System

Abstract

An object of the system according to the embodiment is to allow a user to easily create and share a video work according to the user's preference.SOLUTION: A system includes a genre selection part, a story generation part, a character generation part, a video music generation part, and a sharing feedback part. The genre selection unit selects a genre based on the genre or theme selected by the user. The story generation unit generates a story on the basis of the genre or theme selected by the genre selection unit. The character generation unit generates a character on the basis of the story generated by the story generation unit. The video / music generation unit generates video and music based on the story and the character generated by the story generation unit and the character generation unit. A sharing feedback part shares the video work generated by the video music generation part and receives feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to easily create and share video works that suit their preferences.

[0005] The system according to the embodiment aims to enable users to easily create and share video works that suit their preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a genre selection unit, a story generation unit, a character generation unit, a video and music generation unit, and a shared feedback unit. The genre selection unit selects a genre based on a genre or theme selected by a user. The story generation unit generates a story based on the genre or theme selected by the genre selection unit. The character generation unit generates a character based on the story generated by the story generation unit. The video and music generation unit generates video and music based on the story and characters generated by the story generation unit and character generation unit. The shared feedback unit shares the video work generated by the video and music generation unit and receives feedback. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create and share video works that suit their preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The CineAI Studio system according to an embodiment of the present invention uses a generation AI to automatically create stories, characters, videos, music, etc. based on a genre or theme selected by a user, and then shares the created work on an online platform and receives feedback. This allows users to easily create and share original video works.

[0029] The CineAI Studio system according to the embodiment includes a genre selection unit, a story generation unit, a character generation unit, a video and music generation unit, and a sharing and feedback unit. The genre selection unit selects a genre based on a genre or theme selected by a user. For example, the user can select genres such as action, romance, science fiction, and horror, or themes such as "future city" and "magical world." The story generation unit generates a story based on the genre and theme selected by the genre selection unit. For example, the generation AI generates a story plot, character settings, dialogue, etc. that matches the theme selected by the user. The character generation unit generates characters based on the story generated by the story generation unit. For example, the generation AI sets the appearance, personality, background, etc. of the characters appearing in the story in detail. The video and music generation unit generates video and music based on the story and characters generated by the story generation unit and the character generation unit. For example, the generation AI creates video including scene settings and character movements, and music that matches the atmosphere of the scene. The sharing and feedback unit shares the video generated by the video and music generation unit and receives feedback. For example, the generated video can be shared on an online platform where other users can view, comment, and rate it. As a result, the CineAI Studio system according to the embodiment allows users to easily create and share original video works. For example, users can create video works based on their own ideas and share them with other users, forming new creative communities. In addition, feedback can be used to improve the works, resulting in higher quality video works.

[0030] The genre selection unit allows the generation AI to analyze past popular works and trend data based on the genre and theme selected by the user, and propose optimal sub-themes and plots. For example, if the user selects "future city," the generation AI will propose sub-themes such as "cyberpunk" and "dystopia" from past popular works. This allows the generation AI to propose optimal sub-themes and plots based on the genre and theme selected by the user, thereby generating more appealing video works.

[0031] The genre selection unit can automatically incorporate historical background and cultural elements related to the genre or theme selected by the user. For example, if the user selects "Medieval Europe," the generation AI will reflect the historical background and cultural elements of that era in the story. This allows for the generation of a more in-depth video work by incorporating historical background and cultural elements related to the genre or theme selected by the user.

[0032] The genre selection unit allows the generation AI to draw inspiration from different media based on the genre and theme selected by the user and reflect that inspiration in the video work. For example, the genre selection unit allows the generation AI to draw inspiration from books based on the genre and theme selected by the user and reflect that inspiration in the video work. For example, if the user selects "fantasy," the generation AI will incorporate ideas from fantasy novels. This allows the generation AI to draw inspiration from different media based on the genre and theme selected by the user and reflect that inspiration in the video work, thereby generating a wider variety of video works.

[0033] The genre selection unit can automatically suggest genre and theme selection based on the user's past viewing history and rating history. The genre selection unit, for example, analyzes the user's past viewing history and rating history and automatically suggests genre and theme selection. For example, the unit suggests an optimal genre based on the genres of works the user has viewed in the past. This allows for the generation of more personalized video works by automatically suggesting genres and themes based on the user's past viewing history and rating history.

[0034] When the generation AI generates a story, the story generation unit learns the user's past choices and feedback, and can generate a more personalized story. For example, when the generation AI generates a story, the story generation unit learns the user's past choices and feedback, and can generate a more personalized story. For example, it generates an optimal story based on themes and genres selected by the user in the past. In this way, by learning the user's past choices and feedback and generating a more personalized story, it is possible to provide a video work that suits the user's preferences.

[0035] The story generation unit can add a function that allows the generation AI to incorporate different perspectives and timelines when generating a story, creating a complex plot. For example, the story generation unit adds a function that allows the generation AI to incorporate different perspectives and create a complex plot when generating a story. For example, the story can unfold from the perspectives of multiple characters. This allows the creation of a complex plot that incorporates different perspectives and timelines, making it possible to provide a deeper story.

[0036] When generating a story, the story generation unit allows the generation AI to incorporate elements from different cultures and regions to create a story from a global perspective. For example, when generating a story, the story generation unit allows the generation AI to incorporate elements from different cultures to create a story from a global perspective. For example, it can reflect the cultures of Asia and Europe. This allows for the creation of a story from a global perspective by incorporating elements from different cultures and regions, making it possible to provide video works with a more diverse range of perspectives.

[0037] The story generation unit can add an interactive function that allows the user to monitor the story generation process in real time and make corrections or additions along the way. The story generation unit can add an interactive function that allows the user to monitor the story generation process in real time and make corrections or additions along the way. For example, the user can change the plot or add characters. In this way, by adding an interactive function that allows the user to monitor the story generation process in real time and make corrections or additions along the way, a video work can be generated that is more in line with the user's intentions.

[0038] When the generation AI generates a character, the character generation unit learns the user's past choices and feedback, and can generate a more personalized character. For example, when the generation AI generates a character, the character generation unit learns the user's past choices and feedback, and generates a more personalized character. For example, it incorporates the characteristics of characters previously selected by the user. In this way, by learning the user's past choices and feedback and generating a more personalized character, it is possible to provide a character that suits the user's preferences.

[0039] The character generation unit can add a function that allows the generation AI to incorporate elements of different cultures and historical backgrounds when generating characters, creating more diverse characters. For example, the character generation unit can add a function that allows the generation AI to incorporate elements of different cultures when generating characters, creating more diverse characters. For example, Asian and European cultures can be reflected. This allows for the creation of more diverse characters that incorporate different cultures and historical backgrounds, making it possible to provide video works with more diverse perspectives.

[0040] The character generation unit allows the AI ​​to draw inspiration from different media and reflect it in the character when generating a character. For example, when generating a character, the AI ​​draws inspiration from books and reflects it in the character. For example, if the user selects "fantasy," the AI ​​will incorporate character characteristics from fantasy novels. This allows for the generation of more diverse characters by drawing inspiration from different media and reflecting it in the character.

[0041] The character generation unit can add an interactive function that allows the user to monitor the character generation process in real time and make corrections or additions along the way. The character generation unit can add, for example, an interactive function that allows the user to monitor the character generation process in real time and make corrections or additions along the way. For example, the user can change the character's appearance or personality. In this way, by adding an interactive function that allows the user to monitor the character generation process in real time and make corrections or additions along the way, it is possible to generate a character that is more in line with the user's intentions.

[0042] When the generation AI generates video and music, the video and music generation unit learns the user's past choices and feedback, and can generate more personalized video and music. For example, when the generation AI generates video and music, the video and music generation unit learns the user's past choices and feedback, and generates more personalized video and music. For example, it incorporates the style of video and music that the user has previously selected. In this way, the generation AI can learn the user's past choices and feedback and generate more personalized video and music, thereby providing a video work that suits the user's preferences.

[0043] The video and music generation unit can add a function that allows the AI ​​to incorporate elements of different cultures and historical backgrounds when generating video and music, creating more diverse video and music. For example, the video and music generation unit can add a function that allows the AI ​​to incorporate elements of different cultures when generating video and music, creating more diverse video and music. For example, it can reflect Asian and European cultures. This allows the AI ​​to incorporate different cultures and historical backgrounds to create more diverse video and music, thereby providing video works with more diverse perspectives.

[0044] The visual and music generation unit allows the generation AI to draw inspiration from different media and reflect that in the visuals and music. For example, when generating visuals and music, the visual and music generation unit allows the generation AI to draw inspiration from books and reflect that in the visuals and music. For example, if a user selects "fantasy," the generation AI will incorporate visual and musical elements from fantasy novels. This allows for the generation of a wider variety of visual works by drawing inspiration from different media and reflecting that in the visuals and music.

[0045] The video and music generation unit can add an interactive function that allows the user to monitor the video and music generation process in real time and make corrections or additions along the way.The video and music generation unit can add an interactive function that allows the user to monitor the video and music generation process in real time and make corrections or additions along the way.For example, the user can change the order of scenes or add new scenes.In this way, by adding an interactive function that allows the user to monitor the video and music generation process in real time and make corrections or additions along the way, a video work can be created that is more in line with the user's intentions.

[0046] The shared feedback unit uses a generation AI to analyze feedback on shared works and automatically generate specific improvement suggestions for the user. For example, the shared feedback unit uses a generation AI to analyze feedback on shared works and automatically generate specific improvement suggestions for the user. For example, the shared feedback unit suggests improvements to the story based on the feedback the user received. In this way, the generation AI analyzes feedback on shared works and automatically generates specific improvement suggestions, allowing users to create higher quality video works.

[0047] The sharing feedback unit can add a feature to the work sharing platform that allows users to collaboratively edit in real time, allowing multiple users to improve the work simultaneously. For example, the sharing feedback unit can add a feature to the work sharing platform that allows users to collaboratively edit in real time, allowing multiple users to improve the work simultaneously. For example, users can edit stories or characters simultaneously. By adding a feature to the work sharing platform that allows collaborative editing in real time, multiple users can improve the work simultaneously.

[0048] The sharing feedback unit can add a function to the work sharing platform that allows users to add visual notes or mind maps to the work, making it easier to understand visually. For example, the sharing feedback unit can add a function to the work sharing platform that allows users to add visual notes to the work, making it easier to understand visually. For example, important points can be indicated with diagrams or icons. In this way, adding a function to the work sharing platform that allows users to add visual notes or mind maps makes it easier to understand visually.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The CineAI Studio system can also be equipped with a history analysis unit that analyzes a user's past viewing history and rating history. The history analysis unit collects data on works the user has previously viewed and rated, and analyzes the user's preferences and trends. For example, it can suggest optimal genres and themes based on the genres and themes of works the user has previously viewed. This allows the system to generate more personalized video works by automatically suggesting genres and themes based on the user's past viewing history and rating history.

[0051] The CineAI Studio system can also be equipped with a cultural fusion section that incorporates elements from different cultures and regions. This section reflects elements from different cultures and regions into stories and characters based on the genre and theme selected by the user. For example, it can generate stories and characters that incorporate Asian and European cultures. This allows for the creation of video works with a more diverse perspective by incorporating elements from different cultures and regions.

[0052] The CineAI Studio system can also be equipped with an interactive editing section that allows users to edit stories and characters in real time. The interactive editing section allows users to monitor the story and character generation process in real time and make corrections or additions along the way. For example, users can change the plot or modify the appearance or personality of characters. This allows users to monitor the story and character generation process in real time and make corrections or additions along the way, thereby generating a video work that is more in line with their intentions.

[0053] The CineAI Studio system can also be equipped with a media fusion unit that draws inspiration from different media sources and incorporates it into video and music. Based on a genre or theme selected by the user, the media fusion unit draws inspiration from different media sources, such as books, music, and art, and incorporates it into video and music. For example, it can generate video and music that incorporates ideas from fantasy novels. This allows for the creation of a wider variety of video works by drawing inspiration from different media sources and incorporating it into video and music.

[0054] The CineAI Studio system can also be equipped with a visual notes section that allows users to add visual notes and mind maps to their work, making it easier to understand visually. For example, by indicating important points with diagrams or icons, users can more intuitively understand the content of the work. This allows users to add visual notes and mind maps to the work sharing platform, making it easier to understand visually.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The genre selection unit selects a genre based on the genre or theme selected by the user. For example, the user can select genres such as action, romance, science fiction, horror, etc., or themes such as "future city" and "magical world." Step 2: The story generation unit generates a story based on the genre and theme selected by the genre selection unit. For example, the generation AI generates a story plot, character settings, dialogue, etc. that matches the theme selected by the user. Step 3: The character generation unit generates characters based on the story generated by the story generation unit. For example, the generation AI sets the details of the characters' appearances, personalities, backgrounds, etc. Step 4: The video and music generation unit generates video and music based on the story and characters generated by the story generation unit and character generation unit. For example, the generation AI creates video including scene settings and character movements, and music that matches the atmosphere of the scene. Step 5: The sharing and feedback unit shares the video work generated by the video and music generation unit and receives feedback. For example, the generated video work may be shared on an online platform so that other users can view, comment on, and rate it.

[0057] (Example 2) The CineAI Studio system according to an embodiment of the present invention uses a generation AI to automatically create stories, characters, videos, music, etc. based on a genre or theme selected by a user, and then shares the created work on an online platform and receives feedback. This allows users to easily create and share original video works.

[0058] The CineAI Studio system according to the embodiment includes a genre selection unit, a story generation unit, a character generation unit, a video and music generation unit, and a sharing and feedback unit. The genre selection unit selects a genre based on a genre or theme selected by a user. For example, the user can select genres such as action, romance, science fiction, and horror, or themes such as "future city" and "magical world." The story generation unit generates a story based on the genre and theme selected by the genre selection unit. For example, the generation AI generates a story plot, character settings, dialogue, etc. that matches the theme selected by the user. The character generation unit generates characters based on the story generated by the story generation unit. For example, the generation AI sets the appearance, personality, background, etc. of the characters appearing in the story in detail. The video and music generation unit generates video and music based on the story and characters generated by the story generation unit and the character generation unit. For example, the generation AI creates video including scene settings and character movements, and music that matches the atmosphere of the scene. The sharing and feedback unit shares the video generated by the video and music generation unit and receives feedback. For example, the generated video can be shared on an online platform where other users can view, comment, and rate it. As a result, the CineAI Studio system according to the embodiment allows users to easily create and share original video works. For example, users can create video works based on their own ideas and share them with other users, forming new creative communities. In addition, feedback can be used to improve the works, resulting in higher quality video works.

[0059] The genre selection unit allows the generation AI to analyze past popular works and trend data based on the genre and theme selected by the user, and propose optimal sub-themes and plots. For example, if the user selects "future city," the generation AI will propose sub-themes such as "cyberpunk" and "dystopia" from past popular works. This allows the generation AI to propose optimal sub-themes and plots based on the genre and theme selected by the user, thereby generating more appealing video works.

[0060] The genre selection unit can automatically incorporate historical background and cultural elements related to the genre or theme selected by the user. For example, if the user selects "Medieval Europe," the generation AI will reflect the historical background and cultural elements of that era in the story. This allows for the generation of a more in-depth video work by incorporating historical background and cultural elements related to the genre or theme selected by the user.

[0061] The genre selection unit can use the emotion estimation function to analyze the emotional response to the genre or theme selected by the user and suggest elements that are most likely to resonate emotionally. The genre selection unit can, for example, use the emotion estimation function to analyze the emotional response to the genre or theme selected by the user and suggest elements that are most likely to resonate emotionally. For example, if the user selects "horror," the generation AI will suggest elements that enhance the sense of fear. In this way, by analyzing the emotional response to the genre or theme selected by the user and suggesting elements that are most likely to resonate emotionally, it is possible to generate video works that are more likely to resonate emotionally.

[0062] The genre selection unit allows the generation AI to draw inspiration from different media based on the genre and theme selected by the user and reflect that inspiration in the video work. For example, the genre selection unit allows the generation AI to draw inspiration from books based on the genre and theme selected by the user and reflect that inspiration in the video work. For example, if the user selects "fantasy," the generation AI will incorporate ideas from fantasy novels. This allows the generation AI to draw inspiration from different media based on the genre and theme selected by the user and reflect that inspiration in the video work, thereby generating a wider variety of video works.

[0063] The genre selection unit can automatically suggest genre and theme selection based on the user's past viewing history and rating history. The genre selection unit, for example, analyzes the user's past viewing history and rating history and automatically suggests genre and theme selection. For example, the unit suggests an optimal genre based on the genres of works the user has viewed in the past. This allows for the generation of more personalized video works by automatically suggesting genres and themes based on the user's past viewing history and rating history.

[0064] The genre selection unit can use the emotion estimation function to collect other users' emotional reactions to the genre or theme selected by the user and display popular genres and themes in real time. For example, the genre selection unit can use the emotion estimation function to collect other users' emotional reactions to the genre or theme selected by the user and display popular genres and themes in real time. For example, if the user selects "horror," a popular horror theme is displayed based on the emotional reactions of other users. In this way, by collecting other users' emotional reactions to the genre or theme selected by the user and displaying popular genres and themes in real time, it is possible to create a video work that resonates with more users.

[0065] When the generation AI generates a story, the story generation unit learns the user's past choices and feedback, and can generate a more personalized story. For example, when the generation AI generates a story, the story generation unit learns the user's past choices and feedback, and can generate a more personalized story. For example, it generates an optimal story based on themes and genres selected by the user in the past. In this way, by learning the user's past choices and feedback and generating a more personalized story, it is possible to provide a video work that suits the user's preferences.

[0066] The story generation unit can add a function that allows the generation AI to incorporate different perspectives and timelines when generating a story, creating a complex plot. For example, the story generation unit adds a function that allows the generation AI to incorporate different perspectives and create a complex plot when generating a story. For example, the story can unfold from the perspectives of multiple characters. This allows the creation of a complex plot that incorporates different perspectives and timelines, making it possible to provide a deeper story.

[0067] The story generation unit can use the emotion estimation function to predict the user's emotional reaction to each scene in the story and emphasize scenes that will have a strong emotional impact. For example, the story generation unit can use the emotion estimation function to predict the user's emotional reaction to each scene in the story and emphasize scenes that will have a strong emotional impact. For example, moving scenes can be emphasized. In this way, by using the emotion estimation function to emphasize scenes that will have a strong emotional impact, a story that is more likely to be emotionally relatable can be provided.

[0068] When generating a story, the story generation unit allows the generation AI to incorporate elements from different cultures and regions to create a story from a global perspective. For example, when generating a story, the story generation unit allows the generation AI to incorporate elements from different cultures to create a story from a global perspective. For example, it can reflect the cultures of Asia and Europe. This allows for the creation of a story from a global perspective by incorporating elements from different cultures and regions, making it possible to provide video works with a more diverse range of perspectives.

[0069] The story generation unit can add an interactive function that allows the user to monitor the story generation process in real time and make corrections or additions along the way. The story generation unit can add an interactive function that allows the user to monitor the story generation process in real time and make corrections or additions along the way. For example, the user can change the plot or add characters. In this way, by adding an interactive function that allows the user to monitor the story generation process in real time and make corrections or additions along the way, a video work can be generated that is more in line with the user's intentions.

[0070] The story generation unit can use the emotion estimation function to collect other users' emotional reactions to each scene of the story and automatically insert the scene that is most likely to be emotionally relatable. The story generation unit, for example, uses the emotion estimation function to collect other users' emotional reactions to each scene of the story and automatically inserts the scene that is most likely to be emotionally relatable. For example, it adds a moving scene. In this way, by using the emotion estimation function to collect other users' emotional reactions and automatically inserting the scene that is most likely to be emotionally relatable, it is possible to generate a video work that will resonate with more users.

[0071] When the generation AI generates a character, the character generation unit learns the user's past choices and feedback, and can generate a more personalized character. For example, when the generation AI generates a character, the character generation unit learns the user's past choices and feedback, and generates a more personalized character. For example, it incorporates the characteristics of characters previously selected by the user. In this way, by learning the user's past choices and feedback and generating a more personalized character, it is possible to provide a character that suits the user's preferences.

[0072] The character generation unit can add a function that allows the generation AI to incorporate elements of different cultures and historical backgrounds when generating characters, creating more diverse characters. For example, the character generation unit can add a function that allows the generation AI to incorporate elements of different cultures when generating characters, creating more diverse characters. For example, Asian and European cultures can be reflected. This allows for the creation of more diverse characters that incorporate different cultures and historical backgrounds, making it possible to provide video works with more diverse perspectives.

[0073] The character generation unit can use the emotion estimation function to predict the user's emotional reaction to the character's personality and behavior, and create a character that is easy to empathize with emotionally. The character generation unit, for example, uses the emotion estimation function to predict the user's emotional reaction to the character's personality and behavior, and create a character that is easy to empathize with emotionally. For example, a character with an inspiring personality and behavior is created. In this way, by using the emotion estimation function to create a character that is easy to empathize with emotionally, a video work that can be empathized with by a larger number of users can be generated.

[0074] The character generation unit allows the AI ​​to draw inspiration from different media and reflect it in the character when generating a character. For example, when generating a character, the AI ​​draws inspiration from books and reflects it in the character. For example, if the user selects "fantasy," the AI ​​will incorporate character characteristics from fantasy novels. This allows for the generation of more diverse characters by drawing inspiration from different media and reflecting it in the character.

[0075] The character generation unit can add an interactive function that allows the user to monitor the character generation process in real time and make corrections or additions along the way. The character generation unit can add, for example, an interactive function that allows the user to monitor the character generation process in real time and make corrections or additions along the way. For example, the user can change the character's appearance or personality. In this way, by adding an interactive function that allows the user to monitor the character generation process in real time and make corrections or additions along the way, it is possible to generate a character that is more in line with the user's intentions.

[0076] The character generation unit can use the emotion estimation function to collect other users' emotional reactions to the character's personality and actions, and automatically insert a character that is most likely to be emotionally empathetic. The character generation unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the character's personality and actions, and automatically inserts a character that is most likely to be emotionally empathetic. For example, a character with an inspiring personality or behavior is added. In this way, by using the emotion estimation function to collect other users' emotional reactions and automatically inserting a character that is most likely to be emotionally empathetic, a video work that can be empathized with by more users can be generated.

[0077] When the generation AI generates video and music, the video and music generation unit learns the user's past choices and feedback, and can generate more personalized video and music. For example, when the generation AI generates video and music, the video and music generation unit learns the user's past choices and feedback, and generates more personalized video and music. For example, it incorporates the style of video and music that the user has previously selected. In this way, the generation AI can learn the user's past choices and feedback and generate more personalized video and music, thereby providing a video work that suits the user's preferences.

[0078] The video and music generation unit can add a function that allows the AI ​​to incorporate elements of different cultures and historical backgrounds when generating video and music, creating more diverse video and music. For example, the video and music generation unit can add a function that allows the AI ​​to incorporate elements of different cultures when generating video and music, creating more diverse video and music. For example, it can reflect Asian and European cultures. This allows the AI ​​to incorporate different cultures and historical backgrounds to create more diverse video and music, thereby providing video works with more diverse perspectives.

[0079] The video and music generation unit can use the emotion estimation function to predict the user's emotional response to each video and music scene and emphasize scenes that have a strong emotional impact. The video and music generation unit, for example, uses the emotion estimation function to predict the user's emotional response to each video and music scene and emphasizes scenes that have a strong emotional impact. For example, moving scenes can be emphasized. In this way, by using the emotion estimation function to emphasize scenes that have a strong emotional impact, a video work that is more likely to be emotionally relatable can be provided.

[0080] The visual and music generation unit allows the generation AI to draw inspiration from different media and reflect that in the visuals and music. For example, when generating visuals and music, the visual and music generation unit allows the generation AI to draw inspiration from books and reflect that in the visuals and music. For example, if a user selects "fantasy," the generation AI will incorporate visual and musical elements from fantasy novels. This allows for the generation of a wider variety of visual works by drawing inspiration from different media and reflecting that in the visuals and music.

[0081] The video and music generation unit can add an interactive function that allows the user to monitor the video and music generation process in real time and make corrections or additions along the way.The video and music generation unit can add an interactive function that allows the user to monitor the video and music generation process in real time and make corrections or additions along the way.For example, the user can change the order of scenes or add new scenes.In this way, by adding an interactive function that allows the user to monitor the video and music generation process in real time and make corrections or additions along the way, a video work can be created that is more in line with the user's intentions.

[0082] The video and music generation unit can use the emotion estimation function to collect other users' emotional reactions to each video and music scene and automatically insert the scene that is most likely to be emotionally empathetic. The video and music generation unit, for example, uses the emotion estimation function to collect other users' emotional reactions to each video and music scene and automatically inserts the scene that is most likely to be emotionally empathetic. For example, it adds a moving scene. In this way, by using the emotion estimation function to collect other users' emotional reactions and automatically inserting the scene that is most likely to be emotionally empathetic, a video work that can be empathized with by more users can be generated.

[0083] The shared feedback unit uses a generation AI to analyze feedback on shared works and automatically generate specific improvement suggestions for the user. For example, the shared feedback unit uses a generation AI to analyze feedback on shared works and automatically generate specific improvement suggestions for the user. For example, the shared feedback unit suggests improvements to the story based on the feedback the user received. In this way, the generation AI analyzes feedback on shared works and automatically generates specific improvement suggestions, allowing users to create higher quality video works.

[0084] The sharing feedback unit can add a feature to the work sharing platform that allows users to collaboratively edit in real time, allowing multiple users to improve the work simultaneously. For example, the sharing feedback unit can add a feature to the work sharing platform that allows users to collaboratively edit in real time, allowing multiple users to improve the work simultaneously. For example, users can edit stories or characters simultaneously. By adding a feature to the work sharing platform that allows collaborative editing in real time, multiple users can improve the work simultaneously.

[0085] The sharing feedback unit can use the emotion estimation function to analyze other users' emotional reactions to the shared work and display a ranking of works that are likely to resonate emotionally. The sharing feedback unit, for example, uses the emotion estimation function to analyze other users' emotional reactions to the shared work and display a ranking of works that are likely to resonate emotionally. For example, moving works are displayed at the top. In this way, by using the emotion estimation function to analyze other users' emotional reactions to the shared work and display a ranking of works that are likely to resonate emotionally, it is possible to provide video works that resonate with more users.

[0086] The sharing feedback unit can add a function to the work sharing platform that allows users to add visual notes or mind maps to the work, making it easier to understand visually. For example, the sharing feedback unit can add a function to the work sharing platform that allows users to add visual notes to the work, making it easier to understand visually. For example, important points can be indicated with diagrams or icons. In this way, adding a function to the work sharing platform that allows users to add visual notes or mind maps makes it easier to understand visually.

[0087] The sharing feedback unit can use the emotion estimation function to collect other users' emotional reactions to the shared work and automatically recommend the work that is most likely to resonate emotionally. The sharing feedback unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the shared work and automatically recommends the work that is most likely to resonate emotionally. For example, it recommends moving works. In this way, by using the emotion estimation function to collect other users' emotional reactions to the shared work and automatically recommending the work that is most likely to resonate emotionally, it is possible to provide video works that resonate with more users.

[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] The CineAI Studio system can also be equipped with a history analysis unit that analyzes a user's past viewing history and rating history. The history analysis unit collects data on works the user has previously viewed and rated, and analyzes the user's preferences and trends. For example, it can suggest optimal genres and themes based on the genres and themes of works the user has previously viewed. This allows the system to generate more personalized video works by automatically suggesting genres and themes based on the user's past viewing history and rating history.

[0090] The CineAI Studio system can also be equipped with an emotion adjustment unit that estimates the user's emotions and adjusts the story development based on the estimated emotions. The emotion adjustment unit analyzes the user's emotional reactions in real time as they watch a story and emphasizes scenes that have a strong emotional impact. For example, by emphasizing moving scenes, it is possible to provide a story that appeals to the user's emotions. In this way, by using the emotion estimation function to emphasize scenes that have a strong emotional impact, it is possible to provide a story that is more likely to resonate emotionally.

[0091] The CineAI Studio system can also be equipped with a cultural fusion section that incorporates elements from different cultures and regions. This section reflects elements from different cultures and regions into stories and characters based on the genre and theme selected by the user. For example, it can generate stories and characters that incorporate Asian and European cultures. This allows for the creation of video works with a more diverse perspective by incorporating elements from different cultures and regions.

[0092] The CineAI Studio system can also be equipped with a character adjustment unit that estimates the user's emotions and adjusts the character's personality and behavior based on the estimated emotions. The character adjustment unit analyzes the user's emotions toward the character in real time and creates a character that is easy to empathize with. For example, by creating a character with an inspiring personality and behavior, it is possible to provide a character that appeals to the user's emotions. In this way, by using the emotion estimation function to create a character that is easy to empathize with emotionally, it is possible to generate video works that resonate with more users.

[0093] The CineAI Studio system can also be equipped with an interactive editing section that allows users to edit stories and characters in real time. The interactive editing section allows users to monitor the story and character generation process in real time and make corrections or additions along the way. For example, users can change the plot or modify the appearance or personality of characters. This allows users to monitor the story and character generation process in real time and make corrections or additions along the way, thereby generating a video work that is more in line with their intentions.

[0094] The CineAI Studio system can also be equipped with a scene adjustment unit that estimates the user's emotions and adjusts each video and music scene based on the estimated emotions. The scene adjustment unit analyzes the user's emotions toward the video and music in real time and emphasizes scenes that have a strong emotional impact. For example, by emphasizing moving scenes, it is possible to provide a video work that appeals to the user's emotions. In this way, by using the emotion estimation function to emphasize scenes that have a strong emotional impact, it is possible to provide a video work that is more likely to resonate emotionally.

[0095] The CineAI Studio system can also be equipped with a media fusion unit that draws inspiration from different media sources and incorporates it into video and music. Based on a genre or theme selected by the user, the media fusion unit draws inspiration from different media sources, such as books, music, and art, and incorporates it into video and music. For example, it can generate video and music that incorporates ideas from fantasy novels. This allows for the creation of a wider variety of video works by drawing inspiration from different media sources and incorporating it into video and music.

[0096] The CineAI Studio system can further include a feedback analysis unit that estimates the user's emotions and analyzes feedback on shared works based on the estimated emotions. The feedback analysis unit collects other users' emotional reactions to shared works and displays a ranking of works that are likely to resonate emotionally. For example, by displaying moving works at the top, it becomes easier for users to find works that resonate more. This allows the emotion estimation function to analyze other users' emotional reactions to shared works and display a ranking of works that are likely to resonate emotionally, thereby providing video works that resonate with more users.

[0097] The CineAI Studio system can also be equipped with a visual notes section that allows users to add visual notes and mind maps to their work, making it easier to understand visually. For example, by indicating important points with diagrams or icons, users can more intuitively understand the content of the work. This allows users to add visual notes and mind maps to the work sharing platform, making it easier to understand visually.

[0098] The CineAI Studio system can further include a recommendation unit that estimates a user's emotions and automatically recommends works that are most likely to resonate emotionally based on the estimated emotions. The recommendation unit collects other users' emotional reactions to shared works and automatically recommends works that are most likely to resonate emotionally. For example, by recommending moving works, it becomes easier for users to find works that resonate more. This allows the emotion estimation function to collect other users' emotional reactions to shared works and automatically recommend works that are most likely to resonate emotionally, thereby providing video works that resonate with more users.

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

[0100] Step 1: The genre selection unit selects a genre based on the genre or theme selected by the user. For example, the user can select genres such as action, romance, science fiction, horror, etc., or themes such as "future city" and "magical world." Step 2: The story generation unit generates a story based on the genre and theme selected by the genre selection unit. For example, the generation AI generates a story plot, character settings, dialogue, etc. that matches the theme selected by the user. Step 3: The character generation unit generates characters based on the story generated by the story generation unit. For example, the generation AI sets the details of the characters' appearances, personalities, backgrounds, etc. Step 4: The video and music generation unit generates video and music based on the story and characters generated by the story generation unit and character generation unit. For example, the generation AI creates video including scene settings and character movements, and music that matches the atmosphere of the scene. Step 5: The sharing and feedback unit shares the video work generated by the video and music generation unit and receives feedback. For example, the generated video work may be shared on an online platform so that other users can view, comment on, and rate it.

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

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 type 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

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

[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. a genre selection unit that selects a genre based on a genre or theme selected by a user; a story generation unit that generates a story based on the genre and theme selected by the genre selection unit; a character generation unit that generates a character based on the story generated by the story generation unit; a video and music generation unit that generates video and music based on the story and characters generated by the story generation unit and the character generation unit; a sharing and feedback unit for sharing the video work generated by the video and music generation unit and receiving feedback therefrom. A system characterized by:

2. The genre selection unit Based on the genre and theme selected by the user, the generating AI draws inspiration from different media and reflects it in the video work.

2. The system of claim 1.

3. The story generation unit When the generation AI generates the story, it learns the user's selections and feedback and generates a more personalized story.

2. The system of claim 1.

4. The character generation unit When the generation AI generates the character, it learns the user's selection and feedback and generates a more personalized character.

2. The system of claim 1.

5. The video and music generation unit When the generation AI generates the video and the music, it learns the user's selections and feedback and generates more personalized video and music.

2. The system of claim 1.

6. The shared feedback unit The generation AI analyzes the feedback on the shared work and automatically generates specific improvement suggestions for the user.

2. The system of claim 1.

7. The genre selection unit Analyzing the emotional response of the user to the genre or theme selected by the user and suggesting elements that are most likely to resonate with the user emotionally 2. The system of claim 1.

8. The story generation unit Predicting the user's emotional response to each of the scenes in the story and highlighting scenes that have a strong emotional impact 2. The system of claim 1.

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