system

Abstract

An object of the system according to the embodiment is to efficiently generate a CG using generation AI and provide an interactive video.SOLUTION: A system according to an embodiment includes a generation AI, a prompt input device, a CG generator, and an interactive video generator. The prompt input unit inputs an instruction. The CG generation unit generates a CG based on the instruction input by the prompt input unit. The interactive video generation unit generates an interactive video using the CG generated by the CG generation unit.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, generating CG for movies and videos required advanced skills and a great deal of time, making efficient production difficult.

[0005] The system according to the embodiment aims to efficiently generate CG using generative AI and provide interactive video. [Means for solving the problem]

[0006] A system according to an embodiment includes a generation AI, a prompt input unit, a CG generation unit, and an interactive video generation unit. The prompt input unit inputs instructions. The CG generation unit generates CG based on the instructions input by the prompt input unit. The interactive video generation unit generates interactive video using the CG generated by the CG generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate CG using generative AI and provide interactive images. [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 automatic generation system according to an embodiment of the present invention uses generative AI to automatically generate CG for movies and videos and provide interactive images based on viewer requests. This saves creators time and skill, and allows viewers to enjoy interactive images tailored to their preferences.

[0029] An automatic generation system according to an embodiment includes a generation AI, a prompt input unit, a CG generation unit, and an interactive video generation unit. The generation AI automatically generates CG for movies and videos based on prompts and instructions input by a creator. For example, if a creator inputs a prompt such as "I want you to generate a background for a futuristic city," the generation AI generates a background for the futuristic city based on the instruction. The generation AI uses a pre-fine-tuned model and can generate high-quality CG according to the creator's instructions. The prompt input unit is an interface through which the creator inputs instructions to the generation AI. For example, the prompt can be input using text input or voice input. The CG generation unit generates CG based on the instructions input by the prompt input unit. For example, the futuristic city background generated by the generation AI is output as CG. The interactive video generation unit generates interactive video using the CG generated by the CG generation unit. For example, if a viewer inputs a request such as "I want the background of this scene to be changed to a night view," the interactive video generation unit can change the video in real time based on the request. As a result, the automatic generation system according to an embodiment can automatically generate CG for movies and videos using the generation AI and provide interactive video based on the viewer's request.

[0030] The CG generation unit incorporates physics simulation to realistically reflect movement and changes. In the CG generation unit, for example, the generation AI uses physics simulation to realistically reproduce the movement of CG characters and objects. For example, it generates natural movement influenced by wind and gravity. In addition, the CG generation unit uses physics simulation to realistically reproduce environmental changes in the CG. For example, it accurately represents the flow of water and the burning of fire. In addition, the CG generation unit uses physics simulation to realistically reproduce CG collision and destruction scenes. For example, it accurately represents the collapse of a building or a car crash. This makes it possible to automatically reflect realistic movement and changes using physics simulation.

[0031] The CG generation unit automatically generates environmental sounds and sound effects, providing a realistic visual and auditory experience. For example, the CG generation unit uses a generation AI to automatically generate environmental sounds that match the scene, providing a realistic visual and auditory experience. For example, in a forest scene, it generates the sounds of birds chirping and wind. The CG generation unit also uses a generation AI to automatically generate sound effects that match the character's movements. For example, it realistically reproduces the sound of a character's footsteps when walking or the sound of lifting an object. The CG generation unit also uses a generation AI to automatically generate music that matches scene changes, enhancing emotions in both the visual and auditory senses. For example, it plays tense music in a tense scene. This allows the automatic generation of environmental sounds and sound effects, providing a realistic visual and auditory experience.

[0032] Generative AI can automatically generate not only movie and video games, but also game backgrounds and character designs. For example, generative AI can automatically generate game backgrounds, realistically representing the worlds players explore. For example, it can generate backgrounds for fantasy worlds or futuristic cities. Generative AI can also automatically generate game character designs, creating diverse representations of characters controlled by players. For example, it can generate characters of different races and occupations. Generative AI can also automatically generate designs for game items and equipment, allowing players to enjoy collecting them. For example, it can generate designs for weapons and armor. This makes it possible to automatically generate game backgrounds and character designs, not only for movies and videos, but also for games.

[0033] Generative AI can apply CG to educational simulations and training programs to provide a realistic learning environment. For example, generative AI can automatically generate educational simulations to allow students to learn in a realistic environment. For example, it can generate simulations that recreate historical events. Generative AI can also automatically generate training program scenarios to provide an environment in which learners can acquire practical skills. For example, it can generate scenarios for medical training or disaster response. Generative AI can also automatically generate interactive educational content to allow students to learn independently. For example, it can generate simulated science experiments or virtual field trips. This allows generative AI to be applied to educational simulations and training programs to provide a realistic learning environment.

[0034] Generative AI can learn from a creator's past works and automatically generate CG that reflects that style. For example, generative AI can import a creator's past works into a database and learn their style. For example, it can learn specific color usage and composition and reflect them in new CG. Generative AI can also automatically generate CG for new projects based on a creator's style. For example, it can generate designs that are consistent with past works. Generative AI can also analyze a creator's style, extract its features, and apply them to new CG. For example, it can reflect specific textures and patterns in new works. This allows generative AI to learn from a creator's past works and automatically generate CG that reflects that style.

[0035] Generative AI can analyze a creator's work process and propose an optimal work flow. For example, generative AI collects a creator's work process as data and proposes an efficient work flow. For example, it optimizes the order of tasks and time allocation. Generative AI can also analyze a creator's work data and identify bottlenecks and wasteful tasks. For example, it can make suggestions to automate time-consuming tasks. Generative AI can also monitor a creator's work flow in real time and propose efficient work methods. For example, it can present the optimal next step based on the progress of the work. This allows generative AI to analyze a creator's work process and propose an optimal work flow.

[0036] Generative AI not only automates the work of creators, but can also be applied to creative fields such as music production and design. For example, generative AI automates the music production process. For example, it can automatically generate parts of composing and arranging music, reducing the burden on creators. Generative AI can also automate the design process. For example, it can automatically generate logo and poster designs, saving creators time. Generative AI can also automate the video production process. For example, it can automatically generate video editing and the addition of effects, making creators' work more efficient. This means that generative AI can be used to automate creators' work and can be applied to other creative fields.

[0037] Generative AI can monitor a creator's work in real time and suggest efficient work methods. For example, generative AI can monitor a creator's work in real time and suggest efficient work methods. For example, it can suggest the optimal next step based on the progress of the work. Generative AI can also analyze a creator's work data and identify bottlenecks and wasteful tasks. For example, it can suggest automating time-consuming tasks. Generative AI can also monitor a creator's workflow in real time and suggest efficient work methods. For example, it can optimize the order of tasks and time allocation. This allows generative AI to monitor a creator's work in real time and suggest efficient work methods.

[0038] It is possible to analyze a viewer's past viewing history and suggest optimal video customization. A viewer's past viewing history is stored in a database, and optimal video customization is suggested based on that data. For example, videos are customized based on the viewer's preferred genres and themes. Viewing history can also be analyzed to identify scenes and characters that the viewer is particularly interested in, and videos can be customized based on that. For example, scenes centered around the viewer's favorite characters can be generated. Viewing history data can also be used to automatically generate video scenarios that match the viewer's preferences. For example, a scenario that reflects the viewer's preferred story patterns can be suggested. This makes it possible to analyze a viewer's past viewing history and suggest optimal video customization.

[0039] Interactive video can be applied to the fields of education and training to provide personalized learning experiences. Interactive video can be used to provide educational simulations. For example, simulations that recreate historical events can be generated to allow students to learn independently. Interactive video can also be used to provide training programs. For example, medical training or disaster response scenarios can be generated to provide an environment in which learners can acquire practical skills. Interactive video can also be used to provide personalized learning experiences. For example, the content of the video can be dynamically changed according to the student's level of understanding to support effective learning. In this way, interactive video can be applied to the fields of education and training to provide personalized learning experiences.

[0040] Interactive video can be applied to advertising and marketing to provide customized advertisements that match the viewer's preferences. Interactive video can be used to provide customized advertisements that match the viewer's preferences. For example, products and services that interest the viewer can be introduced within the video. In addition, the viewer's real-time reactions can be analyzed and the content of the advertisement can be dynamically changed based on that data. For example, advertisements can be generated that focus on products that the viewer has shown interest in. In addition, optimal advertisements can be suggested based on the viewer's past viewing history. For example, advertisements related to products and services that the viewer has been interested in in the past can be displayed. In this way, interactive video can be applied to advertising and marketing to provide customized advertisements that match the viewer's preferences.

[0041] The generative AI can automatically evaluate ideas entered by viewers and suggest the best idea to the producer. The generative AI can automatically evaluate ideas entered by viewers and suggest the best idea to the producer based on the evaluation results. For example, it evaluates the feasibility and creativity of the viewer's idea. The generative AI can also analyze the viewer's idea and identify the strengths and weaknesses of the idea. For example, it can evaluate the idea's originality and marketability. The generative AI can also suggest the best scenario or scene based on the viewer's idea. For example, it can generate a storyboard based on the viewer's idea. This allows the generative AI to automatically evaluate ideas entered by viewers and suggest the best idea to the producer.

[0042] It is possible to monitor the collaborative work between viewers and producers in real time and suggest efficient collaboration methods. It is possible to monitor the collaborative work between viewers and producers in real time and suggest efficient collaboration methods. For example, it may suggest the best next step based on the progress of the work. It is also possible to analyze the collaborative work data and identify bottlenecks and wasteful work. For example, it may suggest automating time-consuming tasks. It is also possible to monitor the collaborative work flow in real time and suggest efficient work methods. For example, it may optimize the order of work and time allocation. This makes it possible to monitor the collaborative work between viewers and producers in real time and suggest efficient collaboration methods.

[0043] Collaboration between viewers and creators can also be applied to other creative fields such as music production and design. Collaboration between viewers and creators can be applied to music production, where they jointly create a piece of music based on the viewers' ideas. For example, a piece of music can be created based on a melody or rhythm suggested by the viewers. Collaboration between viewers and creators can also be applied to design, where they jointly create a design based on the viewers' ideas. For example, a design can be created based on a concept or color scheme suggested by the viewers. Collaboration between viewers and creators can also be applied to video production, where they jointly create a video based on the viewers' ideas. For example, a video can be created based on a scenario or scene suggested by the viewers. In this way, collaboration between viewers and creators can be applied to other creative fields.

[0044] Viewers and producers can work together on an online platform, enabling global collaboration. Viewers and producers can work together on an online platform, enabling global collaboration. For example, viewers and producers from different countries or regions can work together to create a video. Also, on the online platform, viewers can propose ideas, and producers can create a video based on those ideas. For example, a video can be created based on a scenario or scene proposed by a viewer. Also, through the online platform, viewers and producers can communicate with each other in real time and work together. For example, they can exchange opinions using chat or video calls. In this way, viewers and producers can work together on an online platform, enabling global collaboration.

[0045] Generative AI can learn viewer preferences and automatically generate new entertainment content based on those preferences. For example, generative AI can learn a viewer's past viewing history and preferences, and automatically generate new entertainment content based on that data. For example, it can generate videos based on the viewer's favorite genres and themes. Generative AI can also analyze viewer preferences and automatically generate scenarios and scenes that match those preferences. For example, it can generate videos that reflect the viewer's favorite characters and story patterns. Generative AI can also provide a personalized entertainment experience based on the viewer's preferences. For example, it can dynamically change the content and development of videos according to the viewer's preferences. This allows generative AI to learn viewer preferences and automatically generate new entertainment content based on those preferences.

[0046] The new entertainment experiences can be applied to the fields of education and training to provide personalized learning experiences. The new entertainment experiences can be used to provide educational simulations. For example, simulations that recreate historical events can be generated to allow students to learn independently. The new entertainment experiences can also be used to provide training programs. For example, medical training or disaster response scenarios can be generated to provide an environment in which learners can acquire practical skills. The new entertainment experiences can also be used to provide personalized learning experiences. For example, the content of videos can be dynamically changed according to the student's level of understanding to support effective learning. In this way, the new entertainment experiences can be applied to the fields of education and training to provide personalized learning experiences.

[0047] This new entertainment experience can be applied to advertising and marketing, making it possible to provide customized advertisements that match the viewer's preferences. This new entertainment experience can be used to provide customized advertisements that match the viewer's preferences. For example, products and services that interest the viewer can be introduced within the video. In addition, the viewer's real-time reactions can be analyzed and the content of the advertisement can be dynamically changed based on that data. For example, advertisements can be generated that focus on products that the viewer has shown interest in. In addition, the most suitable advertisements can be suggested based on the viewer's past viewing history. For example, advertisements related to products and services that the viewer has been interested in in the past can be displayed. This makes it possible to apply this new entertainment experience to advertising and marketing, making it possible to provide customized advertisements that match the viewer's preferences.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] Generative AI can analyze a viewer's past viewing history and suggest optimal video customizations. It stores the viewer's past viewing history in a database and uses that data to suggest optimal video customizations. For example, it can customize videos based on the viewer's preferred genres and themes. It can also analyze viewing history to identify scenes and characters that the viewer is particularly interested in and customize videos based on that. For example, it can generate scenes centered around the viewer's favorite characters. Furthermore, it can use viewing history data to automatically generate video scenarios tailored to the viewer's preferences. For example, it can suggest scenarios that reflect the viewer's preferred story patterns. This makes it possible to analyze a viewer's past viewing history and suggest optimal video customizations.

[0050] Generative AI can learn from a creator's past works and automatically generate CG that reflects that style. For example, generative AI can import a creator's past works into a database and learn their style. For example, it can learn specific color usage and composition and reflect them in new CG. Generative AI can also automatically generate CG for new projects based on a creator's style. For example, it can generate designs that are consistent with past works. Furthermore, generative AI can analyze a creator's style, extract its features, and apply them to new CG. For example, it can reflect specific textures and patterns in new works. This allows generative AI to learn from a creator's past works and automatically generate CG that reflects that style.

[0051] Generative AI can analyze a creator's work process and propose an optimal workflow. For example, generative AI can collect a creator's work process as data and propose an efficient workflow. For example, it can optimize the order of tasks and time allocation. Generative AI can also analyze a creator's work data and identify bottlenecks and wasteful tasks. For example, it can make suggestions to automate time-consuming tasks. Furthermore, generative AI can monitor a creator's work flow in real time and propose efficient work methods. For example, it can present the optimal next step based on the progress of the work. This allows generative AI to analyze a creator's work process and propose an optimal workflow.

[0052] Generative AI can learn viewer preferences and automatically generate new entertainment content based on those preferences. For example, generative AI can learn a viewer's past viewing history and preferences and automatically generate new entertainment content based on that data. For example, it can generate videos based on the viewer's favorite genres and themes. Generative AI can also analyze viewer preferences and automatically generate scenarios and scenes that match those preferences. For example, it can generate videos that reflect the viewer's favorite characters and story patterns. Furthermore, generative AI can provide a personalized entertainment experience based on the viewer's preferences. For example, it can dynamically change the content and development of videos based on the viewer's preferences. This allows generative AI to learn viewer preferences and automatically generate new entertainment content based on those preferences.

[0053] Generative AI enables viewers and producers to collaborate on an online platform, realizing global collaboration. Using an online platform, viewers and producers can collaborate on global collaboration. For example, viewers and producers from different countries or regions can collaborate to create videos. Viewers can also propose ideas on the online platform, and producers can create videos based on those ideas. For example, a video can be created based on a scenario or scene proposed by the viewer. Furthermore, viewers and producers can collaborate through real-time communication via the online platform. For example, they can exchange opinions via chat or video calls. This allows viewers and producers to collaborate on an online platform, realizing global collaboration.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The prompt input section is an interface through which creators can input instructions to the generation AI. For example, prompts can be input using text input or voice input. Step 2: The CG generation unit generates CG based on the instructions input by the prompt input unit. For example, the background of a future city generated by the generation AI is output as CG. Step 3: The interactive video generation unit generates interactive video using the CG generated by the CG generation unit. For example, if a viewer inputs a request such as "I want the background of this scene to be changed to a night view," the interactive video generation unit can change the video in real time based on that request.

[0056] (Example 2) The automatic generation system according to an embodiment of the present invention uses generative AI to automatically generate CG for movies and videos and provide interactive images based on viewer requests. This saves creators time and skill, and allows viewers to enjoy interactive images tailored to their preferences.

[0057] An automatic generation system according to an embodiment includes a generation AI, a prompt input unit, a CG generation unit, and an interactive video generation unit. The generation AI automatically generates CG for movies and videos based on prompts and instructions input by a creator. For example, if a creator inputs a prompt such as "I want you to generate a background for a futuristic city," the generation AI generates a background for the futuristic city based on the instruction. The generation AI uses a pre-fine-tuned model and can generate high-quality CG according to the creator's instructions. The prompt input unit is an interface through which the creator inputs instructions to the generation AI. For example, the prompt can be input using text input or voice input. The CG generation unit generates CG based on the instructions input by the prompt input unit. For example, the futuristic city background generated by the generation AI is output as CG. The interactive video generation unit generates interactive video using the CG generated by the CG generation unit. For example, if a viewer inputs a request such as "I want the background of this scene to be changed to a night view," the interactive video generation unit can change the video in real time based on the request. As a result, the automatic generation system according to an embodiment can automatically generate CG for movies and videos using the generation AI and provide interactive video based on the viewer's request.

[0058] The interactive video generation unit can reflect the viewer's emotions in real time and change the scene according to the emotion. For example, the interactive video generation unit uses a generation AI to analyze the user's emotions in real time and dynamically change the CG scene according to that emotion. For example, if the user makes a surprised expression, an element of surprise is added to the scene. The interactive video generation unit also uses the user's emotion data to change the color tone and music of the scene. For example, if the user makes a sad expression, the color tone of the scene is darkened and sad music is played. The interactive video generation unit also uses emotion analysis data to change the facial expressions and movements of the characters in the scene in real time. For example, if the user smiles, the character also smiles. This makes it possible to change the scene in real time according to the viewer's emotions.

[0059] The CG generation unit incorporates physics simulation to realistically reflect movement and changes. In the CG generation unit, for example, the generation AI uses physics simulation to realistically reproduce the movement of CG characters and objects. For example, it generates natural movement influenced by wind and gravity. In addition, the CG generation unit uses physics simulation to realistically reproduce environmental changes in the CG. For example, it accurately represents the flow of water and the burning of fire. In addition, the CG generation unit uses physics simulation to realistically reproduce CG collision and destruction scenes. For example, it accurately represents the collapse of a building or a car crash. This makes it possible to automatically reflect realistic movement and changes using physics simulation.

[0060] The CG generation unit automatically generates environmental sounds and sound effects, providing a realistic visual and auditory experience. For example, the CG generation unit uses a generation AI to automatically generate environmental sounds that match the scene, providing a realistic visual and auditory experience. For example, in a forest scene, it generates the sounds of birds chirping and wind. The CG generation unit also uses a generation AI to automatically generate sound effects that match the character's movements. For example, it realistically reproduces the sound of a character's footsteps when walking or the sound of lifting an object. The CG generation unit also uses a generation AI to automatically generate music that matches scene changes, enhancing emotions in both the visual and auditory senses. For example, it plays tense music in a tense scene. This allows the automatic generation of environmental sounds and sound effects, providing a realistic visual and auditory experience.

[0061] Generative AI can automatically generate not only movie and video games, but also game backgrounds and character designs. For example, generative AI can automatically generate game backgrounds, realistically representing the worlds players explore. For example, it can generate backgrounds for fantasy worlds or futuristic cities. Generative AI can also automatically generate game character designs, creating diverse representations of characters controlled by players. For example, it can generate characters of different races and occupations. Generative AI can also automatically generate designs for game items and equipment, allowing players to enjoy collecting them. For example, it can generate designs for weapons and armor. This makes it possible to automatically generate game backgrounds and character designs, not only for movies and videos, but also for games.

[0062] Generative AI can apply CG to educational simulations and training programs to provide a realistic learning environment. For example, generative AI can automatically generate educational simulations to allow students to learn in a realistic environment. For example, it can generate simulations that recreate historical events. Generative AI can also automatically generate training program scenarios to provide an environment in which learners can acquire practical skills. For example, it can generate scenarios for medical training or disaster response. Generative AI can also automatically generate interactive educational content to allow students to learn independently. For example, it can generate simulated science experiments or virtual field trips. This allows generative AI to be applied to educational simulations and training programs to provide a realistic learning environment.

[0063] The generation AI can analyze the user's emotional response to the CG and suggest optimal scenes. For example, the generation AI uses an emotion estimation function to analyze in real time what emotions the user feels toward the generated CG. For example, it identifies scenes in which the user felt surprise or joy. The generation AI also suggests optimal scenes based on the user's emotional response data. For example, it prioritizes displaying scenes in which the user felt positive emotions. The generation AI also uses the emotion estimation data to identify areas for improvement in scenes and make suggestions to enhance the user's emotions. For example, it makes suggestions to improve scenes with low emotion scores. This allows the generation AI to analyze the user's emotional response and suggest optimal scenes.

[0064] Generative AI can learn from a creator's past works and automatically generate CG that reflects that style. For example, generative AI can import a creator's past works into a database and learn their style. For example, it can learn specific color usage and composition and reflect them in new CG. Generative AI can also automatically generate CG for new projects based on a creator's style. For example, it can generate designs that are consistent with past works. Generative AI can also analyze a creator's style, extract its features, and apply them to new CG. For example, it can reflect specific textures and patterns in new works. This allows generative AI to learn from a creator's past works and automatically generate CG that reflects that style.

[0065] Generative AI can analyze a creator's work process and propose an optimal work flow. For example, generative AI collects a creator's work process as data and proposes an efficient work flow. For example, it optimizes the order of tasks and time allocation. Generative AI can also analyze a creator's work data and identify bottlenecks and wasteful tasks. For example, it can make suggestions to automate time-consuming tasks. Generative AI can also monitor a creator's work flow in real time and propose efficient work methods. For example, it can present the optimal next step based on the progress of the work. This allows generative AI to analyze a creator's work process and propose an optimal work flow.

[0066] Generative AI can estimate a creator's emotional state and suggest the optimal work environment and tools. For example, generative AI can analyze a creator's emotional state in real time and suggest the optimal work environment. For example, if stress is high, it can suggest a relaxing environment. Generative AI can also suggest the optimal work tools based on the creator's emotional data. For example, if concentration is declining, it can suggest simple work tools. Generative AI can also monitor a creator's emotional state and provide advice to improve work efficiency. For example, it can suggest taking a break if fatigue is building up. This allows generative AI to estimate a creator's emotional state and suggest the optimal work environment and tools.

[0067] Generative AI not only automates the work of creators, but can also be applied to creative fields such as music production and design. For example, generative AI automates the music production process. For example, it can automatically generate parts of composing and arranging music, reducing the burden on creators. Generative AI can also automate the design process. For example, it can automatically generate logo and poster designs, saving creators time. Generative AI can also automate the video production process. For example, it can automatically generate video editing and the addition of effects, making creators' work more efficient. This means that generative AI can be used to automate creators' work and can be applied to other creative fields.

[0068] Generative AI can monitor a creator's work in real time and suggest efficient work methods. For example, generative AI can monitor a creator's work in real time and suggest efficient work methods. For example, it can suggest the optimal next step based on the progress of the work. Generative AI can also analyze a creator's work data and identify bottlenecks and wasteful tasks. For example, it can suggest automating time-consuming tasks. Generative AI can also monitor a creator's workflow in real time and suggest efficient work methods. For example, it can optimize the order of tasks and time allocation. This allows generative AI to monitor a creator's work in real time and suggest efficient work methods.

[0069] The emotion estimation function can automatically adjust the work environment according to the creator's emotional state. For example, the emotion estimation function analyzes the creator's emotional state in real time and automatically adjusts the optimal work environment. For example, if stress is high, it will suggest a relaxing environment. The emotion estimation function also suggests the optimal work tools based on the creator's emotional data. For example, if concentration is declining, it will suggest simple work tools. The emotion estimation function also monitors the creator's emotional state and provides advice to improve work efficiency. For example, if fatigue is building up, it will suggest taking a break. In this way, the emotion estimation function can automatically adjust the work environment according to the creator's emotional state.

[0070] It is possible to estimate the viewer's emotions in real time and automatically generate changes to the video in response to their emotions. It analyzes the viewer's emotions in real time and dynamically changes the video scene in response to those emotions. For example, if the viewer has a surprised expression, an element of surprise is added to the scene. It also changes the color tone and music of the video based on the viewer's emotional data. For example, if the viewer has a sad expression, the color tone of the scene is darkened and sad music is played. It also uses emotion analysis data to change the facial expressions and movements of characters in the video in real time. For example, if the viewer smiles, the character will also smile. This makes it possible to estimate the viewer's emotions in real time and automatically generate changes to the video in response to those emotions.

[0071] It is possible to analyze a viewer's past viewing history and suggest optimal video customization. A viewer's past viewing history is stored in a database, and optimal video customization is suggested based on that data. For example, videos are customized based on the viewer's preferred genres and themes. Viewing history can also be analyzed to identify scenes and characters that the viewer is particularly interested in, and videos can be customized based on that. For example, scenes centered around the viewer's favorite characters can be generated. Viewing history data can also be used to automatically generate video scenarios that match the viewer's preferences. For example, a scenario that reflects the viewer's preferred story patterns can be suggested. This makes it possible to analyze a viewer's past viewing history and suggest optimal video customization.

[0072] It is possible to collect real-time reactions from viewers and dynamically change the video based on those reactions. Real-time reactions from viewers are collected using cameras and microphones, and the video is dynamically changed based on that data. For example, if a viewer looks surprised, an element of surprise is added to the scene. It is also possible to analyze the viewer's real-time reactions and change the color tone and music of the video. For example, if a viewer looks sad, the color tone of the scene is darkened and sad music is played. It is also possible to change the facial expressions and movements of characters in the video based on the viewer's real-time reactions. For example, if a viewer smiles, the character will also smile. This makes it possible to collect real-time reactions from viewers and dynamically change the video based on those reactions.

[0073] Interactive video can be applied to the fields of education and training to provide personalized learning experiences. Interactive video can be used to provide educational simulations. For example, simulations that recreate historical events can be generated to allow students to learn independently. Interactive video can also be used to provide training programs. For example, medical training or disaster response scenarios can be generated to provide an environment in which learners can acquire practical skills. Interactive video can also be used to provide personalized learning experiences. For example, the content of the video can be dynamically changed according to the student's level of understanding to support effective learning. In this way, interactive video can be applied to the fields of education and training to provide personalized learning experiences.

[0074] Interactive video can be applied to advertising and marketing to provide customized advertisements that match the viewer's preferences. Interactive video can be used to provide customized advertisements that match the viewer's preferences. For example, products and services that interest the viewer can be introduced within the video. In addition, the viewer's real-time reactions can be analyzed and the content of the advertisement can be dynamically changed based on that data. For example, advertisements can be generated that focus on products that the viewer has shown interest in. In addition, optimal advertisements can be suggested based on the viewer's past viewing history. For example, advertisements related to products and services that the viewer has been interested in in the past can be displayed. In this way, interactive video can be applied to advertising and marketing to provide customized advertisements that match the viewer's preferences.

[0075] The emotion estimation function can be used to provide interactive video that corresponds to the viewer's emotions, thereby achieving deeper engagement. The emotion estimation function can be used to provide interactive video that corresponds to the viewer's emotions. For example, if the viewer has a surprised expression, an element of surprise can be added to the scene. The color tone of the video and music can also be changed based on the viewer's emotional data. For example, if the viewer has a sad expression, the color tone of the scene can be darkened and sad music can be played. The emotion analysis data can also be used to change the facial expressions and movements of characters in the video in real time. For example, if the viewer smiles, the character can also be made to smile. In this way, the emotion estimation function can be used to provide interactive video that corresponds to the viewer's emotions, thereby achieving deeper engagement.

[0076] It is possible to estimate viewer emotions and promote collaboration between producers and viewers based on those emotions. It is possible to analyze viewer emotions in real time and promote collaboration between producers and viewers based on those emotions. For example, if a viewer is excited, that information is shared with the producer and they can jointly create a scene. It is also possible for producers to suggest scenes that correspond to the viewer's emotions based on the viewer's emotion data. For example, if a viewer is moved, an emotional scene is added. It is also possible to use emotion analysis data to monitor collaboration between viewers and producers in real time and propose efficient collaboration methods. For example, it can propose a workflow that corresponds to the viewer's emotions. This makes it possible to estimate viewer emotions and promote collaboration between producers and viewers based on those emotions.

[0077] The generative AI can automatically evaluate ideas entered by viewers and suggest the best idea to the producer. The generative AI can automatically evaluate ideas entered by viewers and suggest the best idea to the producer based on the evaluation results. For example, it evaluates the feasibility and creativity of the viewer's idea. The generative AI can also analyze the viewer's idea and identify the strengths and weaknesses of the idea. For example, it can evaluate the idea's originality and marketability. The generative AI can also suggest the best scenario or scene based on the viewer's idea. For example, it can generate a storyboard based on the viewer's idea. This allows the generative AI to automatically evaluate ideas entered by viewers and suggest the best idea to the producer.

[0078] It is possible to monitor the collaborative work between viewers and producers in real time and suggest efficient collaboration methods. It is possible to monitor the collaborative work between viewers and producers in real time and suggest efficient collaboration methods. For example, it may suggest the best next step based on the progress of the work. It is also possible to analyze the collaborative work data and identify bottlenecks and wasteful work. For example, it may suggest automating time-consuming tasks. It is also possible to monitor the collaborative work flow in real time and suggest efficient work methods. For example, it may optimize the order of work and time allocation. This makes it possible to monitor the collaborative work between viewers and producers in real time and suggest efficient collaboration methods.

[0079] Collaboration between viewers and creators can also be applied to other creative fields such as music production and design. Collaboration between viewers and creators can be applied to music production, where they jointly create a piece of music based on the viewers' ideas. For example, a piece of music can be created based on a melody or rhythm suggested by the viewers. Collaboration between viewers and creators can also be applied to design, where they jointly create a design based on the viewers' ideas. For example, a design can be created based on a concept or color scheme suggested by the viewers. Collaboration between viewers and creators can also be applied to video production, where they jointly create a video based on the viewers' ideas. For example, a video can be created based on a scenario or scene suggested by the viewers. In this way, collaboration between viewers and creators can be applied to other creative fields.

[0080] Viewers and producers can work together on an online platform, enabling global collaboration. Viewers and producers can work together on an online platform, enabling global collaboration. For example, viewers and producers from different countries or regions can work together to create a video. Also, on the online platform, viewers can propose ideas, and producers can create a video based on those ideas. For example, a video can be created based on a scenario or scene proposed by a viewer. Also, through the online platform, viewers and producers can communicate with each other in real time and work together. For example, they can exchange opinions using chat or video calls. In this way, viewers and producers can work together on an online platform, enabling global collaboration.

[0081] The emotion estimation function can be used to analyze the emotional state of viewers and producers and suggest the optimal collaboration method. The emotion estimation function can be used to analyze the emotional state of viewers and producers in real time and suggest the optimal collaboration method. For example, if stress levels are high, a relaxing environment can be suggested. The emotion estimation function can also be used to suggest an efficient work flow based on the emotional data of viewers and producers. For example, if concentration levels are declining, an easy task can be suggested. The emotion estimation function can also be used to monitor the emotional state of viewers and producers and provide advice to improve work efficiency. For example, if fatigue is building up, a break can be suggested. In this way, the emotion estimation function can be used to analyze the emotional state of viewers and producers and suggest the optimal collaboration method.

[0082] It is possible to estimate the viewer's emotions in real time and provide a new entertainment experience that corresponds to those emotions. It is possible to analyze the viewer's emotions in real time and provide a new entertainment experience that corresponds to those emotions. For example, if the viewer has a surprised expression, an element of surprise can be added to the scene. The content of the entertainment experience can also be dynamically changed based on the viewer's emotional data. For example, if the viewer has a sad expression, the color tone of the scene can be darkened and sad music can be played. The emotion analysis data can also be used to change the expressions and movements of characters in the entertainment experience in real time. For example, if the viewer smiles, the character can also be made to smile. This makes it possible to estimate the viewer's emotions in real time and provide a new entertainment experience that corresponds to those emotions.

[0083] Generative AI can learn viewer preferences and automatically generate new entertainment content based on those preferences. For example, generative AI can learn a viewer's past viewing history and preferences, and automatically generate new entertainment content based on that data. For example, it can generate videos based on the viewer's favorite genres and themes. Generative AI can also analyze viewer preferences and automatically generate scenarios and scenes that match those preferences. For example, it can generate videos that reflect the viewer's favorite characters and story patterns. Generative AI can also provide a personalized entertainment experience based on the viewer's preferences. For example, it can dynamically change the content and development of videos according to the viewer's preferences. This allows generative AI to learn viewer preferences and automatically generate new entertainment content based on those preferences.

[0084] It is possible to collect real-time reactions from viewers and dynamically change the entertainment experience based on those reactions. Real-time reactions from viewers are collected using cameras and microphones, and the entertainment experience is dynamically changed based on that data. For example, if a viewer makes a surprised expression, an element of surprise is added to the scene. It is also possible to analyze the viewer's real-time reactions and change the color tone and music of the entertainment experience. For example, if a viewer makes a sad expression, the color tone of the scene is darkened and sad music is played. It is also possible to change the expressions and movements of characters in the entertainment experience based on the viewer's real-time reactions. For example, if a viewer smiles, the character also smiles. This makes it possible to collect real-time reactions from viewers and dynamically change the entertainment experience based on those reactions.

[0085] The new entertainment experiences can be applied to the fields of education and training to provide personalized learning experiences. The new entertainment experiences can be used to provide educational simulations. For example, simulations that recreate historical events can be generated to allow students to learn independently. The new entertainment experiences can also be used to provide training programs. For example, medical training or disaster response scenarios can be generated to provide an environment in which learners can acquire practical skills. The new entertainment experiences can also be used to provide personalized learning experiences. For example, the content of videos can be dynamically changed according to the student's level of understanding to support effective learning. In this way, the new entertainment experiences can be applied to the fields of education and training to provide personalized learning experiences.

[0086] This new entertainment experience can be applied to advertising and marketing, making it possible to provide customized advertisements that match the viewer's preferences. This new entertainment experience can be used to provide customized advertisements that match the viewer's preferences. For example, products and services that interest the viewer can be introduced within the video. In addition, the viewer's real-time reactions can be analyzed and the content of the advertisement can be dynamically changed based on that data. For example, advertisements can be generated that focus on products that the viewer has shown interest in. In addition, the most suitable advertisements can be suggested based on the viewer's past viewing history. For example, advertisements related to products and services that the viewer has been interested in in the past can be displayed. This makes it possible to apply this new entertainment experience to advertising and marketing, making it possible to provide customized advertisements that match the viewer's preferences.

[0087] The emotion estimation function can be used to provide new entertainment experiences that correspond to the viewer's emotions, thereby achieving deeper engagement. The emotion estimation function can be used to provide new entertainment experiences that correspond to the viewer's emotions. For example, if the viewer has a surprised expression, an element of surprise can be added to the scene. The content of the entertainment experience can also be dynamically changed based on the viewer's emotional data. For example, if the viewer has a sad expression, the color tone of the scene can be darkened and sad music can be played. The emotion analysis data can also be used to change the expressions and movements of characters in the entertainment experience in real time. For example, if the viewer smiles, the character can also be made to smile. In this way, the emotion estimation function can be used to provide new entertainment experiences that correspond to the viewer's emotions, thereby achieving deeper engagement.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] Generative AI can estimate the viewer's emotions and dynamically change video scenes based on those emotions. For example, if the viewer has a surprised expression, an element of surprise can be added to the scene. It can also change the color tone and music of the video based on the viewer's emotional data. For example, if the viewer has a sad expression, the scene's color tone can be darkened and sad music can be played. Furthermore, it is possible to use emotion analysis data to change the facial expressions and movements of characters in the video in real time. For example, if the viewer smiles, the character can also smile. This makes it possible to change scenes in real time according to the viewer's emotions.

[0090] Generative AI can analyze a viewer's past viewing history and suggest optimal video customizations. It stores the viewer's past viewing history in a database and uses that data to suggest optimal video customizations. For example, it can customize videos based on the viewer's preferred genres and themes. It can also analyze viewing history to identify scenes and characters that the viewer is particularly interested in and customize videos based on that. For example, it can generate scenes centered around the viewer's favorite characters. Furthermore, it can use viewing history data to automatically generate video scenarios tailored to the viewer's preferences. For example, it can suggest scenarios that reflect the viewer's preferred story patterns. This makes it possible to analyze a viewer's past viewing history and suggest optimal video customizations.

[0091] Generative AI can learn from a creator's past works and automatically generate CG that reflects that style. For example, generative AI can import a creator's past works into a database and learn their style. For example, it can learn specific color usage and composition and reflect them in new CG. Generative AI can also automatically generate CG for new projects based on a creator's style. For example, it can generate designs that are consistent with past works. Furthermore, generative AI can analyze a creator's style, extract its features, and apply them to new CG. For example, it can reflect specific textures and patterns in new works. This allows generative AI to learn from a creator's past works and automatically generate CG that reflects that style.

[0092] Generative AI can analyze a creator's work process and propose an optimal workflow. For example, generative AI can collect a creator's work process as data and propose an efficient workflow. For example, it can optimize the order of tasks and time allocation. Generative AI can also analyze a creator's work data and identify bottlenecks and wasteful tasks. For example, it can make suggestions to automate time-consuming tasks. Furthermore, generative AI can monitor a creator's work flow in real time and propose efficient work methods. For example, it can present the optimal next step based on the progress of the work. This allows generative AI to analyze a creator's work process and propose an optimal workflow.

[0093] Generative AI can estimate a creator's emotional state and suggest the optimal work environment and tools. For example, generative AI can analyze a creator's emotional state in real time and suggest the optimal work environment. For example, if stress is high, it can suggest a relaxing environment. Generative AI can also suggest the optimal work tools based on the creator's emotional data. For example, if concentration is declining, it can suggest simple work tools. Furthermore, generative AI can monitor a creator's emotional state and provide advice to improve work efficiency. For example, it can suggest taking a break if fatigue is building up. This allows generative AI to estimate a creator's emotional state and suggest the optimal work environment and tools.

[0094] Generative AI can estimate viewer emotions and promote collaboration between creators and viewers based on those emotions. It can analyze viewer emotions in real time and promote collaboration between creators and viewers based on those emotions. For example, if a viewer is excited, that information can be shared with the producer, and they can jointly create a scene. It can also suggest scenes based on the viewer's emotion data, depending on the viewer's emotion. For example, if a viewer is moved, an emotional scene can be added. Furthermore, emotion analysis data can be used to monitor collaboration between viewers and producers in real time and suggest efficient collaboration methods. For example, it can suggest a workflow based on the viewer's emotion. This makes it possible to estimate viewer emotions and promote collaboration between creators and viewers based on those emotions.

[0095] Generative AI can learn viewer preferences and automatically generate new entertainment content based on those preferences. For example, generative AI can learn a viewer's past viewing history and preferences and automatically generate new entertainment content based on that data. For example, it can generate videos based on the viewer's favorite genres and themes. Generative AI can also analyze viewer preferences and automatically generate scenarios and scenes that match those preferences. For example, it can generate videos that reflect the viewer's favorite characters and story patterns. Furthermore, generative AI can provide a personalized entertainment experience based on the viewer's preferences. For example, it can dynamically change the content and development of videos based on the viewer's preferences. This allows generative AI to learn viewer preferences and automatically generate new entertainment content based on those preferences.

[0096] Generative AI can collect real-time reactions from viewers and dynamically change the entertainment experience based on those reactions. Real-time reactions from viewers can be collected using cameras and microphones, and the entertainment experience can be dynamically changed based on that data. For example, if a viewer expresses surprise, an element of surprise can be added to the scene. It can also analyze real-time reactions from viewers and change the color tone and music of the entertainment experience. For example, if a viewer expresses sadness, the scene's color tone can be darkened and sad music can be played. Furthermore, the expressions and movements of characters in the entertainment experience can be changed based on real-time reactions from viewers. For example, if a viewer smiles, the character can also smile. This allows for real-time reactions from viewers to be collected and the entertainment experience can be dynamically changed based on those reactions.

[0097] Generative AI can estimate viewer emotions in real time and provide new entertainment experiences tailored to those emotions. Generative AI can analyze viewer emotions in real time and provide new entertainment experiences tailored to those emotions. For example, if a viewer has a surprised expression, an element of surprise can be added to the scene. It is also possible to dynamically change the content of the entertainment experience based on the viewer's emotional data. For example, if a viewer has a sad expression, the scene's color tone can be darkened and sad music can be played. Furthermore, emotion analysis data can be used to change the expressions and movements of characters in the entertainment experience in real time. For example, if a viewer smiles, the character can also smile. This makes it possible to estimate viewer emotions in real time and provide new entertainment experiences tailored to those emotions.

[0098] Generative AI enables viewers and producers to collaborate on an online platform, realizing global collaboration. Using an online platform, viewers and producers can collaborate on global collaboration. For example, viewers and producers from different countries or regions can collaborate to create videos. Viewers can also propose ideas on the online platform, and producers can create videos based on those ideas. For example, a video can be created based on a scenario or scene proposed by the viewer. Furthermore, viewers and producers can collaborate through real-time communication via the online platform. For example, they can exchange opinions via chat or video calls. This allows viewers and producers to collaborate on an online platform, realizing global collaboration.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The prompt input section is an interface through which creators can input instructions to the generation AI. For example, prompts can be input using text input or voice input. Step 2: The CG generation unit generates CG based on the instructions input by the prompt input unit. For example, the background of a future city generated by the generation AI is output as CG. Step 3: The interactive video generation unit generates interactive video using the CG generated by the CG generation unit. For example, if a viewer inputs a request such as "I want the background of this scene to be changed to a night view," the interactive video generation unit can change the video in real time based on that request.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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. Equipped with generative AI, a prompt input section; a CG generation unit that generates CG based on the instruction input by the prompt input unit; an interactive video generation unit that generates an interactive video using the CG generated by the CG generation unit; A system characterized by:

2. The interactive video generation unit Reflects the viewer's emotions in real time and changes the scene accordingly 2. The system of claim 1.

3. The CG generation unit Incorporating physical simulation to realistically reflect the above movements and changes 2. The system of claim 1.

4. The CG generation unit Automatically generate ambient sounds and sound effects to provide a realistic experience both visually and aurally.

2. The system of claim 1.

5. The generated AI is Automatically generates not only movies and videos, but also game backgrounds and character designs 2. The system of claim 1.

Images