system

Abstract

The system according to this embodiment aims to generate, modify, and convert a story into a video based on character settings and a synopsis. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an instruction reception unit, a modification unit, and a conversion unit. The reception unit receives input of character settings and a synopsis. The generation unit generates a story based on the information received by the reception unit. The instruction reception unit receives user instructions for the story generated by the generation unit. The modification unit modifies the story based on the instructions received by the instruction reception unit. The conversion unit converts the story modified by the modification unit into a video.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, although character settings and outlines can be considered, there are problems that it is difficult to generate, modify, or animate a story.

[0005] The system according to the embodiment aims to generate, modify, and convert a story based on character settings and outlines into a video.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an instruction reception unit, a modification unit, and a conversion unit. The reception unit receives input of character settings and a synopsis. The generation unit generates a story based on the information received by the reception unit. The instruction reception unit receives user instructions for the story generated by the generation unit. The modification unit modifies the story based on the instructions received by the instruction reception unit. The conversion unit converts the story modified by the modification unit into a video. [Effects of the Invention]

[0007] The system according to this embodiment can generate, modify, and convert a story into a video based on character settings and a synopsis. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied 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 reception 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 reception 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 system according to an embodiment of the present invention is a system that uses AI to create original stories for manga and anime. This system allows a user to input character settings and a synopsis, and the AI ​​automatically generates a story, further revises it based on user instructions, and finally converts it into a video. For example, the user sets the protagonist's personality and behavioral patterns and inputs a synopsis. The AI ​​analyzes this information and determines the story's development. Furthermore, the AI ​​automatically generates detailed elements such as panel layouts and dialogue. The user can give instructions on the generated story, and the AI ​​revises the story based on these instructions. Finally, the AI ​​can convert the generated manga or anime into a video. For example, the AI ​​can lower the protagonist's gaze to create a serious atmosphere, further slow down the slow motion to match the heroine's emotions, or change the music to a sad melody. In this way, by simply inputting character settings and a synopsis, the AI ​​automatically generates a manga or anime story and further revises it based on user instructions, allowing users to easily create original works. This system enables users to easily create original stories for manga and anime.

[0029] The system according to the embodiment comprises a reception unit, a generation unit, an instruction reception unit, a modification unit, and a conversion unit. The reception unit receives input of character settings and a synopsis. For example, the reception unit allows the user to input the protagonist's personality and behavioral patterns. The generation unit generates a story based on the information received by the reception unit. The generation unit generates a story while considering the character's personality and behavioral patterns, for example. The generation unit can use a generation AI to generate a story while considering the character's personality and behavioral patterns. The generation unit determines the story's development based on the character's personality and behavioral patterns, for example. The generation unit can also automatically generate detailed parts such as panel layouts and dialogue. The instruction reception unit receives user instructions for the story generated by the generation unit. For example, the instruction reception unit allows the user to input instructions to modify a specific part of the story. The modification unit modifies the story based on the instructions received by the instruction reception unit. The modification unit modifies a specific part of the story based on the user's instructions, for example. The modification unit can use AI to modify the story based on the user's instructions. The conversion unit converts the story, which has been modified by the modification unit, into a video. The conversion unit converts, for example, the generated manga or anime into a video. The conversion unit can use AI to convert the generated manga or anime into a video. As a result, the system according to the embodiment allows users to easily create the original story for manga or anime.

[0030] The reception desk accepts input for character settings and plot summaries. For example, the reception desk allows users to input the protagonist's personality and behavioral patterns. Specifically, users can input detailed information such as the protagonist's name, age, gender, appearance, personality, hobbies, special skills, and background story. In the plot summary input, users can describe in detail the story's setting, major events, relationships between characters, and the story's theme and message. The reception desk has the function to efficiently collect this information and store it in a database. Furthermore, the reception desk provides an interface that allows users to check the information they have entered in real time and make corrections or additions as needed. For example, if there are deficiencies in the input or if additional information is needed, the reception desk will provide appropriate feedback to the user, prompting them to correct or complete the input. The reception desk can also automatically generate character and story prototypes based on the information entered by the user and present them to the user. This allows users to visually check the input and make corrections or adjustments as needed. The reception desk plays a crucial role in efficiently collecting user input and providing accurate information to the generation department.

[0031] The generation unit generates a story based on the information received by the reception unit. For example, the generation unit generates a story while considering the characters' personalities and behavioral patterns. The generation unit can generate a story while considering the characters' personalities and behavioral patterns using a generation AI. Specifically, the generation AI uses natural language processing technology to analyze the character settings and synopsis entered by the user and automatically constructs the story's development. For example, if the protagonist has a brave and strong sense of justice, the generation AI will generate a story based on that personality in which the protagonist faces difficult situations and solves problems in cooperation with friends. The generation AI also naturally constructs the relationships between characters and the flow of dialogue, maintaining the consistency of the story. Furthermore, the generation unit can automatically generate not only the story's development but also detailed aspects such as panel layouts and dialogue. For example, the generation AI selects appropriate lines and expressions according to the characters' emotions and situations, enhancing the realism of the story. The generation unit can also adjust the structure and development of the story to reflect themes and messages specified by the user. This allows the generation unit to automatically generate engaging stories that reflect the user's intentions, helping users easily create original stories for manga and anime.

[0032] The instruction receiving unit receives user instructions for the story generated by the generation unit. For example, the instruction receiving unit allows users to input instructions to modify specific parts of the story. Specifically, users can input instructions for modifications or additions to each scene or line of dialogue in the generated story. The instruction receiving unit provides an interface that visually displays each part of the story, making it easy for users to identify the areas they want to modify. For example, a user can select a specific scene and input instructions to modify the dialogue or character movements within that scene. The instruction receiving unit also allows users to input instructions to add new scenes or characters. This allows users to freely customize the generated story and incorporate their own ideas. Furthermore, the instruction receiving unit plays a crucial role in efficiently collecting user instructions and providing accurate information to the modification unit. For example, the instruction receiving unit analyzes the modification instructions entered by the user and converts them into a format that the modification unit can process appropriately. The instruction receiving unit also manages the history of user-entered instructions and allows users to refer to past modifications. This allows users to modify the story while reviewing past modifications. The instruction receiving unit plays a crucial role in efficiently collecting user instructions and providing accurate information to the correction unit.

[0033] The editing unit modifies the story based on instructions received by the instruction receiving unit. For example, the editing unit modifies specific parts of the story based on user instructions. The editing unit can use AI to modify the story based on user instructions. Specifically, the editing unit uses natural language processing technology to analyze the modification instructions entered by the user and automatically modifies the relevant parts of the story. For example, if the user enters an instruction to change the dialogue in a specific scene, the editing unit modifies the dialogue to fit the appropriate context. Also, if the user enters an instruction to add a new scene, the editing unit adjusts it so that it is naturally incorporated into the flow of the story. Furthermore, the editing unit works in conjunction with the generation AI to optimize the modification content in order to accurately reflect the intent of the user's instructions. For example, the editing unit modifies the story based on user instructions while maintaining the consistency and coherence of the story generated by the generation AI. The editing unit also has a function to manage the history of user-entered instructions and allow users to refer to past modification content. This allows users to modify the story while checking past modification content. The editing unit plays a crucial role in efficiently revising stories based on user instructions and improving the quality of stories generated by the generation unit.

[0034] The conversion unit converts the story, corrected by the editing unit, into a video. For example, the conversion unit converts generated manga or anime into a video. The conversion unit can use AI to convert generated manga or anime into videos. Specifically, the conversion unit utilizes image processing and video generation technologies to convert the corrected story into a high-quality video. For example, the conversion unit uses motion capture and animation technologies to naturally express character movements and expressions. The conversion unit also generates backgrounds and effects in real time to enhance the video's realism. Furthermore, the conversion unit can incorporate user-specified music and sound effects into the video. This allows users to create high-quality videos that are enjoyable both visually and aurally. The conversion unit works in conjunction with the generation AI to optimize video quality, accurately reflecting the corrections provided by the editing unit. For example, the conversion unit automatically generates each scene of the video based on storyboards and character designs generated by the generation AI. The conversion unit also manages the history of user-input instructions, allowing users to refer to past corrections. This allows users to generate videos while reviewing past revisions. The conversion unit plays a crucial role in converting the revised story into a high-quality video, enabling users to easily enjoy the original manga or anime as a video.

[0035] The generation unit can generate stories while considering the characters' personalities and behavioral patterns. For example, the generation unit determines the story's progression based on the characters' personalities and behavioral patterns. The generation unit can generate stories while considering the characters' personalities and behavioral patterns using a generation AI. For example, the generation unit determines the story's progression based on the characters' personalities and behavioral patterns. The generation unit can generate stories while considering the characters' personalities and behavioral patterns. This makes it possible to generate stories based on the characters' personalities and behavioral patterns.

[0036] The generation unit can automatically generate detailed elements such as panel layouts and dialogue. For example, the generation unit automatically determines the size and placement of panel layouts, and the length and tone of dialogue. The generation unit can automatically generate detailed elements such as panel layouts and dialogue using generation AI. For example, the generation unit automatically determines the size and placement of panel layouts, and the length and tone of dialogue. The generation unit can automatically generate detailed elements such as panel layouts and dialogue. This allows for the efficient creation of detailed story elements through the automatic generation of panel layouts and dialogue.

[0037] The instruction receiving unit can receive user instructions. For example, the instruction receiving unit allows users to input instructions to modify specific parts of a story. The instruction receiving unit can use AI to receive user instructions. For example, the instruction receiving unit allows users to input instructions to modify specific parts of a story. The instruction receiving unit can receive user instructions. This allows for modifications to the generated story by receiving user instructions.

[0038] The editing unit can revise the story based on user instructions. For example, the editing unit can revise specific parts of the story based on user instructions. The editing unit can use AI to revise the story based on user instructions. For example, the editing unit can revise specific parts of the story based on user instructions. The editing unit can revise the story based on user instructions. This makes it possible to revise the story based on user instructions.

[0039] The conversion unit can convert generated manga and anime into videos. For example, the conversion unit converts generated manga and anime into videos. The conversion unit can use AI to convert generated manga and anime into videos. For example, the conversion unit converts generated manga and anime into videos. The conversion unit can convert generated manga and anime into videos. This allows for visual expression by converting generated manga and anime into videos.

[0040] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can suggest similar settings based on character settings and synopses previously entered by the user. The reception desk can also prioritize suggesting input methods (voice, text, etc.) previously used by the user. The reception desk can also suggest settings based on specific genres or themes from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0041] The reception desk can filter character settings and synopses based on the user's current interests and trends. For example, the reception desk can suggest character settings and synopses based on themes and genres the user has recently been interested in. The reception desk can also analyze the user's social media activity and suggest relevant settings. The reception desk can also suggest character settings and synopses based on current trends and popular themes. This makes it possible to suggest character settings and synopses based on the user's interests and trends. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0042] The reception desk can prioritize inputting highly relevant settings when users input character settings and plot summaries, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest character settings and plot summaries related to that region. If the user is traveling, the reception desk can also prioritize inputting settings related to the travel destination. If the user is participating in a specific event, the reception desk can also prioritize inputting settings related to that event. This makes it possible to suggest character settings and plot summaries based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0043] The reception desk can analyze the user's social media activity when inputting character settings and synopses, and suggest relevant settings. For example, the reception desk can suggest character settings and synopses based on the user's recent posts. The reception desk can also suggest settings based on the trends of the accounts the user follows. The reception desk can also suggest settings based on the interests of the groups and communities the user participates in. This makes it possible to suggest character settings and synopses based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0044] The generation unit can adjust the level of detail in the story based on the importance of the characters during story generation. For example, the generation unit may describe scenes involving main characters in detail and simplify scenes involving supporting characters. The generation unit can also describe the emotions and actions of main characters in detail to increase the depth of the story. The generation unit can also simplify the appearance of supporting characters and focus on the story development of the main characters. This makes it possible to adjust the level of detail in the story based on the importance of the characters. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0045] The generation unit can apply different generation algorithms depending on the character category when generating a story. For example, the generation unit can apply an algorithm that includes many action scenes to hero characters. The generation unit can also apply an algorithm that includes many humorous scenes to comedy characters. The generation unit can also apply an algorithm that includes many emotional scenes to romance characters. This makes it possible to apply generation algorithms according to the character category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0046] The generation unit can determine the priority of stories based on when character settings were submitted during story generation. For example, the generation unit can prioritize stories based on character settings submitted early. The generation unit can also prioritize stories based on character settings submitted sooner. The generation unit can also adjust the order of story generation according to the submission dates. This makes it possible to determine the priority of stories based on when character settings were submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0047] The generation unit can adjust the order of the story based on the relationships between characters when generating the story. For example, the generation unit can prioritize generating scenes where the main characters have a high degree of relevance. The generation unit can also postpone scenes where the supporting characters have a low degree of relevance. The generation unit can also adjust the order of the story according to the relationships between characters. This makes it possible to adjust the order of the story based on the relationships between characters. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0048] The instruction receiving unit can select the optimal instruction receiving method by referring to the user's past instruction history when an instruction is received. For example, the instruction receiving unit can suggest similar instructions based on the user's past instructions. The instruction receiving unit can also prioritize suggesting instruction methods (voice, text, etc.) that the user has used in the past. The instruction receiving unit can also suggest instructions based on a specific genre or theme from the user's past instruction history. This makes it possible to select the optimal instruction receiving method based on the user's past instruction history. Some or all of the above processing in the instruction receiving unit may be performed using AI, for example, or without using AI.

[0049] The instruction receiving unit can select the optimal instruction receiving method when it receives an instruction, taking into account the user's device information. For example, if the user is using a smartphone, the instruction receiving unit provides an instruction receiving interface that matches the screen size. If the user is using a tablet, the instruction receiving unit can also provide an instruction receiving interface optimized for a larger screen. If the user is using a smartwatch, the instruction receiving unit can also provide a simple and highly visible instruction receiving interface. This makes it possible to select the optimal instruction receiving method based on the user's device information. Some or all of the above processing in the instruction receiving unit may be performed using AI, for example, or without using AI.

[0050] The editing unit can select the optimal editing method when revising a story by referring to the user's past revision history. For example, the editing unit can suggest similar revisions based on revisions the user has made in the past. The editing unit can also prioritize suggesting revision methods (audio, text, etc.) that the user has used in the past. The editing unit can also suggest revisions based on specific genres or themes from the user's past revision history. This makes it possible to select the optimal editing method based on the user's past revision history. Some or all of the above processes in the editing unit may be performed using AI, for example, or not using AI.

[0051] The editing unit can select the optimal editing method when editing a story, taking into account the user's geographical location. For example, if the user is in a specific region, the editing unit can suggest editing content related to that region. If the user is traveling, the editing unit can also suggest editing content related to their travel destination. If the user is participating in a specific event, the editing unit can also suggest editing content related to that event. This makes it possible to select the optimal editing method based on the user's geographical location. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI.

[0052] The editing unit can analyze the user's social media activity and suggest revisions when revising a story. For example, it can suggest revisions based on the user's recent posts. It can also suggest revisions based on the trends of accounts the user follows. It can also suggest revisions based on the interests of groups and communities the user participates in. This makes it possible to suggest revisions based on the user's social media activity. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI.

[0053] The conversion unit can select the optimal conversion method by referring to the user's past conversion history when converting videos. For example, the conversion unit can suggest a similar conversion method based on the user's past video conversions. The conversion unit can also prioritize suggesting conversion methods (effects, filters, etc.) that the user has used in the past. The conversion unit can also suggest conversion methods based on a specific genre or theme from the user's past conversion history. This makes it possible to select the optimal conversion method based on the user's past conversion history. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI.

[0054] The conversion unit can select the optimal conversion method when converting videos, taking into account the user's device information. For example, if the user is using a smartphone, the conversion unit will perform video conversion that is adapted to the screen size. If the user is using a tablet, the conversion unit can also perform video conversion optimized for a larger screen. If the user is using a smartwatch, the conversion unit can also perform video conversion that is concise and highly visible. This makes it possible to select the optimal video conversion method based on the user's device information. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze user input in real time and provide appropriate advice and suggestions based on that input. For example, when a user is inputting character settings, the reception desk can suggest story development and relationships with other characters based on those settings. Similarly, when a user is inputting an outline, the reception desk can suggest the direction of the story and important events based on that outline. Furthermore, the reception desk can provide feedback on the user's input, suggesting areas for improvement and additional information. This allows users to create higher-quality character settings and outlines.

[0057] The generation unit can automatically set the story's theme and tone based on user input. For example, based on the character settings and synopsis entered by the user, the generation unit can set the story's theme to "adventure" or "romance." It can also set the story's tone to "serious" or "comedy." Furthermore, the generation unit can automatically generate appropriate scenes and events according to the story's theme and tone. This allows users to easily create a consistent story.

[0058] The instruction receiving unit can analyze the user's instructions and propose appropriate revisions based on those instructions. For example, if a user inputs instructions to revise a specific part of the story, the instruction receiving unit can propose multiple revisions based on those instructions. Also, if a user inputs instructions to revise a character's personality or behavioral patterns, the instruction receiving unit can propose revisions to maintain the overall consistency of the story based on those revisions. Furthermore, the instruction receiving unit can provide feedback on the user's instructions and suggest additional information to broaden the range of revision options. This allows the user to select a more appropriate revision.

[0059] The conversion unit can automatically set video effects and music based on user input. For example, it can select appropriate effects and music based on character settings and a synopsis entered by the user. It can also adjust the timing of effects and music according to the story's progression. Furthermore, it can change the type of effects and music based on user instructions. This allows users to easily create visually and aurally engaging videos.

[0060] The reception desk can analyze the user's past input history and suggest the most suitable input method. For example, based on character settings and synopses previously entered by the user, the reception desk can suggest similar settings. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can suggest settings based on specific genres or themes from the user's past input history. This makes it possible to suggest the most suitable input method based on the user's past input history.

[0061] The conversion unit can select the optimal video conversion method by considering the user's device information. For example, if the user is using a smartphone, the conversion unit can perform video conversion that is optimized for the screen size. If the user is using a tablet, the conversion unit can also perform video conversion that is optimized for the larger screen. Furthermore, if the user is using a smartwatch, the conversion unit can perform video conversion that is simple and highly visible. This makes it possible to select the optimal video conversion method based on the user's device information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk accepts input for character settings and synopsis. For example, it allows users to input the protagonist's personality and behavioral patterns. Step 2: The generation unit generates a story based on the information received by the reception unit. The generation unit generates the story while considering the characters' personalities and behavioral patterns, and can also automatically generate detailed parts using a generation AI. Step 3: The instruction receiving unit receives user instructions for the story generated by the generation unit. For example, it allows the user to input instructions to modify a specific part of the story. Step 4: The revision unit revises the story based on the instructions received by the instruction receiving unit. The revision unit can revise specific parts of the story based on user instructions and can use AI to make the revisions. Step 5: The conversion unit converts the story, corrected by the correction unit, into a video. The conversion unit can convert the generated manga or anime into a video, and this conversion can be done using AI.

[0064] (Example of form 2) The system according to an embodiment of the present invention is a system that uses AI to create original stories for manga and anime. This system allows a user to input character settings and a synopsis, and the AI ​​automatically generates a story, further revises it based on user instructions, and finally converts it into a video. For example, the user sets the protagonist's personality and behavioral patterns and inputs a synopsis. The AI ​​analyzes this information and determines the story's development. Furthermore, the AI ​​automatically generates detailed elements such as panel layouts and dialogue. The user can give instructions on the generated story, and the AI ​​revises the story based on these instructions. Finally, the AI ​​can convert the generated manga or anime into a video. For example, the AI ​​can lower the protagonist's gaze to create a serious atmosphere, further slow down the slow motion to match the heroine's emotions, or change the music to a sad melody. In this way, by simply inputting character settings and a synopsis, the AI ​​automatically generates a manga or anime story and further revises it based on user instructions, allowing users to easily create original works. This system enables users to easily create original stories for manga and anime.

[0065] The system according to the embodiment comprises a reception unit, a generation unit, an instruction reception unit, a modification unit, and a conversion unit. The reception unit receives input of character settings and a synopsis. For example, the reception unit allows the user to input the protagonist's personality and behavioral patterns. The generation unit generates a story based on the information received by the reception unit. The generation unit generates a story while considering the character's personality and behavioral patterns, for example. The generation unit can use a generation AI to generate a story while considering the character's personality and behavioral patterns. The generation unit determines the story's development based on the character's personality and behavioral patterns, for example. The generation unit can also automatically generate detailed parts such as panel layouts and dialogue. The instruction reception unit receives user instructions for the story generated by the generation unit. For example, the instruction reception unit allows the user to input instructions to modify a specific part of the story. The modification unit modifies the story based on the instructions received by the instruction reception unit. The modification unit modifies a specific part of the story based on the user's instructions, for example. The modification unit can use AI to modify the story based on the user's instructions. The conversion unit converts the story, which has been modified by the modification unit, into a video. The conversion unit converts, for example, the generated manga or anime into a video. The conversion unit can use AI to convert the generated manga or anime into a video. As a result, the system according to the embodiment allows users to easily create the original story for manga or anime.

[0066] The reception desk accepts input for character settings and plot summaries. For example, the reception desk allows users to input the protagonist's personality and behavioral patterns. Specifically, users can input detailed information such as the protagonist's name, age, gender, appearance, personality, hobbies, special skills, and background story. In the plot summary input, users can describe in detail the story's setting, major events, relationships between characters, and the story's theme and message. The reception desk has the function to efficiently collect this information and store it in a database. Furthermore, the reception desk provides an interface that allows users to check the information they have entered in real time and make corrections or additions as needed. For example, if there are deficiencies in the input or if additional information is needed, the reception desk will provide appropriate feedback to the user, prompting them to correct or complete the input. The reception desk can also automatically generate character and story prototypes based on the information entered by the user and present them to the user. This allows users to visually check the input and make corrections or adjustments as needed. The reception desk plays a crucial role in efficiently collecting user input and providing accurate information to the generation department.

[0067] The generation unit generates a story based on the information received by the reception unit. For example, the generation unit generates a story while considering the characters' personalities and behavioral patterns. The generation unit can generate a story while considering the characters' personalities and behavioral patterns using a generation AI. Specifically, the generation AI uses natural language processing technology to analyze the character settings and synopsis entered by the user and automatically constructs the story's development. For example, if the protagonist has a brave and strong sense of justice, the generation AI will generate a story based on that personality in which the protagonist faces difficult situations and solves problems in cooperation with friends. The generation AI also naturally constructs the relationships between characters and the flow of dialogue, maintaining the consistency of the story. Furthermore, the generation unit can automatically generate not only the story's development but also detailed aspects such as panel layouts and dialogue. For example, the generation AI selects appropriate lines and expressions according to the characters' emotions and situations, enhancing the realism of the story. The generation unit can also adjust the structure and development of the story to reflect themes and messages specified by the user. This allows the generation unit to automatically generate engaging stories that reflect the user's intentions, helping users easily create original stories for manga and anime.

[0068] The instruction receiving unit receives user instructions for the story generated by the generation unit. For example, the instruction receiving unit allows users to input instructions to modify specific parts of the story. Specifically, users can input instructions for modifications or additions to each scene or line of dialogue in the generated story. The instruction receiving unit provides an interface that visually displays each part of the story, making it easy for users to identify the areas they want to modify. For example, a user can select a specific scene and input instructions to modify the dialogue or character movements within that scene. The instruction receiving unit also allows users to input instructions to add new scenes or characters. This allows users to freely customize the generated story and incorporate their own ideas. Furthermore, the instruction receiving unit plays a crucial role in efficiently collecting user instructions and providing accurate information to the modification unit. For example, the instruction receiving unit analyzes the modification instructions entered by the user and converts them into a format that the modification unit can process appropriately. The instruction receiving unit also manages the history of user-entered instructions and allows users to refer to past modifications. This allows users to modify the story while reviewing past modifications. The instruction receiving unit plays a crucial role in efficiently collecting user instructions and providing accurate information to the correction unit.

[0069] The editing unit modifies the story based on instructions received by the instruction receiving unit. For example, the editing unit modifies specific parts of the story based on user instructions. The editing unit can use AI to modify the story based on user instructions. Specifically, the editing unit uses natural language processing technology to analyze the modification instructions entered by the user and automatically modifies the relevant parts of the story. For example, if the user enters an instruction to change the dialogue in a specific scene, the editing unit modifies the dialogue to fit the appropriate context. Also, if the user enters an instruction to add a new scene, the editing unit adjusts it so that it is naturally incorporated into the flow of the story. Furthermore, the editing unit works in conjunction with the generation AI to optimize the modification content in order to accurately reflect the intent of the user's instructions. For example, the editing unit modifies the story based on user instructions while maintaining the consistency and coherence of the story generated by the generation AI. The editing unit also has a function to manage the history of user-entered instructions and allow users to refer to past modification content. This allows users to modify the story while checking past modification content. The editing unit plays a crucial role in efficiently revising stories based on user instructions and improving the quality of stories generated by the generation unit.

[0070] The conversion unit converts the story, corrected by the editing unit, into a video. For example, the conversion unit converts generated manga or anime into a video. The conversion unit can use AI to convert generated manga or anime into videos. Specifically, the conversion unit utilizes image processing and video generation technologies to convert the corrected story into a high-quality video. For example, the conversion unit uses motion capture and animation technologies to naturally express character movements and expressions. The conversion unit also generates backgrounds and effects in real time to enhance the video's realism. Furthermore, the conversion unit can incorporate user-specified music and sound effects into the video. This allows users to create high-quality videos that are enjoyable both visually and aurally. The conversion unit works in conjunction with the generation AI to optimize video quality, accurately reflecting the corrections provided by the editing unit. For example, the conversion unit automatically generates each scene of the video based on storyboards and character designs generated by the generation AI. The conversion unit also manages the history of user-input instructions, allowing users to refer to past corrections. This allows users to generate videos while reviewing past revisions. The conversion unit plays a crucial role in converting the revised story into a high-quality video, enabling users to easily enjoy the original manga or anime as a video.

[0071] The generation unit can generate stories while considering the characters' personalities and behavioral patterns. For example, the generation unit determines the story's progression based on the characters' personalities and behavioral patterns. The generation unit can generate stories while considering the characters' personalities and behavioral patterns using a generation AI. For example, the generation unit determines the story's progression based on the characters' personalities and behavioral patterns. The generation unit can generate stories while considering the characters' personalities and behavioral patterns. This makes it possible to generate stories based on the characters' personalities and behavioral patterns.

[0072] The generation unit can automatically generate detailed elements such as panel layouts and dialogue. For example, the generation unit automatically determines the size and placement of panel layouts, and the length and tone of dialogue. The generation unit can automatically generate detailed elements such as panel layouts and dialogue using generation AI. For example, the generation unit automatically determines the size and placement of panel layouts, and the length and tone of dialogue. The generation unit can automatically generate detailed elements such as panel layouts and dialogue. This allows for the efficient creation of detailed story elements through the automatic generation of panel layouts and dialogue.

[0073] The instruction receiving unit can receive user instructions. For example, the instruction receiving unit allows users to input instructions to modify specific parts of a story. The instruction receiving unit can use AI to receive user instructions. For example, the instruction receiving unit allows users to input instructions to modify specific parts of a story. The instruction receiving unit can receive user instructions. This allows for modifications to the generated story by receiving user instructions.

[0074] The editing unit can revise the story based on user instructions. For example, the editing unit can revise specific parts of the story based on user instructions. The editing unit can use AI to revise the story based on user instructions. For example, the editing unit can revise specific parts of the story based on user instructions. The editing unit can revise the story based on user instructions. This makes it possible to revise the story based on user instructions.

[0075] The conversion unit can convert generated manga and anime into videos. For example, the conversion unit converts generated manga and anime into videos. The conversion unit can use AI to convert generated manga and anime into videos. For example, the conversion unit converts generated manga and anime into videos. The conversion unit can convert generated manga and anime into videos. This allows for visual expression by converting generated manga and anime into videos.

[0076] The reception desk can estimate the user's emotions and adjust the timing of character setting and synopsis input based on the estimated emotions. For example, if the user is excited, the reception desk can display an interface prompting immediate input of character setting and synopsis. If the user is relaxed, the reception desk can also display an interface prompting input at a slower pace. If the user is stressed, the reception desk can support input of character setting and synopsis in the form of simple questions. This allows for adjustment of input timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can suggest similar settings based on character settings and synopses previously entered by the user. The reception desk can also prioritize suggesting input methods (voice, text, etc.) previously used by the user. The reception desk can also suggest settings based on specific genres or themes from the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0078] The reception desk can filter character settings and synopses based on the user's current interests and trends. For example, the reception desk can suggest character settings and synopses based on themes and genres the user has recently been interested in. The reception desk can also analyze the user's social media activity and suggest relevant settings. The reception desk can also suggest character settings and synopses based on current trends and popular themes. This makes it possible to suggest character settings and synopses based on the user's interests and trends. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0079] The reception system can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is excited, the reception system might prompt the user to prioritize inputting important character details or plot summaries. If the user is relaxed, the reception system might also prioritize prompting the user to prioritize inputting detailed settings or background information. If the user is stressed, the reception system might also prioritize prompting the user to prioritize inputting simple settings or plot summaries. This allows for the prioritization of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception desk can prioritize inputting highly relevant settings when users input character settings and plot summaries, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest character settings and plot summaries related to that region. If the user is traveling, the reception desk can also prioritize inputting settings related to the travel destination. If the user is participating in a specific event, the reception desk can also prioritize inputting settings related to that event. This makes it possible to suggest character settings and plot summaries based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0081] The reception desk can analyze the user's social media activity when inputting character settings and synopses, and suggest relevant settings. For example, the reception desk can suggest character settings and synopses based on the user's recent posts. The reception desk can also suggest settings based on the trends of the accounts the user follows. The reception desk can also suggest settings based on the interests of the groups and communities the user participates in. This makes it possible to suggest character settings and synopses based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI.

[0082] The generation unit can estimate the user's emotions and adjust the story's development based on those emotions. For example, if the user is excited, the generation unit can generate a story with many action scenes. If the user is relaxed, the generation unit can also generate a story with many everyday scenes. If the user is sad, the generation unit can also generate a story with many emotional scenes. This allows for adjustment of the story's development according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The generation unit can adjust the level of detail in the story based on the importance of the characters during story generation. For example, the generation unit may describe scenes involving main characters in detail and simplify scenes involving supporting characters. The generation unit can also describe the emotions and actions of main characters in detail to increase the depth of the story. The generation unit can also simplify the appearance of supporting characters and focus on the story development of the main characters. This makes it possible to adjust the level of detail in the story based on the importance of the characters. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0084] The generation unit can apply different generation algorithms depending on the character category when generating a story. For example, the generation unit can apply an algorithm that includes many action scenes to hero characters. The generation unit can also apply an algorithm that includes many humorous scenes to comedy characters. The generation unit can also apply an algorithm that includes many emotional scenes to romance characters. This makes it possible to apply generation algorithms according to the character category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0085] The generation unit can estimate the user's emotions and adjust the story length based on those emotions. For example, if the user is in a hurry, the generation unit will generate a short, concise story. If the user is relaxed, the generation unit can also generate a longer story with more detailed explanations. If the user is excited, the generation unit can also generate a fast-paced story. This allows for adjustment of story length according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, 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.

[0086] The generation unit can determine the priority of stories based on when character settings were submitted during story generation. For example, the generation unit can prioritize stories based on character settings submitted early. The generation unit can also prioritize stories based on character settings submitted sooner. The generation unit can also adjust the order of story generation according to the submission dates. This makes it possible to determine the priority of stories based on when character settings were submitted. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0087] The generation unit can adjust the order of the story based on the relationships between characters when generating the story. For example, the generation unit can prioritize generating scenes where the main characters have a high degree of relevance. The generation unit can also postpone scenes where the supporting characters have a low degree of relevance. The generation unit can also adjust the order of the story according to the relationships between characters. This makes it possible to adjust the order of the story based on the relationships between characters. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI.

[0088] The instruction receiving unit can estimate the user's emotions and adjust the instruction receiving method based on the estimated emotions. For example, if the user is excited, the instruction receiving unit can display an interface that accepts instructions quickly. If the user is relaxed, the instruction receiving unit can also display an interface that accepts instructions at a slower pace. If the user is stressed, the instruction receiving unit can also display an interface that accepts instructions in the form of simple questions. This makes it possible to adjust the instruction receiving method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The instruction receiving unit can select the optimal instruction receiving method by referring to the user's past instruction history when an instruction is received. For example, the instruction receiving unit can suggest similar instructions based on the user's past instructions. The instruction receiving unit can also prioritize suggesting instruction methods (voice, text, etc.) that the user has used in the past. The instruction receiving unit can also suggest instructions based on a specific genre or theme from the user's past instruction history. This makes it possible to select the optimal instruction receiving method based on the user's past instruction history. Some or all of the above processing in the instruction receiving unit may be performed using AI, for example, or without using AI.

[0090] The instruction receiving unit can estimate the user's emotions and determine the priority of instructions based on the estimated emotions. For example, if the user is excited, the instruction receiving unit will prioritize important instructions. If the user is relaxed, the instruction receiving unit may also prioritize detailed instructions. If the user is stressed, the instruction receiving unit may also prioritize simple instructions. This makes it possible to determine the priority of instructions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0091] The instruction receiving unit can select the optimal instruction receiving method when it receives an instruction, taking into account the user's device information. For example, if the user is using a smartphone, the instruction receiving unit provides an instruction receiving interface that matches the screen size. If the user is using a tablet, the instruction receiving unit can also provide an instruction receiving interface optimized for a larger screen. If the user is using a smartwatch, the instruction receiving unit can also provide a simple and highly visible instruction receiving interface. This makes it possible to select the optimal instruction receiving method based on the user's device information. Some or all of the above processing in the instruction receiving unit may be performed using AI, for example, or without using AI.

[0092] The editing unit can estimate the user's emotions and adjust how the story is modified based on those emotions. For example, if the user is excited, the editing unit can emphasize action scenes. If the user is relaxed, the editing unit can also emphasize everyday scenes. If the user is sad, the editing unit can also emphasize emotional scenes. This allows for adjustments to the story modification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The editing unit can select the optimal editing method when revising a story by referring to the user's past revision history. For example, the editing unit can suggest similar revisions based on revisions the user has made in the past. The editing unit can also prioritize suggesting revision methods (audio, text, etc.) that the user has used in the past. The editing unit can also suggest revisions based on specific genres or themes from the user's past revision history. This makes it possible to select the optimal editing method based on the user's past revision history. Some or all of the above processes in the editing unit may be performed using AI, for example, or not using AI.

[0094] The editing unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is excited, the editing unit will prioritize important corrections. If the user is relaxed, the editing unit may also prioritize detailed corrections. If the user is stressed, the editing unit may also prioritize simple corrections. This makes it possible to determine the priority of corrections according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The editing unit can select the optimal editing method when editing a story, taking into account the user's geographical location. For example, if the user is in a specific region, the editing unit can suggest editing content related to that region. If the user is traveling, the editing unit can also suggest editing content related to their travel destination. If the user is participating in a specific event, the editing unit can also suggest editing content related to that event. This makes it possible to select the optimal editing method based on the user's geographical location. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI.

[0096] The editing unit can analyze the user's social media activity and suggest revisions when revising a story. For example, it can suggest revisions based on the user's recent posts. It can also suggest revisions based on the trends of accounts the user follows. It can also suggest revisions based on the interests of groups and communities the user participates in. This makes it possible to suggest revisions based on the user's social media activity. Some or all of the above processing in the editing unit may be performed using AI, for example, or not using AI.

[0097] The conversion unit can estimate the user's emotions and adjust the video conversion method based on the estimated emotions. For example, if the user is excited, the conversion unit can perform a fast-paced video conversion. If the user is relaxed, the conversion unit can perform a slow-paced video conversion. If the user is sad, the conversion unit can perform a video conversion with emotional effects. This makes it possible to adjust the video conversion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The conversion unit can select the optimal conversion method by referring to the user's past conversion history when converting videos. For example, the conversion unit can suggest a similar conversion method based on the user's past video conversions. The conversion unit can also prioritize suggesting conversion methods (effects, filters, etc.) that the user has used in the past. The conversion unit can also suggest conversion methods based on a specific genre or theme from the user's past conversion history. This makes it possible to select the optimal conversion method based on the user's past conversion history. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI.

[0099] The conversion unit can estimate the user's emotions and determine the priority of video conversions based on the estimated emotions. For example, if the user is excited, the conversion unit will prioritize important video conversions. If the user is relaxed, the conversion unit may also prioritize detailed video conversions. If the user is stressed, the conversion unit may also prioritize simple video conversions. This makes it possible to determine the priority of video conversions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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.

[0100] The conversion unit can select the optimal conversion method when converting videos, taking into account the user's device information. For example, if the user is using a smartphone, the conversion unit will perform video conversion that is adapted to the screen size. If the user is using a tablet, the conversion unit can also perform video conversion optimized for a larger screen. If the user is using a smartwatch, the conversion unit can also perform video conversion that is concise and highly visible. This makes it possible to select the optimal video conversion method based on the user's device information. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The reception desk can analyze user input in real time and provide appropriate advice and suggestions based on that input. For example, when a user is inputting character settings, the reception desk can suggest story development and relationships with other characters based on those settings. Similarly, when a user is inputting an outline, the reception desk can suggest the direction of the story and important events based on that outline. Furthermore, the reception desk can provide feedback on the user's input, suggesting areas for improvement and additional information. This allows users to create higher-quality character settings and outlines.

[0103] The generation unit can automatically set the story's theme and tone based on user input. For example, based on the character settings and synopsis entered by the user, the generation unit can set the story's theme to "adventure" or "romance." It can also set the story's tone to "serious" or "comedy." Furthermore, the generation unit can automatically generate appropriate scenes and events according to the story's theme and tone. This allows users to easily create a consistent story.

[0104] The generation unit can estimate the user's emotions and adjust the story's progression based on those emotions. For example, if the user is excited, the generation unit can generate a story with many action scenes. If the user is relaxed, the generation unit can generate a story with many everyday scenes. Furthermore, if the user is sad, the generation unit can generate a story with many emotional scenes. This allows for adjustment of the story's progression according to the user's emotions.

[0105] The instruction receiving unit can analyze the user's instructions and propose appropriate revisions based on those instructions. For example, if a user inputs instructions to revise a specific part of the story, the instruction receiving unit can propose multiple revisions based on those instructions. Also, if a user inputs instructions to revise a character's personality or behavioral patterns, the instruction receiving unit can propose revisions to maintain the overall consistency of the story based on those revisions. Furthermore, the instruction receiving unit can provide feedback on the user's instructions and suggest additional information to broaden the range of revision options. This allows the user to select a more appropriate revision.

[0106] The editing unit can estimate the user's emotions and adjust the edits based on those emotions. For example, if the user is excited, the editing unit can emphasize action scenes. If the user is relaxed, the editing unit can emphasize everyday scenes. Furthermore, if the user is sad, the editing unit can emphasize emotional scenes. This allows for adjustments to the edits according to the user's emotions.

[0107] The conversion unit can automatically set video effects and music based on user input. For example, it can select appropriate effects and music based on character settings and a synopsis entered by the user. It can also adjust the timing of effects and music according to the story's progression. Furthermore, it can change the type of effects and music based on user instructions. This allows users to easily create visually and aurally engaging videos.

[0108] The reception desk can estimate the user's emotions and adjust the timing of character setting and synopsis input based on those estimates. For example, if the user is excited, the reception desk can immediately display an interface prompting them to input character settings and synopsis. If the user is relaxed, the reception desk can display an interface prompting input at a slower pace. Furthermore, if the user is stressed, the reception desk can support the input of character settings and synopsis in the form of simple questions. This allows for adjustment of input timing according to the user's emotions.

[0109] The reception desk can analyze the user's past input history and suggest the most suitable input method. For example, based on character settings and synopses previously entered by the user, the reception desk can suggest similar settings. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can suggest settings based on specific genres or themes from the user's past input history. This makes it possible to suggest the most suitable input method based on the user's past input history.

[0110] The generation unit can estimate the user's emotions and adjust the story length based on those emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise story. If the user is relaxed, the generation unit can generate a longer story with more detailed explanations. Furthermore, if the user is excited, the generation unit can generate a fast-paced story. This allows for adjustment of story length according to the user's emotions.

[0111] The conversion unit can select the optimal video conversion method by considering the user's device information. For example, if the user is using a smartphone, the conversion unit can perform video conversion that is optimized for the screen size. If the user is using a tablet, the conversion unit can also perform video conversion that is optimized for the larger screen. Furthermore, if the user is using a smartwatch, the conversion unit can perform video conversion that is simple and highly visible. This makes it possible to select the optimal video conversion method based on the user's device information.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The reception desk accepts input for character settings and synopsis. For example, it allows users to input the protagonist's personality and behavioral patterns. Step 2: The generation unit generates a story based on the information received by the reception unit. The generation unit generates the story while considering the characters' personalities and behavioral patterns, and can also automatically generate detailed parts using a generation AI. Step 3: The instruction receiving unit receives user instructions for the story generated by the generation unit. For example, it allows the user to input instructions to modify a specific part of the story. Step 4: The revision unit revises the story based on the instructions received by the instruction receiving unit. The revision unit can revise specific parts of the story based on user instructions and can use AI to make the revisions. Step 5: The conversion unit converts the story, corrected by the correction unit, into a video. The conversion unit can convert the generated manga or anime into a video, and this conversion can be done using AI.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the reception unit, generation unit, instruction reception unit, modification unit, and conversion unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, enabling the user to input character settings and a synopsis. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, generating a story while considering the character's personality and behavioral patterns. The instruction reception unit is implemented by the control unit 46A of the smart device 14, enabling the user to input instructions for the generated story. The modification unit is implemented by the specific processing unit 290 of the data processing device 12, modifying the story based on user instructions. The conversion unit is implemented by the specific processing unit 290 of the data processing device 12, converting the modified story into a video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the reception unit, generation unit, instruction reception unit, modification unit, and conversion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, enabling the user to input character settings and a synopsis. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating a story while considering the character's personality and behavioral patterns. The instruction reception unit is implemented by the control unit 46A of the smart glasses 214, enabling the user to input instructions for the generated story. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12, modifying the story based on user instructions. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, converting the modified story into a video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, instruction reception unit, modification unit, and conversion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, enabling the user to input character settings and a synopsis. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating a story while considering the character's personality and behavioral patterns. The instruction reception unit is implemented by the control unit 46A of the headset terminal 314, enabling the user to input instructions for the generated story. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12, modifying the story based on user instructions. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, converting the modified story into a video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the reception unit, generation unit, instruction reception unit, modification unit, and conversion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, enabling the user to input character settings and a synopsis. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, generating a story while considering the character's personality and behavioral patterns. The instruction reception unit is implemented by the control unit 46A of the robot 414, enabling the user to input instructions for the generated story. The modification unit is implemented by the specific processing unit 290 of the data processing unit 12, modifying the story based on user instructions. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12, converting the modified story into a video. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A reception desk that accepts input of character settings and synopsis, A generation unit that generates a story based on the information received by the reception unit, An instruction receiving unit that receives user instructions for the story generated by the generation unit, A revision unit that modifies the story based on instructions received by the instruction receiving unit, The system includes a conversion unit that converts the story modified by the aforementioned modification unit into a video. A system characterized by the following features. (Note 2) The generating unit is The story is generated while taking into account the characters' personalities and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It automatically generates detailed elements such as panel layouts and dialogue. The system described in Appendix 1, characterized by the features described herein. (Note 4) The instruction receiving unit is, Accepts user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned modification section is, Modify the story based on user instructions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The conversion unit is Convert generated manga and anime into videos. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of character settings and plot input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned reception unit is When entering character settings and plot summaries, filtering is performed based on the user's current interests and trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned reception unit is When entering character settings and plot summaries, the system prioritizes inputting settings that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input character settings and plot summaries, the system analyzes their social media activity and suggests relevant settings. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the story's progression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a story, adjust the level of detail based on the importance of the characters. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating a story, different generation algorithms are applied depending on the character category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the story length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a story, prioritize the story based on when the character settings were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating a story, the order of the story is adjusted based on the relationships between the characters. The system described in Appendix 1, characterized by the features described herein. (Note 19) The instruction receiving unit is, It estimates the user's emotions and adjusts how instructions are received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The instruction receiving unit is, When receiving instructions, the system selects the most suitable method of receiving them by referring to the user's past instruction history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The instruction receiving unit is, It estimates the user's emotions and determines the priority of instructions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The instruction receiving unit is, When receiving instructions, the system selects the optimal reception method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned modification section is, It estimates the user's emotions and adjusts how the story is modified based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned modification section is, When revising a story, the system will refer to the user's past revision history to select the most suitable revision method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned modification section is, It estimates user sentiment and determines the priority of modifications based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned modification section is, When revising a story, the optimal revision method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned modification section is, When revising a story, we analyze users' social media activity and suggest revisions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The conversion unit is It estimates the user's emotions and adjusts the video conversion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The conversion unit is During video conversion, the system selects the optimal conversion method by referring to the user's past conversion history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The conversion unit is It estimates the user's emotions and determines the priority of video conversion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The conversion unit is When converting videos, the system selects the optimal conversion method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 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 desk that accepts input of character settings and synopsis, A generation unit that generates a story based on the information received by the reception unit, An instruction receiving unit that receives user instructions for the story generated by the generation unit, A revision unit that modifies the story based on instructions received by the instruction receiving unit, The system includes a conversion unit that converts the story modified by the aforementioned modification unit into a video. A system characterized by the following features.

2. The generating unit is The story is generated while taking into account the characters' personalities and behavioral patterns. The system according to feature 1.

3. The generating unit is It automatically generates detailed elements such as panel layouts and dialogue. The system according to feature 1.

4. The instruction receiving unit is, Accepts user instructions. The system according to feature 1.

5. The aforementioned modification section is, Modify the story based on user instructions. The system according to feature 1.

6. The conversion unit is Convert generated manga and anime into videos. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of character settings and plot input based on those estimated emotions. The system according to feature 1.

8. 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.

Images