system

Abstract

The system according to this embodiment aims to automate the entire content creation process. [Solution] The system according to the embodiment comprises a reception unit, a scenario creation unit, an actor generation unit, and a filming location generation unit. The reception unit receives user input. The scenario creation unit generates a scenario based on the input received by the reception unit. The actor generation unit generates actors based on the scenario generated by the scenario creation unit. The filming location generation unit generates filming locations based on the actors generated by the actor generation unit.

Description

Technical Field

[0006] , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to automate the entire process of content production and efficient production is difficult.

[0005] The system according to the embodiment aims to automate the entire process of content production.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a scenario creation unit, an actor generation unit, and a filming location generation unit. The reception unit receives user input. The scenario creation unit generates a scenario based on the input received by the reception unit. The actor generation unit generates actors based on the scenario generated by the scenario creation unit. The filming location generation unit generates filming locations based on the actors generated by the actor generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the entire content creation process. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The internet broadcasting station system according to an embodiment of the present invention is an internet broadcasting station in which the entire process is supported by AI. This internet broadcasting station system can automate content production of all genres, including scenario creation, actor generation, and location generation. For example, if a user inputs "I want you to create a scenario for an action movie," the generation AI will generate a scenario based on that instruction, the actor generation unit will generate actors based on the scenario, and the location generation unit will generate locations based on the scenario. In this way, the internet broadcasting station system, in which the entire process is supported by AI, can quickly produce content based on user instructions. As a result, the internet broadcasting station system can significantly improve the efficiency of content production and quickly provide content of various genres.

[0029] The network broadcasting station system according to this embodiment comprises a reception unit, a scenario creation unit, an actor generation unit, and a filming location generation unit. The reception unit receives user input. The reception unit can receive user input in the form of, for example, text input, voice input, or image input. The scenario creation unit generates a scenario based on the input received by the reception unit, using a generation AI. The scenario creation unit generates a scenario that includes, for example, a story plot, character settings, and scene details, based on the user input analyzed by the generation AI. The actor generation unit generates actors based on the scenario generated by the scenario creation unit. The actor generation unit generates actors based on, for example, the appearance and personality of the characters appearing in the scenario, and generates actors such as 3D models, animated characters, or real actors based on that. The filming location generation unit generates filming locations based on the actors generated by the actor generation unit. The filming location generation unit generates scenery suitable for the scenes included in the scenario and provides filming locations such as virtual backgrounds, actual locations, and studio sets. As a result, the network broadcasting station system according to this embodiment can automatically generate scenarios, actors, and filming locations based on user input.

[0030] The reception desk receives user input. The reception desk can accept user input in various formats, such as text input, voice input, and image input. Specifically, for text input, users can input story outlines and character settings using a keyboard or touchscreen. For voice input, speech recognition technology converts what the user speaks into text via a microphone, and the system analyzes the content. For image input, users upload images of character appearances or scenes, and image recognition technology analyzes the content. This allows the reception desk to accept diverse input formats and flexibly respond to user needs. Furthermore, the reception desk can temporarily save user input and allow editing and modification as needed. For example, users can later modify text they have entered or upload additional images. The reception desk also analyzes user input and passes the data to the scenario creation and actor generation departments in the appropriate format. This allows the reception desk to efficiently process user input and support the smooth operation of the entire system.

[0031] The scenario creation unit uses a generative AI to generate scenarios based on input received by the reception unit. For example, the generative AI analyzes user input and generates a scenario that includes the story's plot, character settings, and scene details. Specifically, the generative AI analyzes text, audio, and images entered by the user and automatically generates the story's theme, character relationships, and scene development. The generative AI uses natural language processing technology to understand the user's input and generate an appropriate scenario. For example, if a user enters "a story about adventure in a medieval fantasy world," the generative AI will generate a scenario that incorporates medieval settings and fantasy elements. Regarding character settings, it will also create detailed character settings based on the personality and appearance of the characters entered by the user. Furthermore, regarding scene details, it will provide specific scene descriptions based on the user's input to ensure the smooth progression of the story. In this way, the scenario creation unit can automatically generate high-quality scenarios based on user input and support the user's creativity.

[0032] The actor generation unit generates actors based on the scenario created by the scenario creation unit. For example, the actor generation unit analyzes the appearance and personality of the characters appearing in the scenario and generates actors such as 3D models, animated characters, and real actors based on that analysis. Specifically, it generates 3D models of characters using 3D modeling technology based on the character's appearance information described in the scenario. For example, it sets details such as the character's hairstyle, clothing, and facial expressions to create a realistic 3D model. It also analyzes the character's personality and behavioral patterns and generates animations based on that analysis. This allows the characters to move naturally in accordance with the scenario. Furthermore, when using real actors, it can select appropriate actors based on the scenario and have them perform the characters using the actor's video and audio. In this way, the actor generation unit can generate characters that are faithful to the scenario and enhance the realism of the story.

[0033] The location generation unit generates filming locations based on the actors generated by the actor generation unit. For example, the location generation unit generates scenery suitable for the scenes included in the scenario and provides filming locations such as virtual backgrounds, actual locations, and studio sets. Specifically, it generates virtual backgrounds based on the scene settings described in the scenario. For example, it creates backgrounds that match the scenario, such as a medieval castle or a futuristic city, using 3D modeling technology. When using actual locations, it selects an appropriate location based on the scenario and allows filming to take place there. Furthermore, when using studio sets, it constructs a set that matches the scenario, allowing actors to perform within that set. In this way, the location generation unit can provide filming locations that are faithful to the scenario and realistically recreate the world of the story. In addition, the location generation unit can dynamically change the generated filming locations in accordance with the actors' movements and the progress of the scene. For example, it can change the background according to the progress of the scene or adjust the camera angle according to the actors' movements. In this way, the location generation unit can provide a flexible filming environment based on the scenario and enhance the expressiveness of the story.

[0034] The scenario creation unit can generate scenarios using generative AI. For example, the generative AI analyzes user input and generates a scenario that includes the story's plot, character settings, and scene details. The generative AI can generate scenarios using, for example, text generation AI. This improves the accuracy of scenario generation by using generative AI.

[0035] The actor generation unit can analyze the appearance and personality of characters appearing in a scenario and generate actors based on that analysis. For example, the actor generation unit can analyze the facial features, clothing, and personality traits of characters appearing in a scenario and generate actors such as 3D models, animated characters, or real-life actors based on that analysis. This allows for the provision of more realistic actors by generating actors based on the appearance and personality of the characters. Some or all of the above-described processes in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input data on the appearance and personality of characters appearing in a scenario into a generation AI and have the generation AI perform the actor generation.

[0036] The location generation unit can generate scenery suitable for the scenes included in the scenario. For example, the location generation unit can generate cityscapes, natural landscapes, historical backgrounds, etc., suitable for the scenes included in the scenario, and provide shooting locations such as virtual backgrounds, actual locations, and studio sets. This makes it easier to select shooting locations by generating scenery suitable for the scenario. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input scene data included in the scenario into a generation AI and have the generation AI perform scenery generation.

[0037] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions genres that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest genres that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0038] The reception unit can filter input content based on the user's current areas of interest. For example, the reception unit prioritizes receiving input scenarios related to topics the user is currently interested in. The reception unit can also suggest relevant scenario inputs based on keywords the user has recently searched for. Furthermore, the reception unit can filter input content based on social media trends the user follows. This allows for the provision of more relevant content by filtering input content based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's current areas of interest data into a generating AI and have the generating AI perform the filtering of input content.

[0039] The reception desk can prioritize receiving input content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving input related to that region. Furthermore, if the user is traveling, the reception desk can suggest input related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving input related to their home area. This allows for the provision of highly relevant content to the user by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize receiving highly relevant input content.

[0040] The reception desk can analyze a user's social media activity and suggest relevant inputs. For example, it can suggest relevant scenario inputs based on posts the user has recently "liked." It can also suggest inputs based on the topics of accounts the user follows. Furthermore, it can suggest relevant scenario inputs based on content the user has shared. In this way, by analyzing social media activity, it is possible to suggest content relevant to the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant inputs.

[0041] The scenario creation unit can customize scenario content by referring to the user's past preferences and ratings during the scenario creation process. For example, the scenario creation unit can incorporate elements from scenarios that the user has previously given high ratings to. It can also customize scenarios based on genres that the user has previously enjoyed. Furthermore, the scenario creation unit can create scenarios by excluding elements that the user has previously avoided. This allows the system to provide the user with the most suitable scenario by referring to past preferences and ratings. Some or all of the above processes in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input the user's past preferences and rating data into a generating AI and have the generating AI perform the scenario customization.

[0042] The scenario creation unit can generate new scenarios by combining elements from different genres. For example, it can create scenarios that combine action and romance. It can also create scenarios that combine comedy and horror. Furthermore, it can create scenarios that combine drama and science fiction. In this way, new scenarios can be provided by combining elements from different genres. Some or all of the above processes in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input element data from different genres into a generation AI and have the generation AI execute the generation of a new scenario.

[0043] The scenario creation unit can prioritize scenarios based on user submission timing during scenario creation. For example, the scenario creation unit can prioritize generating scenarios with approaching deadlines. It can also prioritize generating scenarios submitted early by users. Furthermore, it can prioritize generating scenarios frequently requested by users. This enables efficient scenario creation by prioritizing scenarios based on submission timing. Some or all of the above processes in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input user submission timing data into a generation AI and have the generation AI determine the scenario priorities.

[0044] The scenario creation unit can improve the accuracy of its scenarios by referring to relevant literature and materials during the scenario creation process. For example, when creating a historical scenario, the scenario creation unit can refer to relevant historical documents. It can also refer to the latest research papers when creating a scientific scenario. Furthermore, when creating a fictional scenario, the scenario creation unit can refer to relevant novels and films. This improves the accuracy of the scenarios by referring to relevant literature and materials. Some or all of the above processes in the scenario creation unit may be performed using AI, or not. For example, the scenario creation unit can input relevant literature and material data into a generation AI and have the generation AI perform the scenario accuracy improvement.

[0045] The actor generation unit can customize the actors' movements and expressions based on the scenario content during actor generation. For example, in action scenes, the actor generation unit can generate actors with dynamic movements. In romance scenes, it can also generate actors with graceful movements. Furthermore, in comedy scenes, it can generate actors with humorous expressions. By customizing the actors' movements and expressions based on the scenario content, it is possible to provide more realistic actors. Some or all of the above processing in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input scenario content data into a generation AI and have the generation AI perform the customization of the actors' movements and expressions.

[0046] The actor generation unit can generate new actors by combining characters with different cultures and backgrounds. For example, it can generate multicultural actors by combining characters of different nationalities. It can also generate unique actors by combining characters from different historical periods. Furthermore, it can generate diverse actors by combining characters with different occupations. This allows for the provision of diverse actors by combining characters with different cultures and backgrounds. Some or all of the above processes in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input data on different cultures and backgrounds into a generation AI and have the generation AI perform the generation of new actors.

[0047] The actor generation unit can prioritize generating actors with high relevance by considering the user's geographical location information during actor generation. For example, if the user is in a specific region, the actor generation unit can generate actors related to that region. Furthermore, if the user is traveling, the actor generation unit can generate actors related to the travel destination. Additionally, if the user is at home, the actor generation unit can generate actors related to the area around the user's home. This allows the system to provide the user with actors with high relevance by considering geographical location information. Some or all of the above processing in the actor generation unit may be performed using AI, or not. For example, the actor generation unit can input the user's geographical location data into a generation AI and have the generation AI generate actors with high relevance.

[0048] The actor generation unit can improve the accuracy of actors by referencing characters from relevant movies and dramas during the actor generation process. For example, the actor generation unit can generate actors by referencing characters from popular movies. It can also generate actors by referencing characters from hit dramas. Furthermore, it can generate actors by referencing characters from classic movies. This improves the accuracy of actors by referencing characters from relevant movies and dramas. Some or all of the above processing in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input character data from relevant movies and dramas into a generation AI and have the generation AI perform the actor accuracy improvement.

[0049] The location generation unit can customize the details of the location based on the scenario content when generating it. For example, in action scenes, the location generation unit can generate vast spaces or urban environments. In romance scenes, it can also generate romantic landscapes or quiet places. Furthermore, in horror scenes, it can generate dark and eerie places. By customizing the details of the location based on the scenario content, it is possible to provide more appropriate locations. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input scenario content data into a generation AI and have the generation AI perform the customization of the location details.

[0050] The location generation unit can generate new locations by combining different natural and urban environments. For example, it can generate locations that combine mountains and the sea. It can also generate locations that combine cities and nature. Furthermore, it can generate locations that combine futuristic cities and ancient ruins. In this way, new locations can be provided by combining different natural and urban environments. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input data of different natural and urban environments into a generation AI and have the generation AI perform the generation of new locations.

[0051] The shooting location generation unit can prioritize the generation of highly relevant shooting locations by considering the user's geographical location information when generating shooting locations. For example, if the user is in a specific region, the shooting location generation unit can generate shooting locations related to that region. Furthermore, if the user is traveling, the shooting location generation unit can generate shooting locations related to the travel destination. Additionally, if the user is at home, the shooting location generation unit can generate shooting locations related to the user's home area. This allows the system to provide the user with highly relevant shooting locations by considering geographical location information. Some or all of the above processing in the shooting location generation unit may be performed using AI, or without AI. For example, the shooting location generation unit can input the user's geographical location data into a generation AI and have the generation AI generate highly relevant shooting locations.

[0052] The location generation unit can improve the accuracy of location generation by referencing relevant tourist destinations and landmarks. For example, the location generation unit can generate location locations by referencing popular tourist destinations. It can also generate location locations by referencing historical landmarks. Furthermore, it can generate location locations by referencing natural landmarks. This improves the accuracy of location generation by referencing relevant tourist destinations and landmarks. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input data on relevant tourist destinations and landmarks into a generation AI and have the generation AI perform the task of improving the accuracy of location generation.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The online broadcasting system can further analyze the user's viewing history and suggest scenarios based on that history. For example, if a user has watched many action movies in the past, the scenario creation department will prioritize suggesting action movie scenarios. Similarly, if a user prefers watching comedy movies, it can suggest comedy movie scenarios. Furthermore, if a user has watched many movies featuring a particular actor, it can suggest scenarios featuring that actor. This allows for more personalized content by suggesting scenarios based on the user's viewing history. The analysis of viewing history may be performed using AI or not. For example, viewing history data can be input into a generation AI, which can then generate scenario suggestions.

[0055] The online broadcasting system can further analyze users' social media activity and suggest scenario content based on social media trends. For example, it can incorporate topics related to posts that users have recently "liked" into its scenarios. It can also suggest scenario content based on topics from accounts that users follow. Furthermore, it can suggest scenarios related to content that users have shared. In this way, by analyzing social media activity, it can suggest content relevant to the user. The analysis of social media activity may be performed using AI or not. For example, social media activity data can be input into a generating AI, and the generating AI can then be made to suggest scenario content.

[0056] The internet broadcasting system can further consider the user's geographical location to suggest highly relevant scenarios. For example, if the user is in a specific region, it can suggest scenarios related to that region. If the user is traveling, it can suggest scenarios related to their travel destination. Furthermore, if the user is at home, it can suggest scenarios related to their home area. This allows for the provision of highly relevant content to users by considering their geographical location. The analysis of geographical location information may be performed using AI or not. For example, geographical location data can be input into a generating AI, and the generating AI can then perform scenario suggestions.

[0057] The online broadcasting system can further analyze the user's past input history and suggest the most suitable scenario genre. For example, if a user has previously entered many action movie scenarios, the system will prioritize suggesting action movie scenarios. Similarly, if a user prefers comedy movie scenarios, comedy movie scenarios can be suggested. Furthermore, if a user has entered many scenarios on a specific theme, scenarios related to that theme can be suggested. This allows the system to suggest the most suitable scenario genre for the user by analyzing their past input history. The analysis of input history may be performed using AI or without AI. For example, input history data can be input into a generating AI, which can then perform scenario genre suggestions.

[0058] The online broadcasting system can further analyze a user's past viewing history and customize the appearance and personality of actors based on that history. For example, it can generate actors by referencing the appearance and personality of characters in movies the user has watched in the past. It can also generate actors by incorporating the characteristics of actors in movies the user has enjoyed watching. Furthermore, it can generate actors by excluding the characteristics of characters in movies the user has avoided. In this way, by analyzing past viewing history, the system can provide the user with the most suitable actors. The analysis of viewing history may be performed using AI or not. For example, viewing history data can be input into a generation AI, and the generation AI can be made to customize the appearance and personality of actors.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives user input. The reception desk can receive user input in various formats, such as text input, voice input, or image input. Step 2: The scenario creation unit uses a generation AI to generate a scenario based on the input received by the reception unit. For example, the generation AI analyzes the user's input and generates a scenario that includes the story's plot, character settings, scene details, etc. Step 3: The actor generation unit generates actors based on the scenario generated by the scenario creation unit. For example, the actor generation unit analyzes the appearance and personality of the characters appearing in the scenario and generates actors such as 3D models, animated characters, or real actors based on that analysis. Step 4: The location generation unit generates locations based on the actors generated by the actor generation unit. The location generation unit generates scenery suitable for scenes included in the scenario and provides locations such as virtual backgrounds, actual locations, and studio sets.

[0061] (Example of form 2) The internet broadcasting station system according to an embodiment of the present invention is an internet broadcasting station in which the entire process is supported by AI. This internet broadcasting station system can automate content production of all genres, including scenario creation, actor generation, and location generation. For example, if a user inputs "I want you to create a scenario for an action movie," the generation AI will generate a scenario based on that instruction, the actor generation unit will generate actors based on the scenario, and the location generation unit will generate locations based on the scenario. In this way, the internet broadcasting station system, in which the entire process is supported by AI, can quickly produce content based on user instructions. As a result, the internet broadcasting station system can significantly improve the efficiency of content production and quickly provide content of various genres.

[0062] The network broadcasting station system according to this embodiment comprises a reception unit, a scenario creation unit, an actor generation unit, and a filming location generation unit. The reception unit receives user input. The reception unit can receive user input in the form of, for example, text input, voice input, or image input. The scenario creation unit generates a scenario based on the input received by the reception unit, using a generation AI. The scenario creation unit generates a scenario that includes, for example, a story plot, character settings, and scene details, based on the user input analyzed by the generation AI. The actor generation unit generates actors based on the scenario generated by the scenario creation unit. The actor generation unit generates actors based on, for example, the appearance and personality of the characters appearing in the scenario, and generates actors such as 3D models, animated characters, or real actors based on that. The filming location generation unit generates filming locations based on the actors generated by the actor generation unit. The filming location generation unit generates scenery suitable for the scenes included in the scenario and provides filming locations such as virtual backgrounds, actual locations, and studio sets. As a result, the network broadcasting station system according to this embodiment can automatically generate scenarios, actors, and filming locations based on user input.

[0063] The reception desk receives user input. The reception desk can accept user input in various formats, such as text input, voice input, and image input. Specifically, for text input, users can input story outlines and character settings using a keyboard or touchscreen. For voice input, speech recognition technology converts what the user speaks into text via a microphone, and the system analyzes the content. For image input, users upload images of character appearances or scenes, and image recognition technology analyzes the content. This allows the reception desk to accept diverse input formats and flexibly respond to user needs. Furthermore, the reception desk can temporarily save user input and allow editing and modification as needed. For example, users can later modify text they have entered or upload additional images. The reception desk also analyzes user input and passes the data to the scenario creation and actor generation departments in the appropriate format. This allows the reception desk to efficiently process user input and support the smooth operation of the entire system.

[0064] The scenario creation unit uses a generative AI to generate scenarios based on input received by the reception unit. For example, the generative AI analyzes user input and generates a scenario that includes the story's plot, character settings, and scene details. Specifically, the generative AI analyzes text, audio, and images entered by the user and automatically generates the story's theme, character relationships, and scene development. The generative AI uses natural language processing technology to understand the user's input and generate an appropriate scenario. For example, if a user enters "a story about adventure in a medieval fantasy world," the generative AI will generate a scenario that incorporates medieval settings and fantasy elements. Regarding character settings, it will also create detailed character settings based on the personality and appearance of the characters entered by the user. Furthermore, regarding scene details, it will provide specific scene descriptions based on the user's input to ensure the smooth progression of the story. In this way, the scenario creation unit can automatically generate high-quality scenarios based on user input and support the user's creativity.

[0065] The actor generation unit generates actors based on the scenario created by the scenario creation unit. For example, the actor generation unit analyzes the appearance and personality of the characters appearing in the scenario and generates actors such as 3D models, animated characters, and real actors based on that analysis. Specifically, it generates 3D models of characters using 3D modeling technology based on the character's appearance information described in the scenario. For example, it sets details such as the character's hairstyle, clothing, and facial expressions to create a realistic 3D model. It also analyzes the character's personality and behavioral patterns and generates animations based on that analysis. This allows the characters to move naturally in accordance with the scenario. Furthermore, when using real actors, it can select appropriate actors based on the scenario and have them perform the characters using the actor's video and audio. In this way, the actor generation unit can generate characters that are faithful to the scenario and enhance the realism of the story.

[0066] The location generation unit generates filming locations based on the actors generated by the actor generation unit. For example, the location generation unit generates scenery suitable for the scenes included in the scenario and provides filming locations such as virtual backgrounds, actual locations, and studio sets. Specifically, it generates virtual backgrounds based on the scene settings described in the scenario. For example, it creates backgrounds that match the scenario, such as a medieval castle or a futuristic city, using 3D modeling technology. When using actual locations, it selects an appropriate location based on the scenario and allows filming to take place there. Furthermore, when using studio sets, it constructs a set that matches the scenario, allowing actors to perform within that set. In this way, the location generation unit can provide filming locations that are faithful to the scenario and realistically recreate the world of the story. In addition, the location generation unit can dynamically change the generated filming locations in accordance with the actors' movements and the progress of the scene. For example, it can change the background according to the progress of the scene or adjust the camera angle according to the actors' movements. In this way, the location generation unit can provide a flexible filming environment based on the scenario and enhance the expressiveness of the story.

[0067] The scenario creation unit can generate scenarios using generative AI. For example, the generative AI analyzes user input and generates a scenario that includes the story's plot, character settings, and scene details. The generative AI can generate scenarios using, for example, text generation AI. This improves the accuracy of scenario generation by using generative AI.

[0068] The actor generation unit can analyze the appearance and personality of characters appearing in a scenario and generate actors based on that analysis. For example, the actor generation unit can analyze the facial features, clothing, and personality traits of characters appearing in a scenario and generate actors such as 3D models, animated characters, or real-life actors based on that analysis. This allows for the provision of more realistic actors by generating actors based on the appearance and personality of the characters. Some or all of the above-described processes in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input data on the appearance and personality of characters appearing in a scenario into a generation AI and have the generation AI perform the actor generation.

[0069] The location generation unit can generate scenery suitable for the scenes included in the scenario. For example, the location generation unit can generate cityscapes, natural landscapes, historical backgrounds, etc., suitable for the scenes included in the scenario, and provide shooting locations such as virtual backgrounds, actual locations, and studio sets. This makes it easier to select shooting locations by generating scenery suitable for the scenario. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input scene data included in the scenario into a generation AI and have the generation AI perform scenery generation.

[0070] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is excited, the reception desk may prioritize accepting input for highly entertaining scenarios. Similarly, if the user is calm, it may prioritize accepting input for documentary or educational scenarios. Furthermore, if the user is stressed, it may prioritize accepting input for relaxing scenarios. This allows for the provision of more appropriate content by prioritizing input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0071] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions genres that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest genres that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0072] The reception unit can filter input content based on the user's current areas of interest. For example, the reception unit prioritizes receiving input scenarios related to topics the user is currently interested in. The reception unit can also suggest relevant scenario inputs based on keywords the user has recently searched for. Furthermore, the reception unit can filter input content based on social media trends the user follows. This allows for the provision of more relevant content by filtering input content based on the user's areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the user's current areas of interest data into a generating AI and have the generating AI perform the filtering of input content.

[0073] The reception unit can estimate the user's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This allows for a more comfortable input environment by adjusting the interface display based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the interface display.

[0074] The reception desk can prioritize receiving input content that is highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving input related to that region. Furthermore, if the user is traveling, the reception desk can suggest input related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving input related to their home area. This allows for the provision of highly relevant content to the user by considering their geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI prioritize receiving highly relevant input content.

[0075] The reception desk can analyze a user's social media activity and suggest relevant inputs. For example, it can suggest relevant scenario inputs based on posts the user has recently "liked." It can also suggest inputs based on the topics of accounts the user follows. Furthermore, it can suggest relevant scenario inputs based on content the user has shared. In this way, by analyzing social media activity, it is possible to suggest content relevant to the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant inputs.

[0076] The scenario creation unit can estimate the user's emotions and adjust the tone and style of the scenario based on those emotions. For example, if the user is relaxed, the scenario creation unit can generate a scenario with a calm tone. It can also generate an action-packed or thrilling scenario if the user is excited. Furthermore, if the user is sad, it can generate an emotionally charged scenario. This allows for the provision of more appropriate scenarios by adjusting the tone and style based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input user emotion data into the generative AI and have the generative AI adjust the tone and style of the scenario.

[0077] The scenario creation unit can customize scenario content by referring to the user's past preferences and ratings during the scenario creation process. For example, the scenario creation unit can incorporate elements from scenarios that the user has previously given high ratings to. It can also customize scenarios based on genres that the user has previously enjoyed. Furthermore, the scenario creation unit can create scenarios by excluding elements that the user has previously avoided. This allows the system to provide the user with the most suitable scenario by referring to past preferences and ratings. Some or all of the above processes in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input the user's past preferences and rating data into a generating AI and have the generating AI perform the scenario customization.

[0078] The scenario creation unit can generate new scenarios by combining elements from different genres. For example, it can create scenarios that combine action and romance. It can also create scenarios that combine comedy and horror. Furthermore, it can create scenarios that combine drama and science fiction. In this way, new scenarios can be provided by combining elements from different genres. Some or all of the above processes in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input element data from different genres into a generation AI and have the generation AI execute the generation of a new scenario.

[0079] The scenario generation unit can estimate the user's emotions and adjust the length of the scenario based on the estimated emotions. For example, if the user is in a hurry, the scenario generation unit can generate a short scenario. It can also generate a longer scenario if the user is relaxed. Furthermore, if the user is excited, the scenario generation unit can generate a fast-paced scenario. This allows for the provision of more appropriate scenarios by adjusting the length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scenario generation unit may be performed using AI or not. For example, the scenario generation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the scenario.

[0080] The scenario creation unit can prioritize scenarios based on user submission timing during scenario creation. For example, the scenario creation unit can prioritize generating scenarios with approaching deadlines. It can also prioritize generating scenarios submitted early by users. Furthermore, it can prioritize generating scenarios frequently requested by users. This enables efficient scenario creation by prioritizing scenarios based on submission timing. Some or all of the above processes in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input user submission timing data into a generation AI and have the generation AI determine the scenario priorities.

[0081] The scenario creation unit can improve the accuracy of its scenarios by referring to relevant literature and materials during the scenario creation process. For example, when creating a historical scenario, the scenario creation unit can refer to relevant historical documents. It can also refer to the latest research papers when creating a scientific scenario. Furthermore, when creating a fictional scenario, the scenario creation unit can refer to relevant novels and films. This improves the accuracy of the scenarios by referring to relevant literature and materials. Some or all of the above processes in the scenario creation unit may be performed using AI, or not. For example, the scenario creation unit can input relevant literature and material data into a generation AI and have the generation AI perform the scenario accuracy improvement.

[0082] The actor generation unit can estimate the user's emotions and adjust the actor's appearance and personality based on the estimated emotions. For example, if the user is relaxed, the actor generation unit can generate an actor with a calm personality. It can also generate an energetic actor if the user is excited. Furthermore, if the user is sad, it can generate an emotionally expressive actor. This allows for the provision of more appropriate actors by adjusting the actor's appearance and personality based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input user emotion data into a generative AI and have the generative AI adjust the actor's appearance and personality.

[0083] The actor generation unit can customize the actors' movements and expressions based on the scenario content during actor generation. For example, in action scenes, the actor generation unit can generate actors with dynamic movements. In romance scenes, it can also generate actors with graceful movements. Furthermore, in comedy scenes, it can generate actors with humorous expressions. By customizing the actors' movements and expressions based on the scenario content, it is possible to provide more realistic actors. Some or all of the above processing in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input scenario content data into a generation AI and have the generation AI perform the customization of the actors' movements and expressions.

[0084] The actor generation unit can generate new actors by combining characters with different cultures and backgrounds. For example, it can generate multicultural actors by combining characters of different nationalities. It can also generate unique actors by combining characters from different historical periods. Furthermore, it can generate diverse actors by combining characters with different occupations. This allows for the provision of diverse actors by combining characters with different cultures and backgrounds. Some or all of the above processes in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input data on different cultures and backgrounds into a generation AI and have the generation AI perform the generation of new actors.

[0085] The actor generation unit can estimate the user's emotions and adjust the actor's appearance scenes based on the estimated emotions. For example, if the user is relaxed, the actor generation unit can introduce an actor in a calm scene. If the user is excited, the actor generation unit can introduce an actor in an action scene. Furthermore, if the user is sad, the actor generation unit can introduce an actor in an emotional scene. In this way, by adjusting the actor's appearance scenes based on the user's emotions, more appropriate scenes can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the actor's appearance scenes.

[0086] The actor generation unit can prioritize generating actors with high relevance by considering the user's geographical location information during actor generation. For example, if the user is in a specific region, the actor generation unit can generate actors related to that region. Furthermore, if the user is traveling, the actor generation unit can generate actors related to the travel destination. Additionally, if the user is at home, the actor generation unit can generate actors related to the area around the user's home. This allows the system to provide the user with actors with high relevance by considering geographical location information. Some or all of the above processing in the actor generation unit may be performed using AI, or not. For example, the actor generation unit can input the user's geographical location data into a generation AI and have the generation AI generate actors with high relevance.

[0087] The actor generation unit can improve the accuracy of actors by referencing characters from relevant movies and dramas during the actor generation process. For example, the actor generation unit can generate actors by referencing characters from popular movies. It can also generate actors by referencing characters from hit dramas. Furthermore, it can generate actors by referencing characters from classic movies. This improves the accuracy of actors by referencing characters from relevant movies and dramas. Some or all of the above processing in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input character data from relevant movies and dramas into a generation AI and have the generation AI perform the actor accuracy improvement.

[0088] The shooting location generation unit can estimate the user's emotions and adjust the atmosphere and environment of the shooting location based on the estimated user emotions. For example, if the user is relaxed, the shooting location generation unit can generate a shooting location with a calm atmosphere. It can also generate a shooting location with a dynamic atmosphere if the user is excited. Furthermore, if the user is sad, it can generate a shooting location with an emotional atmosphere. This allows for the provision of more appropriate shooting locations by adjusting the atmosphere and environment based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the shooting location generation unit may be performed using AI or not. For example, the shooting location generation unit can input user emotion data into a generative AI and have the generative AI adjust the atmosphere and environment of the shooting location.

[0089] The location generation unit can customize the details of the location based on the scenario content when generating it. For example, in action scenes, the location generation unit can generate vast spaces or urban environments. In romance scenes, it can also generate romantic landscapes or quiet places. Furthermore, in horror scenes, it can generate dark and eerie places. By customizing the details of the location based on the scenario content, it is possible to provide more appropriate locations. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input scenario content data into a generation AI and have the generation AI perform the customization of the location details.

[0090] The location generation unit can generate new locations by combining different natural and urban environments. For example, it can generate locations that combine mountains and the sea. It can also generate locations that combine cities and nature. Furthermore, it can generate locations that combine futuristic cities and ancient ruins. In this way, new locations can be provided by combining different natural and urban environments. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input data of different natural and urban environments into a generation AI and have the generation AI perform the generation of new locations.

[0091] The location generation unit can estimate the user's emotions and adjust the display method of the locations based on the estimated emotions. For example, if the user is relaxed, the location generation unit can display the locations in calm colors. If the user is excited, the location generation unit can also display the locations in vivid colors. Furthermore, if the user is sad, the location generation unit can also display the locations in subdued colors. In this way, by adjusting the display method of the locations based on the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the locations.

[0092] The shooting location generation unit can prioritize the generation of highly relevant shooting locations by considering the user's geographical location information when generating shooting locations. For example, if the user is in a specific region, the shooting location generation unit can generate shooting locations related to that region. Furthermore, if the user is traveling, the shooting location generation unit can generate shooting locations related to the travel destination. Additionally, if the user is at home, the shooting location generation unit can generate shooting locations related to the user's home area. This allows the system to provide the user with highly relevant shooting locations by considering geographical location information. Some or all of the above processing in the shooting location generation unit may be performed using AI, or without AI. For example, the shooting location generation unit can input the user's geographical location data into a generation AI and have the generation AI generate highly relevant shooting locations.

[0093] The location generation unit can improve the accuracy of location generation by referencing relevant tourist destinations and landmarks. For example, the location generation unit can generate location locations by referencing popular tourist destinations. It can also generate location locations by referencing historical landmarks. Furthermore, it can generate location locations by referencing natural landmarks. This improves the accuracy of location generation by referencing relevant tourist destinations and landmarks. Some or all of the above processing in the location generation unit may be performed using AI or not. For example, the location generation unit can input data on relevant tourist destinations and landmarks into a generation AI and have the generation AI perform the task of improving the accuracy of location generation.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The online broadcasting system can further analyze the user's viewing history and suggest scenarios based on that history. For example, if a user has watched many action movies in the past, the scenario creation department will prioritize suggesting action movie scenarios. Similarly, if a user prefers watching comedy movies, it can suggest comedy movie scenarios. Furthermore, if a user has watched many movies featuring a particular actor, it can suggest scenarios featuring that actor. This allows for more personalized content by suggesting scenarios based on the user's viewing history. The analysis of viewing history may be performed using AI or not. For example, viewing history data can be input into a generation AI, which can then generate scenario suggestions.

[0096] The online broadcasting system can further estimate the user's emotions and suggest scenario genres based on those emotions. For example, if the user is stressed, it can suggest relaxing comedy or drama scenarios. If the user is excited, it can suggest action or thriller scenarios. Furthermore, if the user is sad, it can suggest emotional drama or romance scenarios. This allows for the provision of more appropriate content by suggesting scenario genres based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input user emotion data into the generative AI and have the generative AI suggest scenario genres.

[0097] The online broadcasting system can further analyze users' social media activity and suggest scenario content based on social media trends. For example, it can incorporate topics related to posts that users have recently "liked" into its scenarios. It can also suggest scenario content based on topics from accounts that users follow. Furthermore, it can suggest scenarios related to content that users have shared. In this way, by analyzing social media activity, it can suggest content relevant to the user. The analysis of social media activity may be performed using AI or not. For example, social media activity data can be input into a generating AI, and the generating AI can then be made to suggest scenario content.

[0098] The online broadcasting system can further estimate the user's emotions and adjust the actors' performance styles based on those estimated emotions. For example, if the user is relaxed, it can generate actors with a calm performance style. If the user is excited, it can generate actors with an energetic performance style. Furthermore, if the user is sad, it can generate actors with an emotionally expressive performance style. This allows for the provision of more appropriate actors by adjusting the actors' performance styles based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the actors' performance styles.

[0099] The internet broadcasting system can further consider the user's geographical location to suggest highly relevant scenarios. For example, if the user is in a specific region, it can suggest scenarios related to that region. If the user is traveling, it can suggest scenarios related to their travel destination. Furthermore, if the user is at home, it can suggest scenarios related to their home area. This allows for the provision of highly relevant content to users by considering their geographical location. The analysis of geographical location information may be performed using AI or not. For example, geographical location data can be input into a generating AI, and the generating AI can then perform scenario suggestions.

[0100] The internet broadcasting system can further estimate the user's emotions and adjust the atmosphere of the filming location based on the estimated emotions. For example, if the user is relaxed, it can generate a filming location with a calm atmosphere. If the user is excited, it can generate a filming location with a dynamic atmosphere. Furthermore, if the user is sad, it can generate a filming location with an emotional atmosphere. In this way, by adjusting the atmosphere of the filming location based on the user's emotions, more appropriate filming locations can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the filming location generation unit may be performed using AI or not. For example, the filming location generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the atmosphere of the filming location.

[0101] The online broadcasting system can further analyze the user's past input history and suggest the most suitable scenario genre. For example, if a user has previously entered many action movie scenarios, the system will prioritize suggesting action movie scenarios. Similarly, if a user prefers comedy movie scenarios, comedy movie scenarios can be suggested. Furthermore, if a user has entered many scenarios on a specific theme, scenarios related to that theme can be suggested. This allows the system to suggest the most suitable scenario genre for the user by analyzing their past input history. The analysis of input history may be performed using AI or without AI. For example, input history data can be input into a generating AI, which can then perform scenario genre suggestions.

[0102] The online broadcasting system can further estimate the user's emotions and adjust the scenario length based on the estimated emotions. For example, if the user is in a hurry, a shorter scenario can be generated. If the user is relaxed, a longer scenario can be generated. Furthermore, if the user is excited, a faster-paced scenario can be generated. This allows for the provision of more appropriate scenarios by adjusting the scenario length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scenario creation unit may be performed using AI or not. For example, the scenario creation unit can input user emotion data into the generative AI and have the generative AI adjust the scenario length.

[0103] The online broadcasting system can further analyze a user's past viewing history and customize the appearance and personality of actors based on that history. For example, it can generate actors by referencing the appearance and personality of characters in movies the user has watched in the past. It can also generate actors by incorporating the characteristics of actors in movies the user has enjoyed watching. Furthermore, it can generate actors by excluding the characteristics of characters in movies the user has avoided. In this way, by analyzing past viewing history, the system can provide the user with the most suitable actors. The analysis of viewing history may be performed using AI or not. For example, viewing history data can be input into a generation AI, and the generation AI can be made to customize the appearance and personality of actors.

[0104] The online broadcasting system can further estimate the user's emotions and adjust the actor's appearance scenes based on those emotions. For example, if the user is relaxed, the actor may appear in a calm scene. If the user is excited, the actor may appear in an action scene. Furthermore, if the user is sad, the actor may appear in an emotional scene. By adjusting the actor's appearance scenes based on the user's emotions, more appropriate scenes can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the actor generation unit may be performed using AI or not. For example, the actor generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the actor's appearance scenes.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives user input. The reception desk can receive user input in various formats, such as text input, voice input, or image input. Step 2: The scenario creation unit uses a generation AI to generate a scenario based on the input received by the reception unit. For example, the generation AI analyzes the user's input and generates a scenario that includes the story's plot, character settings, scene details, etc. Step 3: The actor generation unit generates actors based on the scenario generated by the scenario creation unit. For example, the actor generation unit analyzes the appearance and personality of the characters appearing in the scenario and generates actors such as 3D models, animated characters, or real actors based on that analysis. Step 4: The location generation unit generates locations based on the actors generated by the actor generation unit. The location generation unit generates scenery suitable for scenes included in the scenario and provides locations such as virtual backgrounds, actual locations, and studio sets.

[0107] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0110] Each of the multiple elements described above, including the reception unit, scenario creation unit, actor generation unit, and shooting location generation unit, is implemented by, for example, at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts text input or voice input from the user. The scenario creation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and generates a scenario using a generation AI. The actor generation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and generates actors based on the scenario. The shooting location generation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12 and generates shooting locations based on the scenario. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the reception unit, scenario creation unit, actor generation unit, and shooting location generation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The scenario creation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates a scenario using a generation AI. The actor generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates actors based on the scenario. The shooting location generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates shooting locations based on the scenario. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, scenario creation unit, actor generation unit, and shooting location generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The scenario creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a scenario using a generation AI. The actor generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates actors based on the scenario. The shooting location generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates shooting locations based on the scenario. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, scenario creation unit, actor generation unit, and shooting location generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The scenario creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a scenario using a generation AI. The actor generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates actors based on the scenario. The shooting location generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates shooting locations based on the scenario. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A reception area that receives user input, A scenario creation unit generates a scenario based on the input received by the aforementioned reception unit, An actor generation unit generates actors based on the scenario generated by the aforementioned scenario creation unit, The system includes a shooting location generation unit that generates shooting locations based on actors generated by the actor generation unit. A system characterized by the following features. (Note 2) The aforementioned scenario creation unit, Generate scenarios using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned actor generation unit, The system analyzes the appearance and personality of the characters in the scenario and generates actors based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned shooting location generation unit, Generates landscapes suitable for the scenes included in the scenario. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Filter input based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system prioritizes accepting input that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned scenario creation unit, It estimates the user's emotions and adjusts the tone and style of the scenario based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned scenario creation unit, When creating a scenario, the content of the scenario is customized by referring to the user's past preferences and ratings. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned scenario creation unit, When creating a scenario, combine elements from different genres to generate a new scenario. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned scenario creation unit, It estimates the user's emotions and adjusts the scenario length based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned scenario creation unit, When creating scenarios, prioritize them based on when users submit them. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned scenario creation unit, When creating a scenario, refer to relevant literature and materials to improve the accuracy of the scenario. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned actor generation unit, The system estimates the user's emotions and adjusts the actor's appearance and personality based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned actor generation unit, When generating actors, their actions and facial expressions are customized based on the content of the scenario. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned actor generation unit, When creating actors, combine characters with different cultures and backgrounds to generate new actors. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned actor generation unit, The system estimates the user's emotions and adjusts the actors' scenes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned actor generation unit, When generating actors, the system prioritizes generating actors with high relevance by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned actor generation unit, When generating actors, we improve the accuracy of the actors by referencing characters from related movies and dramas. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned shooting location generation unit, The system estimates the user's emotions and adjusts the atmosphere and environment of the shooting location based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned shooting location generation unit, When generating shooting locations, customize the details of the shooting locations based on the scenario content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shooting location generation unit, When generating shooting locations, new shooting locations are generated by combining different natural and urban environments. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shooting location generation unit, The system estimates the user's emotions and adjusts how the shooting locations are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shooting location generation unit, When generating shooting locations, the system prioritizes generating highly relevant locations by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shooting location generation unit, When generating shooting locations, the accuracy of the locations is improved by referencing related tourist destinations and landmarks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, A scenario creation unit generates a scenario based on the input received by the aforementioned reception unit, An actor generation unit generates actors based on the scenario generated by the aforementioned scenario creation unit, The system includes a shooting location generation unit that generates shooting locations based on actors generated by the actor generation unit. A system characterized by the following features.

2. The aforementioned scenario creation unit, Generate scenarios using generative AI. The system according to feature 1.

3. The aforementioned actor generation unit, The system analyzes the appearance and personality of the characters in the scenario and generates actors based on that analysis. The system according to feature 1.

4. The aforementioned shooting location generation unit, Generates landscapes suitable for the scenes included in the scenario. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is Filter input based on the user's current areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts how the input interface is displayed based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is The system prioritizes accepting input that is highly relevant, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system according to feature 1.

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