system

Abstract

The system according to the embodiment aims to allow users to easily express their memories in the form of comics. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a scene generation unit, an illustration generation unit, a customization unit, and a sharing unit. The reception unit receives text input from a user. The analysis unit analyzes the text received by the reception unit and understands the flow of the story. The scene generation unit generates scenes based on the story understood by the analysis unit. The illustration generation unit generates illustrations corresponding to the scenes generated by the scene generation unit. The customization unit allows the user to customize the illustrations generated by the illustration generation unit. The sharing unit downloads or shares the manga customized by the customization 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 techniques have had the problem of making it difficult for users to easily express their memories as comics.

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

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a scene generation unit, an illustration generation unit, a customization unit, and a sharing unit. The reception unit receives text input from a user. The analysis unit analyzes the text received by the reception unit and understands the flow of the story. The scene generation unit generates scenes based on the story understood by the analysis unit. The illustration generation unit generates illustrations corresponding to the scenes generated by the scene generation unit. The customization unit allows the user to customize the illustrations generated by the illustration generation unit. The sharing unit downloads or shares the manga customized by the customization unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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 system according to an embodiment of the present invention allows anyone to easily turn their memories into a manga. In this system, users input their memories in text format, and a generation AI analyzes the text to understand the flow of the story, automatically generates scenes, and creates illustrations corresponding to each scene. Furthermore, users can customize the generated illustrations and download or share the final manga. This allows the system to easily express their memories as a manga, even without specialized knowledge.

[0029] A manga generation system according to an embodiment includes a reception unit, an analysis unit, a scene generation unit, an illustration generation unit, a customization unit, and a sharing unit. The reception unit receives text input from a user. The text input from the user includes, but is not limited to, keyboard input, voice input, and handwritten input. The analysis unit uses a generation AI to analyze the text received by the reception unit and understand the flow of the story. The generation AI analyzes the content of the text and understands the structure of the story and the relationships between characters, for example, using a natural language processing model or a machine learning algorithm. The scene generation unit uses the generation AI to generate scenes based on the story understood by the analysis unit. The scene generation unit extracts, for example, important moments in the story and generates scenes based on the extracted moments. The illustration generation unit uses the generation AI to generate illustrations corresponding to the scenes generated by the scene generation unit. The illustration generation unit generates illustrations by selecting, for example, appropriate styles and colors according to the content of the scene. The customization unit allows the user to customize the illustrations generated by the illustration generation unit. The customization unit provides customization options, for example, changing colors, adding characters, and changing backgrounds. The sharing unit downloads or shares the manga customized by the customization unit. The sharing unit selects, for example, a file format or a sharing platform, and downloads or shares the manga. This allows the manga generation system according to the embodiment to easily express a user's memories as a manga. For example, a user can input their memories in text format, and the generation AI can analyze the text to understand the flow of the story, automatically generate scenes, and create illustrations corresponding to each scene. Furthermore, the user can customize the generated illustrations and download or share the final manga.

[0030] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays phrases and keywords that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest phrases and keywords that will be used in a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0031] When inputting text, the reception unit can filter the input content based on the user's current situation and areas of interest. For example, if the user is traveling, the reception unit can prioritize displaying phrases and keywords related to travel. Furthermore, if the user is working, the reception unit can also prioritize displaying phrases and keywords related to work. Furthermore, if the user is inputting information related to a hobby, the reception unit can also prioritize displaying phrases and keywords related to the hobby. This improves the accuracy of input by providing input content that is appropriate for the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0032] The reception unit can select an appropriate input means depending on the user's input method when inputting text. For example, if the user selects voice input, the reception unit automatically generates text using voice recognition technology. Furthermore, if the user selects image input, the reception unit can also automatically generate text using image recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. This improves input convenience by providing the optimal means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0033] The reception unit can automatically complete the input content by referring to the user's past input history. The reception unit automatically completes the input content based on, for example, phrases and keywords that the user has previously input. The reception unit can also automatically complete the input content based on the writing style and expressions that the user has previously used. The reception unit can also automatically complete phrases and keywords that are appropriate for a specific context from the user's past input history. This improves input efficiency by automatically completing the input content based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0034] When inputting text, the reception unit can prioritize receiving input content that is highly relevant based on the user's current location information. For example, when the user is in a specific location, the reception unit can prioritize displaying phrases and keywords related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize displaying phrases and keywords related to traveling. Furthermore, when the user is participating in a specific event, the reception unit can also prioritize displaying phrases and keywords related to the event. This improves the accuracy of input by providing input content based on the current location information. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0035] The reception unit can analyze the user's social media activity when entering text and suggest related input content. The reception unit can, for example, suggest related phrases and keywords based on content posted by the user on social media. The reception unit can also suggest related phrases and keywords based on the activity of the user's friends on social media. The reception unit can also analyze the user's interests and concerns on social media and suggest related phrases and keywords. This improves the accuracy of input by providing input content based on social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI.

[0036] The analysis unit can use the generation AI to analyze the text received by the reception unit and understand the flow of the story. For example, the generation AI analyzes the text in the analysis unit to understand the structure of the story and the relationships between characters. The generation AI analyzes the content of the text using a natural language processing model or a machine learning algorithm. This allows the generation AI to accurately understand the flow of the story. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the generation AI receives text as input and outputs the flow of the story.

[0037] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, the analysis unit performs a detailed analysis on important scenes. The analysis unit can also perform a concise analysis on less important scenes. The analysis unit can also evaluate the importance of the entire text and adjust the level of detail of the analysis. This improves the efficiency of the analysis by performing analysis according to the importance of the text. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the generation AI receives text as input and outputs the level of detail of the analysis based on the importance.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the text category. For example, in the case of a romance story, the analysis unit can apply an analysis algorithm that emphasizes emotional changes. In addition, in the case of an adventure story, the analysis unit can apply an analysis algorithm that emphasizes action scenes. In addition, in the case of a comedy story, the analysis unit can apply an analysis algorithm that emphasizes humorous elements. In this way, analysis is performed according to the text category, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the generation AI receives text as input and outputs an analysis algorithm according to the category.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to stories created by the user in the past. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the analysis algorithm based on the user's past feedback to improve the accuracy. In this way, the efficiency of the analysis is improved by improving the accuracy of the analysis based on the past analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the generation AI receives past analysis results as input and outputs an algorithm that improves the accuracy of the analysis.

[0040] The scene generation unit can use the generative AI to generate scenes based on the story understood by the analysis unit. For example, the generative AI extracts important moments from the story and generates scenes based on them. The generative AI analyzes the content of the story and generates scenes using a natural language processing model or a machine learning algorithm. This makes it possible to accurately generate scenes based on the story using the generative AI. Some or all of the above-mentioned processing in the scene generation unit is performed using the generative AI. For example, the generative AI receives a story as input and outputs scenes.

[0041] The scene generation unit can adjust the level of detail of a scene based on the importance of the story when generating a scene. For example, the scene generation unit generates a detailed scene for an important scene. The scene generation unit can also generate a concise scene for a scene with low importance. The scene generation unit can also evaluate the importance of the entire story and adjust the level of detail of a scene. This improves the efficiency of generation by generating scenes according to the importance of the story. Some or all of the above-mentioned processing in the scene generation unit is performed using a generation AI. For example, the generation AI receives a story as input and outputs the level of detail of a scene based on its importance.

[0042] When generating scenes, the scene generation unit can apply different scene generation algorithms depending on the story category. For example, in the case of a romance story, the scene generation unit can apply a scene generation algorithm that emphasizes changes in emotions. In addition, in the case of an adventure story, the scene generation unit can also apply a scene generation algorithm that emphasizes action scenes. In addition, in the case of a comedy story, the scene generation unit can also apply a scene generation algorithm that emphasizes humorous elements. In this way, scene generation according to the story category improves the accuracy of generation. Some or all of the above-mentioned processing in the scene generation unit is performed using a generation AI. For example, the generation AI receives a story as input and outputs a scene generation algorithm according to the category.

[0043] When generating a scene, the scene generation unit can improve the accuracy of scene generation by referring to the user's past scene generation results. For example, the scene generation unit improves the accuracy of scene generation by referring to scenes created by the user in the past. The scene generation unit can also extract specific patterns from the user's past scene generation results to improve the accuracy of scene generation. The scene generation unit can also adjust the scene generation algorithm based on the user's past feedback to improve accuracy. This improves the efficiency of generation by improving the accuracy of generation based on past scene generation results. Some or all of the above-mentioned processing in the scene generation unit is performed using a generation AI. For example, the generation AI receives past scene generation results as input and outputs an algorithm that improves the accuracy of scene generation.

[0044] The illustration generation unit can use the generation AI to generate an illustration corresponding to the scene generated by the scene generation unit. For example, the generation AI selects an appropriate style and color according to the content of the scene and generates the illustration. The generation AI uses a natural language processing model or a machine learning algorithm to analyze the content of the scene and generate the illustration. In this way, the generation AI can accurately generate an illustration corresponding to the scene. Some or all of the above-mentioned processing in the illustration generation unit is performed using the generation AI. For example, the generation AI receives a scene as input and outputs an illustration.

[0045] When generating an illustration, the illustration generation unit can adjust the level of detail of the illustration based on the importance of the scene. For example, the illustration generation unit generates a detailed illustration for an important scene. The illustration generation unit can also generate a simple illustration for a scene with low importance. The illustration generation unit can also evaluate the importance of the entire scene and adjust the level of detail of the illustration. This improves the efficiency of generation by generating an illustration according to the importance of the scene. Some or all of the above-mentioned processing in the illustration generation unit is performed using a generation AI. For example, the generation AI receives a scene as input and outputs the level of detail of the illustration based on its importance.

[0046] When generating an illustration, the illustration generation unit can apply different illustration generation algorithms depending on the scene category. For example, in the case of a romance scene, the illustration generation unit applies an illustration generation algorithm that emphasizes changes in emotions. In addition, in the case of an adventure scene, the illustration generation unit can also apply an illustration generation algorithm that emphasizes action. In addition, in the case of a comedy scene, the illustration generation unit can also apply an illustration generation algorithm that emphasizes humorous elements. In this way, by generating an illustration according to the scene category, the accuracy of the generation is improved. Some or all of the above-mentioned processing in the illustration generation unit is performed using a generation AI. For example, the generation AI receives a scene as input and outputs an illustration generation algorithm according to the category.

[0047] When generating an illustration, the illustration generation unit can improve the accuracy of the illustration generation by referring to the user's past illustration generation results. For example, the illustration generation unit improves the accuracy of the illustration generation by referring to illustrations created by the user in the past. The illustration generation unit can also extract specific patterns from the user's past illustration generation results to improve the accuracy of the illustration generation. The illustration generation unit can also adjust the illustration generation algorithm based on the user's past feedback to improve accuracy. This improves the efficiency of generation by improving the accuracy of generation based on past illustration generation results. Some or all of the above-mentioned processing in the illustration generation unit is performed using a generation AI. For example, the generation AI receives past illustration generation results as input and outputs an algorithm that improves the accuracy of illustration generation.

[0048] The customization unit allows the user to customize the illustrations generated by the illustration generation unit. For example, the customization unit allows the user to change the color of the generated illustration. The customization unit also allows the user to add characters. The customization unit also allows the user to change the background. In this way, the user can customize the generated illustrations to create a more personalized manga. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI.

[0049] During customization, the customization unit can suggest the optimal customization method by referring to the user's past customization history. For example, the customization unit automatically displays customizations that the user has frequently performed in the past as candidates. The customization unit can also preferentially suggest customization methods (color, style, etc.) that the user has used in the past. The customization unit can also predict and suggest a customization method to be used in a specific time period based on the user's past customization history. This improves the efficiency of customization by suggesting the optimal method based on the past customization history. Some or all of the above-mentioned processing in the customization unit may be performed using AI, or may be performed without using AI.

[0050] During customization, the customization unit can filter the customization content based on the user's current situation and areas of interest. For example, if the user is traveling, the customization unit can prioritize displaying travel-related customization options. Furthermore, if the user is working, the customization unit can also prioritize displaying work-related customization options. Furthermore, if the user is customizing the device for a hobby, the customization unit can also prioritize displaying customization options related to the hobby. This improves the accuracy of customization by providing customization content according to the user's situation and areas of interest. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI.

[0051] During customization, the customization unit can select an appropriate customization means according to the user's input method. For example, if the user selects voice input, the customization unit automatically generates customization content using voice recognition technology. Furthermore, if the user selects image input, the customization unit can also automatically generate customization content using image recognition technology. Furthermore, if the user selects text input, the customization unit can also provide an interface that supports keyboard input. This improves the convenience of customization by providing the optimal means according to the user's input method. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI.

[0052] The sharing unit can download or share the manga customized by the customization unit. For example, the sharing unit downloads the manga customized by the user in PDF format. The sharing unit can also share the manga customized by the user on social networking sites. The sharing unit can also send the manga customized by the user by email. This makes it easy to download or share the customized manga. Some or all of the above-described processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0053] When sharing, the sharing unit can suggest the optimal sharing method by referring to the user's past sharing history. For example, the sharing unit automatically displays platforms that the user has frequently shared on in the past as candidates. The sharing unit can also prioritize suggesting sharing methods (email, SNS, etc.) that the user has used in the past. The sharing unit can also predict and suggest the sharing method to be used at a specific time period based on the user's past sharing history. This improves sharing efficiency by suggesting the optimal method based on the past sharing history. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0054] When sharing, the sharing unit can filter the shared content based on the user's current situation and areas of interest. For example, if the user is traveling, the sharing unit can prioritize displaying travel-related sharing options. Also, if the user is at work, the sharing unit can prioritize displaying work-related sharing options. Also, if the user is sharing information related to a hobby, the sharing unit can prioritize displaying sharing options related to that hobby. This improves the accuracy of sharing by providing shared content that is appropriate for the user's situation and areas of interest. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0055] The sharing unit can select an appropriate sharing means depending on the user's input method when sharing. For example, if the user selects voice input, the sharing unit automatically generates the content to be shared using voice recognition technology. Furthermore, if the user selects image input, the sharing unit can also automatically generate the content to be shared using image recognition technology. Furthermore, if the user selects text input, the sharing unit can also provide an interface that supports keyboard input. This improves the convenience of sharing by providing the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0056] When sharing, the sharing unit can prioritize suggesting highly relevant sharing content by taking into account the user's geographical location information. For example, if the user is in a specific location, the sharing unit can prioritize displaying sharing options related to that location. Furthermore, if the user is traveling, the sharing unit can prioritize displaying sharing options related to travel. Furthermore, if the user is participating in a specific event, the sharing unit can prioritize displaying sharing options related to the event. This improves the accuracy of sharing by providing sharing content based on the geographical location information. Some or all of the above-described processing in the sharing unit may be performed using AI or without AI.

[0057] The sharing unit can analyze the user's social media activity at the time of sharing and suggest relevant sharing content. For example, the sharing unit can suggest relevant sharing options based on content posted by the user on social media. The sharing unit can also suggest relevant sharing options based on the activity of the user's friends on social media. The sharing unit can also analyze the user's interests on social media and suggest relevant sharing options. This improves the accuracy of sharing by providing sharing content based on social media activity. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0058] The sharing unit can customize the sharing method by reflecting the user's past feedback when sharing. For example, the sharing unit can adjust the sharing options based on feedback provided by the user in the past. The sharing unit can also extract specific patterns from the user's past feedback and optimize the sharing method. The sharing unit can also improve the sharing interface by referring to the user's past feedback. In this way, customizing the sharing method based on past feedback improves sharing efficiency. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can also provide related information in real time based on the user's input. For example, if the user inputs information about a specific place or event, the reception unit can display additional information and related images about the place or event. The reception unit can also suggest related news articles or blog articles based on the content entered by the user. Furthermore, the reception unit can suggest products or services related to the content entered by the user. This allows the user to instantly obtain information related to the input content, providing a more fulfilling experience.

[0061] The scene generation unit can also automatically generate background sounds and sound effects for a scene based on user input. For example, if the user inputs a seaside scene, the sound of waves and the cries of seagulls can be added as background sounds. If the user inputs a city scene, the sound of cars and people talking can be added as background sounds. Furthermore, if the user inputs a forest scene, the sound of birds chirping and the sound of the wind can be added as background sounds. This can add a sense of realism to the scene and further enrich the user's experience.

[0062] The customization unit can also make customization suggestions based on user input. For example, if a user wants to add a specific character, the customization unit can suggest customization options related to that character. If a user wants to change a specific background, the customization unit can suggest customization options related to that background. Furthermore, if a user wants to change a specific color, the customization unit can suggest customization options related to that color. This makes customization easier for users and provides a more personalized experience.

[0063] The reception unit can also suggest related templates based on the user's input. For example, if the user inputs travel memories, a travel-related template can be suggested. If the user inputs birthday memories, a birthday-related template can be suggested. If the user inputs wedding memories, a wedding-related template can be suggested. This allows the user to easily select a template that matches the input content, making it easier to create manga.

[0064] The scene generation unit can also automatically add visual effects to a scene based on user input. For example, if the user inputs a night scene, the scene generation unit can add visual effects such as a starry sky or moonlight. If the user inputs a rainy scene, the scene generation unit can add visual effects such as raindrops or lightning. If the user inputs a winter scene, the scene generation unit can add visual effects such as snow or frost. This can add visual depth to the scene and enrich the user's experience.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The accepting unit accepts text input from a user. The text input from a user includes, but is not limited to, keyboard input, voice input, handwritten input, and the like. Step 2: The analysis unit uses the generation AI to analyze the text received by the reception unit and understand the flow of the story. The generation AI uses, for example, natural language processing models or machine learning algorithms to analyze the content of the text and understand the structure of the story and the relationships between characters. Step 3: The scene generation unit uses the generation AI to generate scenes based on the story understood by the analysis unit. For example, the scene generation unit extracts important moments from the story and generates scenes based on them. Step 4: The illustration generation unit uses the generation AI to generate an illustration corresponding to the scene generated by the scene generation unit. For example, the illustration generation unit selects an appropriate style and color depending on the content of the scene and generates the illustration. Step 5: The customization unit allows the user to customize the illustration generated by the illustration generation unit. The customization unit provides customization options such as changing the color, adding characters, and changing the background. Step 6: The sharing unit downloads or shares the comic customized by the customization unit. For example, the sharing unit selects a file format or a sharing platform, and downloads or shares the comic.

[0067] (Example 2) A system according to an embodiment of the present invention allows anyone to easily turn their memories into a manga. In this system, users input their memories in text format, and a generation AI analyzes the text to understand the flow of the story, automatically generates scenes, and creates illustrations corresponding to each scene. Furthermore, users can customize the generated illustrations and download or share the final manga. This allows the system to easily express their memories as a manga, even without specialized knowledge.

[0068] A manga generation system according to an embodiment includes a reception unit, an analysis unit, a scene generation unit, an illustration generation unit, a customization unit, and a sharing unit. The reception unit receives text input from a user. The text input from the user includes, but is not limited to, keyboard input, voice input, and handwritten input. The analysis unit uses a generation AI to analyze the text received by the reception unit and understand the flow of the story. The generation AI analyzes the content of the text and understands the structure of the story and the relationships between characters, for example, using a natural language processing model or a machine learning algorithm. The scene generation unit uses the generation AI to generate scenes based on the story understood by the analysis unit. The scene generation unit extracts, for example, important moments in the story and generates scenes based on the extracted moments. The illustration generation unit uses the generation AI to generate illustrations corresponding to the scenes generated by the scene generation unit. The illustration generation unit generates illustrations by selecting, for example, appropriate styles and colors according to the content of the scene. The customization unit allows the user to customize the illustrations generated by the illustration generation unit. The customization unit provides customization options, for example, changing colors, adding characters, and changing backgrounds. The sharing unit downloads or shares the manga customized by the customization unit. The sharing unit selects, for example, a file format or a sharing platform, and downloads or shares the manga. This allows the manga generation system according to the embodiment to easily express a user's memories as a manga. For example, a user can input their memories in text format, and the generation AI can analyze the text to understand the flow of the story, automatically generate scenes, and create illustrations corresponding to each scene. Furthermore, the user can customize the generated illustrations and download or share the final manga.

[0069] The reception unit estimates the user's emotion and adjusts the text input interface based on the estimated user emotion. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick text input. This improves ease of input by providing an interface that corresponds to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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-described processing in the reception unit may be performed using, for example, AI, or without AI.

[0070] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit automatically displays phrases and keywords that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest phrases and keywords that will be used in a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0071] When inputting text, the reception unit can filter the input content based on the user's current situation and areas of interest. For example, if the user is traveling, the reception unit can prioritize displaying phrases and keywords related to travel. Furthermore, if the user is working, the reception unit can also prioritize displaying phrases and keywords related to work. Furthermore, if the user is inputting information related to a hobby, the reception unit can also prioritize displaying phrases and keywords related to the hobby. This improves the accuracy of input by providing input content that is appropriate for the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0072] The reception unit can select an appropriate input means depending on the user's input method when inputting text. For example, if the user selects voice input, the reception unit automatically generates text using voice recognition technology. Furthermore, if the user selects image input, the reception unit can also automatically generate text using image recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. This improves input convenience by providing the optimal means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0073] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This improves ease of input by providing a design that corresponds 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 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-described processing in the reception unit may be performed using, for example, AI, or without AI.

[0074] The reception unit can automatically complete the input content by referring to the user's past input history. The reception unit automatically completes the input content based on, for example, phrases and keywords that the user has previously input. The reception unit can also automatically complete the input content based on the writing style and expressions that the user has previously used. The reception unit can also automatically complete phrases and keywords that are appropriate for a specific context from the user's past input history. This improves input efficiency by automatically completing the input content based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0075] When inputting text, the reception unit can prioritize receiving input content that is highly relevant based on the user's current location information. For example, when the user is in a specific location, the reception unit can prioritize displaying phrases and keywords related to that location. Furthermore, when the user is traveling, the reception unit can also prioritize displaying phrases and keywords related to traveling. Furthermore, when the user is participating in a specific event, the reception unit can also prioritize displaying phrases and keywords related to the event. This improves the accuracy of input by providing input content based on the current location information. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or without using AI.

[0076] The reception unit can analyze the user's social media activity when entering text and suggest related input content. The reception unit can, for example, suggest related phrases and keywords based on content posted by the user on social media. The reception unit can also suggest related phrases and keywords based on the activity of the user's friends on social media. The reception unit can also analyze the user's interests and concerns on social media and suggest related phrases and keywords. This improves the accuracy of input by providing input content based on social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI.

[0077] The analysis unit can use the generation AI to analyze the text received by the reception unit and understand the flow of the story. For example, the generation AI analyzes the text in the analysis unit to understand the structure of the story and the relationships between characters. The generation AI analyzes the content of the text using a natural language processing model or a machine learning algorithm. This allows the generation AI to accurately understand the flow of the story. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the generation AI receives text as input and outputs the flow of the story.

[0078] The analysis unit can estimate the user's emotions and adjust the story analysis algorithm based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can apply an algorithm that performs detailed story analysis. If the user is in a hurry, the analysis unit can also apply an algorithm that performs concise story analysis. If the user is excited, the analysis unit can also apply an algorithm that performs visually stimulating story analysis. This improves the accuracy of the analysis by performing story analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generation AI. The generation AI can 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 analysis unit is performed using the generation AI.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the text. For example, the analysis unit performs a detailed analysis on important scenes. The analysis unit can also perform a concise analysis on less important scenes. The analysis unit can also evaluate the importance of the entire text and adjust the level of detail of the analysis. This improves the efficiency of the analysis by performing analysis according to the importance of the text. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the generation AI receives text as input and outputs the level of detail of the analysis based on the importance.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the text category. For example, in the case of a romance story, the analysis unit can apply an analysis algorithm that emphasizes emotional changes. In addition, in the case of an adventure story, the analysis unit can apply an analysis algorithm that emphasizes action scenes. In addition, in the case of a comedy story, the analysis unit can apply an analysis algorithm that emphasizes humorous elements. In this way, analysis is performed according to the text category, thereby improving the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the generation AI receives text as input and outputs an analysis algorithm according to the category.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can improve the accuracy of the analysis by referring to stories created by the user in the past. The analysis unit can also extract specific patterns from the user's past analysis results to improve the accuracy of the analysis. The analysis unit can also adjust the analysis algorithm based on the user's past feedback to improve the accuracy. In this way, the efficiency of the analysis is improved by improving the accuracy of the analysis based on the past analysis results. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the generation AI receives past analysis results as input and outputs an algorithm that improves the accuracy of the analysis.

[0082] The scene generation unit can use the generative AI to generate scenes based on the story understood by the analysis unit. For example, the generative AI extracts important moments from the story and generates scenes based on them. The generative AI analyzes the content of the story and generates scenes using a natural language processing model or a machine learning algorithm. This makes it possible to accurately generate scenes based on the story using the generative AI. Some or all of the above-mentioned processing in the scene generation unit is performed using the generative AI. For example, the generative AI receives a story as input and outputs scenes.

[0083] The scene generation unit can estimate the user's emotions and adjust the scene generation algorithm based on the estimated user emotions. For example, if the user is relaxed, the scene generation unit can generate a scene that progresses at a leisurely pace. If the user is in a hurry, the scene generation unit can also generate a scene that emphasizes the shortest route. If the user is excited, the scene generation unit can also generate a scene that adds visually stimulating effects. This improves the accuracy of scene generation by generating scenes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generation AI. The generation AI can 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 scene generation unit is performed using the generation AI.

[0084] The scene generation unit can adjust the level of detail of a scene based on the importance of the story when generating a scene. For example, the scene generation unit generates a detailed scene for an important scene. The scene generation unit can also generate a concise scene for a scene with low importance. The scene generation unit can also evaluate the importance of the entire story and adjust the level of detail of a scene. This improves the efficiency of generation by generating scenes according to the importance of the story. Some or all of the above-mentioned processing in the scene generation unit is performed using a generation AI. For example, the generation AI receives a story as input and outputs the level of detail of a scene based on its importance.

[0085] When generating scenes, the scene generation unit can apply different scene generation algorithms depending on the story category. For example, in the case of a romance story, the scene generation unit can apply a scene generation algorithm that emphasizes changes in emotions. In addition, in the case of an adventure story, the scene generation unit can also apply a scene generation algorithm that emphasizes action scenes. In addition, in the case of a comedy story, the scene generation unit can also apply a scene generation algorithm that emphasizes humorous elements. In this way, scene generation according to the story category improves the accuracy of generation. Some or all of the above-mentioned processing in the scene generation unit is performed using a generation AI. For example, the generation AI receives a story as input and outputs a scene generation algorithm according to the category.

[0086] When generating a scene, the scene generation unit can improve the accuracy of scene generation by referring to the user's past scene generation results. For example, the scene generation unit improves the accuracy of scene generation by referring to scenes created by the user in the past. The scene generation unit can also extract specific patterns from the user's past scene generation results to improve the accuracy of scene generation. The scene generation unit can also adjust the scene generation algorithm based on the user's past feedback to improve accuracy. This improves the efficiency of generation by improving the accuracy of generation based on past scene generation results. Some or all of the above-mentioned processing in the scene generation unit is performed using a generation AI. For example, the generation AI receives past scene generation results as input and outputs an algorithm that improves the accuracy of scene generation.

[0087] The illustration generation unit can use the generation AI to generate an illustration corresponding to the scene generated by the scene generation unit. For example, the generation AI selects an appropriate style and color according to the content of the scene and generates the illustration. The generation AI uses a natural language processing model or a machine learning algorithm to analyze the content of the scene and generate the illustration. In this way, the generation AI can accurately generate an illustration corresponding to the scene. Some or all of the above-mentioned processing in the illustration generation unit is performed using the generation AI. For example, the generation AI receives a scene as input and outputs an illustration.

[0088] The illustration generation unit can estimate the user's emotions and adjust the illustration generation algorithm 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 improves the accuracy of generation by generating illustrations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generation AI. The generation AI can 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 illustration generation unit is performed using the generation AI.

[0089] When generating an illustration, the illustration generation unit can adjust the level of detail of the illustration based on the importance of the scene. For example, the illustration generation unit generates a detailed illustration for an important scene. The illustration generation unit can also generate a simple illustration for a scene with low importance. The illustration generation unit can also evaluate the importance of the entire scene and adjust the level of detail of the illustration. This improves the efficiency of generation by generating an illustration according to the importance of the scene. Some or all of the above-mentioned processing in the illustration generation unit is performed using a generation AI. For example, the generation AI receives a scene as input and outputs the level of detail of the illustration based on its importance.

[0090] When generating an illustration, the illustration generation unit can apply different illustration generation algorithms depending on the scene category. For example, in the case of a romance scene, the illustration generation unit applies an illustration generation algorithm that emphasizes changes in emotions. In addition, in the case of an adventure scene, the illustration generation unit can also apply an illustration generation algorithm that emphasizes action. In addition, in the case of a comedy scene, the illustration generation unit can also apply an illustration generation algorithm that emphasizes humorous elements. In this way, by generating an illustration according to the scene category, the accuracy of the generation is improved. Some or all of the above-mentioned processing in the illustration generation unit is performed using a generation AI. For example, the generation AI receives a scene as input and outputs an illustration generation algorithm according to the category.

[0091] When generating an illustration, the illustration generation unit can improve the accuracy of the illustration generation by referring to the user's past illustration generation results. For example, the illustration generation unit improves the accuracy of the illustration generation by referring to illustrations created by the user in the past. The illustration generation unit can also extract specific patterns from the user's past illustration generation results to improve the accuracy of the illustration generation. The illustration generation unit can also adjust the illustration generation algorithm based on the user's past feedback to improve accuracy. This improves the efficiency of generation by improving the accuracy of generation based on past illustration generation results. Some or all of the above-mentioned processing in the illustration generation unit is performed using a generation AI. For example, the generation AI receives past illustration generation results as input and outputs an algorithm that improves the accuracy of illustration generation.

[0092] The customization unit allows the user to customize the illustrations generated by the illustration generation unit. For example, the customization unit allows the user to change the color of the generated illustration. The customization unit also allows the user to add characters. The customization unit also allows the user to change the background. In this way, the user can customize the generated illustrations to create a more personalized manga. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI.

[0093] The customization unit can estimate the user's emotions and adjust the customization interface based on the estimated user emotions. For example, if the user is feeling stressed, the customization unit can provide a simple interface and minimize the customization steps. Furthermore, if the user is relaxed, the customization unit can provide detailed customization options and suggest customization methods. Furthermore, if the user is in a hurry, the customization unit can prioritize voice input to enable quick customization. This improves the ease of customization by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generation AI. The generation AI can 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-described processing in the customization unit may be performed using AI, or may be performed without AI.

[0094] During customization, the customization unit can suggest the optimal customization method by referring to the user's past customization history. For example, the customization unit automatically displays customizations that the user has frequently performed in the past as candidates. The customization unit can also preferentially suggest customization methods (color, style, etc.) that the user has used in the past. The customization unit can also predict and suggest a customization method to be used in a specific time period based on the user's past customization history. This improves the efficiency of customization by suggesting the optimal method based on the past customization history. Some or all of the above-mentioned processing in the customization unit may be performed using AI, or may be performed without using AI.

[0095] During customization, the customization unit can filter the customization content based on the user's current situation and areas of interest. For example, if the user is traveling, the customization unit can prioritize displaying travel-related customization options. Furthermore, if the user is working, the customization unit can also prioritize displaying work-related customization options. Furthermore, if the user is customizing the device for a hobby, the customization unit can also prioritize displaying customization options related to the hobby. This improves the accuracy of customization by providing customization content according to the user's situation and areas of interest. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI.

[0096] During customization, the customization unit can select an appropriate customization means according to the user's input method. For example, if the user selects voice input, the customization unit automatically generates customization content using voice recognition technology. Furthermore, if the user selects image input, the customization unit can also automatically generate customization content using image recognition technology. Furthermore, if the user selects text input, the customization unit can also provide an interface that supports keyboard input. This improves the convenience of customization by providing the optimal means according to the user's input method. Some or all of the above-described processing in the customization unit may be performed using AI, or may be performed without using AI.

[0097] The sharing unit can download or share the manga customized by the customization unit. For example, the sharing unit downloads the manga customized by the user in PDF format. The sharing unit can also share the manga customized by the user on social networking sites. The sharing unit can also send the manga customized by the user by email. This makes it easy to download or share the customized manga. Some or all of the above-described processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0098] The sharing unit can estimate the user's emotions and adjust the sharing interface based on the estimated user emotions. For example, if the user is feeling stressed, the sharing unit can provide a simple sharing interface and minimize the sharing procedure. Furthermore, if the user is relaxed, the sharing unit can provide detailed sharing options and suggest customizable sharing methods. Furthermore, if the user is in a hurry, the sharing unit can prioritize voice input to enable quick sharing. This improves ease of sharing by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 sharing unit may be performed using AI, or may be performed without AI.

[0099] When sharing, the sharing unit can suggest the optimal sharing method by referring to the user's past sharing history. For example, the sharing unit automatically displays platforms that the user has frequently shared on in the past as candidates. The sharing unit can also prioritize suggesting sharing methods (email, SNS, etc.) that the user has used in the past. The sharing unit can also predict and suggest the sharing method to be used at a specific time period based on the user's past sharing history. This improves sharing efficiency by suggesting the optimal method based on the past sharing history. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0100] When sharing, the sharing unit can filter the shared content based on the user's current situation and areas of interest. For example, if the user is traveling, the sharing unit can prioritize displaying travel-related sharing options. Also, if the user is at work, the sharing unit can prioritize displaying work-related sharing options. Also, if the user is sharing information related to a hobby, the sharing unit can prioritize displaying sharing options related to that hobby. This improves the accuracy of sharing by providing shared content that is appropriate for the user's situation and areas of interest. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0101] The sharing unit can select an appropriate sharing means depending on the user's input method when sharing. For example, if the user selects voice input, the sharing unit automatically generates the content to be shared using voice recognition technology. Furthermore, if the user selects image input, the sharing unit can also automatically generate the content to be shared using image recognition technology. Furthermore, if the user selects text input, the sharing unit can also provide an interface that supports keyboard input. This improves the convenience of sharing by providing the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0102] The sharing unit can estimate the user's emotions and determine the priority of shared content based on the estimated user's emotions. For example, if the user is excited, the sharing unit prioritizes important shared content. Furthermore, if the user is relaxed, the sharing unit can share all content equally. Furthermore, if the user is in a hurry, the sharing unit can prioritize shared content that focuses on the main points. This improves sharing efficiency by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generation AI. The generation AI can 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 sharing unit may be performed using AI or without AI.

[0103] When sharing, the sharing unit can prioritize suggesting highly relevant sharing content by taking into account the user's geographical location information. For example, if the user is in a specific location, the sharing unit can prioritize displaying sharing options related to that location. Furthermore, if the user is traveling, the sharing unit can prioritize displaying sharing options related to travel. Furthermore, if the user is participating in a specific event, the sharing unit can prioritize displaying sharing options related to the event. This improves the accuracy of sharing by providing sharing content based on the geographical location information. Some or all of the above-described processing in the sharing unit may be performed using AI or without AI.

[0104] The sharing unit can analyze the user's social media activity at the time of sharing and suggest relevant sharing content. For example, the sharing unit can suggest relevant sharing options based on content posted by the user on social media. The sharing unit can also suggest relevant sharing options based on the activity of the user's friends on social media. The sharing unit can also analyze the user's interests on social media and suggest relevant sharing options. This improves the accuracy of sharing by providing sharing content based on social media activity. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI.

[0105] The sharing unit can customize the sharing method by reflecting the user's past feedback when sharing. For example, the sharing unit can adjust the sharing options based on feedback provided by the user in the past. The sharing unit can also extract specific patterns from the user's past feedback and optimize the sharing method. The sharing unit can also improve the sharing interface by referring to the user's past feedback. In this way, customizing the sharing method based on past feedback improves sharing efficiency. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, scene generation unit, illustration generation unit, customization unit, and sharing unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts text input from a user. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the text using a generation AI to understand the flow of the story. The scene generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates scenes based on the analyzed story. The illustration generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates illustrations corresponding to the scenes. The customization unit is implemented, for example, by the control unit 46A of the smart device 14 and allows the user to customize the generated illustrations. The sharing unit downloads or shares the customized manga via, for example, the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, scene generation unit, illustration generation unit, customization unit, and sharing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes text using a generation AI to understand the flow of the story. The scene generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates scenes based on the analyzed story. The illustration generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates illustrations corresponding to the scenes. The customization unit is realized, for example, by the control unit 46A of the smart glasses 214 and allows the user to customize the generated illustrations. The sharing unit downloads or shares the customized manga, for example, via the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, analysis unit, scene generation unit, illustration generation unit, customization unit, and sharing unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes text using a generation AI to understand the flow of the story. The scene generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates scenes based on the analyzed story. The illustration generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates illustrations corresponding to the scenes. The customization unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and allows the user to customize the generated illustrations. The sharing unit downloads or shares the customized manga, for example, via the communication I / F 44 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, scene generation unit, illustration generation unit, customization unit, and sharing unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes text using a generation AI to understand the flow of the story. The scene generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates scenes based on the analyzed story. The illustration generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates illustrations corresponding to the scenes. The customization unit is realized, for example, by the control unit 46A of the robot 414 and allows the user to customize the generated illustrations. The sharing unit downloads or shares the customized manga, for example, via the communication I / F 44 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also provide related information in real time based on the user's input. For example, if the user inputs information about a specific place or event, the reception unit can display additional information and related images about the place or event. The reception unit can also suggest related news articles or blog articles based on the content entered by the user. Furthermore, the reception unit can suggest products or services related to the content entered by the user. This allows the user to instantly obtain information related to the input content, providing a more fulfilling experience.

[0108] The analysis unit can also estimate the user's emotions and adjust the tone of the story based on the estimated user's emotions. For example, if the user is sad, the tone of the story can be adjusted to be calm. If the user is excited, the tone of the story can be adjusted to be lively. Furthermore, if the user is relaxed, the tone of the story can be adjusted to be calm. In this way, by providing a story tone that corresponds to the user's emotions, it is possible to provide an experience that is more in tune with the user's emotions.

[0109] The scene generation unit can also automatically generate background sounds and sound effects for a scene based on user input. For example, if the user inputs a seaside scene, the sound of waves and the cries of seagulls can be added as background sounds. If the user inputs a city scene, the sound of cars and people talking can be added as background sounds. Furthermore, if the user inputs a forest scene, the sound of birds chirping and the sound of the wind can be added as background sounds. This can add a sense of realism to the scene and further enrich the user's experience.

[0110] The illustration generation unit can also estimate the user's emotions and adjust the style of the illustration based on the estimated user's emotions. For example, if the user is happy, a bright and colorful illustration can be generated. If the user is sad, a subdued illustration can be generated. Furthermore, if the user is excited, a dynamic style illustration can be generated. This allows the system to provide an experience that is more in tune with the user's emotions by providing illustrations that correspond to the user's emotions.

[0111] The customization unit can also make customization suggestions based on user input. For example, if a user wants to add a specific character, the customization unit can suggest customization options related to that character. If a user wants to change a specific background, the customization unit can suggest customization options related to that background. Furthermore, if a user wants to change a specific color, the customization unit can suggest customization options related to that color. This makes customization easier for users and provides a more personalized experience.

[0112] The sharing unit can also estimate the user's emotions and suggest the timing of sharing based on the estimated user's emotions. For example, if the user is excited, it can suggest sharing immediately. If the user is relaxed, it can also suggest sharing later. Furthermore, if the user is busy, it can also suggest postponing sharing. In this way, by providing the timing of sharing according to the user's emotions, sharing can be done at a more appropriate time.

[0113] The reception unit can also suggest related templates based on the user's input. For example, if the user inputs travel memories, a travel-related template can be suggested. If the user inputs birthday memories, a birthday-related template can be suggested. If the user inputs wedding memories, a wedding-related template can be suggested. This allows the user to easily select a template that matches the input content, making it easier to create manga.

[0114] The analysis unit can also estimate the user's emotions and adjust the story development based on the estimated user emotions. For example, if the user is nervous, the story can be developed slowly. Alternatively, if the user is excited, the story can be developed quickly. Furthermore, if the user is relaxed, the story can be developed gently. This allows the user to have a more emotionally relevant experience by providing a story development that matches the user's emotions.

[0115] The scene generation unit can also automatically add visual effects to a scene based on user input. For example, if the user inputs a night scene, the scene generation unit can add visual effects such as a starry sky or moonlight. If the user inputs a rainy scene, the scene generation unit can add visual effects such as raindrops or lightning. If the user inputs a winter scene, the scene generation unit can add visual effects such as snow or frost. This can add visual depth to the scene and enrich the user's experience.

[0116] The illustration generation unit can also estimate the user's emotions and adjust the details of the illustration based on the estimated user emotions. For example, if the user is happy, it can generate an illustration that is carefully drawn down to the smallest detail. If the user is sad, it can generate a simple and calm illustration. Furthermore, if the user is excited, it can generate a dynamic illustration with movement. This allows the system to provide an experience that is more in tune with the user's emotions by providing illustrations that correspond to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The accepting unit accepts text input from a user. The text input from a user includes, but is not limited to, keyboard input, voice input, handwritten input, and the like. Step 2: The analysis unit uses the generation AI to analyze the text received by the reception unit and understand the flow of the story. The generation AI uses, for example, natural language processing models or machine learning algorithms to analyze the content of the text and understand the structure of the story and the relationships between characters. Step 3: The scene generation unit uses the generation AI to generate scenes based on the story understood by the analysis unit. For example, the scene generation unit extracts important moments from the story and generates scenes based on them. Step 4: The illustration generation unit uses the generation AI to generate an illustration corresponding to the scene generated by the scene generation unit. For example, the illustration generation unit selects an appropriate style and color depending on the content of the scene and generates the illustration. Step 5: The customization unit allows the user to customize the illustration generated by the illustration generation unit. The customization unit provides customization options such as changing the color, adding characters, and changing the background. Step 6: The sharing unit downloads or shares the comic customized by the customization unit. For example, the sharing unit selects a file format or a sharing platform, and downloads or shares the comic.

[0119] 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.

[0120] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0133] 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0149] 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.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives text input from a user; an analysis unit that analyzes the text received by the reception unit and understands the flow of the story; a scene generation unit that generates scenes based on the story understood by the analysis unit; an illustration generation unit that generates an illustration corresponding to the scene generated by the scene generation unit; a customization unit that allows a user to customize the illustration generated by the illustration generation unit; a sharing unit that downloads or shares the manga customized by the customization unit. A system characterized by:

2. The reception unit Estimating user emotions and adjusting a text input interface based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

4. The reception unit As you enter text, filter your input based on your current context and interests 2. The system of claim 1.

5. The reception unit When entering text, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit Estimate user emotions and adjust the design of the input interface based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Auto-complete input contents based on the user's past input history 2. The system of claim 1.

8. The reception unit When entering text, it prioritizes relevant input based on the user's current location.

2. The system of claim 1.

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