production platform can also be equipped with a function that estimates a user's emotions and automatically changes the avatar's costume and background based on those emotions. For example, if the user is having fun, it can select bright and colorful costumes and backgrounds, and if the user is feeling down, it can select calm costumes and backgrounds. This provides a visual expression that matches the user's emotions, eliciting greater empathy from viewers. The emotion estimation function can also change the costume and background in real time according to changes in the user's emotions. For example, if the user's emotions change during a live broadcast, the visuals can be adjusted to match those changes.

[0077] The AITuber production platform can also be equipped with a function that estimates a user's emotions and automatically suggests content themes and topics based on those emotions. For example, if a user is excited, energetic themes and topics are suggested, and if the user is relaxed, calm themes and topics are suggested. This makes it easier for users to create content that matches their emotions and provides viewers with natural expression. The emotion estimation function can also adjust themes and topics in real time according to changes in the user's emotions. For example, if a user's emotions change during a live broadcast, new themes and topics will be suggested to match those changes.

[0078] The AITuber production platform can also be equipped with a function that estimates a user's emotions and automatically generates messages and comments for viewers based on those emotions. For example, if a user is happy, it can generate positive messages and comments, and if the user is sad, it can generate encouraging messages and comments. This allows for emotionally sensitive communication with viewers and increases engagement. The emotion estimation function can also adjust messages and comments in real time according to changes in the user's emotions. For example, if a user's emotions change during a live broadcast, new messages and comments can be generated to match those changes.

[0079] The AITuber production platform can also be equipped with a function that estimates a user's emotions and automatically generates interactive questions and surveys for viewers based on those emotions. For example, if a user is excited, energetic questions and surveys are generated, and if the user is relaxed, calm questions and surveys are generated. This can promote interaction with viewers and increase engagement. The emotion estimation function can also adjust questions and surveys in real time according to changes in the user's emotions. For example, if a user's emotions change during a live broadcast, new questions and surveys can be generated to match those changes.

[0080] The AITuber production platform can also be equipped with a function that estimates a user's emotions and automatically generates reactions and effects for viewers based on those emotions. For example, if a user is happy, a positive reaction or effect is generated, and if the user is angry, an emphasized reaction or effect is generated. This allows viewers to be provided with visual expressions that are in line with their emotions and increases engagement. The emotion estimation function can also adjust reactions and effects in real time according to changes in the user's emotions. For example, if a user's emotions change during a live broadcast, new reactions and effects are generated to match those changes.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The avatar generator generates a 3D or Live2D avatar. For example, the generator AI generates an avatar design based on a prompt entered by the user. For example, if a user enters a prompt such as "Create a cute girl character with cat ears," the generator AI will generate an avatar based on that instruction. Step 2: The script generator generates a script for the content. For example, the generation AI creates a script based on a prompt entered by the user. For example, if a user enters a prompt such as "Please write a script for a game commentary," the generation AI generates a script based on that instruction. Step 3: The speech synthesis unit synthesizes speech. For example, the generation AI synthesizes speech based on the text entered by the user. For example, if you enter text such as "Hello, today I'll introduce a new game," the generation AI will generate speech based on that text. Step 4: The motion synchronization unit synchronizes the motion with the audio. For example, the generation AI synchronizes the avatar's motion with the synthesized audio. For example, the generation AI automatically moves the avatar's mouth or waves its hands in time with the audio. Step 5: The subtitle generator generates subtitles. For example, the AI ​​automatically generates subtitles based on the synthesized speech. For example, it converts the speech into text and adds it to the video as subtitles.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. an avatar generation unit that generates 3D or Live2D avatars; a script generation unit that generates a script of the content; a speech synthesis unit that synthesizes speech; A motion synchronization unit that synchronizes motion with audio; a subtitle generation unit that generates subtitles; A system characterized by:

2. The avatar generation unit The facial expressions and movements of the avatar are generated in real time according to the user's emotions.

2. The system of claim 1.

3. The script generation unit Learns the user's past content and preferences and generates personalized scripts based on them 2. The system of claim 1.

4. The speech synthesis unit Learns the characteristics of the user's voice and synthesizes natural voice based on them 2. The system of claim 1.

5. The motion synchronization unit Fine-tuning the motion of the avatar based on the rhythm and intonation of the voice.

2. The system of claim 1.

6. The subtitle generation unit Converting the audio content into text in real time and instantly generating the subtitles 2. The system of claim 1.

7. The script generation unit Adjusting the tone and content of the script according to the user's emotions 2. The system of claim 1.

8. The motion synchronization unit The motion is generated in real time according to the user's emotion.

2. The system of claim 1.

Images