System

Abstract

An object of a system according to an embodiment is to easily create comics without expert knowledge.SOLUTION: A system includes a character setting input unit, a synopsis input unit, a frame division generation unit, and a scene generation unit. The character setting input component inputs character settings. The synopsis input unit inputs a synopsis. The frame layout generation unit generates a frame layout based on the information input by the character setting input unit and the synopsis input unit. The scene generation unit generates a scene based on the frame division generated by the frame division generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to create frames and scenes for creating manga, and it was difficult to execute without specialized knowledge and skills.

[0005] The system according to the embodiment aims to enable people without specialized knowledge to easily create manga. [Means for solving the problem]

[0006] The system according to the embodiment includes a character setting input unit, a plot input unit, a frame layout generation unit, and a scene generation unit. The character setting input unit inputs character settings. The plot input unit inputs a plot. The frame layout generation unit generates a frame layout based on the information input by the character setting input unit and the plot input unit. The scene generation unit generates a scene based on the frame layout generated by the frame layout generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create manga without specialized knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The manga generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates a manga when a user simply inputs character settings and a plot. This allows even those who are not good at drawing or find panel layout difficult to create manga easily.

[0029] A manga generation system according to an embodiment includes a character setting input unit, a plot input unit, a panel layout generation unit, and a scene generation unit. The character setting input unit allows a user to input character settings. For example, the character's name, age, personality, appearance, special skills, etc. The plot input unit allows a user to input a plot that forms the main plot of the manga. For example, the user may input content such as "the protagonist is reincarnated in another world and goes on an adventure using magical powers." The panel layout generation unit generates panel layouts based on the information input by the character setting input unit and the plot input unit. For example, the panel layout generation unit generates scenes such as a scene in which the protagonist is reincarnated in another world or a scene in which the protagonist uses magical powers to fight enemies. The scene generation unit generates scenes based on the panel layouts generated by the panel layout generation unit. For example, the scene generation unit depicts specific scenes based on the generated panel layouts. This allows the generation AI to automatically generate manga simply by the user inputting character settings and plot.

[0030] The character setting input unit can refer to a database of past popular characters and propose settings based on similar successful examples. For example, the generation AI in the character setting input unit refers to a database of past popular characters and proposes settings based on similar successful examples. For example, the generation AI proposes settings that refer to the characteristics of past popular characters. The generation AI refers to a database of past popular characters and proposes settings based on similar successful examples. This makes it easier for users to create attractive characters by proposing character settings based on past successful examples.

[0031] The character setting input unit can analyze the user's past input history and automatically complete consistent character settings. In the character setting input unit, for example, a generation AI analyzes the user's past input history and automatically completes consistent character settings. For example, a new character is proposed based on the characteristics of characters previously input. The generation AI analyzes the user's past input history and automatically completes consistent character settings. This makes it possible to maintain the consistency of the character by automatically completing consistent character settings based on the user's past input history.

[0032] The character setting input unit can input character settings using multimodal methods such as voice input and gesture input. The character setting input unit, for example, enables character settings to be input using voice input, building a system in which a user can input character settings simply by speaking. For example, the character's name and personality are input using voice recognition technology. The generation AI enables character settings to be input using multimodal methods such as voice input and gesture input, improving user convenience. As a result, user convenience is improved by being able to input character settings using multimodal methods such as voice input and gesture input.

[0033] The character setting input unit can propose settings that take into account different cultural spheres and historical backgrounds. For example, the generation AI proposes settings that take into account the characteristics of different cultural spheres. For example, the generation AI proposes character settings that incorporate elements of traditional Japanese culture and Western fantasy. The generation AI proposes settings that take into account different cultural spheres and historical backgrounds. This makes it possible to support character creation from an international perspective by proposing settings that take into account different cultural spheres and historical backgrounds.

[0034] The synopsis input unit can refer to a database of past successful stories and suggest story developments based on similar successful examples. The synopsis input unit, for example, allows the generation AI to refer to a database of past successful stories and suggest story developments based on similar successful examples. For example, suggestions can be made based on the developments of popular works from the past. The generation AI refers to a database of past successful stories and suggests story developments based on similar successful examples. This makes it easier for users to create compelling stories by suggesting story developments based on past successful examples.

[0035] The synopsis input unit can analyze the user's past input history and automatically complete a consistent story development. In the synopsis input unit, for example, a generation AI analyzes the user's past input history and automatically completes a consistent story development. For example, it proposes a new development based on elements of stories input in the past. The generation AI analyzes the user's past input history and automatically completes a consistent story development. In this way, the consistency of the story can be maintained by automatically completing a consistent story development based on the user's past input history.

[0036] The synopsis input unit can input a synopsis using multimodal methods such as voice input and gesture input. The synopsis input unit, for example, allows the synopsis to be input using voice input, building a system that allows the user to input a synopsis simply by speaking. For example, the story flow is input using voice recognition technology. The generation AI allows the synopsis to be input using multimodal methods such as voice input and gesture input, improving user convenience. This allows the synopsis to be input using multimodal methods such as voice input and gesture input, improving user convenience.

[0037] The synopsis input unit can propose story developments that take into account different cultural spheres and historical backgrounds. For example, the generation AI in the synopsis input unit proposes story developments that take into account the characteristics of different cultural spheres. For example, it proposes story developments that incorporate elements of traditional Japanese culture and Western fantasy. The generation AI proposes story developments that take into account different cultural spheres and historical backgrounds. This makes it possible to support story creation from an international perspective by proposing story developments that take into account different cultural spheres and historical backgrounds.

[0038] The panel layout generation unit can refer to a database of past successful manga and propose panel layouts and scenes based on similar successful examples. For example, the panel layout generation unit makes suggestions based on the panel layouts of past popular works. The panel layout generation unit can refer to a database of past successful manga and propose panel layouts and scenes based on similar successful examples. This makes it easier for users to create attractive manga by proposing panel layouts and scenes based on past successful examples.

[0039] The panel layout generation unit can analyze the user's past input history and automatically complete consistent panel layouts and scenes. For example, the panel layout generation unit uses a generation AI to analyze the user's past input history and automatically complete consistent panel layouts and scenes. For example, it may suggest new scenes based on elements of a story that have been previously input. The generation AI analyzes the user's past input history and automatically completes consistent panel layouts and scenes. This allows the consistency of the manga to be maintained by automatically completing consistent panel layouts and scenes based on the user's past input history.

[0040] The frame layout generation unit can generate frame layouts and scenes using multimodal methods such as voice input and gesture input. For example, the frame layout generation unit can enable frame layouts and scene generation using voice input, building a system that allows users to generate frame layouts and scenes simply by speaking. For example, the flow of scenes can be input using voice recognition technology. The generation AI can generate frame layouts and scenes using multimodal methods such as voice input and gesture input, improving user convenience. This improves user convenience by enabling frame layouts and scene generation using multimodal methods such as voice input and gesture input.

[0041] The frame layout generation unit can propose scenes that take into account different cultural spheres and historical backgrounds. For example, the generation AI in the frame layout generation unit proposes scenes that take into account the characteristics of different cultural spheres. For example, it proposes scenes that incorporate elements of traditional Japanese culture and Western fantasy. The generation AI proposes scenes that take into account different cultural spheres and historical backgrounds. This allows for the creation of scenes from an international perspective.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The character setting input unit can automatically generate a character's voice based on the user's input. For example, it can generate a voice that matches the character's personality and age, allowing the user to check that voice. It can also change the tone of the voice according to the character's emotions. This makes it easier for the user to specifically imagine the character's voice, allowing for more realistic character settings.

[0044] The character setting input unit can automatically generate character movements and poses based on the character settings entered by the user. For example, it can generate movements and poses according to the character's personality and special skills, allowing the user to check those movements. Furthermore, it can also change the movements and poses according to the character's emotions. This makes it easier for the user to visualize the character's movements specifically, allowing for more realistic character settings.

[0045] The character setting input unit can analyze the user's past input history and automatically complete the character's growth process. For example, it can suggest a new character's growth process based on the characteristics of characters previously input. This allows the user to depict the character's growth in a consistent manner, adding depth to the story.

[0046] The character setting input unit can suggest character names that take into account different cultural and historical backgrounds. For example, it can suggest names based on traditional Japanese culture or names that incorporate Western fantasy elements. This allows users to set character names that fit different cultural and historical backgrounds, maintaining consistency within the story.

[0047] The synopsis input unit can automatically generate a story setting based on the synopsis input by the user. For example, it can generate a setting based on the locations and historical background that appear in the synopsis and allow the user to check the setting. This makes it easier for the user to specifically imagine the setting of the story, allowing them to create a more realistic narrative.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: In the character setting input section, the user inputs the character's settings, such as the character's name, age, personality, appearance, special skills, etc. Step 2: In the plot input section, the user inputs the main plot of the manga. For example, the user can input something like "The main character is reincarnated in another world and goes on an adventure using magical powers." Step 3: The frame layout generator generates frame layouts based on the information entered by the character setting input unit and the plot input unit. For example, it generates a scene in which the protagonist is reincarnated into another world, or a scene in which the protagonist uses magical powers to fight enemies. Step 4: The scene generation unit generates a scene based on the frame layout generated by the frame layout generation unit. For example, a specific scene is depicted based on the generated frame layout.

[0050] (Example 2) The manga generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates a manga when a user simply inputs character settings and a plot. This allows even those who are not good at drawing or find panel layout difficult to create manga easily.

[0051] A manga generation system according to an embodiment includes a character setting input unit, a plot input unit, a panel layout generation unit, and a scene generation unit. The character setting input unit allows a user to input character settings. For example, the character's name, age, personality, appearance, special skills, etc. The plot input unit allows a user to input a plot that forms the main plot of the manga. For example, the user may input content such as "the protagonist is reincarnated in another world and goes on an adventure using magical powers." The panel layout generation unit generates panel layouts based on the information input by the character setting input unit and the plot input unit. For example, the panel layout generation unit generates scenes such as a scene in which the protagonist is reincarnated in another world or a scene in which the protagonist uses magical powers to fight enemies. The scene generation unit generates scenes based on the panel layouts generated by the panel layout generation unit. For example, the scene generation unit depicts specific scenes based on the generated panel layouts. This allows the generation AI to automatically generate manga simply by the user inputting character settings and plot.

[0052] The character setting input unit can estimate the user's emotions in real time and suggest character settings that will elicit positive emotions. For example, the generation AI in the character setting input unit analyzes the user's facial expressions and vocal tone to estimate emotions in real time. For example, if the user is inputting with a smile, a positive character setting is suggested. The generation AI estimates the user's emotions in real time and suggests character settings that will elicit positive emotions. This can increase creative motivation by suggesting positive character settings based on the user's emotions.

[0053] The character setting input unit can refer to a database of past popular characters and propose settings based on similar successful examples. For example, the generation AI in the character setting input unit refers to a database of past popular characters and proposes settings based on similar successful examples. For example, the generation AI proposes settings that refer to the characteristics of past popular characters. The generation AI refers to a database of past popular characters and proposes settings based on similar successful examples. This makes it easier for users to create attractive characters by proposing character settings based on past successful examples.

[0054] The character setting input unit can analyze the user's past input history and automatically complete consistent character settings. In the character setting input unit, for example, a generation AI analyzes the user's past input history and automatically completes consistent character settings. For example, a new character is proposed based on the characteristics of characters previously input. The generation AI analyzes the user's past input history and automatically completes consistent character settings. This makes it possible to maintain the consistency of the character by automatically completing consistent character settings based on the user's past input history.

[0055] The character setting input unit can input character settings using multimodal methods such as voice input and gesture input. The character setting input unit, for example, enables character settings to be input using voice input, building a system in which a user can input character settings simply by speaking. For example, the character's name and personality are input using voice recognition technology. The generation AI enables character settings to be input using multimodal methods such as voice input and gesture input, improving user convenience. As a result, user convenience is improved by being able to input character settings using multimodal methods such as voice input and gesture input.

[0056] The character setting input unit can propose settings that take into account different cultural spheres and historical backgrounds. For example, the generation AI proposes settings that take into account the characteristics of different cultural spheres. For example, the generation AI proposes character settings that incorporate elements of traditional Japanese culture and Western fantasy. The generation AI proposes settings that take into account different cultural spheres and historical backgrounds. This makes it possible to support character creation from an international perspective by proposing settings that take into account different cultural spheres and historical backgrounds.

[0057] The character setting input unit can use the emotion estimation function to analyze the user's emotions and suggest a character backstory and special skills based on the emotions. For example, the generation AI in the character setting input unit can use the emotion estimation function to analyze the emotions the user feels when entering character settings and suggest a character backstory based on the emotions. For example, if the user is entering settings with a happy expression, a positive backstory can be suggested. The generation AI can use the emotion estimation function to analyze the emotions the user feels when entering character settings and suggest a character backstory and special skills based on the emotions. This makes it possible to create a more appealing character by suggesting a character backstory and special skills based on the user's emotions.

[0058] The synopsis input unit can estimate the user's emotions in real time and suggest a story development that is likely to resonate emotionally. For example, the generation AI in the synopsis input unit analyzes the user's facial expressions and vocal tone to estimate emotions in real time. For example, if the user is moved, it will suggest a story development that is likely to resonate emotionally. The generation AI estimates the user's emotions in real time and suggests a story development that is likely to resonate emotionally. This makes it possible to create a more compelling story by suggesting a story development that is likely to resonate emotionally based on the user's emotions.

[0059] The synopsis input unit can refer to a database of past successful stories and suggest story developments based on similar successful examples. The synopsis input unit, for example, allows the generation AI to refer to a database of past successful stories and suggest story developments based on similar successful examples. For example, suggestions can be made based on the developments of popular works from the past. The generation AI refers to a database of past successful stories and suggests story developments based on similar successful examples. This makes it easier for users to create compelling stories by suggesting story developments based on past successful examples.

[0060] The synopsis input unit can analyze the user's past input history and automatically complete a consistent story development. In the synopsis input unit, for example, a generation AI analyzes the user's past input history and automatically completes a consistent story development. For example, it proposes a new development based on elements of stories input in the past. The generation AI analyzes the user's past input history and automatically completes a consistent story development. In this way, the consistency of the story can be maintained by automatically completing a consistent story development based on the user's past input history.

[0061] The synopsis input unit can input a synopsis using multimodal methods such as voice input and gesture input. The synopsis input unit, for example, allows the synopsis to be input using voice input, building a system that allows the user to input a synopsis simply by speaking. For example, the story flow is input using voice recognition technology. The generation AI allows the synopsis to be input using multimodal methods such as voice input and gesture input, improving user convenience. This allows the synopsis to be input using multimodal methods such as voice input and gesture input, improving user convenience.

[0062] The synopsis input unit can propose story developments that take into account different cultural spheres and historical backgrounds. For example, the generation AI in the synopsis input unit proposes story developments that take into account the characteristics of different cultural spheres. For example, it proposes story developments that incorporate elements of traditional Japanese culture and Western fantasy. The generation AI proposes story developments that take into account different cultural spheres and historical backgrounds. This makes it possible to support story creation from an international perspective by proposing story developments that take into account different cultural spheres and historical backgrounds.

[0063] The synopsis input unit can use the emotion estimation function to analyze the user's emotions and suggest a story climax or ending based on those emotions. In the synopsis input unit, for example, the generation AI uses the emotion estimation function to analyze the emotions the user has when inputting the synopsis and suggests a story climax based on those emotions. For example, if the user is excited, the generation AI can suggest a climax of an action scene. The generation AI uses the emotion estimation function to analyze the emotions the user has when inputting the synopsis and suggests a story climax or ending based on those emotions. In this way, by suggesting a story climax or ending based on the user's emotions, it is possible to create a story that is more likely to resonate emotionally.

[0064] The panel layout generation unit can refer to a database of past successful manga and propose panel layouts and scenes based on similar successful examples. For example, the panel layout generation unit makes suggestions based on the panel layouts of past popular works. The panel layout generation unit can refer to a database of past successful manga and propose panel layouts and scenes based on similar successful examples. This makes it easier for users to create attractive manga by proposing panel layouts and scenes based on past successful examples.

[0065] The panel layout generation unit can analyze the user's past input history and automatically complete consistent panel layouts and scenes. For example, the panel layout generation unit uses a generation AI to analyze the user's past input history and automatically complete consistent panel layouts and scenes. For example, it may suggest new scenes based on elements of a story that have been previously input. The generation AI analyzes the user's past input history and automatically completes consistent panel layouts and scenes. This allows the consistency of the manga to be maintained by automatically completing consistent panel layouts and scenes based on the user's past input history.

[0066] The frame layout generation unit can generate frame layouts and scenes using multimodal methods such as voice input and gesture input. For example, the frame layout generation unit can enable frame layouts and scene generation using voice input, building a system that allows users to generate frame layouts and scenes simply by speaking. For example, the flow of scenes can be input using voice recognition technology. The generation AI can generate frame layouts and scenes using multimodal methods such as voice input and gesture input, improving user convenience. This improves user convenience by enabling frame layouts and scene generation using multimodal methods such as voice input and gesture input.

[0067] The frame layout generation unit can propose scenes that take into account different cultural spheres and historical backgrounds. For example, the generation AI in the frame layout generation unit proposes scenes that take into account the characteristics of different cultural spheres. For example, it proposes scenes that incorporate elements of traditional Japanese culture and Western fantasy. The generation AI proposes scenes that take into account different cultural spheres and historical backgrounds. This allows for the creation of scenes from an international perspective.

[0068] The panel layout generation unit can use the emotion estimation function to analyze the user's emotions and suggest a scene climax or ending based on the emotions. For example, the panel layout generation unit uses the emotion estimation function to analyze the emotions the user has when generating panel layouts and scenes, and suggest a scene climax based on the emotions. For example, if the user is excited, the panel layout generation unit can suggest a climax for an action scene. The panel layout generation unit uses the emotion estimation function to analyze the emotions the user has when generating panel layouts and scenes, and suggest a scene climax or ending based on the emotions. In this way, by suggesting a scene climax or ending based on the user's emotions, it is possible to create scenes that are more likely to resonate emotionally.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The character setting input unit can automatically generate a character's voice based on the user's input. For example, it can generate a voice that matches the character's personality and age, allowing the user to check that voice. It can also change the tone of the voice according to the character's emotions. This makes it easier for the user to specifically imagine the character's voice, allowing for more realistic character settings.

[0071] The character setting input unit can estimate the user's emotions and suggest character outfits and accessories based on the estimated emotions. For example, if the user is inputting with a happy expression, bright colored outfits and accessories that give a positive impression are suggested. Conversely, if the user is depressed, calm colored outfits and relaxing accessories are suggested. This allows the character's appearance to be set to match the user's emotions.

[0072] The character setting input unit can automatically generate character movements and poses based on the character settings entered by the user. For example, it can generate movements and poses according to the character's personality and special skills, allowing the user to check those movements. Furthermore, it can also change the movements and poses according to the character's emotions. This makes it easier for the user to visualize the character's movements specifically, allowing for more realistic character settings.

[0073] The character setting input unit can analyze the user's past input history and automatically complete the character's growth process. For example, it can suggest a new character's growth process based on the characteristics of characters previously input. This allows the user to depict the character's growth in a consistent manner, adding depth to the story.

[0074] The character setting input unit can estimate the user's emotions and suggest lines for the character based on the estimated emotions. For example, if the user is excited, it can suggest energetic lines. Conversely, if the user is calm, it can suggest calm lines. This allows the character's lines to be set in accordance with the user's emotions, thereby maintaining consistency in the story.

[0075] The character setting input unit can suggest character names that take into account different cultural and historical backgrounds. For example, it can suggest names based on traditional Japanese culture or names that incorporate Western fantasy elements. This allows users to set character names that fit different cultural and historical backgrounds, maintaining consistency within the story.

[0076] The character setting input unit can estimate the user's emotions and suggest character relationships based on the estimated emotions. For example, if the user is inputting in a fun manner, a positive relationship is suggested. Conversely, if the user is nervous, a tense relationship is suggested. This makes it possible to set character relationships that match the user's emotions, thereby maintaining consistency in the story.

[0077] The synopsis input unit can estimate the user's emotions and suggest a story tempo based on the estimated emotions. For example, if the user is excited, a fast-paced story development is suggested. Conversely, if the user is calm, a slower story development is suggested. This allows the story tempo to be set in accordance with the user's emotions, and the consistency of the story can be maintained.

[0078] The synopsis input unit can automatically generate a story setting based on the synopsis input by the user. For example, it can generate a setting based on the locations and historical background that appear in the synopsis and allow the user to check the setting. This makes it easier for the user to specifically imagine the setting of the story, allowing them to create a more realistic narrative.

[0079] The plot input unit can estimate the user's emotions and suggest story subplots based on the estimated emotions. For example, if the user is inputting with a happy expression, a positive subplot is suggested. Conversely, if the user is nervous, a tense subplot is suggested. This allows the story subplot to be set in accordance with the user's emotions, thereby maintaining the consistency of the story.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: In the character setting input section, the user inputs the character's settings, such as the character's name, age, personality, appearance, special skills, etc. Step 2: In the plot input section, the user inputs the main plot of the manga. For example, the user can input something like "The main character is reincarnated in another world and goes on an adventure using magical powers." Step 3: The frame layout generator generates frame layouts based on the information entered by the character setting input unit and the plot input unit. For example, it generates a scene in which the protagonist is reincarnated into another world, or a scene in which the protagonist uses magical powers to fight enemies. Step 4: The scene generation unit generates a scene based on the frame layout generated by the frame layout generation unit. For example, a specific scene is depicted based on the generated frame layout.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] 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.

[0141] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a character setting input section for inputting character settings; a synopsis input section for inputting a synopsis; a frame layout generation unit that generates a frame layout based on the information input by the character setting input unit and the synopsis input unit; a scene generation unit that generates a scene based on the frame layout generated by the frame layout generation unit. A system characterized by:

2. The character setting input unit Estimates user emotions in real time and suggests character settings to elicit positive emotions 2. The system of claim 1.

3. The character setting input unit The character settings can be input in a multimodal way, such as through voice input or gesture input.

2. The system of claim 1.

4. The synopsis input unit Estimates user emotions in real time and suggests story developments that resonate with users emotionally.

2. The system of claim 1.

5. The frame layout generation unit Estimate user emotions in real time and suggest scenes with the highest emotional impact.

2. The system of claim 1.

6. The character setting input unit Analyzes user emotions using emotion estimation function and suggests character backstories and special skills based on emotions 2. The system of claim 1.

7. The synopsis input unit Analyzes user emotions using emotion estimation and suggests story climaxes and endings based on emotions 2. The system of claim 1.

8. The frame layout generation unit Analyze the user's emotions using emotion estimation function and suggest the climax and ending of the scene based on the emotions.

2. The system of claim 1.

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