System

Abstract

An object of the system according to the embodiment is to generate a new material based on an original plan and create a storyboard.SOLUTION: A system according to an embodiment includes a plan analysis unit, an arrangement unit, and a storyboard generation unit. The plan analyzing section analyzes the original plan. The arrangement unit generates a new material on the basis of the result analyzed by the plan analysis unit. The storyboard creation section creates a storyboard based on the material created by the arrangement section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult for YouTubers and live streamers to continuously generate new content, which can lead to a problem of running out of content.

[0005] The system according to the embodiment aims to generate new ideas based on an original plan and create storyboards. [Means for solving the problem]

[0006] The system according to the embodiment includes a plan analysis unit, an arrangement unit, and a storyboard generation unit. The plan analysis unit analyzes the original plan. The arrangement unit generates new content based on the results of the analysis by the plan analysis unit. The storyboard generation unit creates a storyboard based on the content generated by the arrangement unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate new ideas based on an original plan and create storyboards. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The material generation system according to an embodiment of the present invention is a system for YouTubers and live streamers who tend to run out of material for their videos to post. The system recreates material by exquisitely adjusting the rules, tools, locations, and punch lines based on the original plan or idea. This allows YouTubers and live streamers to easily generate new material and keep viewers interested.

[0029] A material generation system according to an embodiment includes a plan analysis unit, an arrangement unit, and a storyboard generation unit. The plan analysis unit analyzes the original plan. For example, the generation AI analyzes the original plan or idea provided by the user and understands its content. The generation AI uses a text generation AI (e.g., LLM) to analyze the plan's achievement conditions, the props used, the location, and punchline settings. The generation AI can also analyze videos and images using a multimodal generation AI. For example, the generation AI extracts and analyzes important scenes from the plan using video analysis technology. The arrangement unit generates new material based on the results of the analysis by the plan analysis unit. For example, the generation AI exquisitely adjusts the rules, the props used, the location, and the punchline settings based on the analysis results. For example, the generation AI can change the rules of the game broadcast or add props to be used. The generation AI can also change the plan's achievement conditions. For example, the generation AI can keep viewers interested by making the plan's achievement conditions more difficult. The storyboard generation unit creates a storyboard based on the material generated by the arrangement unit. For example, the generative AI creates a storyboard using simple pictures or images based on the generated material. The generative AI provides a storyboard that depicts, for example, character movements and backgrounds for each scene. The generative AI can also create storyboards using 3D models. For example, the generative AI reproduces character movements using 3D models and provides a storyboard. This allows the material generation system to enable YouTubers and live streamers to easily generate new material and keep viewers interested.

[0030] The project analysis unit can analyze the original project while taking into account the cultural background and historical context. For example, when the generation AI analyzes the original project, the project analysis unit takes into account the cultural background and historical context from which the project was created. For example, in the case of a project themed on a traditional Japanese festival, the generation AI will understand the history and cultural significance of the festival and generate new material based on that. The generation AI also takes into account the historical development and changes of the genre to which the project belongs. For example, when analyzing a horror movie project, the generation AI will understand the history and trends of horror movies and generate new material based on that. Furthermore, the generation AI will consider other works and cultural elements that influenced the project. For example, in the case of a project themed on a specific anime work, the generation AI will understand the other works and cultural elements that influenced that anime and generate new material based on that. This allows the generation AI to make arrangements based on a deeper understanding by taking into account the cultural background and historical context of the original project.

[0031] The plan analysis unit analyzes the factors that contributed to the success or failure of the original plan and can propose optimal arrangements based on that analysis. For example, when the generation AI analyzes the original plan, the plan analysis unit analyzes the factors that contributed to the success or failure of that plan. For example, it identifies which elements were well-received by viewers based on past viewing data and feedback, and generates new material based on that. The generation AI also analyzes the components of the plan (e.g., story, characters, direction) and evaluates how each element contributed to the success or failure. It generates new material based on this. Furthermore, the generation AI also refers to the factors that contributed to the success or failure of other similar plans. For example, it analyzes data from other videos and streams in the same genre and generates new material based on that. This allows it to propose optimal arrangements by analyzing the factors that contributed to the success or failure of the original plan.

[0032] The arrangement unit can generate arrangements that can be applied to different genres or themes based on the results of the analysis by the plan analysis unit. For example, the generation AI analyzes the original plan and generates arrangements that can be applied to different genres based on the results. For example, when adapting a comedy plan into a horror plan, it replaces humorous elements with scary elements. The generation AI also analyzes the original plan and generates arrangements that can be applied to different themes based on the results. For example, when adapting a sports plan into a cooking plan, it replaces athletic elements with cooking elements. The generation AI also analyzes the original plan and generates arrangements that can be applied to different audiences based on the results. For example, when adapting a plan aimed at children for adults, it adjusts the content and direction to be more adult-oriented. This allows the generation of arrangements that can be applied to different genres and themes, making it possible to accommodate a wide range of content.

[0033] The arrangement department can make arrangements to adapt the program to different languages ​​and cultural spheres based on the results of the analysis by the plan analysis department. For example, the arrangement department has the generation AI analyze the original plan and make arrangements to adapt it to different languages ​​based on the results. For example, when adapting a plan for English-speaking countries to Japanese-speaking countries, the language and cultural elements are adjusted. The generation AI also analyzes the original plan and makes arrangements to adapt it to different cultural spheres based on the results. For example, when adapting a plan based on American culture to Asian culture, the cultural elements are adjusted. Furthermore, the generation AI analyzes the original plan and makes arrangements to make it multilingual based on the results. For example, when providing the same plan in multiple languages, appropriate arrangements are made for each language. In this way, by making arrangements to adapt it to different languages ​​and cultural spheres, it is possible to accommodate international audiences.

[0034] The arrangement department can propose optimal rules, props, locations, and punchline settings based on past viewer reaction data. For example, the arrangement department uses a generation AI to analyze past viewer reaction data and propose optimal rules, props, locations, and punchline settings. For example, it generates new material by combining elements from past projects that viewers liked. Furthermore, the generation AI analyzes the emotional reactions that specific rules, props, locations, and punchline settings evoked based on past viewer reaction data, and generates new material based on that. Furthermore, the generation AI tries different combinations of elements based on past viewer reaction data and proposes combinations that elicit the most positive reactions. For example, it combines the rules, props, locations, and punchline settings that viewers enjoyed the most. This makes it possible to generate material that is likely to attract viewers' interest by proposing optimal rules, props, locations, and punchline settings based on past viewer reaction data.

[0035] The arrangement department can collect viewer feedback in real time and fine-tune the rules, props, locations, and punchline settings based on that feedback. In the arrangement department, for example, the generation AI collects viewer feedback in real time and fine-tunes the rules, props, locations, and punchline settings based on that feedback. For example, it analyzes viewer comments and reactions and adjusts the content of the material. The generation AI also evaluates how particular rules, props, locations, and punchline settings are received based on viewer feedback in real time and makes arrangements based on that. Furthermore, the generation AI tries different combinations of elements based on viewer feedback in real time and fine-tunes the arrangement to elicit the most positive response. For example, it adjusts the content of the material based on viewer reactions. In this way, by collecting viewer feedback in real time and making fine adjustments based on that, it is possible to generate material that is in line with viewer reactions.

[0036] The arrangement department can apply the arranged rules, tools, locations, and punchline settings to projects of different genres to create new content. For example, the arrangement department applies the rules, tools, locations, and punchline settings arranged by the generation AI to projects of different genres. For example, the rules of a game broadcast can be applied to a cooking broadcast to generate new content. The generation AI also applies the arranged rules, tools, locations, and punchline settings to projects of different themes. For example, the rules of a sports project can be applied to an educational project to generate new content. Furthermore, the generation AI applies the arranged rules, tools, locations, and punchline settings to different audiences. For example, a project aimed at children can be adapted for adults to generate new content. This makes it possible to create new content by applying it to projects of different genres.

[0037] The arrangement department can apply the arranged content to viewer-participation projects and provide interactive content in which viewers can directly participate. For example, the arrangement department applies the rules, tools, locations, and punchline settings arranged by the generation AI to viewer-participation projects. For example, it can provide an interactive game broadcast in which viewers can change the rules in real time. The generation AI can also provide interactive content in which viewers can directly participate based on the arranged content. For example, it can create an interactive video in which viewers select options to advance the story. Furthermore, the generation AI can plan live events in which viewers can participate based on the arranged content. For example, it can hold a live broadcast in which viewers vote in real time to decide the progress. This provides interactive content in which viewers can directly participate, thereby increasing their motivation to participate.

[0038] The storyboard generation unit can analyze the detailed movements and facial expressions of each scene and generate more realistic storyboards. For example, the generation AI of the storyboard generation unit analyzes the detailed movements and facial expressions of each scene and generates a realistic storyboard based on that. For example, it provides a storyboard that depicts the character's movements and facial expressions in detail. The generation AI also analyzes the movements and facial expressions of each scene and generates an animation-style storyboard based on that. For example, it provides a storyboard that depicts the character's movements continuously. Furthermore, the generation AI analyzes the movements and facial expressions of each scene and generates a storyboard using a 3D model based on that. For example, it provides a storyboard that reproduces the character's movements using a 3D model. In this way, by analyzing the detailed movements and facial expressions of each scene, it is possible to generate a more realistic storyboard.

[0039] The storyboard generation unit can incorporate visual storytelling techniques to generate storyboards that attract the viewer's interest. In the storyboard generation unit, for example, a generation AI incorporates visual storytelling techniques to generate storyboards that attract the viewer's interest. For example, it provides a storyboard with clever camera angles and scene compositions. The generation AI also incorporates visual storytelling techniques to generate storyboards that elicit the viewer's emotions. For example, it provides a storyboard that emphasizes emotional scenes. Furthermore, the generation AI incorporates visual storytelling techniques to generate storyboards that attract the viewer's attention. For example, it provides a storyboard that incorporates visual effects and direction. In this way, it is possible to generate storyboards that attract the viewer's interest by incorporating visual storytelling techniques.

[0040] The storyboard generation unit can apply the created storyboard to different media and achieve multimedia development. The storyboard generation unit, for example, applies the storyboard created by the generation AI to a manga and achieves multimedia development. For example, it creates a manga based on the storyboard and provides it to viewers. The generation AI also applies the created storyboard to an anime and achieves multimedia development. For example, it creates an anime based on the storyboard and provides it to viewers. The generation AI also applies the created storyboard to a game and achieves multimedia development. For example, it creates a game based on the storyboard and provides it to viewers. In this way, multimedia development can be achieved by applying the created storyboard to different media.

[0041] The storyboard generation unit provides a platform where viewers can freely edit the storyboards they have created, thereby promoting user-generated content. The storyboard generation unit, for example, provides a platform where viewers can freely edit storyboards created by the generation AI. For example, it provides a function where viewers can edit storyboards and create their own original content. The generation AI also provides a platform where viewers can add their own ideas based on the storyboards they have created. For example, it provides a function where viewers can add new scenes or characters to the storyboard. Furthermore, the generation AI provides a platform where viewers can share the storyboards they have created. For example, it provides a function where viewers can share their edited storyboards with other users. This makes it possible to promote user-generated content by providing a platform where viewers can freely edit content.

[0042] A trial platform will be provided that analyzes viewer feedback in real time and adds a function that allows the generation AI to automatically improve the material. The trial platform will, for example, provide a function that analyzes viewer feedback in real time and allows the generation AI to automatically improve the material. For example, the content of the material will be adjusted based on viewer comments and ratings. The generation AI will also be able to change the punchline and settings of the material based on viewer feedback. For example, it will change the punchline based on viewer reactions. Furthermore, the generation AI will be able to add scenes and characters to the material based on viewer feedback. For example, it will add new scenes and characters based on viewer opinions. This will allow the generation AI to analyze viewer feedback in real time and automatically improve the material, making it possible to provide material that is in line with viewer reactions.

[0043] The trial platform will add a function that analyzes viewers' viewing history and preferences and suggests the most suitable material based on that. The trial platform, for example, provides a function that analyzes viewers' viewing history and preferences and suggests the most suitable material based on that. For example, it will suggest new material based on data on videos the viewer has previously watched. In addition, the generation AI can automatically generate material based on the viewer's viewing history and preferences. For example, it will automatically generate material that matches the viewer's preferences. Furthermore, the generation AI will develop an algorithm that suggests the most suitable material based on the viewer's viewing history and preferences. For example, it will adjust the content and settings of the material based on the viewer's preferences. This will allow the platform to analyze viewers' viewing history and preferences and suggest the most suitable material based on that, making it possible to provide material that is likely to attract viewers' interest.

[0044] The trial platform will be compatible with content of different genres, appealing to a wider audience. The trial platform will provide functions to accommodate content of different genres, for example. For example, it will accommodate not only game streaming, but also genres such as cooking, sports, and education. The generation AI will also provide interfaces customized for each genre to appeal to different audiences. For example, it will provide interfaces for children, young people, and adults. Furthermore, the generation AI will provide feedback collection functions for each genre to accommodate content of different genres. For example, it will provide feedback forms specialized for each genre. This will allow the platform to accommodate content of different genres, appealing to a wider audience.

[0045] A function that allows viewers to generate their own material will be added to the trial platform. The trial platform may, for example, provide a function that allows viewers to generate their own material. For example, a function may be provided where viewers input their own ideas using the generation AI to generate new material. The generation AI may also provide a function where viewers can not only rate material but also post their own ideas. For example, a function may be provided where viewers post their own ideas and receive feedback from other viewers. Furthermore, the generation AI may provide a function where viewers can generate material and share it with other viewers. For example, a function may be provided where viewers can share material they have generated and receive evaluations from other viewers. In this way, adding a function that allows viewers to generate material themselves can increase viewers' motivation to participate.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The material generation system can further include a history analysis unit that analyzes the viewer's past viewing history. The history analysis unit identifies the viewer's preferences and interests based on data on videos the viewer has previously viewed. For example, if a viewer watches many videos of a particular genre or theme, it can suggest new material related to that genre or theme. The history analysis unit can also analyze the viewer's viewing patterns and identify what kind of content the viewer prefers at what time of day. This makes it possible to suggest more personalized material based on the viewer's viewing history.

[0048] The material generation system can further include a feedback collection unit that collects viewer feedback. The feedback collection unit collects and analyzes feedback provided by viewers. For example, it provides a function for viewers to post ratings and comments on material, and generates new material based on that. The feedback collection unit can also identify areas for improvement or new ideas for material based on the feedback provided by viewers. This makes it possible to provide material that better meets the needs of viewers based on viewer feedback.

[0049] The material generation system can further include an interest prediction unit that predicts the viewer's interests. The interest prediction unit predicts what kind of material the viewer will be interested in based on the viewer's past viewing history and feedback. For example, it can analyze data on videos the viewer has watched in the past and identify the viewer's favorite genres and themes. It can also predict what elements the viewer will be interested in based on the viewer's feedback. This makes it possible to predict the viewer's interests and suggest new material based on them.

[0050] The material generation system can further include a behavior analysis unit that analyzes viewer behavior. The behavior analysis unit analyzes the viewer's viewing behavior and interactions to identify the viewer's behavior patterns. For example, it can analyze what kind of videos the viewer watches and at what time of day, thereby understanding the viewer's behavior patterns. It can also analyze how the viewer interacts with the videos, thereby identifying the viewer's preferences and interests. This makes it possible to provide more personalized material based on the viewer's behavior.

[0051] The material generation system can further include a preference learning unit that learns viewer preferences. The preference learning unit learns viewer preferences based on the viewer's past viewing history and feedback. For example, it can analyze data on videos the viewer has previously watched and identify the genres and themes the viewer prefers. It can also learn what elements the viewer is interested in based on viewer feedback. This allows the system to learn viewer preferences and suggest new material based on them.

[0052] The material generation system can further include a behavior analysis unit that analyzes viewer behavior. The behavior analysis unit analyzes the viewer's viewing behavior and interactions to identify the viewer's behavior patterns. For example, it can analyze what kind of videos the viewer watches and at what time of day, thereby understanding the viewer's behavior patterns. It can also analyze how the viewer interacts with the videos, thereby identifying the viewer's preferences and interests. This makes it possible to provide more personalized material based on the viewer's behavior.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The plan analysis unit analyzes the original plan. For example, the generation AI analyzes the original plan or idea provided by the user and understands its content. The generation AI uses text generation AI (e.g., LLM) to analyze the plan's achievement conditions, tools used, location, punchline settings, etc. The generation AI can also use multimodal generation AI to analyze video and images. For example, the generation AI uses video analysis technology to extract and analyze important scenes from the plan. Step 2: The arrangement department generates new material based on the results of the analysis by the planning and analysis department. For example, the generation AI exquisitely adjusts the rules, props used, location, and punchline settings based on the analysis results. For example, the generation AI can change the rules of the game broadcast or add props to be used. The generation AI can also change the conditions for achieving the plan. For example, the generation AI can keep viewers interested by making the conditions for achieving the plan more difficult. Step 3: The storyboard generation unit creates a storyboard based on the material generated by the arrangement unit. For example, the generation AI creates a storyboard using simple pictures or images based on the generated material. For example, the generation AI provides a storyboard that depicts character movements and backgrounds for each scene. The generation AI can also create a storyboard using a 3D model. For example, the generation AI reproduces character movements using a 3D model and provides a storyboard.

[0055] (Example 2) The material generation system according to an embodiment of the present invention is a system for YouTubers and live streamers who tend to run out of material for their videos to post. The system recreates material by exquisitely adjusting the rules, tools, locations, and punch lines based on the original plan or idea. This allows YouTubers and live streamers to easily generate new material and keep viewers interested.

[0056] A material generation system according to an embodiment includes a plan analysis unit, an arrangement unit, and a storyboard generation unit. The plan analysis unit analyzes the original plan. For example, the generation AI analyzes the original plan or idea provided by the user and understands its content. The generation AI uses a text generation AI (e.g., LLM) to analyze the plan's achievement conditions, the props used, the location, and punchline settings. The generation AI can also analyze videos and images using a multimodal generation AI. For example, the generation AI extracts and analyzes important scenes from the plan using video analysis technology. The arrangement unit generates new material based on the results of the analysis by the plan analysis unit. For example, the generation AI exquisitely adjusts the rules, the props used, the location, and the punchline settings based on the analysis results. For example, the generation AI can change the rules of the game broadcast or add props to be used. The generation AI can also change the plan's achievement conditions. For example, the generation AI can keep viewers interested by making the plan's achievement conditions more difficult. The storyboard generation unit creates a storyboard based on the material generated by the arrangement unit. For example, the generative AI creates a storyboard using simple pictures or images based on the generated material. The generative AI provides a storyboard that depicts, for example, character movements and backgrounds for each scene. The generative AI can also create storyboards using 3D models. For example, the generative AI reproduces character movements using 3D models and provides a storyboard. This allows the material generation system to enable YouTubers and live streamers to easily generate new material and keep viewers interested.

[0057] The project analysis unit can analyze the original project while taking into account the cultural background and historical context. For example, when the generation AI analyzes the original project, the project analysis unit takes into account the cultural background and historical context from which the project was created. For example, in the case of a project themed on a traditional Japanese festival, the generation AI will understand the history and cultural significance of the festival and generate new material based on that. The generation AI also takes into account the historical development and changes of the genre to which the project belongs. For example, when analyzing a horror movie project, the generation AI will understand the history and trends of horror movies and generate new material based on that. Furthermore, the generation AI will consider other works and cultural elements that influenced the project. For example, in the case of a project themed on a specific anime work, the generation AI will understand the other works and cultural elements that influenced that anime and generate new material based on that. This allows the generation AI to make arrangements based on a deeper understanding by taking into account the cultural background and historical context of the original project.

[0058] The plan analysis unit analyzes the factors that contributed to the success or failure of the original plan and can propose optimal arrangements based on that analysis. For example, when the generation AI analyzes the original plan, the plan analysis unit analyzes the factors that contributed to the success or failure of that plan. For example, it identifies which elements were well-received by viewers based on past viewing data and feedback, and generates new material based on that. The generation AI also analyzes the components of the plan (e.g., story, characters, direction) and evaluates how each element contributed to the success or failure. It generates new material based on this. Furthermore, the generation AI also refers to the factors that contributed to the success or failure of other similar plans. For example, it analyzes data from other videos and streams in the same genre and generates new material based on that. This allows it to propose optimal arrangements by analyzing the factors that contributed to the success or failure of the original plan.

[0059] The project analysis unit uses the emotion estimation function to analyze viewers' emotional reactions to the original project and can make arrangements that elicit positive emotions. The project analysis unit, for example, uses the emotion estimation function to analyze viewers' emotional reactions to the original project. For example, it analyzes viewers' comments and reaction videos to identify parts with strong positive emotions and generates new material based on that. It also uses the emotion estimation function to analyze viewers' emotional reactions to each scene in the original project. For example, it identifies parts of a particular scene that made viewers laugh or be surprised and generates new material based on that. It also uses the emotion estimation function to analyze viewers' emotional reactions to the entire original project. For example, it identifies the elements of the project that viewers enjoyed the most and generates new material based on that. In this way, by analyzing viewers' emotional reactions, it is possible to make arrangements that elicit positive emotions.

[0060] The arrangement unit can generate arrangements that can be applied to different genres or themes based on the results of the analysis by the plan analysis unit. For example, the generation AI analyzes the original plan and generates arrangements that can be applied to different genres based on the results. For example, when adapting a comedy plan into a horror plan, it replaces humorous elements with scary elements. The generation AI also analyzes the original plan and generates arrangements that can be applied to different themes based on the results. For example, when adapting a sports plan into a cooking plan, it replaces athletic elements with cooking elements. The generation AI also analyzes the original plan and generates arrangements that can be applied to different audiences based on the results. For example, when adapting a plan aimed at children for adults, it adjusts the content and direction to be more adult-oriented. This allows the generation of arrangements that can be applied to different genres and themes, making it possible to accommodate a wide range of content.

[0061] The arrangement department can make arrangements to adapt the program to different languages ​​and cultural spheres based on the results of the analysis by the plan analysis department. For example, the arrangement department has the generation AI analyze the original plan and make arrangements to adapt it to different languages ​​based on the results. For example, when adapting a plan for English-speaking countries to Japanese-speaking countries, the language and cultural elements are adjusted. The generation AI also analyzes the original plan and makes arrangements to adapt it to different cultural spheres based on the results. For example, when adapting a plan based on American culture to Asian culture, the cultural elements are adjusted. Furthermore, the generation AI analyzes the original plan and makes arrangements to make it multilingual based on the results. For example, when providing the same plan in multiple languages, appropriate arrangements are made for each language. In this way, by making arrangements to adapt it to different languages ​​and cultural spheres, it is possible to accommodate international audiences.

[0062] The arrangement unit can use the emotion estimation function to analyze emotional responses to different viewer demographics and propose optimal arrangements for each. The arrangement unit, for example, uses the emotion estimation function to analyze emotional responses to viewers of different age groups. For example, when adapting a program aimed at children for adults, the arrangement unit proposes an optimal arrangement based on the emotional responses for each age group. The arrangement unit also uses the emotion estimation function to analyze emotional responses to viewers of different genders. For example, when adapting a program aimed at men for women, the arrangement unit proposes an optimal arrangement based on the emotional responses for each gender. The arrangement unit also uses the emotion estimation function to analyze emotional responses to viewers in different regions. For example, when adapting a program aimed at urban viewers for rural viewers, the arrangement unit proposes an optimal arrangement based on the emotional responses for each region. In this way, by analyzing emotional responses to different viewer demographics, the arrangement unit can propose optimal arrangements for each.

[0063] The arrangement department can propose optimal rules, props, locations, and punchline settings based on past viewer reaction data. For example, the arrangement department uses a generation AI to analyze past viewer reaction data and propose optimal rules, props, locations, and punchline settings. For example, it generates new material by combining elements from past projects that viewers liked. Furthermore, the generation AI analyzes the emotional reactions that specific rules, props, locations, and punchline settings evoked based on past viewer reaction data, and generates new material based on that. Furthermore, the generation AI tries different combinations of elements based on past viewer reaction data and proposes combinations that elicit the most positive reactions. For example, it combines the rules, props, locations, and punchline settings that viewers enjoyed the most. This makes it possible to generate material that is likely to attract viewers' interest by proposing optimal rules, props, locations, and punchline settings based on past viewer reaction data.

[0064] The arrangement department can collect viewer feedback in real time and fine-tune the rules, props, locations, and punchline settings based on that feedback. In the arrangement department, for example, the generation AI collects viewer feedback in real time and fine-tunes the rules, props, locations, and punchline settings based on that feedback. For example, it analyzes viewer comments and reactions and adjusts the content of the material. The generation AI also evaluates how particular rules, props, locations, and punchline settings are received based on viewer feedback in real time and makes arrangements based on that. Furthermore, the generation AI tries different combinations of elements based on viewer feedback in real time and fine-tunes the arrangement to elicit the most positive response. For example, it adjusts the content of the material based on viewer reactions. In this way, by collecting viewer feedback in real time and making fine adjustments based on that, it is possible to generate material that is in line with viewer reactions.

[0065] The arrangement unit can use the emotion estimation function to identify the rules, tools, locations, and punchline settings that viewers will enjoy the most, and arrange the material based on that. For example, the arrangement unit can use the emotion estimation function to identify the rules, tools, locations, and punchline settings that viewers will enjoy the most. For example, the arrangement unit can analyze viewers' emotional reactions and combine elements that evoke strong positive emotions to generate new material. The emotion estimation function can also be used to evaluate how certain rules, tools, locations, and punchline settings are received based on viewers' emotional reactions, and arrange the material based on that. Furthermore, the emotion estimation function can be used to try different combinations of elements based on viewers' emotional reactions, and arrange the material to elicit the most positive reaction. For example, the content of the material can be adjusted based on the viewers' emotional scores. In this way, the arrangement unit can identify the elements that viewers will enjoy the most and arrange the material based on that, thereby increasing viewer satisfaction.

[0066] The arrangement department can apply the arranged rules, tools, locations, and punchline settings to projects of different genres to create new content. For example, the arrangement department applies the rules, tools, locations, and punchline settings arranged by the generation AI to projects of different genres. For example, the rules of a game broadcast can be applied to a cooking broadcast to generate new content. The generation AI also applies the arranged rules, tools, locations, and punchline settings to projects of different themes. For example, the rules of a sports project can be applied to an educational project to generate new content. Furthermore, the generation AI applies the arranged rules, tools, locations, and punchline settings to different audiences. For example, a project aimed at children can be adapted for adults to generate new content. This makes it possible to create new content by applying it to projects of different genres.

[0067] The arrangement department can apply the arranged content to viewer-participation projects and provide interactive content in which viewers can directly participate. For example, the arrangement department applies the rules, tools, locations, and punchline settings arranged by the generation AI to viewer-participation projects. For example, it can provide an interactive game broadcast in which viewers can change the rules in real time. The generation AI can also provide interactive content in which viewers can directly participate based on the arranged content. For example, it can create an interactive video in which viewers select options to advance the story. Furthermore, the generation AI can plan live events in which viewers can participate based on the arranged content. For example, it can hold a live broadcast in which viewers vote in real time to decide the progress. This provides interactive content in which viewers can directly participate, thereby increasing their motivation to participate.

[0068] The arrangement unit can use the emotion estimation function to analyze the emotional responses of different viewer demographics and propose optimal rules, props, locations, and punchline settings for each. The arrangement unit, for example, uses the emotion estimation function to analyze the emotional responses of viewers of different age groups and propose optimal rules, props, locations, and punchline settings for each. For example, when adapting a program aimed at children for adults, the arrangement unit proposes optimal arrangements based on the emotional responses of each age group. The emotion estimation function can also be used to analyze the emotional responses of viewers of different genders and propose optimal rules, props, locations, and punchline settings for each. For example, when adapting a program aimed at men for women, the arrangement unit proposes optimal arrangements based on the emotional responses of each gender. The emotion estimation function can also be used to analyze the emotional responses of viewers in different regions and propose optimal rules, props, locations, and punchline settings for each. For example, when adapting a program aimed at urban viewers for rural viewers, the arrangement unit proposes optimal arrangements based on the emotional responses of each region. In this way, by analyzing the emotional responses of different viewer demographics, it is possible to propose optimal rules, props, locations, and punchline settings for each.

[0069] The storyboard generation unit can analyze the detailed movements and facial expressions of each scene and generate more realistic storyboards. For example, the generation AI of the storyboard generation unit analyzes the detailed movements and facial expressions of each scene and generates a realistic storyboard based on that. For example, it provides a storyboard that depicts the character's movements and facial expressions in detail. The generation AI also analyzes the movements and facial expressions of each scene and generates an animation-style storyboard based on that. For example, it provides a storyboard that depicts the character's movements continuously. Furthermore, the generation AI analyzes the movements and facial expressions of each scene and generates a storyboard using a 3D model based on that. For example, it provides a storyboard that reproduces the character's movements using a 3D model. In this way, by analyzing the detailed movements and facial expressions of each scene, it is possible to generate a more realistic storyboard.

[0070] The storyboard generation unit can incorporate visual storytelling techniques to generate storyboards that attract the viewer's interest. In the storyboard generation unit, for example, a generation AI incorporates visual storytelling techniques to generate storyboards that attract the viewer's interest. For example, it provides a storyboard with clever camera angles and scene compositions. The generation AI also incorporates visual storytelling techniques to generate storyboards that elicit the viewer's emotions. For example, it provides a storyboard that emphasizes emotional scenes. Furthermore, the generation AI incorporates visual storytelling techniques to generate storyboards that attract the viewer's attention. For example, it provides a storyboard that incorporates visual effects and direction. In this way, it is possible to generate storyboards that attract the viewer's interest by incorporating visual storytelling techniques.

[0071] The storyboard generation unit can use the emotion estimation function to identify scenes with which the viewer is most likely to empathize, and create a storyboard based on that. The storyboard generation unit, for example, uses the emotion estimation function to identify scenes with which the viewer is most likely to empathize, and creates a storyboard based on that. For example, it analyzes the viewer's emotional response and provides a storyboard that emphasizes emotional scenes. Furthermore, it uses the emotion estimation function to create a storyboard that emphasizes specific scenes based on the viewer's emotional response. For example, it provides a storyboard that emphasizes scenes that moved the viewer the most. Furthermore, it uses the emotion estimation function to create a storyboard that emphasizes emotional scenes based on the viewer's emotional response. For example, it provides a storyboard that emphasizes scenes with which the viewer most sympathizes. In this way, it is possible to identify scenes with which the viewer is most likely to empathize, and create a storyboard based on that, thereby eliciting the viewer's emotions.

[0072] The storyboard generation unit can apply the created storyboard to different media and achieve multimedia development. The storyboard generation unit, for example, applies the storyboard created by the generation AI to a manga and achieves multimedia development. For example, it creates a manga based on the storyboard and provides it to viewers. The generation AI also applies the created storyboard to an anime and achieves multimedia development. For example, it creates an anime based on the storyboard and provides it to viewers. The generation AI also applies the created storyboard to a game and achieves multimedia development. For example, it creates a game based on the storyboard and provides it to viewers. In this way, multimedia development can be achieved by applying the created storyboard to different media.

[0073] The storyboard generation unit provides a platform where viewers can freely edit the storyboards they have created, thereby promoting user-generated content. The storyboard generation unit, for example, provides a platform where viewers can freely edit storyboards created by the generation AI. For example, it provides a function where viewers can edit storyboards and create their own original content. The generation AI also provides a platform where viewers can add their own ideas based on the storyboards they have created. For example, it provides a function where viewers can add new scenes or characters to the storyboard. Furthermore, the generation AI provides a platform where viewers can share the storyboards they have created. For example, it provides a function where viewers can share their edited storyboards with other users. This makes it possible to promote user-generated content by providing a platform where viewers can freely edit content.

[0074] The storyboard generation unit can use the emotion estimation function to analyze emotional responses to different viewer demographics and propose optimal storyboards for each. The storyboard generation unit, for example, uses the emotion estimation function to analyze emotional responses to viewers of different age groups and propose optimal storyboards for each. For example, when adapting a storyboard for children for adults, the storyboard generation unit proposes an optimal storyboard based on the emotional responses for each age group. The emotion estimation function can also be used to analyze emotional responses to viewers of different genders and propose optimal storyboards for each. For example, when adapting a storyboard for men for women, the storyboard generation unit proposes an optimal storyboard based on the emotional responses for each gender. The emotion estimation function can also be used to analyze emotional responses to viewers in different regions and propose optimal storyboards for each. For example, when adapting a storyboard for urban viewers for rural viewers, the storyboard generation unit proposes an optimal storyboard based on the emotional responses for each region. In this way, by analyzing emotional responses to different viewer demographics, it is possible to propose optimal storyboards for each.

[0075] A trial platform will be provided that analyzes viewer feedback in real time and adds a function that allows the generation AI to automatically improve the material. The trial platform will, for example, provide a function that analyzes viewer feedback in real time and allows the generation AI to automatically improve the material. For example, the content of the material will be adjusted based on viewer comments and ratings. The generation AI will also be able to change the punchline and settings of the material based on viewer feedback. For example, it will change the punchline based on viewer reactions. Furthermore, the generation AI will be able to add scenes and characters to the material based on viewer feedback. For example, it will add new scenes and characters based on viewer opinions. This will allow the generation AI to analyze viewer feedback in real time and automatically improve the material, making it possible to provide material that is in line with viewer reactions.

[0076] The trial platform will add a function that analyzes viewers' viewing history and preferences and suggests the most suitable material based on that. The trial platform, for example, provides a function that analyzes viewers' viewing history and preferences and suggests the most suitable material based on that. For example, it will suggest new material based on data on videos the viewer has previously watched. In addition, the generation AI can automatically generate material based on the viewer's viewing history and preferences. For example, it will automatically generate material that matches the viewer's preferences. Furthermore, the generation AI will develop an algorithm that suggests the most suitable material based on the viewer's viewing history and preferences. For example, it will adjust the content and settings of the material based on the viewer's preferences. This will allow the platform to analyze viewers' viewing history and preferences and suggest the most suitable material based on that, making it possible to provide material that is likely to attract viewers' interest.

[0077] The emotional estimation function is used to monitor viewers' emotional responses in real time, and material that elicits a positive response is prioritized. The trial platform provides a function that uses the emotional estimation function to monitor viewers' emotional responses in real time, and prioritizes displaying material that elicits a positive response. For example, the order in which material is displayed is adjusted based on the viewer's emotional score. The generation AI also develops an algorithm that prioritizes displaying material that elicits a large number of positive responses based on the viewer's emotional response. For example, it analyzes viewer emotional data and selects the most optimal material. The generation AI then builds a system that automatically selects material that elicits a positive response based on the viewer's emotional response. For example, it prioritizes displaying material that has a high viewer emotional score. This makes it possible to monitor viewers' emotional responses in real time and prioritize displaying material that elicits a positive response, thereby increasing viewer satisfaction.

[0078] The trial platform will be compatible with content of different genres, appealing to a wider audience. The trial platform will provide functions to accommodate content of different genres, for example. For example, it will accommodate not only game streaming, but also genres such as cooking, sports, and education. The generation AI will also provide interfaces customized for each genre to appeal to different audiences. For example, it will provide interfaces for children, young people, and adults. Furthermore, the generation AI will provide feedback collection functions for each genre to accommodate content of different genres. For example, it will provide feedback forms specialized for each genre. This will allow the platform to accommodate content of different genres, appealing to a wider audience.

[0079] A function that allows viewers to generate their own material will be added to the trial platform. The trial platform may, for example, provide a function that allows viewers to generate their own material. For example, a function may be provided where viewers input their own ideas using the generation AI to generate new material. The generation AI may also provide a function where viewers can not only rate material but also post their own ideas. For example, a function may be provided where viewers post their own ideas and receive feedback from other viewers. Furthermore, the generation AI may provide a function where viewers can generate material and share it with other viewers. For example, a function may be provided where viewers can share material they have generated and receive evaluations from other viewers. In this way, adding a function that allows viewers to generate material themselves can increase viewers' motivation to participate.

[0080] The emotion estimation function can be used to analyze the emotional responses of different viewer demographics and suggest the most suitable material for each. The trial platform, for example, uses the emotion estimation function to analyze the emotional responses of viewers of different age groups and suggest the most suitable material for each. For example, when adapting material aimed at children for adults, the platform suggests the most suitable material based on the emotional responses of each age group. The emotion estimation function can also be used to analyze the emotional responses of viewers of different genders and suggest the most suitable material for each. For example, when adapting material aimed at men for women, the platform suggests the most suitable material based on the emotional responses of each gender. The emotion estimation function can also be used to analyze the emotional responses of viewers in different regions and suggest the most suitable material for each. For example, when adapting material aimed at urban viewers for rural viewers, the platform suggests the most suitable material based on the emotional responses of each region. In this way, by analyzing the emotional responses of different viewer demographics, the platform can suggest the most suitable material for each.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The material generation system can further include a history analysis unit that analyzes the viewer's past viewing history. The history analysis unit identifies the viewer's preferences and interests based on data on videos the viewer has previously viewed. For example, if a viewer watches many videos of a particular genre or theme, it can suggest new material related to that genre or theme. The history analysis unit can also analyze the viewer's viewing patterns and identify what kind of content the viewer prefers at what time of day. This makes it possible to suggest more personalized material based on the viewer's viewing history.

[0083] The material generation system can further include a real-time analysis unit that analyzes real-time reactions from viewers. The real-time analysis unit collects and analyzes viewers' comments and reactions in real time. For example, it can analyze comments and emojis posted by viewers in real time to identify viewers' emotions and reactions. The real-time analysis unit can also instantly adjust the content of material based on viewers' reactions. This makes it possible to provide more dynamic and interactive material based on viewers' real-time reactions.

[0084] The material generation system can further include a feedback collection unit that collects viewer feedback. The feedback collection unit collects and analyzes feedback provided by viewers. For example, it provides a function for viewers to post ratings and comments on material, and generates new material based on that. The feedback collection unit can also identify areas for improvement or new ideas for material based on the feedback provided by viewers. This makes it possible to provide material that better meets the needs of viewers based on viewer feedback.

[0085] The material generation system can further include an emotion estimation unit that estimates the viewer's emotions. The emotion estimation unit analyzes the viewer's facial expressions and tone of voice to estimate the viewer's emotions. For example, the emotion estimation unit can capture the viewer's facial expressions while watching a video with a camera and identify the viewer's emotions from those expressions. It can also analyze the viewer's tone of voice to estimate the viewer's emotions. This makes it possible to grasp the viewer's emotions in real time and adjust the material based on that.

[0086] The material generation system can further include an interest prediction unit that predicts the viewer's interests. The interest prediction unit predicts what kind of material the viewer will be interested in based on the viewer's past viewing history and feedback. For example, it can analyze data on videos the viewer has watched in the past and identify the viewer's favorite genres and themes. It can also predict what elements the viewer will be interested in based on the viewer's feedback. This makes it possible to predict the viewer's interests and suggest new material based on them.

[0087] The material generation system can further include a behavior analysis unit that analyzes viewer behavior. The behavior analysis unit analyzes the viewer's viewing behavior and interactions to identify the viewer's behavior patterns. For example, it can analyze what kind of videos the viewer watches and at what time of day, thereby understanding the viewer's behavior patterns. It can also analyze how the viewer interacts with the videos, thereby identifying the viewer's preferences and interests. This makes it possible to provide more personalized material based on the viewer's behavior.

[0088] The material generation system can further include an emotion estimation unit that estimates the viewer's emotions. The emotion estimation unit analyzes the viewer's comments and reactions to estimate the viewer's emotions. For example, it can analyze the content and tone of the comments posted by the viewer to identify the viewer's emotions. It can also analyze the viewer's reaction videos to estimate the viewer's emotions from their facial expressions and tone of voice. This makes it possible to grasp the viewer's emotions in real time and adjust the material based on that.

[0089] The material generation system can further include a preference learning unit that learns viewer preferences. The preference learning unit learns viewer preferences based on the viewer's past viewing history and feedback. For example, it can analyze data on videos the viewer has previously watched and identify the genres and themes the viewer prefers. It can also learn what elements the viewer is interested in based on viewer feedback. This allows the system to learn viewer preferences and suggest new material based on them.

[0090] The material generation system can further include an emotion estimation unit that estimates the viewer's emotions. The emotion estimation unit analyzes the viewer's facial expressions and tone of voice to estimate the viewer's emotions. For example, the emotion estimation unit can capture the viewer's facial expressions while watching a video with a camera and identify the viewer's emotions from those expressions. It can also analyze the viewer's tone of voice to estimate the viewer's emotions. This makes it possible to grasp the viewer's emotions in real time and adjust the material based on that.

[0091] The material generation system can further include a behavior analysis unit that analyzes viewer behavior. The behavior analysis unit analyzes the viewer's viewing behavior and interactions to identify the viewer's behavior patterns. For example, it can analyze what kind of videos the viewer watches and at what time of day, thereby understanding the viewer's behavior patterns. It can also analyze how the viewer interacts with the videos, thereby identifying the viewer's preferences and interests. This makes it possible to provide more personalized material based on the viewer's behavior.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The plan analysis unit analyzes the original plan. For example, the generation AI analyzes the original plan or idea provided by the user and understands its content. The generation AI uses text generation AI (e.g., LLM) to analyze the plan's achievement conditions, tools used, location, punchline settings, etc. The generation AI can also use multimodal generation AI to analyze video and images. For example, the generation AI uses video analysis technology to extract and analyze important scenes from the plan. Step 2: The arrangement department generates new material based on the results of the analysis by the planning and analysis department. For example, the generation AI exquisitely adjusts the rules, props used, location, and punchline settings based on the analysis results. For example, the generation AI can change the rules of the game broadcast or add props to be used. The generation AI can also change the conditions for achieving the plan. For example, the generation AI can keep viewers interested by making the conditions for achieving the plan more difficult. Step 3: The storyboard generation unit creates a storyboard based on the material generated by the arrangement unit. For example, the generation AI creates a storyboard using simple pictures or images based on the generated material. For example, the generation AI provides a storyboard that depicts character movements and backgrounds for each scene. The generation AI can also create a storyboard using a 3D model. For example, the generation AI reproduces character movements using a 3D model and provides a storyboard.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 planning analysis department that analyzes the original plan; an arrangement unit that generates new material based on the results of the analysis by the planning analysis unit; a storyboard generation unit that generates a storyboard based on the material generated by the arrangement unit; A system characterized by:

2. The planning and analysis unit Conduct analysis taking into account the cultural and historical context of the original project.

2. The system of claim 1.

3. The planning and analysis unit Analyze the success and failure factors of the original plan and propose the optimal arrangement based on that.

2. The system of claim 1.

4. The planning and analysis unit Analyze viewers' emotional reactions to the original plan and arrange it to elicit positive emotions 2. The system of claim 1.

5. The arranging unit Based on the results of the analysis by the planning analysis unit, arrangements that can be applied to different genres and themes are generated.

2. The system of claim 1.

Images