System

Abstract

An object of a system according to an embodiment is to enable a user to create an original novel or picture book only by selecting an option.SOLUTION: A system according to an embodiment includes an option presentation section, a story generation section, a character setting section, a scene drawing section, and a story output section. The option presentation unit presents a plurality of options to a user. The story generation unit generates a story on the basis of the options presented by the option presentation unit. The character setting unit sets a character on the basis of the story generated by the story generation unit. The scene rendering unit renders a scene based on the character set by the character setting unit. The story output section outputs a story including the scene rendered by the scene rendering section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that when a user creates an original novel or picture book, it is time-consuming to input prompts.

[0005] The system according to the embodiment aims to enable users to create original novels and picture books simply by selecting options. [Means for solving the problem]

[0006] The system according to the embodiment includes a choice presentation unit, a story generation unit, a character setting unit, a scene depiction unit, and a story output unit. The choice presentation unit presents a plurality of choices to the user. The story generation unit generates a story based on the choices presented by the choice presentation unit. The character setting unit sets characters based on the story generated by the story generation unit. The scene depiction unit depicts scenes based on characters set by the character setting unit. The story output unit outputs a story including the scenes depicted by the scene depiction unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to create an original novel or picture book simply by selecting options. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The original novel creation system according to an embodiment of the present invention is a system that allows users to create original novels and picture books simply by selecting options presented by a generation AI without having to enter any prompts. This allows the original novel creation system to allow users to easily create original novels and picture books.

[0029] An original novel creation system according to an embodiment includes a choice presentation unit, a story generation unit, a character setting unit, a scene depiction unit, and a story output unit. The choice presentation unit presents a plurality of choices to a user. For example, the choice presentation unit presents choices such as "a child lost in the forest," "a boy going on an adventure on a spaceship," and "a girl lost in a magical land" as the beginning of a story. The choice presentation unit also presents choices regarding character settings and scene depictions. The story generation unit generates a story based on the choices presented by the choice presentation unit. For example, if "a child lost in the forest" is selected, the generation AI generates the rest of the story based on that setting. The character setting unit sets characters based on the story generated by the story generation unit. For example, the character setting unit presents choices such as "What is the protagonist's personality like?" and "What kind of animal is the protagonist's friend?", and the character's details are determined by the user's selection. The scene depiction unit depicts scenes based on the characters set by the character setting unit. For example, the scene progresses by presenting choices such as "Where does the protagonist go in the next scene?" and "What characters does the protagonist meet?", and the scene progresses by the user's selection. The story output unit outputs a story including the scenes depicted by the scene depiction unit. For example, after the user has selected all options, the generation AI outputs the completed story. This allows the original novel creation system according to the embodiment to create an original novel or picture book simply by selecting options presented by the AI, without the user having to enter any prompts.

[0030] The option presentation unit can learn the user's past selection history and present options optimized for each individual user. For example, the option presentation unit stores options previously selected by the user in a database and customizes the next option based on that history. For example, for a user who likes adventure stories, options related to adventure are presented preferentially. The option presentation unit also analyzes the past selection history and develops an algorithm that learns the user's preferences and tendencies. For example, for a user who likes fantasy elements, many options related to magic and other worlds are presented. The option presentation unit also builds a system that personalizes the next option based on the user's selection history. For example, options related to characters or scenes selected in the past are displayed preferentially. This makes it possible to provide more personalized options based on the user's past selection history.

[0031] The option presentation unit can customize options based on the user's age and interests. The option presentation unit develops a system that presents appropriate options based on, for example, the user's age information. For example, it presents simple and easy-to-understand options for children and complex and in-depth options for adults. The option presentation unit also customizes options based on the user's interests and hobbies. For example, a user who likes animals is presented with options that include scenes and characters featuring animals. The option presentation unit also builds a system that combines the user's age and interests to present optimal options. For example, if a young user likes sports, it presents options related to sports. This allows for a more appropriate story progression by providing options according to the user's age and interests.

[0032] The option presentation unit can present options using not only text but also images, audio, and video. For example, the option presentation unit develops a system that displays related images in addition to text options. For example, an illustration of a character is displayed for a character option. The option presentation unit also introduces audio options to enable the user to confirm the options by audio. For example, a description of a scene is played by audio, making it easier for the user to make a selection. The option presentation unit also uses video options to build a system that displays parts of scenes in video. For example, a short video of part of the next scene is shown to make it easier for the user to make a selection. This allows the user to make a more intuitive selection by providing options both visually and audibly.

[0033] The option presentation unit presents options that correspond to different cultures and languages, making it possible to accommodate international users. The option presentation unit develops, for example, a system that provides options that support multiple languages. For example, options are displayed in multiple languages, such as English, French, and Chinese. The option presentation unit also presents options based on different cultural backgrounds. For example, Japanese-style scenes and characters are presented to Japanese users, and Western-style scenes and characters are presented to American users. The option presentation unit also builds a system that customizes options according to culture and language to accommodate international users. For example, options based on the traditions and customs of each country are presented. This makes the system usable by international users by supporting different cultures and languages.

[0034] The story generation unit can generate a consistent story by taking into account the user's selection history. For example, the story generation unit stores options previously selected by the user in a database and develops a system that maintains the consistency of the story based on that history. For example, the story generation unit generates a story so that a character selected previously reappears. The story generation unit also analyzes the selection history and develops an algorithm that ensures consistency in the progression of the story. For example, it ensures that the settings and scenes selected by the user are reflected in the latter half of the story. The story generation unit also builds a system that automatically generates a scenario to maintain the consistency of the story based on the user's selection history. For example, it ensures that the development of the story continues naturally depending on the options selected. In this way, a consistent story can be generated by taking into account the user's selection history.

[0035] The story generation unit can learn user feedback on the generated story and reflect it in the next story generation. The story generation unit, for example, develops a system that allows users to provide feedback on the generated story. For example, it collects story ratings and comments and reflects them in the next story generation. The story generation unit also analyzes the feedback data and develops an algorithm that learns user preferences and tendencies. For example, it incorporates highly rated elements into the next story. The story generation unit also builds a system that improves the story generation algorithm based on user feedback. For example, it corrects problems pointed out by users and reflects them in the next story generation. In this way, it is possible to learn user feedback and reflect it in the next story generation.

[0036] The story generation unit can generate stories in different genres and styles to provide the user with options. The story generation unit, for example, develops a system that generates stories in different genres. For example, the same story may be generated in genres such as fantasy, science fiction, and mystery to provide the user with options. The story generation unit also develops an algorithm that generates stories in different styles. For example, the same story may be generated in styles such as serious, comedy, and romance to provide the user with options. The story generation unit also builds a system that generates stories according to the genre and style selected by the user. For example, if the user selects fantasy, a story containing many fantasy elements is generated. This allows the user to have a variety of options by generating stories in different genres and styles.

[0037] The story generation unit allows multiple users to collaboratively select options and generate a story through collaborative work. The story generation unit, for example, develops a system in which multiple users simultaneously select options and collaboratively generate a story. For example, multiple users collaborate to create a story on an online platform. The story generation unit also builds a system that reflects each user's selection in real time when collaboratively generating a story. For example, an option selected by user A is instantly reflected on user B's screen. The story generation unit also introduces a voting function to ensure consensus when multiple users collaboratively generate a story. For example, a vote is held for each option, and the next development is decided by majority vote. In this way, multiple users collaboratively generating a story can incorporate a greater variety of perspectives and ideas.

[0038] The character setting unit can perform customization based on the user's selection history and preferences. For example, the character setting unit stores character settings previously selected by the user in a database and customizes the next character setting based on that history. For example, a user who prefers animal characters may be presented with animal character options first. The character setting unit also analyzes the selection history and develops an algorithm that learns the user's preferences and tendencies. For example, a user who prefers fantasy characters may be presented with many options such as wizards and fairies. The character setting unit also builds a system that personalizes the next character setting based on the user's selection history. For example, it may suggest a new character that reflects the characteristics of a previously selected character. This allows for customizing characters based on the user's selection history and preferences, providing a more personalized experience.

[0039] The character setting unit can generate a detailed backstory based on the options selected by the user. The character setting unit, for example, develops a system that automatically generates a detailed backstory based on the character settings selected by the user. For example, it generates past events and family structure of the character. The character setting unit also develops an algorithm that generates a backstory based on the options. For example, it generates episodes related to the occupation and hobbies of the character selected by the user. The character setting unit also builds a system that generates a detailed backstory of the character based on the options selected by the user. For example, it depicts the character's growth process and important events. In this way, by generating a detailed backstory based on the options selected by the user, it is possible to add depth to the character.

[0040] The character setting unit can set characters not only using text, but also using images, audio, and video. The character setting unit, for example, develops a system that displays character illustrations and images in addition to text options. For example, it allows the character's appearance and clothing to be confirmed using images. The character setting unit also introduces audio options and builds a system that plays the character's voice and lines as audio. For example, it allows the user to select options by listening to the character's voice. The character setting unit also develops a system that uses video options to display the character's movements and expressions as video. For example, it shows a short video of a character's action scene, making it easier for the user to select. This allows the character to be set visually and audibly, making character setting more intuitive.

[0041] The character setting unit creates character settings that correspond to different cultures and languages, making it possible to accommodate international users. The character setting unit, for example, develops a system that provides character settings that correspond to multiple languages. For example, character settings are displayed in multiple languages, such as English, French, and Chinese. The character setting unit also presents character settings based on different cultural backgrounds. For example, Japanese-style characters are presented to Japanese users, and Western-style characters are presented to American users. The character setting unit also builds a system that customizes according to culture and language to accommodate international users. For example, character settings based on the traditions and customs of each country are provided. This makes the system usable by international users by supporting different cultures and languages.

[0042] The scene depiction unit can perform customization based on a user's selection history and preferences. For example, the scene depiction unit stores scene settings previously selected by the user in a database and customizes the next scene depiction based on that history. For example, a user who prefers natural scenery can be presented with depictions that include many natural scenes. The scene depiction unit also analyzes the selection history and develops an algorithm that learns the user's preferences and tendencies. For example, a user who prefers urban scenery can be presented with depictions that include many urban scenes. The scene depiction unit also builds a system that personalizes the next scene depiction based on the user's selection history. For example, it can suggest new scenes that reflect the characteristics of previously selected scenes. This allows for customizing scenes based on the user's selection history and preferences, providing a more personalized experience.

[0043] The scene depiction unit can generate a detailed environmental depiction based on the options selected by the user. The scene depiction unit develops, for example, a system that automatically generates a detailed environmental depiction based on a scene setting selected by the user. For example, the scene's weather and time of day are depicted in detail. The scene depiction unit also develops an algorithm that generates an environmental depiction based on the options. For example, a detailed depiction related to the location and background of the scene selected by the user is generated. The scene depiction unit also builds a system that generates a detailed environmental depiction of a scene based on the options selected by the user. For example, a depiction is created that includes sensory information such as the sounds and smells of the scene. This allows the scene to have depth by generating a detailed environmental depiction based on the options selected by the user.

[0044] The scene description unit can describe a scene not only using text, but also using images, audio, and video. For example, the scene description unit develops a system that displays illustrations and images of a scene in addition to text description. For example, it makes it possible to confirm the scenery of a scene and the movements of characters through images. The scene description unit also introduces audio description and builds a system that plays the sounds and sound effects of a scene through audio. For example, it makes it possible to select a scene by listening to the background sounds and character voices of the scene. The scene description unit also develops a system that uses video description to display the movements and development of a scene through video. For example, it shows the action scenes of a scene in a short video, making it easier for the user to select. In this way, by describing a scene visually and aurally, more intuitive scene description becomes possible.

[0045] The scene depiction unit can depict scenes in accordance with different cultures and languages, and can accommodate international users. The scene depiction unit, for example, develops a system that provides scene depictions in multiple languages. For example, it displays scene depictions in multiple languages, such as English, French, and Chinese. The scene depiction unit also presents scene depictions based on different cultural backgrounds. For example, it presents Japanese-style scenes to Japanese users and Western-style scenes to American users. The scene depiction unit also builds a system that customizes according to culture and language to accommodate international users. For example, it provides scene depictions based on the traditions and customs of each country. This makes the system usable by international users by being able to accommodate different cultures and languages.

[0046] The story output unit can perform customization based on the user's selection history and preferences. For example, the story output unit stores options previously selected by the user in a database and customizes the completed story based on that history. For example, it generates a story that includes many of the user's favorite characters and scenes. The story output unit also analyzes the selection history and develops an algorithm that learns the user's preferences and tendencies. For example, it customizes the story to match the user's favorite genre or style. The story output unit also builds a system that personalizes the completed story based on the user's selection history. For example, it generates a story that reflects settings and scenes previously selected. This makes it possible to provide a more personalized experience by customizing the story based on the user's selection history and preferences.

[0047] The story output unit can customize the output format of the completed story according to the user's preferences. The story output unit develops a system that customizes the story output format according to the user's preferences, for example. For example, the story is output in multiple formats, such as text format, PDF format, and e-book format. The story output unit also develops an algorithm that takes into account the user's past selection history when customizing the output format. For example, if a user previously selected PDF format, the unit will preferentially present PDF format next time. The story output unit also builds a system that customizes the layout and design of the story according to the user's preferences. For example, the unit allows the user to select fonts, colors, and page layout. This makes it possible to output a more appropriate story by providing an output format according to the user's preferences.

[0048] The story output unit can output the completed story not only using text, but also using images, audio, and video. The story output unit develops a system that outputs the story in a format that includes illustrations and images of the story in addition to text output. For example, it generates a PDF that includes illustrations of story scenes and characters. The story output unit also introduces audio output and builds a system that outputs the story in a format that includes narration and character voices. For example, it generates an audiobook of the story. The story output unit also develops a system that uses video output to display the movement and development of the story in video. For example, it generates an animated video of the story. This makes it possible to output the story more intuitively by outputting the story both visually and audibly.

[0049] The story output unit can output stories that correspond to different cultures and languages, and can also accommodate international users. The story output unit develops, for example, a system that provides story output that corresponds to multiple languages. For example, it outputs stories in multiple languages, such as English, French, and Chinese. The story output unit also presents story output based on different cultural backgrounds. For example, it outputs Japanese-style stories for Japanese users and Western-style stories for American users. The story output unit also builds a system that customizes according to culture and language to accommodate international users. For example, it provides story output based on the traditions and customs of each country. This makes the system usable by supporting different cultures and languages.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The original novel creation system can further include an illustration generation unit that automatically generates illustrations based on user selections. For example, the AI ​​can automatically generate illustrations based on scenes and characters selected by the user and insert them into the story. The illustration generation unit can also change the design of the illustrations according to the style selected by the user. For example, it can generate fantasy-style illustrations for a fantasy-style story and futuristic illustrations for a science fiction story. The illustration generation unit can also provide a function that allows the user to customize some of the illustrations. For example, it can allow the user to select the character's clothing and background color. This allows the user to create an original story that is visually enjoyable.

[0052] The original novel creation system may further include an audio generation unit that automatically generates music and sound effects based on user selections. For example, background music and sound effects may be automatically generated and inserted into the story according to a scene selected by the user. The audio generation unit may also change the style of music according to a genre selected by the user. For example, orchestral music may be generated for a fantasy-style story, and electronic music may be generated for a science fiction story. The audio generation unit may also provide a function that allows the user to customize some of the music and sound effects. For example, the user may be able to adjust the tempo and volume of music used in a particular scene. This allows the user to create an original story that is also auditorily enjoyable.

[0053] The original novel creation system can further include a 3D model generation unit that generates a 3D model of a character based on a user's selections. For example, the AI ​​can automatically generate a 3D model based on the character settings selected by the user and insert it into the story. The 3D model generation unit can also change the design of the 3D model according to the style selected by the user. For example, it can generate a fantasy-style 3D model for a fantasy-style story and a futuristic 3D model for a science fiction story. The 3D model generation unit can also provide a function that allows the user to customize parts of the 3D model. For example, it can allow the user to select the character's pose and facial expression. This allows the user to create an original story that is visually enjoyable.

[0054] The original novel creation system may further include a visualization unit that visualizes the progression of the story in real time based on the user's selections. For example, the AI ​​may automatically graphically display the progression of the story based on scenes and characters selected by the user. The visualization unit may also change the design of the visualization according to the style selected by the user. For example, it may generate fantasy-style graphics for a fantasy-style story and futuristic graphics for a science fiction story. The visualization unit may also provide a function that allows the user to customize parts of the visualization. For example, it may allow the user to adjust the color and shape of graphics used in a particular scene. This allows the user to create an original story that is also visually enjoyable.

[0055] The original novel creation system can further include a voice navigation unit that provides voice navigation through the story based on the user's selections. For example, the AI ​​automatically provides voice guidance through the story based on the scenes and characters selected by the user. The voice navigation unit can also change the tone and accent of the voice according to the style selected by the user. For example, it can generate voice with an elegant tone for a fantasy-style story and voice with a futuristic tone for science fiction. The voice navigation unit can also provide a function that allows the user to customize parts of the voice. For example, it can allow the user to adjust the speed and volume of the voice used in a particular scene. This allows the user to create an original story that is also auditorily enjoyable.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The option presentation unit presents multiple options to the user. For example, options for the beginning of the story include "a child lost in the forest," "a boy going on an adventure on a spaceship," and "a girl lost in a magical land." Options for character settings and scene descriptions are also presented. Step 2: The story generator generates a story based on the options presented by the option presenter. For example, if you select "A child lost in the woods," the AI ​​generates the rest of the story based on that setting. Step 3: The character setting unit sets up the characters based on the story generated by the story generation unit. For example, it presents options such as "What is the main character's personality?" and "What kind of animal is the main character's friend?", and the details of the character are determined by the user's selection. Step 4: The scene depiction unit depicts the scene based on the characters set by the character setting unit. For example, it presents options such as "Where will the protagonist go in the next scene?" and "Which characters will the protagonist meet?", and the scene progresses as the user makes their choice. Step 5: The story output unit outputs a story including the scenes described by the scene description unit. For example, after the user has selected all the options, the generation AI outputs the completed story.

[0058] (Example 2) The original novel creation system according to an embodiment of the present invention is a system that allows users to create original novels and picture books simply by selecting options presented by a generation AI without having to enter any prompts. This allows the original novel creation system to allow users to easily create original novels and picture books.

[0059] An original novel creation system according to an embodiment includes a choice presentation unit, a story generation unit, a character setting unit, a scene depiction unit, and a story output unit. The choice presentation unit presents a plurality of choices to a user. For example, the choice presentation unit presents choices such as "a child lost in the forest," "a boy going on an adventure on a spaceship," and "a girl lost in a magical land" as the beginning of a story. The choice presentation unit also presents choices regarding character settings and scene depictions. The story generation unit generates a story based on the choices presented by the choice presentation unit. For example, if "a child lost in the forest" is selected, the generation AI generates the rest of the story based on that setting. The character setting unit sets characters based on the story generated by the story generation unit. For example, the character setting unit presents choices such as "What is the protagonist's personality like?" and "What kind of animal is the protagonist's friend?", and the character's details are determined by the user's selection. The scene depiction unit depicts scenes based on the characters set by the character setting unit. For example, the scene progresses by presenting choices such as "Where does the protagonist go in the next scene?" and "What characters does the protagonist meet?", and the scene progresses by the user's selection. The story output unit outputs a story including the scenes depicted by the scene depiction unit. For example, after the user has selected all options, the generation AI outputs the completed story. This allows the original novel creation system according to the embodiment to create an original novel or picture book simply by selecting options presented by the AI, without the user having to enter any prompts.

[0060] The option presentation unit can learn the user's past selection history and present options optimized for each individual user. For example, the option presentation unit stores options previously selected by the user in a database and customizes the next option based on that history. For example, for a user who likes adventure stories, options related to adventure are presented preferentially. The option presentation unit also analyzes the past selection history and develops an algorithm that learns the user's preferences and tendencies. For example, for a user who likes fantasy elements, many options related to magic and other worlds are presented. The option presentation unit also builds a system that personalizes the next option based on the user's selection history. For example, options related to characters or scenes selected in the past are displayed preferentially. This makes it possible to provide more personalized options based on the user's past selection history.

[0061] The option presentation unit can infer emotions from the user's facial expressions and voice and present options corresponding to those emotions. The option presentation unit, for example, develops a system that analyzes the user's facial expressions with a camera and infers emotions in real time. For example, if the user is smiling, options including fun scenes and characters are presented. The option presentation unit also collects the user's voice with a microphone and infers emotions using voice analysis technology. For example, if the user's voice sounds excited, options related to action scenes and adventures are presented. The option presentation unit also analyzes both facial expressions and voice to build a system that comprehensively infers emotions. For example, if the user is tired, options including relaxing scenes and characters are presented. This allows for more appropriate story progression by providing options according to the user's emotions.

[0062] The option presentation unit can customize options based on the user's age and interests. The option presentation unit develops a system that presents appropriate options based on, for example, the user's age information. For example, it presents simple and easy-to-understand options for children and complex and in-depth options for adults. The option presentation unit also customizes options based on the user's interests and hobbies. For example, a user who likes animals is presented with options that include scenes and characters featuring animals. The option presentation unit also builds a system that combines the user's age and interests to present optimal options. For example, if a young user likes sports, it presents options related to sports. This allows for a more appropriate story progression by providing options according to the user's age and interests.

[0063] The option presentation unit can present options using not only text but also images, audio, and video. For example, the option presentation unit develops a system that displays related images in addition to text options. For example, an illustration of a character is displayed for a character option. The option presentation unit also introduces audio options to enable the user to confirm the options by audio. For example, a description of a scene is played by audio, making it easier for the user to make a selection. The option presentation unit also uses video options to build a system that displays parts of scenes in video. For example, a short video of part of the next scene is shown to make it easier for the user to make a selection. This allows the user to make a more intuitive selection by providing options both visually and audibly.

[0064] The option presentation unit presents options that correspond to different cultures and languages, making it possible to accommodate international users. The option presentation unit develops, for example, a system that provides options that support multiple languages. For example, options are displayed in multiple languages, such as English, French, and Chinese. The option presentation unit also presents options based on different cultural backgrounds. For example, Japanese-style scenes and characters are presented to Japanese users, and Western-style scenes and characters are presented to American users. The option presentation unit also builds a system that customizes options according to culture and language to accommodate international users. For example, options based on the traditions and customs of each country are presented. This makes the system usable by international users by supporting different cultures and languages.

[0065] The option presentation unit can use the emotion estimation function to present the option that most interests the user in real time. The option presentation unit develops a system that analyzes the user's facial expressions and voice and estimates the user's emotions in real time. For example, if the user shows interest, options that correspond to that interest are presented. The option presentation unit also prioritizes displaying options that are likely to interest the user based on the emotion estimation data. For example, if the user smiles, options that include fun scenes and characters are presented. The option presentation unit also builds a system that estimates emotions in real time and dynamically adjusts options according to the user's interests. For example, the options are updated every time the user's emotions change. This allows for a more interesting story progression by providing optimal options in real time based on the user's emotions.

[0066] The story generation unit can generate a consistent story by taking into account the user's selection history. For example, the story generation unit stores options previously selected by the user in a database and develops a system that maintains the consistency of the story based on that history. For example, the story generation unit generates a story so that a character selected previously reappears. The story generation unit also analyzes the selection history and develops an algorithm that ensures consistency in the progression of the story. For example, it ensures that the settings and scenes selected by the user are reflected in the latter half of the story. The story generation unit also builds a system that automatically generates a scenario to maintain the consistency of the story based on the user's selection history. For example, it ensures that the development of the story continues naturally depending on the options selected. In this way, a consistent story can be generated by taking into account the user's selection history.

[0067] The story generation unit can learn user feedback on the generated story and reflect it in the next story generation. The story generation unit, for example, develops a system that allows users to provide feedback on the generated story. For example, it collects story ratings and comments and reflects them in the next story generation. The story generation unit also analyzes the feedback data and develops an algorithm that learns user preferences and tendencies. For example, it incorporates highly rated elements into the next story. The story generation unit also builds a system that improves the story generation algorithm based on user feedback. For example, it corrects problems pointed out by users and reflects them in the next story generation. In this way, it is possible to learn user feedback and reflect it in the next story generation.

[0068] The story generation unit can estimate the user's emotions and develop a story in accordance with the emotions. The story generation unit, for example, develops a system that analyzes the user's facial expressions and voice and estimates emotions in real time. For example, if the user is excited, it generates a story that includes many action scenes. The story generation unit also develops an algorithm that develops a story in accordance with the user's emotions based on the emotion estimation data. For example, if the user is sad, it generates a story that includes many moving scenes. The story generation unit also builds a system that estimates emotions in real time and dynamically adjusts the development of the story in accordance with the user's emotions. For example, the development of the story is updated every time the user's emotions change. In this way, a story that is easier for the user to empathize with can be generated by developing the story in accordance with the user's emotions.

[0069] The story generation unit can generate stories in different genres and styles to provide the user with options. The story generation unit, for example, develops a system that generates stories in different genres. For example, the same story may be generated in genres such as fantasy, science fiction, and mystery to provide the user with options. The story generation unit also develops an algorithm that generates stories in different styles. For example, the same story may be generated in styles such as serious, comedy, and romance to provide the user with options. The story generation unit also builds a system that generates stories according to the genre and style selected by the user. For example, if the user selects fantasy, a story containing many fantasy elements is generated. This allows the user to have a variety of options by generating stories in different genres and styles.

[0070] The story generation unit allows multiple users to collaboratively select options and generate a story through collaborative work. The story generation unit, for example, develops a system in which multiple users simultaneously select options and collaboratively generate a story. For example, multiple users collaborate to create a story on an online platform. The story generation unit also builds a system that reflects each user's selection in real time when collaboratively generating a story. For example, an option selected by user A is instantly reflected on user B's screen. The story generation unit also introduces a voting function to ensure consensus when multiple users collaboratively generate a story. For example, a vote is held for each option, and the next development is decided by majority vote. In this way, multiple users collaboratively generating a story can incorporate a greater variety of perspectives and ideas.

[0071] The story generation unit can use the emotion estimation function to generate a story that the user can easily empathize with emotionally. The story generation unit, for example, develops a system that analyzes the user's facial expressions and voice and estimates emotions in real time. For example, if the user is moved, the story generation unit generates a story that includes many moving scenes. The story generation unit also develops an algorithm that generates a story that the user can easily empathize with emotionally based on the emotion estimation data. For example, if the user smiles, the story generation unit generates a story that includes many happy scenes. The story generation unit also performs emotion estimation in real time and builds a system that dynamically adjusts the development of the story according to the user's emotions. For example, the development of the story is updated every time the user's emotions change. This makes it possible to generate a story that the user can easily empathize with emotionally, providing a deeper experience.

[0072] The character setting unit can perform customization based on the user's selection history and preferences. For example, the character setting unit stores character settings previously selected by the user in a database and customizes the next character setting based on that history. For example, a user who prefers animal characters may be presented with animal character options first. The character setting unit also analyzes the selection history and develops an algorithm that learns the user's preferences and tendencies. For example, a user who prefers fantasy characters may be presented with many options such as wizards and fairies. The character setting unit also builds a system that personalizes the next character setting based on the user's selection history. For example, it may suggest a new character that reflects the characteristics of a previously selected character. This allows for customizing characters based on the user's selection history and preferences, providing a more personalized experience.

[0073] The character setting unit can estimate the user's emotions and depict the character according to those emotions. The character setting unit develops a system that analyzes the user's facial expressions and voice, for example, and estimates emotions in real time. For example, if the user is excited, the character is depicted more lively and energetic. The character setting unit also develops an algorithm that depicts the character according to the user's emotions based on the emotion estimation data. For example, if the user is sad, the character is depicted more movingly. The character setting unit also builds a system that estimates emotions in real time and dynamically adjusts the character depiction according to the user's emotions. For example, the character's facial expressions and actions are updated every time the user's emotions change. This allows the character to be depicted according to the user's emotions, making it easier for the user to empathize with the character.

[0074] The character setting unit can generate a detailed backstory based on the options selected by the user. The character setting unit, for example, develops a system that automatically generates a detailed backstory based on the character settings selected by the user. For example, it generates past events and family structure of the character. The character setting unit also develops an algorithm that generates a backstory based on the options. For example, it generates episodes related to the occupation and hobbies of the character selected by the user. The character setting unit also builds a system that generates a detailed backstory of the character based on the options selected by the user. For example, it depicts the character's growth process and important events. In this way, by generating a detailed backstory based on the options selected by the user, it is possible to add depth to the character.

[0075] The character setting unit can set characters not only using text, but also using images, audio, and video. The character setting unit, for example, develops a system that displays character illustrations and images in addition to text options. For example, it allows the character's appearance and clothing to be confirmed using images. The character setting unit also introduces audio options and builds a system that plays the character's voice and lines as audio. For example, it allows the user to select options by listening to the character's voice. The character setting unit also develops a system that uses video options to display the character's movements and expressions as video. For example, it shows a short video of a character's action scene, making it easier for the user to select. This allows the character to be set visually and audibly, making character setting more intuitive.

[0076] The character setting unit creates character settings that correspond to different cultures and languages, making it possible to accommodate international users. The character setting unit, for example, develops a system that provides character settings that correspond to multiple languages. For example, character settings are displayed in multiple languages, such as English, French, and Chinese. The character setting unit also presents character settings based on different cultural backgrounds. For example, Japanese-style characters are presented to Japanese users, and Western-style characters are presented to American users. The character setting unit also builds a system that customizes according to culture and language to accommodate international users. For example, character settings based on the traditions and customs of each country are provided. This makes the system usable by international users by supporting different cultures and languages.

[0077] The character setting unit can use the emotion estimation function to set a character that the user can easily empathize with emotionally. The character setting unit develops a system that analyzes the user's facial expressions and voice, for example, and estimates emotions in real time. For example, if the user is moved, a character with an emotional backstory is set. The character setting unit also develops an algorithm that sets a character that the user can easily empathize with emotionally based on the emotion estimation data. For example, if the user smiles, a character with a fun personality is set. The character setting unit also builds a system that estimates emotions in real time and dynamically adjusts the character settings according to the user's emotions. For example, the character's characteristics are updated every time the user's emotions change. This allows the user to easily empathize with a character, providing a deeper experience.

[0078] The scene depiction unit can perform customization based on a user's selection history and preferences. For example, the scene depiction unit stores scene settings previously selected by the user in a database and customizes the next scene depiction based on that history. For example, a user who prefers natural scenery can be presented with depictions that include many natural scenes. The scene depiction unit also analyzes the selection history and develops an algorithm that learns the user's preferences and tendencies. For example, a user who prefers urban scenery can be presented with depictions that include many urban scenes. The scene depiction unit also builds a system that personalizes the next scene depiction based on the user's selection history. For example, it can suggest new scenes that reflect the characteristics of previously selected scenes. This allows for customizing scenes based on the user's selection history and preferences, providing a more personalized experience.

[0079] The scene depiction unit can estimate the user's emotions and develop a scene according to the emotions. The scene depiction unit, for example, develops a system that analyzes the user's facial expressions and voice and estimates emotions in real time. For example, if the user is excited, the scene is developed to include many action scenes. The scene depiction unit also develops an algorithm that develops a scene according to the user's emotions based on the emotion estimation data. For example, if the user is sad, the scene is developed to include many moving scenes. The scene depiction unit also builds a system that estimates emotions in real time and dynamically adjusts the scene development according to the user's emotions. For example, the scene development is updated every time the user's emotions change. This allows the user to develop a scene according to their emotions, providing a story that is easier for the user to empathize with.

[0080] The scene depiction unit can generate a detailed environmental depiction based on the options selected by the user. The scene depiction unit develops, for example, a system that automatically generates a detailed environmental depiction based on a scene setting selected by the user. For example, the scene's weather and time of day are depicted in detail. The scene depiction unit also develops an algorithm that generates an environmental depiction based on the options. For example, a detailed depiction related to the location and background of the scene selected by the user is generated. The scene depiction unit also builds a system that generates a detailed environmental depiction of a scene based on the options selected by the user. For example, a depiction is created that includes sensory information such as the sounds and smells of the scene. This allows the scene to have depth by generating a detailed environmental depiction based on the options selected by the user.

[0081] The scene description unit can describe a scene not only using text, but also using images, audio, and video. For example, the scene description unit develops a system that displays illustrations and images of a scene in addition to text description. For example, it makes it possible to confirm the scenery of a scene and the movements of characters through images. The scene description unit also introduces audio description and builds a system that plays the sounds and sound effects of a scene through audio. For example, it makes it possible to select a scene by listening to the background sounds and character voices of the scene. The scene description unit also develops a system that uses video description to display the movements and development of a scene through video. For example, it shows the action scenes of a scene in a short video, making it easier for the user to select. In this way, by describing a scene visually and aurally, more intuitive scene description becomes possible.

[0082] The scene depiction unit can depict scenes in accordance with different cultures and languages, and can accommodate international users. The scene depiction unit, for example, develops a system that provides scene depictions in multiple languages. For example, it displays scene depictions in multiple languages, such as English, French, and Chinese. The scene depiction unit also presents scene depictions based on different cultural backgrounds. For example, it presents Japanese-style scenes to Japanese users and Western-style scenes to American users. The scene depiction unit also builds a system that customizes according to culture and language to accommodate international users. For example, it provides scene depictions based on the traditions and customs of each country. This makes the system usable by international users by being able to accommodate different cultures and languages.

[0083] The scene depiction unit can use the emotion estimation function to depict scenes that users can easily empathize with emotionally. The scene depiction unit develops a system that analyzes the user's facial expressions and voice, for example, and estimates emotions in real time. For example, if the user is moved, the system creates a depiction that includes many moving scenes. The scene depiction unit also develops an algorithm that depicts scenes that users can easily empathize with emotionally, based on the emotion estimation data. For example, if the user smiles, the system creates a depiction that includes many happy scenes. The scene depiction unit also performs emotion estimation in real time, and builds a system that dynamically adjusts the scene depiction according to the user's emotions. For example, the scene depiction is updated every time the user's emotions change. This allows the system to depict scenes that users can easily empathize with emotionally, providing a deeper experience.

[0084] The story output unit can perform customization based on the user's selection history and preferences. For example, the story output unit stores options previously selected by the user in a database and customizes the completed story based on that history. For example, it generates a story that includes many of the user's favorite characters and scenes. The story output unit also analyzes the selection history and develops an algorithm that learns the user's preferences and tendencies. For example, it customizes the story to match the user's favorite genre or style. The story output unit also builds a system that personalizes the completed story based on the user's selection history. For example, it generates a story that reflects settings and scenes previously selected. This makes it possible to provide a more personalized experience by customizing the story based on the user's selection history and preferences.

[0085] The story output unit can estimate the user's emotions regarding the completed story and provide feedback according to the emotions. The story output unit develops a system that, for example, analyzes the user's facial expressions and voice and estimates emotions in real time. For example, if the user is moved, it provides emotional feedback. The story output unit also develops an algorithm that provides feedback according to the user's emotions based on the emotion estimation data. For example, if the user smiles, it provides happy feedback. The story output unit also builds a system that estimates emotions in real time and dynamically adjusts feedback according to the user's emotions. For example, it updates the feedback every time the user's emotions change. This enables more appropriate feedback to be provided by providing feedback according to the user's emotions.

[0086] The story output unit can customize the output format of the completed story according to the user's preferences. The story output unit develops a system that customizes the story output format according to the user's preferences, for example. For example, the story is output in multiple formats, such as text format, PDF format, and e-book format. The story output unit also develops an algorithm that takes into account the user's past selection history when customizing the output format. For example, if a user previously selected PDF format, the unit will preferentially present PDF format next time. The story output unit also builds a system that customizes the layout and design of the story according to the user's preferences. For example, the unit allows the user to select fonts, colors, and page layout. This makes it possible to output a more appropriate story by providing an output format according to the user's preferences.

[0087] The story output unit can output the completed story not only using text, but also using images, audio, and video. The story output unit develops a system that outputs the story in a format that includes illustrations and images of the story in addition to text output. For example, it generates a PDF that includes illustrations of story scenes and characters. The story output unit also introduces audio output and builds a system that outputs the story in a format that includes narration and character voices. For example, it generates an audiobook of the story. The story output unit also develops a system that uses video output to display the movement and development of the story in video. For example, it generates an animated video of the story. This makes it possible to output the story more intuitively by outputting the story both visually and audibly.

[0088] The story output unit can output stories that correspond to different cultures and languages, and can also accommodate international users. The story output unit develops, for example, a system that provides story output that corresponds to multiple languages. For example, it outputs stories in multiple languages, such as English, French, and Chinese. The story output unit also presents story output based on different cultural backgrounds. For example, it outputs Japanese-style stories for Japanese users and Western-style stories for American users. The story output unit also builds a system that customizes according to culture and language to accommodate international users. For example, it provides story output based on the traditions and customs of each country. This makes the system usable by supporting different cultures and languages.

[0089] The story output unit can use the emotion estimation function to output a story that the user can easily empathize with emotionally. The story output unit develops a system that analyzes the user's facial expressions and voice, for example, and estimates emotions in real time. For example, if the user is moved, it outputs a story that includes many moving scenes. The story output unit also develops an algorithm that outputs a story that the user can easily empathize with emotionally, based on the emotion estimation data. For example, if the user smiles, it outputs a story that includes many happy scenes. The story output unit also constructs a system that estimates emotions in real time and dynamically adjusts the story output according to the user's emotions. For example, it updates the story output every time the user's emotions change. This allows the output of a story that the user can easily empathize with emotionally, providing a deeper experience.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The original novel creation system can further include an illustration generation unit that automatically generates illustrations based on user selections. For example, the AI ​​can automatically generate illustrations based on scenes and characters selected by the user and insert them into the story. The illustration generation unit can also change the design of the illustrations according to the style selected by the user. For example, it can generate fantasy-style illustrations for a fantasy-style story and futuristic illustrations for a science fiction story. The illustration generation unit can also provide a function that allows the user to customize some of the illustrations. For example, it can allow the user to select the character's clothing and background color. This allows the user to create an original story that is visually enjoyable.

[0092] The original novel creation system may further include an audio generation unit that automatically generates music and sound effects based on user selections. For example, background music and sound effects may be automatically generated and inserted into the story according to a scene selected by the user. The audio generation unit may also change the style of music according to a genre selected by the user. For example, orchestral music may be generated for a fantasy-style story, and electronic music may be generated for a science fiction story. The audio generation unit may also provide a function that allows the user to customize some of the music and sound effects. For example, the user may be able to adjust the tempo and volume of music used in a particular scene. This allows the user to create an original story that is also auditorily enjoyable.

[0093] The original novel creation system can further include a 3D model generation unit that generates a 3D model of a character based on a user's selections. For example, the AI ​​can automatically generate a 3D model based on the character settings selected by the user and insert it into the story. The 3D model generation unit can also change the design of the 3D model according to the style selected by the user. For example, it can generate a fantasy-style 3D model for a fantasy-style story and a futuristic 3D model for a science fiction story. The 3D model generation unit can also provide a function that allows the user to customize parts of the 3D model. For example, it can allow the user to select the character's pose and facial expression. This allows the user to create an original story that is visually enjoyable.

[0094] The original novel creation system may further include a visualization unit that visualizes the progression of the story in real time based on the user's selections. For example, the AI ​​may automatically graphically display the progression of the story based on scenes and characters selected by the user. The visualization unit may also change the design of the visualization according to the style selected by the user. For example, it may generate fantasy-style graphics for a fantasy-style story and futuristic graphics for a science fiction story. The visualization unit may also provide a function that allows the user to customize parts of the visualization. For example, it may allow the user to adjust the color and shape of graphics used in a particular scene. This allows the user to create an original story that is also visually enjoyable.

[0095] The original novel creation system can further include a voice navigation unit that provides voice navigation through the story based on the user's selections. For example, the AI ​​automatically provides voice guidance through the story based on the scenes and characters selected by the user. The voice navigation unit can also change the tone and accent of the voice according to the style selected by the user. For example, it can generate voice with an elegant tone for a fantasy-style story and voice with a futuristic tone for science fiction. The voice navigation unit can also provide a function that allows the user to customize parts of the voice. For example, it can allow the user to adjust the speed and volume of the voice used in a particular scene. This allows the user to create an original story that is also auditorily enjoyable.

[0096] The original novel creation system can also estimate the user's emotions and dynamically change the character's behavior in response to the user's emotions. For example, if the user is excited, the character's behavior can be made more active. On the other hand, if the user is sad, the character's behavior can be made more moving. It is also possible to build a system that adjusts the character's behavior in real time in response to the user's emotions based on the emotion estimation data. For example, the character's behavior can be updated every time the user's emotions change. This allows the character's behavior to be tailored to the user's emotions, making it easier for the reader to empathize with the story.

[0097] The original novel creation system can also estimate the user's emotions and dynamically change the scene descriptions according to the emotions. For example, if the user is excited, the scene descriptions can be made more active. If the user is sad, the scene descriptions can be made more moving. It is also possible to build a system that adjusts the scene descriptions according to the user's emotions in real time based on the emotion estimation data. For example, the scene descriptions can be updated every time the user's emotions change. This allows the story to progress in a way that is easier for the user to empathize with by providing scene descriptions that correspond to the user's emotions.

[0098] The original novel creation system can also estimate the user's emotions and dynamically change the story development according to the emotions. For example, if the user is excited, the story development can be made more active. On the other hand, if the user is sad, the story development can be made more moving. Furthermore, a system can be constructed that adjusts the story development according to the user's emotions in real time based on the emotion estimation data. For example, the story development can be updated every time the user's emotions change. This allows the story to progress in a way that is more relatable to the user by providing a story development that corresponds to the user's emotions.

[0099] The original novel creation system can further estimate the user's emotions and dynamically change the story ending according to the user's emotions. For example, if the user is excited, the story ending can be made more active. If the user is sad, the story ending can be made more moving. Furthermore, a system can be constructed that adjusts the story ending in real time according to the user's emotions based on the emotion estimation data. For example, the story ending can be updated every time the user's emotions change. This allows the story to progress in a way that is more relatable by providing a story ending that corresponds to the user's emotions.

[0100] The original novel creation system can further estimate the user's emotions and dynamically change the story theme according to the emotions. For example, if the user is excited, the story theme can be made more active. If the user is sad, the story theme can be made more moving. It is also possible to build a system that adjusts the story theme according to the user's emotions in real time based on the emotion estimation data. For example, the story theme can be updated every time the user's emotions change. This allows the story to progress in a way that is easier for the user to empathize with by providing a story theme that corresponds to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The option presentation unit presents multiple options to the user. For example, options for the beginning of the story include "a child lost in the forest," "a boy going on an adventure on a spaceship," and "a girl lost in a magical land." Options for character settings and scene descriptions are also presented. Step 2: The story generator generates a story based on the options presented by the option presenter. For example, if you select "A child lost in the woods," the AI ​​generates the rest of the story based on that setting. Step 3: The character setting unit sets up the characters based on the story generated by the story generation unit. For example, it presents options such as "What is the main character's personality?" and "What kind of animal is the main character's friend?", and the details of the character are determined by the user's selection. Step 4: The scene depiction unit depicts the scene based on the characters set by the character setting unit. For example, it presents options such as "Where will the protagonist go in the next scene?" and "Which characters will the protagonist meet?", and the scene progresses as the user makes their choice. Step 5: The story output unit outputs a story including the scenes described by the scene description unit. For example, after the user has selected all the options, the generation AI outputs the completed story.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. an option presentation unit that presents a plurality of options to a user; a story generation unit that generates a story based on the options presented by the option presentation unit; a character setting unit that sets characters based on the story generated by the story generation unit; a scene depiction unit that depicts a scene based on the character set by the character setting unit; a story output unit that outputs a story including the scene depicted by the scene depiction unit. A system characterized by:

2. The option presentation unit Learn the user's past selection history and present options optimized for each individual user.

2. The system of claim 1.

3. The option presentation unit The system estimates the user's emotions from their facial expressions and voice, and presents options according to their emotions.

2. The system of claim 1.

4. The option presentation unit Customize based on the user's age and interests 2. The system of claim 1.

5. The option presentation unit Present options not only through text but also through images, audio, and video.

2. The system of claim 1.

6. The option presentation unit Accommodate international users by presenting options that cater to different cultures and languages 2. The system of claim 1.

Images