system

Abstract

The system according to the embodiment aims to enable users to easily create novels and picture books. [Solution] A system according to an embodiment includes an acquisition unit, a story generation unit, and an illustration generation unit. The acquisition unit acquires information about the setting, characters, and scenes of a story from a user. The story generation unit generates a story based on the information acquired by the acquisition unit. The illustration generation unit generates illustrations based on the story generated by the story generation unit.

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 technology has had the problem that it is difficult for users to easily create novels or picture books.

[0005] The system according to the embodiment aims to enable users to easily create novels and picture books. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a story generation unit, and an illustration generation unit. The acquisition unit acquires information about the setting, characters, and scenes of a story from a user. The story generation unit generates a story based on the information acquired by the acquisition unit. The illustration generation unit generates illustrations based on the story generated by the story generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily create novels and picture books. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A novel / picture book creation system according to an embodiment of the present invention allows users to easily create novels and picture books simply by selecting a story setting, characters, scenes, and so on. This system allows users to select a story setting, characters, scenes, and so on, and a generation AI generates a story based on the selected settings. Furthermore, an image generation AI generates illustrations that match the story. For example, a user selects an "adventure story" and selects a "hero" and a "dragon" as characters. This information is input to the generation AI. The generation AI then analyzes the input information and generates a story. The generation AI determines the story development based on the selected settings. For example, it generates an "adventure story in which a hero fights a dragon." Furthermore, the image generation AI generates illustrations that match the story. The image generation AI generates illustrations appropriate for each scene based on the content of the story. For example, it generates an illustration for a "scene in which a hero fights a dragon." This system allows users to easily create novels and picture books. Users simply select a story setting, characters, scenes, and so on, and the generation AI and image generation AI automatically generate a story and illustrations, allowing them to complete their work without any effort. This novel / picture book creation system allows users to easily create novels and picture books.

[0029] The novel / picture book creation system according to this embodiment comprises an acquisition unit, a story generation unit, and an illustration generation unit. The acquisition unit acquires information such as the story setting, characters, and scenes from the user. For example, the acquisition unit can acquire information that the user has selected "adventure story" and selected "hero" and "dragon" as characters. The acquisition unit can also input the information selected by the user into the generation AI. The story generation unit uses the generation AI to generate a story based on the information acquired by the acquisition unit. For example, the story generation unit generates an "adventure story in which a hero fights a dragon" using the generation AI. The generation AI can generate a story using a text generation AI (e.g., LLM). The story generation unit can also have the generation AI determine the development of the story. For example, the generation AI generates a "scene in which the hero fights a dragon". The illustration generation unit uses an image generation AI to generate illustrations based on the story generated by the story generation unit. For example, the illustration generation unit generates an illustration of the "scene in which the hero fights a dragon" using the image generation AI. The image generation AI can generate illustrations suitable for each scene based on the content of the story. For example, the image generation AI generates an illustration of a scene in which a hero fights a dragon. This allows the novel / picture book creation system according to the embodiment to allow users to easily create novels and picture books.

[0030] The acquisition unit can analyze the user's past selection history and suggest appropriate settings, characters, and scenes. For example, based on the genre of stories the user has previously selected, the acquisition unit can suggest settings of the same genre. It can also suggest characters with similar characteristics based on the characteristics of characters the user has previously selected. Furthermore, it can suggest scenes with a similar atmosphere based on the atmosphere of scenes the user has previously selected. This allows the system to suggest optimal settings, characters, and scenes based on the user's past selection history. The analysis of past selection history is performed, for example, using a selection history storage method and an analysis algorithm. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past selection history data into a generating AI and have the generating AI suggest optimal settings, characters, and scenes.

[0031] The acquisition unit can narrow down the options based on the user's current interests when selecting story settings, characters, and scenes. For example, the acquisition unit can suggest relevant story settings based on keywords the user has recently searched for. It can also suggest characters from the same genre as books the user has recently read. Furthermore, it can suggest relevant scenes based on information about places the user has recently visited. This allows the system to suggest appropriate story settings, characters, and scenes based on the user's current interests. Current interests can be identified, for example, using surveys or analysis of behavioral history. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input the user's behavioral history data into a generating AI and have the generating AI identify the user's interests.

[0032] The acquisition unit can prioritize presenting highly relevant options based on the user's geographical location when selecting story settings, characters, and scenes. For example, if the user is at the beach, the acquisition unit will prioritize presenting story settings set in the sea. Similarly, if the user is in a mountainous area, the acquisition unit can prioritize presenting characters set in mountains. Furthermore, if the user is in an urban area, the acquisition unit can prioritize presenting scenes set in cities. This allows the system to prioritize presenting highly relevant options based on the user's geographical location. Geographical location information is acquired, for example, using GPS data or location services. Some or all of the processing described above in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the user's location data into a generating AI, causing the generating AI to present highly relevant options.

[0033] The data acquisition unit can analyze the user's social media activity and suggest relevant options when selecting story settings, characters, and scenes. For example, the data acquisition unit can suggest relevant story settings based on the content of posts the user has shared on social media. It can also suggest relevant characters based on the content of posts the user has "liked." Furthermore, it can suggest relevant scenes based on the content of accounts the user follows. This allows the system to present relevant options based on the user's social media activity. Social media activity analysis is performed, for example, by analyzing post content and follower analysis. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media data into a generating AI and have the generating AI present relevant options.

[0034] The story generation unit can adjust the development of the story based on the selected setting and level of character detail when generating the story. For example, if a detailed setting is selected, the story generation unit generates a story including detailed descriptions. Furthermore, if a simple setting is selected, the story generation unit can generate a story with a simple development. Furthermore, if a character has many details, the story generation unit can generate a story that describes the character's background and personality in detail. This allows the development of the story to be adjusted based on the selected setting and level of character detail. The level of detail of the setting and characters is evaluated using, for example, a level of detail evaluation standard or evaluation method. Some or all of the above-mentioned processing in the story generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the story generation unit can input detailed data of the selected setting and characters into the generation AI and cause the generation AI to adjust the development of the story.

[0035] When generating a story, the story generation unit can apply different generation algorithms depending on different genres and themes. For example, in the case of a fantasy genre, the story generation unit can apply a generation algorithm that emphasizes magic and adventure. In addition, in the case of a mystery genre, the story generation unit can apply a generation algorithm that emphasizes solving mysteries and suspense. In addition, in the case of a romance genre, the story generation unit can apply a generation algorithm that emphasizes portraying emotions. This allows an appropriate generation algorithm to be applied depending on different genres and themes. The specific content and scope of the genre or theme are identified using classifications such as fantasy, mystery, and romance. Some or all of the above-mentioned processing in the story generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the story generation unit can input genre and theme data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0036] The story generation unit can adjust the order of the stories based on the relevance of the selected settings and characters when generating a story. For example, if the setting and characters are closely related, the story generation unit smooths the development of the story. The story generation unit can also generate a tense development when the setting and characters are in conflict. The story generation unit can also generate a cooperative development when the setting and characters complement each other. This allows the order of the stories to be adjusted based on the relevance of the selected settings and characters. The relevance of the settings and characters is evaluated using, for example, relevance evaluation criteria or evaluation methods. Some or all of the above-mentioned processing in the story generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the story generation unit can input related data of the selected settings and characters into the generation AI and cause the generation AI to adjust the order of the stories.

[0037] When generating a story, the story generation unit can suggest an optimal story development by referring to the user's past story generation history. For example, the story generation unit can suggest a similar development based on a story development that the user has previously preferred. The story generation unit can also suggest a different development based on a story development that the user has previously avoided. The story generation unit can also analyze the user's past story generation history and suggest a new development. This makes it possible to suggest an optimal story development based on the user's past story generation history. The past story generation history is referenced using, for example, a history storage method or a history reference method. Some or all of the above-mentioned processing in the story generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the story generation unit can input the user's past story generation history data into the generation AI and cause the generation AI to suggest an optimal story development.

[0038] When generating an illustration, the illustration generation unit can adjust the level of detail of the illustration based on important scenes in the story. For example, the illustration generation unit generates an illustration including detailed depictions for a climax scene. The illustration generation unit can also generate illustrations with simple depictions for everyday scenes. The illustration generation unit can also generate illustrations including dynamic depictions for action scenes. This allows the level of detail of the illustration to be adjusted based on important scenes in the story. Important scenes in the story are identified using classifications such as climax, turning point, and ending. Some or all of the above-mentioned processing in the illustration generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the illustration generation unit can input data of important scenes in the story into the generation AI and have the generation AI adjust the level of detail of the illustration.

[0039] When generating an illustration, the illustration generation unit can apply different generation algorithms depending on different art styles and techniques. For example, in the case of a watercolor style, the illustration generation unit generates an illustration with a soft touch. In addition, in the case of a manga style, the illustration generation unit can generate an illustration with emphasized line contrast. In addition, in the case of a realistic style, the illustration generation unit can generate an illustration with detailed depictions. This allows an appropriate generation algorithm to be applied depending on different art styles and techniques. The specific content and scope of the art style or technique can be identified using classifications such as realism, abstract painting, and digital art. Some or all of the above-mentioned processing in the illustration generation unit may be performed using, for example, AI, or may be performed without AI. For example, the illustration generation unit can input data on the art style and technique into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0040] The illustration generation unit can adjust the order of illustrations based on the progress of the story when generating illustrations. For example, the illustration generation unit generates illustrations for each scene in accordance with the progress of the story. The illustration generation unit can also increase the level of detail of the illustrations towards the climax. The illustration generation unit can also adjust the arrangement of the illustrations in accordance with the development of the story. This allows the order of illustrations to be adjusted based on the progress of the story. The adjustment of the order of illustrations based on the progress of the story is performed, for example, by changing the order in accordance with the progress. Some or all of the above-mentioned processing in the illustration generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the illustration generation unit can input story progress data into the generation AI and cause the generation AI to adjust the order of the illustrations.

[0041] When generating an illustration, the illustration generation unit can suggest an optimal illustration by referring to the user's past illustration generation history. For example, the illustration generation unit can suggest an illustration in the same style based on an illustration style that the user previously preferred. The illustration generation unit can also suggest an illustration in a different style based on an illustration style that the user previously avoided. The illustration generation unit can also analyze the user's past illustration generation history and suggest an illustration in a new style. This allows the optimal illustration to be suggested based on the user's past illustration generation history. The past illustration generation history is referenced using, for example, a history storage method or reference method. Some or all of the above-mentioned processing in the illustration generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the illustration generation unit can input the user's past illustration generation history data into the generation AI and have the generation AI suggest an optimal illustration.

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

[0043] The acquisition unit can also analyze the user's voice input and generate options for story settings, characters, and scenes from the voice. For example, if the user voice-inputs, "I like adventure stories," the acquisition unit analyzes the voice and suggests settings and characters related to adventure stories. Also, if the user voice-inputs, "I like stories with dragons," the acquisition unit can suggest scenes featuring dragons based on that information. Furthermore, if the user voice-inputs, "I want to see a scene where a hero fights a dragon," the acquisition unit can suggest specific scenes based on that information. This allows the user to more intuitively select story settings, characters, and scenes through voice input.

[0044] The acquisition unit can also analyze the user's past selection history and predict the development of the story based on the selection history. For example, it can predict and suggest the development that the user is likely to choose next based on the story patterns that the user has selected in the past. It can also predict the next action that the user should take based on the behavior patterns of characters that the user has selected in the past. It can also predict the scene that the user is likely to choose next based on the atmosphere of scenes that the user has selected in the past. This makes it possible to provide a more personalized story development based on the user's past selection history.

[0045] The acquisition unit can also refer to the user's real-time web browsing history when narrowing down options based on the user's current interests. For example, the acquisition unit can suggest related story settings based on the content of websites the user has recently visited. The acquisition unit can also suggest related characters based on keywords the user has recently searched for. Furthermore, the acquisition unit can suggest related scenes based on the content of videos the user has recently watched. This allows the acquisition unit to suggest more appropriate story settings, characters, and scenes based on the user's latest interests.

[0046] The acquisition unit can also refer to the user's current weather information when presenting relevant options based on the user's geographical location information. For example, if the user is at the seaside and it is sunny, a story setting set at the sea may be suggested, and if it is raining, a stormy scene may be suggested. Also, if the user is in a mountainous area and it is snowing, a character set in a snowy mountain may be suggested. Furthermore, if the user is in an urban area and it is night, a scene set in a night view may be suggested. This makes it possible to present more appropriate options based on the user's geographical location information and weather information.

[0047] The acquisition unit can analyze the user's social media activity and present options taking into consideration the activities of the user's friends and followers. For example, it can suggest related story settings based on the content of articles shared by the user's friends. It can also suggest related characters based on the content of posts that the user's followers have "liked." It can also suggest related scenes based on the content of accounts followed by the user's friends. This makes it possible to present more relevant options based on the user's social media activity and the activities of their friends and followers.

[0048] The story generation unit can also adjust the development of the story based on the selected setting and character interactions when generating the story. For example, if the setting and character are in a cooperative relationship, a cooperative development can be generated. Also, if the setting and character are in a conflicting relationship, a conflicting development can be generated. Furthermore, if the setting and character are unrelated, an independent development can be generated. This makes it possible to adjust the development of the story based on the selected setting and character interactions.

[0049] When generating a story, the story generation unit can apply different generation algorithms depending on different cultures and backgrounds. For example, for a story set against a Japanese cultural background, a generation algorithm that emphasizes Japanese elements can be applied. For a story set against a Western cultural background, a generation algorithm that emphasizes medieval elements can be applied. Furthermore, for a story set against an African cultural background, a generation algorithm that emphasizes tribal elements can be applied. This makes it possible to apply an appropriate generation algorithm depending on different cultures and backgrounds.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The acquisition unit acquires information such as the story setting, characters, and scenes from the user. For example, the acquisition unit acquires information such as the user selecting an "adventure story" and selecting a "hero" and a "dragon" as characters. The acquisition unit can also input the information selected by the user into the generation AI. Step 2: The story generation unit uses a generation AI to generate a story based on the information acquired by the acquisition unit. For example, the generation AI generates an adventure story about a hero fighting a dragon. The generation AI uses a text generation AI (e.g., LLM) to generate the story and determine its development. Step 3: The illustration generation unit uses image generation AI to generate illustrations based on the story generated by the story generation unit. For example, the image generation AI generates an illustration of a scene in which a hero fights a dragon. The image generation AI generates illustrations appropriate for each scene based on the content of the story.

[0052] (Example 2) A novel / picture book creation system according to an embodiment of the present invention allows users to easily create novels and picture books simply by selecting a story setting, characters, scenes, and so on. This system allows users to select a story setting, characters, scenes, and so on, and a generation AI generates a story based on the selected settings. Furthermore, an image generation AI generates illustrations that match the story. For example, a user selects an "adventure story" and selects a "hero" and a "dragon" as characters. This information is input to the generation AI. The generation AI then analyzes the input information and generates a story. The generation AI determines the story development based on the selected settings. For example, it generates an "adventure story in which a hero fights a dragon." Furthermore, the image generation AI generates illustrations that match the story. The image generation AI generates illustrations appropriate for each scene based on the content of the story. For example, it generates an illustration for a "scene in which a hero fights a dragon." This system allows users to easily create novels and picture books. Users simply select a story setting, characters, scenes, and so on, and the generation AI and image generation AI automatically generate a story and illustrations, allowing them to complete their work without any effort. This novel / picture book creation system allows users to easily create novels and picture books.

[0053] The novel / picture book creation system according to this embodiment comprises an acquisition unit, a story generation unit, and an illustration generation unit. The acquisition unit acquires information such as the story setting, characters, and scenes from the user. For example, the acquisition unit can acquire information that the user has selected "adventure story" and selected "hero" and "dragon" as characters. The acquisition unit can also input the information selected by the user into the generation AI. The story generation unit uses the generation AI to generate a story based on the information acquired by the acquisition unit. For example, the story generation unit generates an "adventure story in which a hero fights a dragon" using the generation AI. The generation AI can generate a story using a text generation AI (e.g., LLM). The story generation unit can also have the generation AI determine the development of the story. For example, the generation AI generates a "scene in which the hero fights a dragon". The illustration generation unit uses an image generation AI to generate illustrations based on the story generated by the story generation unit. For example, the illustration generation unit generates an illustration of the "scene in which the hero fights a dragon" using the image generation AI. The image generation AI can generate illustrations suitable for each scene based on the content of the story. For example, the image generation AI generates an illustration of a scene in which a hero fights a dragon. This allows the novel / picture book creation system according to the embodiment to allow users to easily create novels and picture books.

[0054] The acquisition unit can estimate the user's emotions and present options for story settings, characters, and scenes based on the estimated user emotions. For example, if the user is feeling sad, the acquisition unit can suggest an inspiring story or a soothing character. Alternatively, if the user is excited, the acquisition unit can suggest an action-packed story or a brave character. Alternatively, if the user is feeling relaxed, the acquisition unit can suggest a calm story or a soothing scene. This allows options for story settings, characters, and scenes to be presented according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0055] The acquisition unit can analyze the user's past selection history and suggest appropriate settings, characters, and scenes. For example, based on the genre of stories the user has previously selected, the acquisition unit can suggest settings of the same genre. It can also suggest characters with similar characteristics based on the characteristics of characters the user has previously selected. Furthermore, it can suggest scenes with a similar atmosphere based on the atmosphere of scenes the user has previously selected. This allows the system to suggest optimal settings, characters, and scenes based on the user's past selection history. The analysis of past selection history is performed, for example, using a selection history storage method and an analysis algorithm. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past selection history data into a generating AI and have the generating AI suggest optimal settings, characters, and scenes.

[0056] The acquisition unit can narrow down the options based on the user's current interests when selecting story settings, characters, and scenes. For example, the acquisition unit can suggest relevant story settings based on keywords the user has recently searched for. It can also suggest characters from the same genre as books the user has recently read. Furthermore, it can suggest relevant scenes based on information about places the user has recently visited. This allows the system to suggest appropriate story settings, characters, and scenes based on the user's current interests. Current interests can be identified, for example, using surveys or analysis of behavioral history. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can input the user's behavioral history data into a generating AI and have the generating AI identify the user's interests.

[0057] The acquisition unit can estimate the user's emotions and prioritize options based on the estimated user emotions. For example, if the user is tired, the acquisition unit can prioritize presenting a relaxing story setting. Furthermore, if the user is excited, the acquisition unit can prioritize presenting action-packed characters. Furthermore, if the user is sad, the acquisition unit can prioritize presenting moving scenes. This allows the prioritization of options to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI, or without an AI. For example, the acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0058] The acquisition unit can prioritize presenting highly relevant options based on the user's geographical location when selecting story settings, characters, and scenes. For example, if the user is at the beach, the acquisition unit will prioritize presenting story settings set in the sea. Similarly, if the user is in a mountainous area, the acquisition unit can prioritize presenting characters set in mountains. Furthermore, if the user is in an urban area, the acquisition unit can prioritize presenting scenes set in cities. This allows the system to prioritize presenting highly relevant options based on the user's geographical location. Geographical location information is acquired, for example, using GPS data or location services. Some or all of the processing described above in the acquisition unit may be performed using AI, or without AI. For example, the acquisition unit can input the user's location data into a generating AI, causing the generating AI to present highly relevant options.

[0059] The data acquisition unit can analyze the user's social media activity and suggest relevant options when selecting story settings, characters, and scenes. For example, the data acquisition unit can suggest relevant story settings based on the content of posts the user has shared on social media. It can also suggest relevant characters based on the content of posts the user has "liked." Furthermore, it can suggest relevant scenes based on the content of accounts the user follows. This allows the system to present relevant options based on the user's social media activity. Social media activity analysis is performed, for example, by analyzing post content and follower analysis. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media data into a generating AI and have the generating AI present relevant options.

[0060] The story generation unit can estimate the user's emotions and adjust the way the story is presented based on those emotions. For example, if the user is relaxed, the story generation unit can generate a story in a calm tone. If the user is excited, the story generation unit can generate a story in a tense tone. If the user is sad, the story generation unit can generate a story in an emotional tone. This allows the story's presentation to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the story generation unit may be performed using AI, or not. For example, the story generation unit can input user emotion data into the generative AI and have the generative AI adjust the way the story is presented.

[0061] The story generation unit can adjust the development of the story based on the selected setting and level of character detail when generating the story. For example, if a detailed setting is selected, the story generation unit generates a story including detailed descriptions. Furthermore, if a simple setting is selected, the story generation unit can generate a story with a simple development. Furthermore, if a character has many details, the story generation unit can generate a story that describes the character's background and personality in detail. This allows the development of the story to be adjusted based on the selected setting and level of character detail. The level of detail of the setting and characters is evaluated using, for example, a level of detail evaluation standard or evaluation method. Some or all of the above-mentioned processing in the story generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the story generation unit can input detailed data of the selected setting and characters into the generation AI and cause the generation AI to adjust the development of the story.

[0062] When generating a story, the story generation unit can apply different generation algorithms depending on different genres and themes. For example, in the case of a fantasy genre, the story generation unit can apply a generation algorithm that emphasizes magic and adventure. In addition, in the case of a mystery genre, the story generation unit can apply a generation algorithm that emphasizes solving mysteries and suspense. In addition, in the case of a romance genre, the story generation unit can apply a generation algorithm that emphasizes portraying emotions. This allows an appropriate generation algorithm to be applied depending on different genres and themes. The specific content and scope of the genre or theme are identified using classifications such as fantasy, mystery, and romance. Some or all of the above-mentioned processing in the story generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the story generation unit can input genre and theme data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0063] The story generation unit can estimate the user's emotions and adjust the length of the story based on the estimated user's emotions. For example, the story generation unit generates a short story when the user is in a hurry. The story generation unit can also generate a longer story when the user is relaxed. The story generation unit can also generate a fast-paced story when the user is excited. This allows the length of the story to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the story generation unit may be performed using AI, or may be performed without AI. For example, the story generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the story.

[0064] The story generation unit can adjust the order of the stories based on the relevance of the selected settings and characters when generating a story. For example, if the setting and characters are closely related, the story generation unit smooths the development of the story. The story generation unit can also generate a tense development when the setting and characters are in conflict. The story generation unit can also generate a cooperative development when the setting and characters complement each other. This allows the order of the stories to be adjusted based on the relevance of the selected settings and characters. The relevance of the settings and characters is evaluated using, for example, relevance evaluation criteria or evaluation methods. Some or all of the above-mentioned processing in the story generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the story generation unit can input related data of the selected settings and characters into the generation AI and cause the generation AI to adjust the order of the stories.

[0065] When generating a story, the story generation unit can suggest an optimal story development by referring to the user's past story generation history. For example, the story generation unit can suggest a similar development based on a story development that the user has previously preferred. The story generation unit can also suggest a different development based on a story development that the user has previously avoided. The story generation unit can also analyze the user's past story generation history and suggest a new development. This makes it possible to suggest an optimal story development based on the user's past story generation history. The past story generation history is referenced using, for example, a history storage method or a history reference method. Some or all of the above-mentioned processing in the story generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the story generation unit can input the user's past story generation history data into the generation AI and cause the generation AI to suggest an optimal story development.

[0066] The illustration generation unit can estimate the user's emotions and adjust the style and color tone of the illustration based on the estimated user's emotions. For example, if the user is relaxed, the illustration generation unit can generate an illustration with soft colors. If the user is excited, the illustration generation unit can also generate an illustration with vivid colors. If the user is sad, the illustration generation unit can also generate an illustration with subdued colors. This allows the style and color tone of the illustration to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the illustration generation unit can be performed using, for example, AI, or without AI. For example, the illustration generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the style and color tone of the illustration.

[0067] When generating an illustration, the illustration generation unit can adjust the level of detail of the illustration based on important scenes in the story. For example, the illustration generation unit generates an illustration including detailed depictions for a climax scene. The illustration generation unit can also generate illustrations with simple depictions for everyday scenes. The illustration generation unit can also generate illustrations including dynamic depictions for action scenes. This allows the level of detail of the illustration to be adjusted based on important scenes in the story. Important scenes in the story are identified using classifications such as climax, turning point, and ending. Some or all of the above-mentioned processing in the illustration generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the illustration generation unit can input data of important scenes in the story into the generation AI and have the generation AI adjust the level of detail of the illustration.

[0068] When generating an illustration, the illustration generation unit can apply different generation algorithms depending on different art styles and techniques. For example, in the case of a watercolor style, the illustration generation unit generates an illustration with a soft touch. In addition, in the case of a manga style, the illustration generation unit can generate an illustration with emphasized line contrast. In addition, in the case of a realistic style, the illustration generation unit can generate an illustration with detailed depictions. This allows an appropriate generation algorithm to be applied depending on different art styles and techniques. The specific content and scope of the art style or technique can be identified using classifications such as realism, abstract painting, and digital art. Some or all of the above-mentioned processing in the illustration generation unit may be performed using, for example, AI, or may be performed without AI. For example, the illustration generation unit can input data on the art style and technique into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0069] The illustration generation unit can estimate the user's emotions and adjust the size and placement of the illustration based on the estimated user's emotions. For example, if the user is relaxed, the illustration generation unit can generate a large, spaciously arranged illustration. Furthermore, if the user is excited, the illustration generation unit can generate a small, densely arranged illustration. Furthermore, if the user is sad, the illustration generation unit can generate a centrally placed illustration. This allows the size and placement of the illustration to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the illustration generation unit can be performed using, for example, AI, or without AI. For example, the illustration generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the size and placement of the illustration.

[0070] The illustration generation unit can adjust the order of illustrations based on the progress of the story when generating illustrations. For example, the illustration generation unit generates illustrations for each scene in accordance with the progress of the story. The illustration generation unit can also increase the level of detail of the illustrations towards the climax. The illustration generation unit can also adjust the arrangement of the illustrations in accordance with the development of the story. This allows the order of illustrations to be adjusted based on the progress of the story. The adjustment of the order of illustrations based on the progress of the story is performed, for example, by changing the order in accordance with the progress. Some or all of the above-mentioned processing in the illustration generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the illustration generation unit can input story progress data into the generation AI and cause the generation AI to adjust the order of the illustrations.

[0071] When generating an illustration, the illustration generation unit can suggest an optimal illustration by referring to the user's past illustration generation history. For example, the illustration generation unit can suggest an illustration in the same style based on an illustration style that the user previously preferred. The illustration generation unit can also suggest an illustration in a different style based on an illustration style that the user previously avoided. The illustration generation unit can also analyze the user's past illustration generation history and suggest an illustration in a new style. This allows the optimal illustration to be suggested based on the user's past illustration generation history. The past illustration generation history is referenced using, for example, a history storage method or reference method. Some or all of the above-mentioned processing in the illustration generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the illustration generation unit can input the user's past illustration generation history data into the generation AI and have the generation AI suggest an optimal illustration. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, story generation unit, and illustration generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is realized by a user inputting information such as story settings, characters, and scenes via the reception device 38 of the smart device 14. The story generation unit is realized by a specific processing unit 290 of the data processing device 12 generating a story using a generation AI. The illustration generation unit is realized by a specific processing unit 290 of the data processing device 12 generating illustrations using an image generation AI. Some or all of the acquisition unit, story generation unit, and illustration generation unit may be realized by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described acquisition unit, story generation unit, and illustration generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by a user using the microphone 238 of the smart glasses 214 to input information such as story settings, characters, and scenes through voice. The story generation unit is realized by the specific processing unit 290 of the data processing device 12 generating a story using a generation AI. The illustration generation unit is realized by the specific processing unit 290 of the data processing device 12 generating illustrations using an image generation AI. Some or all of the acquisition unit, story generation unit, and illustration generation unit may be realized by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, story generation unit, and illustration generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the user voice-inputting information such as story settings, characters, and scenes using the microphone 238 of the headset terminal 314. The story generation unit is implemented by the specific processing unit 290 of the data processing unit 12 generating a story using a generation AI. The illustration generation unit is implemented by the specific processing unit 290 of the data processing unit 12 generating an illustration using an image generation AI. Some or all of the acquisition unit, story generation unit, and illustration generation unit may be implemented by the control unit 46A of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, story generation unit, and illustration generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the user voice-inputting information such as story settings, characters, and scenes via the microphone 238 of the robot 414. The story generation unit is implemented by the specific processing unit 290 of the data processing unit 12 generating a story using a generation AI. The illustration generation unit is implemented by the specific processing unit 290 of the data processing unit 12 generating an illustration using an image generation AI. Some or all of the acquisition unit, story generation unit, and illustration generation unit may be implemented by the control unit 46A of the robot 414.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The acquisition unit can also analyze the user's voice input and generate options for story settings, characters, and scenes from the voice. For example, if the user voice-inputs, "I like adventure stories," the acquisition unit analyzes the voice and suggests settings and characters related to adventure stories. Also, if the user voice-inputs, "I like stories with dragons," the acquisition unit can suggest scenes featuring dragons based on that information. Furthermore, if the user voice-inputs, "I want to see a scene where a hero fights a dragon," the acquisition unit can suggest specific scenes based on that information. This allows the user to more intuitively select story settings, characters, and scenes through voice input.

[0074] The acquisition unit can also estimate the user's emotions and dynamically change the story settings, characters, and scene options based on the estimated user emotions. For example, if the user's emotions change during the story, the acquisition unit detects the change and updates the story settings, characters, and scene options in real time. Also, if the user's emotions increase as the story progresses, the acquisition unit can present new options according to those emotions. Furthermore, if the user becomes dissatisfied with the development of the story, the acquisition unit can detect that emotion and suggest options that will provide greater satisfaction. This enables flexible story development according to the user's emotions.

[0075] The acquisition unit can also analyze the user's past selection history and predict the development of the story based on the selection history. For example, it can predict and suggest the development that the user is likely to choose next based on the story patterns that the user has selected in the past. It can also predict the next action that the user should take based on the behavior patterns of characters that the user has selected in the past. It can also predict the scene that the user is likely to choose next based on the atmosphere of scenes that the user has selected in the past. This makes it possible to provide a more personalized story development based on the user's past selection history.

[0076] The acquisition unit can also refer to the user's real-time web browsing history when narrowing down options based on the user's current interests. For example, the acquisition unit can suggest related story settings based on the content of websites the user has recently visited. The acquisition unit can also suggest related characters based on keywords the user has recently searched for. Furthermore, the acquisition unit can suggest related scenes based on the content of videos the user has recently watched. This allows the acquisition unit to suggest more appropriate story settings, characters, and scenes based on the user's latest interests.

[0077] The acquisition unit can also estimate the user's emotions and dynamically change the order in which options are presented based on the estimated user emotions. For example, if the user's emotions change during the story, the acquisition unit detects the change and updates the order in which options are presented in real time. Also, if the user's emotions increase as the story progresses, the acquisition unit can prioritize presenting new options in accordance with those emotions. Furthermore, if the user feels dissatisfied with the development of the story, the acquisition unit can detect that emotion and prioritize presenting options that will provide greater satisfaction. This makes it possible to flexibly present options in accordance with the user's emotions.

[0078] The acquisition unit can also refer to the user's current weather information when presenting relevant options based on the user's geographical location information. For example, if the user is at the seaside and it is sunny, a story setting set at the sea may be suggested, and if it is raining, a stormy scene may be suggested. Also, if the user is in a mountainous area and it is snowing, a character set in a snowy mountain may be suggested. Furthermore, if the user is in an urban area and it is night, a scene set in a night view may be suggested. This makes it possible to present more appropriate options based on the user's geographical location information and weather information.

[0079] The acquisition unit can analyze the user's social media activity and present options taking into consideration the activities of the user's friends and followers. For example, it can suggest related story settings based on the content of articles shared by the user's friends. It can also suggest related characters based on the content of posts that the user's followers have "liked." It can also suggest related scenes based on the content of accounts followed by the user's friends. This makes it possible to present more relevant options based on the user's social media activity and the activities of their friends and followers.

[0080] The story generation unit can also estimate the user's emotions and adjust the tempo of the story based on the estimated user's emotions. For example, if the user is relaxed, the story can be generated at a slow tempo. If the user is excited, the story can be generated at a fast tempo. Furthermore, if the user is sad, the story can be generated at an emotional tempo. In this way, the tempo of the story can be adjusted according to the user's emotions.

[0081] The story generation unit can also adjust the development of the story based on the selected setting and character interactions when generating the story. For example, if the setting and character are in a cooperative relationship, a cooperative development can be generated. Also, if the setting and character are in a conflicting relationship, a conflicting development can be generated. Furthermore, if the setting and character are unrelated, an independent development can be generated. This makes it possible to adjust the development of the story based on the selected setting and character interactions.

[0082] When generating a story, the story generation unit can apply different generation algorithms depending on different cultures and backgrounds. For example, for a story set against a Japanese cultural background, a generation algorithm that emphasizes Japanese elements can be applied. For a story set against a Western cultural background, a generation algorithm that emphasizes medieval elements can be applied. Furthermore, for a story set against an African cultural background, a generation algorithm that emphasizes tribal elements can be applied. This makes it possible to apply an appropriate generation algorithm depending on different cultures and backgrounds.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The acquisition unit acquires information such as the story setting, characters, and scenes from the user. For example, the acquisition unit acquires information such as the user selecting an "adventure story" and selecting a "hero" and a "dragon" as characters. The acquisition unit can also input the information selected by the user into the generation AI. Step 2: The story generation unit uses a generation AI to generate a story based on the information acquired by the acquisition unit. For example, the generation AI generates an adventure story about a hero fighting a dragon. The generation AI uses a text generation AI (e.g., LLM) to generate the story and determine its development. Step 3: The illustration generation unit uses image generation AI to generate illustrations based on the story generated by the story generation unit. For example, the image generation AI generates an illustration of a scene in which a hero fights a dragon. The image generation AI generates illustrations appropriate for each scene based on the content of the story.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0087] 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.

[0088] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0100] 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.

[0101] 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.

[0102] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, a 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit for acquiring information about the setting, characters, and scenes of a story by a user; a unit for generating a story based on the information acquired by the acquisition unit; a unit for generating illustrations based on the story generated by the unit; Equipped with A system characterized by:

2. The acquisition unit It estimates the user's emotions and presents options for story settings, characters, and scenes based on the estimated user emotions.

2. The system of claim 1.

3. The acquisition unit Analyzes the user's past selection history and suggests appropriate settings, characters, and scenes 2. The system of claim 1.

4. The acquisition unit When selecting story settings, characters, and scenes, narrow your choices based on your current interests.

2. The system of claim 1.

5. The acquisition unit Estimate the user's emotions and prioritize options based on the estimated user emotions.

2. The system of claim 1.

6. The acquisition unit Prioritize relevant options based on the user's geographic location when selecting story settings, characters, and scenes.

2. The system of claim 1.

7. The acquisition unit Analyzes users' social media activity and presents relevant options when selecting story settings, characters, and scenes 2. The system of claim 1.

8. The story generation unit Inferring user emotions and adjusting story presentation based on the inferred user emotions 2. The system of claim 1.

9. The story generation unit During story generation, story development is adjusted based on the selected setting and character detail.

2. The system of claim 1.

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