System

Abstract

An object of the system according to the embodiment is to allow a user to easily express his / her memories as comics.SOLUTION: A system according to an embodiment includes a user input reception unit, a story generation unit, a character generation unit, and a comic generation unit. The user input reception unit receives text or an image input by a user. The story generation unit generates a story on the basis of the text or the image received by the user input reception unit. The character generation unit generates a character on the basis of the story generated by the story generation unit. The cartoon generation unit generates a cartoon based on the story and the character generated by the story generation unit and the character generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult for users to easily express their memories as comics.

[0005] The system according to the embodiment aims to allow users to easily express their memories in the form of comics. [Means for solving the problem]

[0006] The system according to the embodiment includes a user input accepting unit, a story generation unit, a character generation unit, and a manga generation unit. The user input accepting unit accepts text or images input by a user. The story generation unit generates a story based on the text or images accepted by the user input accepting unit. The character generation unit generates characters based on the story generated by the story generation unit. The manga generation unit generates a manga based on the story and characters generated by the story generation unit and the character generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily express their memories as comics. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The manga generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates a manga based on the contents of memories input by a user. This allows the manga generation system to easily turn the user's memories into a manga.

[0029] A manga creation system according to an embodiment includes a user input accepting unit, a story generating unit, a character generating unit, and a manga creating unit. The user input accepting unit accepts text or images entered by a user. For example, a user can enter text about their travel memories. The user can also upload family photos. The user input accepting unit can also accept voice input. For example, a user can enter their travel memories by voice. The story generating unit generates a story based on the text or images accepted by the user input accepting unit. For example, the generation AI creates a story based on travel episodes based on the travel memories entered by the user. The generation AI also creates a story based on family episodes based on family photos entered by the user. The character generating unit generates characters based on the story generated by the story generating unit. For example, the generation AI analyzes family photos provided by the user and makes family members appear as characters in the manga. The generation AI also automatically sets the character's appearance and personality based on the text entered by the user. The manga creating unit generates a manga based on the story and characters generated by the story generating unit and the character generating unit. For example, the generation AI automatically generates manga panels and dialogue based on the generated story and characters. The generation AI also outputs manga in the form of a manga based on the memories entered by the user. This allows the manga generation system according to the embodiment to easily turn the user's memories into a manga. For example, the user can download the generated manga or share it on social media. The user can also customize the generated manga. For example, the user can change the dialogue or adjust the character's facial expressions.

[0030] The user input accepting unit accepts voice input from the user, and the generation AI can analyze the voice to generate a story. For example, the user may input travel memories by voice in the user input accepting unit, and the generation AI analyzes the voice to generate a story. For example, when the user talks about events that occurred on a trip, the content is automatically converted into text and reflected in the story. The user input accepting unit also accepts voice input of family anecdotes, and the generation AI analyzes the voice to generate a character. For example, when the user talks about the characteristics of their family, the content is reflected in the character's appearance and personality. The user input accepting unit also accepts voice input from the user, and the generation AI automatically generates the manga panels and dialogue based on the content input by the user. For example, what the user says is reflected directly as dialogue in the manga. This makes it easier to turn a user's memories into a manga through voice input.

[0031] The user input accepting unit allows the generation AI to automatically collect relevant background or supplemental information from the Internet for the content of memories entered by the user, and the story generating unit can reflect that information in the story. For example, when the user enters travel memories, the user input accepting unit allows the generation AI to collect information about the history and tourist spots of the travel destination from the Internet and reflect that information in the story. For example, information about the famous places and culture of the travel destination is added to the story. Furthermore, when the user inputs a family episode, the generation AI collects the historical background and related events from the Internet and reflects that information in the story. For example, the social situation and trends of the era in which the family episode occurred are added to the story. Furthermore, the user input accepting unit collects images and videos related to the content entered by the user from the Internet and reflects that information in the story. For example, scenic photos of the travel destination and related videos are incorporated into the story. In this way, by automatically collecting background and supplemental information and reflecting it in the story, it is possible to generate manga with richer content.

[0032] The user input accepting unit allows the generation AI to automatically tag text or images entered by the user, and the story generating unit can set the story theme based on the tags. For example, when a user enters travel memories in the user input accepting unit, the generation AI automatically tags them with "travel," "adventure," "sightseeing," etc., and sets the story theme based on the tags. For example, a story with an "adventure" theme is generated. When a user enters a family episode in the user input accepting unit, the generation AI automatically tags them with "family," "memories," "moving," etc., and sets the story theme based on the tags. For example, a story with an "moving ... The user input accepting unit also allows the generation AI to automatically tag content entered by the user, and adjusts character settings and story development based on the tags. For example, if a "friendship" tag is added, a story with a friendship theme is generated. This allows for automatic tagging and story theme setting, resulting in the generation of more consistent stories.

[0033] The user input accepting unit combines memories input by different users, and the story generating unit can generate a joint story told from multiple perspectives. The user input accepting unit, for example, combines travel memories input by different users to generate a joint story told from multiple perspectives. For example, different experiences at the same travel destination are compiled into one story. The user input accepting unit also inputs family episodes from different family members and combines them to generate a joint story. For example, an episode told from the perspective of the entire family is generated. The user input accepting unit also combines content input by different users to generate a story told from multiple perspectives. For example, memories between friends are combined to generate a story themed around friendships. In this way, by combining memories from different users, stories from more diverse perspectives can be generated.

[0034] The story generation unit can refer to the user's past input history and create a consistent series of stories. The story generation unit, for example, refers to travel memories input by the user in the past and generates a consistent series of stories. For example, a sequel story is created based on a past travel episode. The story generation unit also refers to family episodes from the past input history and generates a consistent series of stories. For example, a series of stories depicting the growth and changes of a family is generated. The story generation unit also refers to the user's past input history and generates a consistent series of stories. For example, a sequel story is created based on memories with friends. In this way, a consistent series of stories can be generated by referring to the past input history.

[0035] The story generation unit allows the generation AI to automatically set plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. The story generation unit, for example, sets plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, by selecting different tourist spots in travel memories, different endings are generated. The story generation unit also sets plot branching points in family episodes, allowing the user to enjoy different endings by selecting options. For example, by selecting different actions in family events, different endings are generated. The story generation unit also sets plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, by making different choices in memories with friends, different endings are generated. In this way, it is possible to generate stories in which the user can enjoy different endings by selecting options.

[0036] The story generation unit can incorporate elements from different cultures or regions when the generation AI generates a story, creating a story from a global perspective. For example, the story generation unit can incorporate elements from different cultures or regions when the generation AI generates a story, creating a story from a global perspective. For example, it can add elements from different cultures to travel memories. The story generation unit can also incorporate elements from different regions into family episodes, creating a story from a global perspective. For example, it can incorporate customs and habits of different regions into the story. The story generation unit can also incorporate elements from different cultures or regions when the generation AI generates a story, creating a story from a global perspective. For example, it can add elements from different cultures to memories with friends. In this way, it can generate a story from a global perspective by incorporating elements from different cultures or regions.

[0037] The story generation unit allows the generation AI to automatically add music or sound effects when generating a story, thereby creating a multimedia story that appeals to both the visual and the auditory senses. For example, the story generation unit automatically adds music and sound effects when generating a story, thereby creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for travel scenes are added. The story generation unit also automatically adds music and sound effects to family episodes, thereby creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for moving scenes are added. The story generation unit also allows the generation AI to automatically add music and sound effects when generating a story, thereby creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for memories with friends are added. In this way, by adding music and sound effects, it is possible to create a multimedia story that appeals to both the visual and the auditory senses.

[0038] The character generation unit can depict consistent character growth or changes by referring to the user's past input data. The character generation unit can depict consistent character growth or changes by referring to, for example, family photos input by the user in the past. For example, a character depicting a child's growth process is generated. The character generation unit can also depict consistent character growth or changes by referring to photos of friends from the past input data. For example, a character depicting changes in friendships is generated. The character generation unit can also depict consistent character growth or changes by referring to the user's past input data. For example, a character depicting a pet's growth process is generated. In this way, consistent character growth and changes can be depicted by referring to the past input data.

[0039] The character generation unit allows the generation AI to automatically create a character's backstory when generating a character, giving the character depth. For example, the character generation unit automatically creates a character's backstory when generating a character, giving the character depth. For example, adding a past episode of the character that appears in travel memories. The character generation unit also allows the generation AI to automatically create a backstory of the character that appears in family episodes, giving the character depth. For example, adding family history and background. The character generation unit also allows the generation AI to automatically create a character's backstory when generating a character, giving the character depth. For example, adding a past episode of the character that appears in memories with friends. In this way, by giving the character a backstory, the character can be given depth.

[0040] The character generation unit can incorporate different art styles or designs when the generation AI generates a character, allowing the user to select from them. For example, the character generation unit can incorporate different art styles and designs when the generation AI generates a character, allowing the user to select from them. For example, styles such as anime, realistic, and comic book can be selected from among others. The character generation unit also allows the user to select the art style and design of characters that appear in family episodes. For example, the user can select their preferred style to generate a character. The character generation unit can also incorporate different art styles and designs when the generation AI generates a character, allowing the user to select from them. For example, the style of characters that appear in memories with friends can be selected from among others. This allows the generation of characters that can be selected from by incorporating different art styles and designs.

[0041] The character generation unit allows the generation AI to automatically generate character voices when generating characters, thereby creating comics with audio. For example, the character generation unit automatically generates character voices when generating characters, thereby creating comics with audio. For example, the voices of characters appearing in travel memories are automatically generated. The character generation unit also allows the generation AI to automatically generate voices for characters appearing in family episodes, thereby creating comics with audio. For example, the voices of family members are automatically generated. The character generation unit also allows the generation AI to automatically generate character voices when generating characters, thereby creating comics with audio. For example, the voices of characters appearing in memories with friends are automatically generated. In this way, comics with audio can be created by automatically generating character voices.

[0042] The manga generation unit can refer to the user's past works and create a consistent manga series. The manga generation unit, for example, refers to a travel manga created by the user in the past and creates a consistent manga series. For example, a sequel manga is created based on a past travel episode. The manga generation unit also refers to family episodes from past works and creates a consistent manga series. For example, a manga series depicting the growth and change of a family is created. The manga generation unit also refers to the user's past works and creates a consistent manga series. For example, a sequel manga is created based on memories with friends. In this way, a consistent manga series can be created by referring to past works.

[0043] The manga generation unit allows the generation AI to automatically optimize the page layout or panel layout when generating a manga, thereby creating a visually appealing manga. For example, the manga generation unit allows the generation AI to automatically optimize the page layout and panel layout when generating a manga, thereby creating a visually appealing manga. For example, the generation AI automatically generates a layout and panel layout that is suitable for travel scenes. The manga generation unit also allows the generation AI to automatically optimize the page layout and panel layout that is suitable for family episodes, thereby creating a visually appealing manga. For example, the generation AI automatically generates a layout and panel layout that is suitable for moving scenes. The manga generation unit also allows the generation AI to automatically optimize the page layout and panel layout when generating a manga, thereby creating a visually appealing manga. For example, the generation AI automatically generates a layout and panel layout that is suitable for memories with friends. In this way, by optimizing the page layout and panel layout, a visually appealing manga can be created.

[0044] The manga generation unit can enable the generation AI to convert into different media formats when generating a manga. The manga generation unit, for example, enables the generation AI to convert into different media formats when generating a manga. For example, it converts travel memories into animation or video format. The manga generation unit also enables the generation AI to convert family episodes into different media formats. For example, it converts moving scenes into animation or video format. The manga generation unit also enables the generation AI to convert into different media formats when generating a manga. For example, it converts memories with friends into animation or video format. This makes it possible to generate manga that can be enjoyed as animation or video by converting into different media formats.

[0045] The manga generation unit allows the generation AI to automatically translate when generating a manga, thereby creating a manga in different languages. For example, the manga generation unit automatically translates when generating a manga, thereby creating a manga in different languages. For example, it converts travel memories into a manga that supports multiple languages. The manga generation unit also automatically translates family episodes and creates a manga in different languages. For example, it converts moving scenes into a manga that supports multiple languages. The manga generation unit also automatically translates when generating a manga, thereby creating a manga in different languages. For example, it converts memories with friends into a manga that supports multiple languages. This allows manga to be created in different languages ​​by automatically translating.

[0046] When a user customizes, the generation AI can automatically make suggestions and provide customization options. For example, when a user tries to change the lines of a manga, the generation AI automatically suggests multiple lines. For example, it suggests lines that are appropriate for a moving scene. When a user tries to adjust a character's facial expression, the generation AI automatically provides multiple facial expression options. For example, it suggests expressions such as joy, surprise, and sadness. When a user tries to change the background, the generation AI automatically provides multiple background options. For example, it suggests scenery from a travel destination or a background of the family home. This makes customization easier for users by making automatic suggestions.

[0047] When customizing, the customization function allows the generation AI to refer to the user's past customization history and suggest consistent customizations. For example, the customization function may refer to lines or facial expressions that the user has changed in the past and suggest consistent customizations. For example, new suggestions may be made based on lines or facial expressions used in the past. The customization function may also refer to backgrounds and character designs that the user has selected in the past and suggest consistent customizations. For example, new suggestions may be made based on backgrounds and designs used in the past. The customization function may also refer to the user's past customization history and suggest consistent customizations. For example, new suggestions may be made based on colors and styles selected in the past. In this way, by referring to past customization history, consistent customizations may be suggested.

[0048] The customization function allows the generation AI to automatically suggest different art styles or designs when a user customizes. For example, when a user tries to change the dialogue in a manga, the generation AI automatically suggests dialogue in different art styles and designs. For example, it suggests styles such as hand-drawn or digital. Also, when a user tries to adjust a character's facial expression, the generation AI automatically suggests facial expressions in different art styles and designs. For example, it suggests styles such as anime or realistic. Also, when a user tries to change the background, the generation AI automatically suggests backgrounds in different art styles and designs. For example, it suggests styles such as hand-drawn or photographic. This expands the range of customizations available to users by suggesting different art styles and designs.

[0049] The customization function allows the generation AI to automatically add character voices or music when customizing, thereby providing customization with audio. For example, when a user attempts to change lines in a manga, the generation AI automatically generates a character voice and provides customization with audio. For example, a voice that matches the lines is automatically generated. Furthermore, when a user attempts to adjust a character's facial expression, the generation AI automatically adds music and provides customization with audio. For example, music that is suitable for an emotional scene is added. Furthermore, when a user attempts to change the background, the generation AI automatically adds music and sound effects and provides customization with audio. For example, music and sound effects that match the scenery of a travel destination are added. This allows customization with audio to be provided by adding character voices and music.

[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 user input accepting unit allows the generation AI to automatically tag text or images entered by the user, and the story generating unit can set the story theme based on the tags. For example, if a user enters travel memories, the generation AI automatically tags them with "travel," "adventure," "sightseeing," etc., and sets the story theme based on the tags. For example, a story with an "adventure" theme is generated. Also, if the user input accepting unit enters a family episode, the generation AI automatically tags them with "family," "memories," "moving," etc., and sets the story theme based on the tags. For example, a story with an "moving" theme is generated. Also, the user input accepting unit allows the generation AI to automatically tag content entered by the user, and adjusts character settings and story development based on the tags. For example, if a "friendship" tag is added, a story with a friendship theme is generated. This allows for automatic tagging and story theme setting, resulting in the generation of more consistent stories.

[0052] The user input accepting unit combines memories input by different users, and the story generating unit can generate a joint story told from multiple perspectives. For example, travel memories input by different users can be combined to generate a joint story told from multiple perspectives. For example, different experiences at the same travel destination can be compiled into a single story. The user input accepting unit also allows different family members to input family episodes and combines them to generate a joint story. For example, an episode told from the perspective of the entire family can be generated. The user input accepting unit also combines content input by different users to generate a story told from multiple perspectives. For example, memories between friends can be combined to generate a story themed around friendships. In this way, by combining memories from different users, stories can be generated from a wider variety of perspectives.

[0053] The story generation unit can refer to the user's past input history to create a consistent series of stories. For example, it generates a consistent series of stories by referring to travel memories input by the user in the past. For example, it creates a sequel story based on past travel episodes. The story generation unit also generates a consistent series of stories by referring to family episodes from the past input history. For example, it generates a series of stories depicting the growth and changes of a family. The story generation unit also generates a consistent series of stories by referring to the user's past input history. For example, it creates a sequel story based on memories with friends. In this way, it is possible to generate a consistent series of stories by referring to the past input history.

[0054] The story generation unit allows the generation AI to automatically set plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, the generation AI sets plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, by selecting different tourist spots in travel memories, different endings are generated. The story generation unit also sets plot branching points in family episodes, allowing the user to enjoy different endings by selecting options. For example, by selecting different actions in family events, different endings are generated. The story generation unit also sets plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, by making different choices in memories with friends, different endings are generated. This makes it possible to generate stories in which the user can enjoy different endings by selecting options.

[0055] When the generation AI generates a story, the story generation unit can incorporate elements from different cultures or regions to create a story from a global perspective. For example, when the generation AI generates a story, it can incorporate elements from different cultures or regions to create a story from a global perspective. For example, it can add elements from different cultures to travel memories. The story generation unit can also incorporate elements from different regions into family episodes to create a story from a global perspective. For example, it can incorporate customs and habits from different regions into the story. The story generation unit can also incorporate elements from different cultures or regions to create a story from a global perspective when the generation AI generates a story. For example, it can add elements from different cultures to memories with friends. In this way, by incorporating elements from different cultures and regions, it can generate a story from a global perspective.

[0056] The story generation unit allows the generation AI to automatically add music and sound effects when generating a story, creating a multimedia story that appeals to both the visual and the auditory senses. For example, the generation AI automatically adds music and sound effects when generating a story, creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for travel scenes are added. The story generation unit also automatically adds music and sound effects to family episodes, creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for moving scenes are added. The story generation unit also allows the generation AI to automatically add music and sound effects when generating a story, creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for memories with friends are added. In this way, by adding music and sound effects, it is possible to create a multimedia story that appeals to both the visual and the auditory senses.

[0057] The character generation unit can refer to the user's past input data to depict consistent character growth or changes. For example, by referring to family photos previously input by the user, consistent character growth or changes can be depicted. For example, a character depicting a child's growth process can be generated. The character generation unit can also refer to photos of friends from the past input data to depict consistent character growth or changes. For example, a character depicting changes in friendships can be generated. The character generation unit can also refer to the user's past input data to depict consistent character growth or changes. For example, a character depicting a pet's growth process can be generated. In this way, consistent character growth and changes can be depicted by referring to the past input data.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The user input accepting unit accepts text or images entered by the user. For example, the user can enter travel memories in text. The user can also upload family photos. Furthermore, the user input accepting unit can also accept voice input. For example, the user can enter travel memories in voice. Step 2: The story generation unit generates a story based on the text or images received by the user input reception unit. For example, the generation AI creates a story based on travel episodes based on travel memories input by the user. Also, the generation AI creates a story based on family episodes based on family photos input by the user. Step 3: The character generation unit generates characters based on the story generated by the story generation unit. For example, the generation AI analyzes family photos provided by the user and creates characters from the family in the manga. The generation AI also automatically sets the character's appearance and personality based on the text entered by the user. Step 4: The manga generation unit generates a manga based on the story and characters generated by the story generation unit and character generation unit. For example, the generation AI automatically generates the manga's panels and dialogue based on the generated story and characters. The generation AI also outputs the memories entered by the user in manga format.

[0060] (Example 2) The manga generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates a manga based on the contents of memories input by a user. This allows the manga generation system to easily turn the user's memories into a manga.

[0061] A manga creation system according to an embodiment includes a user input accepting unit, a story generating unit, a character generating unit, and a manga creating unit. The user input accepting unit accepts text or images entered by a user. For example, a user can enter text about their travel memories. The user can also upload family photos. The user input accepting unit can also accept voice input. For example, a user can enter their travel memories by voice. The story generating unit generates a story based on the text or images accepted by the user input accepting unit. For example, the generation AI creates a story based on travel episodes based on the travel memories entered by the user. The generation AI also creates a story based on family episodes based on family photos entered by the user. The character generating unit generates characters based on the story generated by the story generating unit. For example, the generation AI analyzes family photos provided by the user and makes family members appear as characters in the manga. The generation AI also automatically sets the character's appearance and personality based on the text entered by the user. The manga creating unit generates a manga based on the story and characters generated by the story generating unit and the character generating unit. For example, the generation AI automatically generates manga panels and dialogue based on the generated story and characters. The generation AI also outputs manga in the form of a manga based on the memories entered by the user. This allows the manga generation system according to the embodiment to easily turn the user's memories into a manga. For example, the user can download the generated manga or share it on social media. The user can also customize the generated manga. For example, the user can change the dialogue or adjust the character's facial expressions.

[0062] The user input accepting unit accepts voice input from the user, and the generation AI can analyze the voice to generate a story. For example, the user may input travel memories by voice in the user input accepting unit, and the generation AI analyzes the voice to generate a story. For example, when the user talks about events that occurred on a trip, the content is automatically converted into text and reflected in the story. The user input accepting unit also accepts voice input of family anecdotes, and the generation AI analyzes the voice to generate a character. For example, when the user talks about the characteristics of their family, the content is reflected in the character's appearance and personality. The user input accepting unit also accepts voice input from the user, and the generation AI automatically generates the manga panels and dialogue based on the content input by the user. For example, what the user says is reflected directly as dialogue in the manga. This makes it easier to turn a user's memories into a manga through voice input.

[0063] The user input accepting unit allows the generation AI to automatically collect relevant background or supplemental information from the Internet for the content of memories entered by the user, and the story generating unit can reflect that information in the story. For example, when the user enters travel memories, the user input accepting unit allows the generation AI to collect information about the history and tourist spots of the travel destination from the Internet and reflect that information in the story. For example, information about the famous places and culture of the travel destination is added to the story. Furthermore, when the user inputs a family episode, the generation AI collects the historical background and related events from the Internet and reflects that information in the story. For example, the social situation and trends of the era in which the family episode occurred are added to the story. Furthermore, the user input accepting unit collects images and videos related to the content entered by the user from the Internet and reflects that information in the story. For example, scenic photos of the travel destination and related videos are incorporated into the story. In this way, by automatically collecting background and supplemental information and reflecting it in the story, it is possible to generate manga with richer content.

[0064] The user input accepting unit uses an emotion estimation function to analyze the emotions expressed by the user when inputting information, and the story generating unit can adjust the tone or atmosphere of the story based on those emotions. For example, the user input accepting unit analyzes the emotions expressed by the user when inputting travel memories, and the generation AI adjusts the tone of the story based on those emotions. For example, if the user is talking happily, the story will have a bright and cheerful atmosphere. The user input accepting unit also analyzes the emotions expressed by the user when inputting a family episode, and the generation AI adjusts the character's facial expressions and movements based on those emotions. For example, if the user is telling a moving story, the character will also have a moving facial expression. The user input accepting unit also analyzes the emotions expressed by the user when inputting information in real time, and adjusts the panel layout and dialogue of the manga based on those emotions. For example, if the user is talking excitedly, the panels of the manga will also be more dynamic. By adjusting the tone and atmosphere of the story based on the user's emotions, a manga that resonates with users more emotionally can be generated.

[0065] The user input accepting unit allows the generation AI to automatically tag text or images entered by the user, and the story generating unit can set the story theme based on the tags. For example, when a user enters travel memories in the user input accepting unit, the generation AI automatically tags them with "travel," "adventure," "sightseeing," etc., and sets the story theme based on the tags. For example, a story with an "adventure" theme is generated. When a user enters a family episode in the user input accepting unit, the generation AI automatically tags them with "family," "memories," "moving," etc., and sets the story theme based on the tags. For example, a story with an "moving ... The user input accepting unit also allows the generation AI to automatically tag content entered by the user, and adjusts character settings and story development based on the tags. For example, if a "friendship" tag is added, a story with a friendship theme is generated. This allows for automatic tagging and story theme setting, resulting in the generation of more consistent stories.

[0066] The user input accepting unit combines memories input by different users, and the story generating unit can generate a joint story told from multiple perspectives. The user input accepting unit, for example, combines travel memories input by different users to generate a joint story told from multiple perspectives. For example, different experiences at the same travel destination are compiled into one story. The user input accepting unit also inputs family episodes from different family members and combines them to generate a joint story. For example, an episode told from the perspective of the entire family is generated. The user input accepting unit also combines content input by different users to generate a story told from multiple perspectives. For example, memories between friends are combined to generate a story themed around friendships. In this way, by combining memories from different users, stories from more diverse perspectives can be generated.

[0067] The user input accepting unit can use the emotion estimation function to provide real-time feedback on the emotions of the user when inputting information and provide advice to elicit positive emotions. For example, when a user inputs travel memories, the emotion estimation function analyzes the emotions in real time and provides advice to elicit positive emotions. For example, the emotion estimation function displays advice such as, "Please tell us more about the fun things that happened at that time." Furthermore, when a user inputs a family episode, the emotion estimation function analyzes the emotions in real time and provides advice to elicit positive emotions. For example, the emotion estimation function displays advice such as, "Please tell us more about the touching moment at that time." Furthermore, the user input accepting unit analyzes the emotions of the user when inputting information in real time and provides advice to elicit positive emotions. For example, the emotion estimation function displays advice such as, "Please tell us more about the happy things that happened at that time." In this way, the user's emotions are fed back in real time and positive emotions are elicited, thereby generating a better story.

[0068] The story generation unit can refer to the user's past input history and create a consistent series of stories. The story generation unit, for example, refers to travel memories input by the user in the past and generates a consistent series of stories. For example, a sequel story is created based on a past travel episode. The story generation unit also refers to family episodes from the past input history and generates a consistent series of stories. For example, a series of stories depicting the growth and changes of a family is generated. The story generation unit also refers to the user's past input history and generates a consistent series of stories. For example, a sequel story is created based on memories with friends. In this way, a consistent series of stories can be generated by referring to the past input history.

[0069] The story generation unit allows the generation AI to automatically set plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. The story generation unit, for example, sets plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, by selecting different tourist spots in travel memories, different endings are generated. The story generation unit also sets plot branching points in family episodes, allowing the user to enjoy different endings by selecting options. For example, by selecting different actions in family events, different endings are generated. The story generation unit also sets plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, by making different choices in memories with friends, different endings are generated. In this way, it is possible to generate stories in which the user can enjoy different endings by selecting options.

[0070] The story generation unit uses the emotion estimation function to adjust the development of the story in real time according to the user's emotions, thereby increasing emotional empathy. The story generation unit, for example, uses the emotion estimation function to adjust the development of the story in real time according to the emotions of the user when inputting travel memories. For example, if the user is talking happily, the story will also develop in a bright and fun manner. The story generation unit also adjusts the development of the story in real time according to the emotions of the user when inputting a family episode. For example, if the user is telling a moving story, the story will also develop in a moving manner. The story generation unit also adjusts the development of the story in real time according to the user's emotions, thereby increasing emotional empathy. For example, if the user is talking excitedly, the story will also develop in a dynamic manner. In this way, emotional empathy can be increased by adjusting the development of the story in real time according to the user's emotions.

[0071] The story generation unit can incorporate elements from different cultures or regions when the generation AI generates a story, creating a story from a global perspective. For example, the story generation unit can incorporate elements from different cultures or regions when the generation AI generates a story, creating a story from a global perspective. For example, it can add elements from different cultures to travel memories. The story generation unit can also incorporate elements from different regions into family episodes, creating a story from a global perspective. For example, it can incorporate customs and habits of different regions into the story. The story generation unit can also incorporate elements from different cultures or regions when the generation AI generates a story, creating a story from a global perspective. For example, it can add elements from different cultures to memories with friends. In this way, it can generate a story from a global perspective by incorporating elements from different cultures or regions.

[0072] The story generation unit allows the generation AI to automatically add music or sound effects when generating a story, thereby creating a multimedia story that appeals to both the visual and the auditory senses. For example, the story generation unit automatically adds music and sound effects when generating a story, thereby creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for travel scenes are added. The story generation unit also automatically adds music and sound effects to family episodes, thereby creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for moving scenes are added. The story generation unit also allows the generation AI to automatically add music and sound effects when generating a story, thereby creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for memories with friends are added. In this way, by adding music and sound effects, it is possible to create a multimedia story that appeals to both the visual and the auditory senses.

[0073] The story generation unit can use the emotion estimation function to identify story elements to which the user responds most emotionally and emphasize those elements. For example, the story generation unit uses the emotion estimation function to identify story elements to which the user responds most emotionally when inputting travel memories and emphasize those elements. For example, it emphasizes parts where the user is talking happily. The story generation unit can also identify story elements to which the user responds most emotionally when inputting family episodes and emphasize those elements. For example, it emphasizes parts where the user is talking movingly. The story generation unit can also identify story elements to which the user responds most emotionally and emphasize those elements. For example, it emphasizes parts where the user is talking excitedly. In this way, by emphasizing the elements to which the user responds most emotionally, emotional empathy can be increased.

[0074] The character generation unit can depict consistent character growth or changes by referring to the user's past input data. The character generation unit can depict consistent character growth or changes by referring to, for example, family photos input by the user in the past. For example, a character depicting a child's growth process is generated. The character generation unit can also depict consistent character growth or changes by referring to photos of friends from the past input data. For example, a character depicting changes in friendships is generated. The character generation unit can also depict consistent character growth or changes by referring to the user's past input data. For example, a character depicting a pet's growth process is generated. In this way, consistent character growth and changes can be depicted by referring to the past input data.

[0075] The character generation unit allows the generation AI to automatically create a character's backstory when generating a character, giving the character depth. For example, the character generation unit automatically creates a character's backstory when generating a character, giving the character depth. For example, adding a past episode of the character that appears in travel memories. The character generation unit also allows the generation AI to automatically create a backstory of the character that appears in family episodes, giving the character depth. For example, adding family history and background. The character generation unit also allows the generation AI to automatically create a character's backstory when generating a character, giving the character depth. For example, adding a past episode of the character that appears in memories with friends. In this way, by giving the character a backstory, the character can be given depth.

[0076] The character generation unit can use the emotion estimation function to adjust the character's facial expression or behavior in real time according to the user's emotions. For example, the character generation unit uses the emotion estimation function to adjust the character's facial expression and behavior in real time according to the emotions the user feels when inputting travel memories. For example, if the user is talking happily, the character also has a happy expression. The character generation unit also adjusts the character's facial expression and behavior in real time according to the emotions the user feels when inputting a family episode. For example, if the user is telling a moving story, the character also has a moving expression. The character generation unit also adjusts the character's facial expression and behavior in real time according to the user's emotions. For example, if the user is talking excitedly, the character also has a dynamic facial expression and behavior. In this way, by adjusting the character's facial expression and behavior in real time according to the user's emotions, it is possible to generate a character that can evoke greater emotional empathy.

[0077] The character generation unit can incorporate different art styles or designs when the generation AI generates a character, allowing the user to select from them. For example, the character generation unit can incorporate different art styles and designs when the generation AI generates a character, allowing the user to select from them. For example, styles such as anime, realistic, and comic book can be selected from among others. The character generation unit also allows the user to select the art style and design of characters that appear in family episodes. For example, the user can select their preferred style to generate a character. The character generation unit can also incorporate different art styles and designs when the generation AI generates a character, allowing the user to select from them. For example, the style of characters that appear in memories with friends can be selected from among others. This allows the generation of characters that can be selected from by incorporating different art styles and designs.

[0078] The character generation unit allows the generation AI to automatically generate character voices when generating characters, thereby creating comics with audio. For example, the character generation unit automatically generates character voices when generating characters, thereby creating comics with audio. For example, the voices of characters appearing in travel memories are automatically generated. The character generation unit also allows the generation AI to automatically generate voices for characters appearing in family episodes, thereby creating comics with audio. For example, the voices of family members are automatically generated. The character generation unit also allows the generation AI to automatically generate character voices when generating characters, thereby creating comics with audio. For example, the voices of characters appearing in memories with friends are automatically generated. In this way, comics with audio can be created by automatically generating character voices.

[0079] The character generation unit can use the emotion estimation function to identify the characteristics of a character that the user most emotionally empathizes with and emphasize those characteristics. For example, the character generation unit uses the emotion estimation function to identify the characteristics of a character that the user most emotionally empathizes with when inputting travel memories and emphasize those characteristics. For example, the character generation unit emphasizes the characteristics of a character that the user is speaking happily. The character generation unit can also identify the characteristics of a character that the user most emotionally empathizes with when inputting a family episode and emphasize those characteristics. For example, the character generation unit emphasizes the characteristics of a character that the user is speaking emotionally. The character generation unit can also identify the characteristics of a character that the user most emotionally empathizes with and emphasize those characteristics. For example, the character generation unit emphasizes the characteristics of a character that the user is speaking excitedly. In this way, by emphasizing the characteristics of a character that the user most emotionally empathizes with, emotional empathy can be increased.

[0080] The manga generation unit can refer to the user's past works and create a consistent manga series. The manga generation unit, for example, refers to a travel manga created by the user in the past and creates a consistent manga series. For example, a sequel manga is created based on a past travel episode. The manga generation unit also refers to family episodes from past works and creates a consistent manga series. For example, a manga series depicting the growth and change of a family is created. The manga generation unit also refers to the user's past works and creates a consistent manga series. For example, a sequel manga is created based on memories with friends. In this way, a consistent manga series can be created by referring to past works.

[0081] The manga generation unit allows the generation AI to automatically optimize the page layout or panel layout when generating a manga, thereby creating a visually appealing manga. For example, the manga generation unit allows the generation AI to automatically optimize the page layout and panel layout when generating a manga, thereby creating a visually appealing manga. For example, the generation AI automatically generates a layout and panel layout that is suitable for travel scenes. The manga generation unit also allows the generation AI to automatically optimize the page layout and panel layout that is suitable for family episodes, thereby creating a visually appealing manga. For example, the generation AI automatically generates a layout and panel layout that is suitable for moving scenes. The manga generation unit also allows the generation AI to automatically optimize the page layout and panel layout when generating a manga, thereby creating a visually appealing manga. For example, the generation AI automatically generates a layout and panel layout that is suitable for memories with friends. In this way, by optimizing the page layout and panel layout, a visually appealing manga can be created.

[0082] The cartoon generation unit can use the emotion estimation function to adjust the tone or atmosphere of the cartoon in real time according to the user's emotions. For example, the cartoon generation unit uses the emotion estimation function to adjust the tone and atmosphere of the cartoon in real time according to the emotions the user feels when inputting their travel memories. For example, if the user is talking happily, the cartoon will have a bright and cheerful atmosphere. The cartoon generation unit also adjusts the tone and atmosphere of the cartoon in real time according to the emotions the user feels when inputting a family episode. For example, if the user is telling a moving story, the cartoon will also have a moving atmosphere. The cartoon generation unit also adjusts the tone and atmosphere of the cartoon in real time according to the user's emotions. For example, if the user is talking excitedly, the cartoon will have a dynamic atmosphere. In this way, by adjusting the tone and atmosphere of the cartoon in real time according to the user's emotions, a cartoon that will resonate with more emotionally is generated.

[0083] The manga generation unit can enable the generation AI to convert into different media formats when generating a manga. The manga generation unit, for example, enables the generation AI to convert into different media formats when generating a manga. For example, it converts travel memories into animation or video format. The manga generation unit also enables the generation AI to convert family episodes into different media formats. For example, it converts moving scenes into animation or video format. The manga generation unit also enables the generation AI to convert into different media formats when generating a manga. For example, it converts memories with friends into animation or video format. This makes it possible to generate manga that can be enjoyed as animation or video by converting into different media formats.

[0084] The manga generation unit allows the generation AI to automatically translate when generating a manga, thereby creating a manga in different languages. For example, the manga generation unit automatically translates when generating a manga, thereby creating a manga in different languages. For example, it converts travel memories into a manga that supports multiple languages. The manga generation unit also automatically translates family episodes and creates a manga in different languages. For example, it converts moving scenes into a manga that supports multiple languages. The manga generation unit also automatically translates when generating a manga, thereby creating a manga in different languages. For example, it converts memories with friends into a manga that supports multiple languages. This allows manga to be created in different languages ​​by automatically translating.

[0085] The cartoon generation unit can use the emotion estimation function to identify scenes to which the user responds most emotionally and emphasize those scenes. For example, the cartoon generation unit uses the emotion estimation function to identify scenes to which the user responds most emotionally when inputting travel memories and emphasize those scenes. For example, it emphasizes scenes in which the user is talking happily. The cartoon generation unit can also identify scenes to which the user responds most emotionally when inputting family episodes and emphasize those scenes. For example, it emphasizes scenes in which the user is talking movingly. The cartoon generation unit can also identify scenes to which the user responds most emotionally and emphasize those scenes. For example, it emphasizes scenes in which the user is talking excitedly. In this way, by emphasizing the scenes to which the user responds most emotionally, emotional empathy can be increased.

[0086] When a user customizes, the generation AI can automatically make suggestions and provide customization options. For example, when a user tries to change the lines of a manga, the generation AI automatically suggests multiple lines. For example, it suggests lines that are appropriate for a moving scene. When a user tries to adjust a character's facial expression, the generation AI automatically provides multiple facial expression options. For example, it suggests expressions such as joy, surprise, and sadness. When a user tries to change the background, the generation AI automatically provides multiple background options. For example, it suggests scenery from a travel destination or a background of the family home. This makes customization easier for users by making automatic suggestions.

[0087] When customizing, the customization function allows the generation AI to refer to the user's past customization history and suggest consistent customizations. For example, the customization function may refer to lines or facial expressions that the user has changed in the past and suggest consistent customizations. For example, new suggestions may be made based on lines or facial expressions used in the past. The customization function may also refer to backgrounds and character designs that the user has selected in the past and suggest consistent customizations. For example, new suggestions may be made based on backgrounds and designs used in the past. The customization function may also refer to the user's past customization history and suggest consistent customizations. For example, new suggestions may be made based on colors and styles selected in the past. In this way, by referring to past customization history, consistent customizations may be suggested.

[0088] The customization function can use the emotion estimation function to suggest customizations in real time according to the user's emotions. For example, when a user tries to change lines in a manga, the customization function uses the emotion estimation function to suggest lines in real time according to the user's emotions. For example, if the user looks happy, the customization function suggests cheerful lines. When a user tries to adjust a character's facial expression, the emotion estimation function suggests facial expressions in real time according to the user's emotions. For example, if the user is moved, the customization function suggests an emotional facial expression. When a user tries to change the background, the emotion estimation function suggests backgrounds in real time according to the user's emotions. For example, if the user is relaxed, the customization function suggests a calm landscape. In this way, by suggesting customizations in real time according to the user's emotions, customization that can gain greater emotional empathy is possible.

[0089] The customization function allows the generation AI to automatically suggest different art styles or designs when a user customizes. For example, when a user tries to change the dialogue in a manga, the generation AI automatically suggests dialogue in different art styles and designs. For example, it suggests styles such as hand-drawn or digital. Also, when a user tries to adjust a character's facial expression, the generation AI automatically suggests facial expressions in different art styles and designs. For example, it suggests styles such as anime or realistic. Also, when a user tries to change the background, the generation AI automatically suggests backgrounds in different art styles and designs. For example, it suggests styles such as hand-drawn or photographic. This expands the range of customizations available to users by suggesting different art styles and designs.

[0090] The customization function allows the generation AI to automatically add character voices or music when customizing, thereby providing customization with audio. For example, when a user attempts to change lines in a manga, the generation AI automatically generates a character voice and provides customization with audio. For example, a voice that matches the lines is automatically generated. Furthermore, when a user attempts to adjust a character's facial expression, the generation AI automatically adds music and provides customization with audio. For example, music that is suitable for an emotional scene is added. Furthermore, when a user attempts to change the background, the generation AI automatically adds music and sound effects and provides customization with audio. For example, music and sound effects that match the scenery of a travel destination are added. This allows customization with audio to be provided by adding character voices and music.

[0091] The customization function can use the emotion estimation function to identify customization elements that the user most emotionally identifies with and emphasize those elements. For example, when the user is trying to change the dialogue of a manga, the customization function can use the emotion estimation function to identify dialogue elements that the user most emotionally identifies with and emphasize those elements. For example, the customization function can emphasize dialogue in which the user appears to be enjoying themselves. Furthermore, when the user is trying to adjust a character's facial expression, the customization function can identify facial expression elements that the user most emotionally identifies with and emphasize those elements. For example, the customization function can emphasize an expression in which the user is moved. Furthermore, when the user is trying to change a background, the customization function can identify background elements that the user most emotionally identifies with and emphasize those elements. For example, the customization function can emphasize a background in which the user appears relaxed. In this way, by emphasizing the customization elements that the user most emotionally identifies with, emotional empathy can be increased.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The user input accepting unit allows the generation AI to automatically tag text or images entered by the user, and the story generating unit can set the story theme based on the tags. For example, if a user enters travel memories, the generation AI automatically tags them with "travel," "adventure," "sightseeing," etc., and sets the story theme based on the tags. For example, a story with an "adventure" theme is generated. Also, if the user input accepting unit enters a family episode, the generation AI automatically tags them with "family," "memories," "moving," etc., and sets the story theme based on the tags. For example, a story with an "moving" theme is generated. Also, the user input accepting unit allows the generation AI to automatically tag content entered by the user, and adjusts character settings and story development based on the tags. For example, if a "friendship" tag is added, a story with a friendship theme is generated. This allows for automatic tagging and story theme setting, resulting in the generation of more consistent stories.

[0094] The user input accepting unit combines memories input by different users, and the story generating unit can generate a joint story told from multiple perspectives. For example, travel memories input by different users can be combined to generate a joint story told from multiple perspectives. For example, different experiences at the same travel destination can be compiled into a single story. The user input accepting unit also allows different family members to input family episodes and combines them to generate a joint story. For example, an episode told from the perspective of the entire family can be generated. The user input accepting unit also combines content input by different users to generate a story told from multiple perspectives. For example, memories between friends can be combined to generate a story themed around friendships. In this way, by combining memories from different users, stories can be generated from a wider variety of perspectives.

[0095] The user input accepting unit can use the emotion estimation function to provide real-time feedback on the emotions felt by the user when the user inputs information and offer advice to elicit positive emotions. For example, when a user inputs travel memories, the emotion estimation function analyzes the emotions in real time and offers advice to elicit positive emotions. For example, advice such as "Please tell us more about the fun things that happened at that time" is displayed. Furthermore, when a user inputs a family episode, the emotion estimation function analyzes the emotions in real time and offers advice to elicit positive emotions. For example, advice such as "Please tell us more about the touching moment at that time" is displayed. Furthermore, the user input accepting unit analyzes the emotions felt by the user when the user inputs information and offers advice to elicit positive emotions. For example, advice such as "Please tell us more about the happy things that happened at that time" is displayed. In this way, the user's emotions are fed back in real time and positive emotions are elicited, thereby generating a better story.

[0096] The story generation unit can refer to the user's past input history to create a consistent series of stories. For example, it generates a consistent series of stories by referring to travel memories input by the user in the past. For example, it creates a sequel story based on past travel episodes. The story generation unit also generates a consistent series of stories by referring to family episodes from the past input history. For example, it generates a series of stories depicting the growth and changes of a family. The story generation unit also generates a consistent series of stories by referring to the user's past input history. For example, it creates a sequel story based on memories with friends. In this way, it is possible to generate a consistent series of stories by referring to the past input history.

[0097] The story generation unit allows the generation AI to automatically set plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, the generation AI sets plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, by selecting different tourist spots in travel memories, different endings are generated. The story generation unit also sets plot branching points in family episodes, allowing the user to enjoy different endings by selecting options. For example, by selecting different actions in family events, different endings are generated. The story generation unit also sets plot branching points when generating a story, allowing the user to enjoy different endings by selecting options. For example, by making different choices in memories with friends, different endings are generated. This makes it possible to generate stories in which the user can enjoy different endings by selecting options.

[0098] The story generation unit uses the emotion estimation function to adjust the development of the story in real time according to the user's emotions, thereby increasing emotional empathy. For example, the emotion estimation function is used to adjust the development of the story in real time according to the emotions expressed when the user inputs their travel memories. For example, if the user is talking happily, the story will develop in a bright and fun manner. The story generation unit also adjusts the development of the story in real time according to the emotions expressed when inputting a family episode. For example, if the user is telling a moving story, the story will also develop in a moving manner. The story generation unit also adjusts the development of the story in real time according to the user's emotions, thereby increasing emotional empathy. For example, if the user is talking excitedly, the story will develop in a dynamic manner. In this way, emotional empathy can be increased by adjusting the development of the story in real time according to the user's emotions.

[0099] When the generation AI generates a story, the story generation unit can incorporate elements from different cultures or regions to create a story from a global perspective. For example, when the generation AI generates a story, it can incorporate elements from different cultures or regions to create a story from a global perspective. For example, it can add elements from different cultures to travel memories. The story generation unit can also incorporate elements from different regions into family episodes to create a story from a global perspective. For example, it can incorporate customs and habits from different regions into the story. The story generation unit can also incorporate elements from different cultures or regions to create a story from a global perspective when the generation AI generates a story. For example, it can add elements from different cultures to memories with friends. In this way, by incorporating elements from different cultures and regions, it can generate a story from a global perspective.

[0100] The story generation unit allows the generation AI to automatically add music and sound effects when generating a story, creating a multimedia story that appeals to both the visual and the auditory senses. For example, the generation AI automatically adds music and sound effects when generating a story, creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for travel scenes are added. The story generation unit also automatically adds music and sound effects to family episodes, creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for moving scenes are added. The story generation unit also allows the generation AI to automatically add music and sound effects when generating a story, creating a multimedia story that appeals to both the visual and the auditory senses. For example, music and sound effects appropriate for memories with friends are added. In this way, by adding music and sound effects, it is possible to create a multimedia story that appeals to both the visual and the auditory senses.

[0101] The story generation unit can use the emotion estimation function to identify the story elements to which the user responds most emotionally and emphasize those elements. For example, the emotion estimation function can be used to identify the story elements to which the user responds most emotionally when inputting travel memories, and emphasize those elements. For example, the function can emphasize parts where the user is talking happily. The story generation unit can also identify the story elements to which the user responds most emotionally when inputting family episodes, and emphasize those elements. For example, the function can emphasize parts where the user is talking movingly. The story generation unit can also identify the story elements to which the user responds most emotionally and emphasize those elements. For example, the function can emphasize parts where the user is talking excitedly. In this way, by emphasizing the elements to which the user responds most emotionally, emotional empathy can be increased.

[0102] The character generation unit can refer to the user's past input data to depict consistent character growth or changes. For example, by referring to family photos previously input by the user, consistent character growth or changes can be depicted. For example, a character depicting a child's growth process can be generated. The character generation unit can also refer to photos of friends from the past input data to depict consistent character growth or changes. For example, a character depicting changes in friendships can be generated. The character generation unit can also refer to the user's past input data to depict consistent character growth or changes. For example, a character depicting a pet's growth process can be generated. In this way, consistent character growth and changes can be depicted by referring to the past input data.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The user input accepting unit accepts text or images entered by the user. For example, the user can enter travel memories in text. The user can also upload family photos. Furthermore, the user input accepting unit can also accept voice input. For example, the user can enter travel memories in voice. Step 2: The story generation unit generates a story based on the text or images received by the user input reception unit. For example, the generation AI creates a story based on travel episodes based on travel memories input by the user. Also, the generation AI creates a story based on family episodes based on family photos input by the user. Step 3: The character generation unit generates characters based on the story generated by the story generation unit. For example, the generation AI analyzes family photos provided by the user and creates characters from the family in the manga. The generation AI also automatically sets the character's appearance and personality based on the text entered by the user. Step 4: The manga generation unit generates a manga based on the story and characters generated by the story generation unit and character generation unit. For example, the generation AI automatically generates the manga's panels and dialogue based on the generated story and characters. The generation AI also outputs the memories entered by the user in manga format.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user input receiving unit that receives text or an image input by a user; a story generation unit that generates a story based on the text or images received by the user input reception unit; a character generation unit that generates a character based on the story generated by the story generation unit; a comic book generation unit that generates a comic book based on the story and characters generated by the story generation unit and the character generation unit. A system characterized by:

2. The user input receiving unit The generation AI automatically tags the text or images entered by the user, and the story generation unit sets the theme of the story based on the tags.

2. The system of claim 1.

3. The story generation unit Create a coherent series of stories by referencing the user's past input history 2. The system of claim 1.

4. The character generation unit Referencing the user's past input data to depict consistent character growth or change 2. The system of claim 1.

5. The cartoon generation unit Referencing users' past works to create a consistent series of manga 2. The system of claim 1.

6. The user input receiving unit Analyzing the emotion of the user's input and adjusting the tone or atmosphere of the story based on the emotion.

2. The system of claim 1.

Images