System

Abstract

An object of the system according to the embodiment is to enable a user to easily express his / her experience or idea as a cartoon.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives an input from a user. The analysis unit analyzes the input received by the reception unit and generates a story line. The generation unit generates each page of the comic based on the story line generated by the analysis unit. The provision unit provides the cartoon generated by the generation unit to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult for users to easily express their experiences and ideas in the form of manga.

[0005] The system according to the embodiment aims to enable users to easily express their experiences and ideas as comics. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit receives input from a user. The analysis unit analyzes the input received by the receiving unit and generates a storyline. The generation unit generates each page of the manga based on the storyline generated by the analysis unit. The provision unit provides the manga generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to easily express their experiences and ideas 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 manga generation system according to an embodiment of the present invention automatically converts user-input content into a manga. This manga generation system analyzes the user's input, generates a storyline, generates each page of the manga, and ultimately provides it to the user. For example, a user can input a story about a mishap that occurred during a trip, a success story at work, or an explanation of a specific technology. The generation AI analyzes the content and generates a storyline. The user can then specify detailed settings, such as the number of pages, art style, perspective, emotional intensity, and target audience. The generation AI then generates each page of the manga based on these settings and ultimately provides it to the user. This allows users to easily create high-quality manga that can be used for entertainment, self-promotion, advertising, seminar materials, and more. The manga generation system automatically generates and provides manga based on user input and settings. For example, users can show the manga to friends and family, share it on social media, and use it as a means of self-promotion. For example, users can turn their own success stories into manga and use them in presentations or interviews. Furthermore, they can also be used as materials for advertising and seminars. For example, explaining a product's features and usage through manga can be visually and easily conveyed. In addition, turning the seminar content into manga can help deepen participants' understanding.

[0029] A manga generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives input from a user. The input from the user includes, but is not limited to, text, audio, and images. The receiving unit receives, for example, text data input by the user. The receiving unit can also receive audio input and convert it into text data using audio recognition technology. The receiving unit can also receive image input and analyze the content using image analysis technology. The analysis unit uses a generation AI to analyze the input received by the receiving unit and generate a storyline. The storyline is generated based on, for example, a plot structure and character settings, but is not limited to, examples. For example, the analysis unit analyzes input text data and generates a storyline using the generation AI. The analysis unit can also analyze audio data and generate a storyline using the generation AI. The analysis unit can also analyze image data and generate a storyline using the generation AI. The generation unit uses the generation AI to generate each page of the manga based on the storyline generated by the analysis unit. The generation unit generates a manga based on, for example, the number of pages, art style, viewpoint, emotional intensity, and target specified by the user. For example, the generation unit generates a 10-page manga specified by the user. The generation unit can also adjust the character and background designs to match the realistic art style selected by the user. The generation unit can also generate a manga based on a first-person viewpoint selected by the user. The providing unit provides the generated manga to the user. The providing unit can display the generated manga to the user through, for example, a web application or a mobile application. The providing unit can also send the generated manga by email. The providing unit can also print and provide the generated manga. In this way, the manga generation system according to the embodiment can automatically generate and provide a manga based on user input.

[0030] The generation unit can generate a manga based on the number of pages, art style, viewpoint, emotional intensity, and target specified by the user. The number of pages includes, but is not limited to, a minimum number of pages and a maximum number of pages. For example, the generation unit generates a 10-page manga specified by the user. The generation unit can also generate a 20-page manga specified by the user. The generation unit can also generate a 5-page manga specified by the user. The art style includes, but is not limited to, realistic, deformed, anime-style, etc. For example, the generation unit adjusts the character and background designs to match the realistic art style selected by the user. The generation unit can also adjust the character and background designs to match the deformed art style selected by the user. The generation unit can also adjust the character and background designs to match the anime-style art style selected by the user. The viewpoint includes, but is not limited to, a first-person viewpoint, a third-person viewpoint, etc. For example, the generation unit generates a manga based on a first-person viewpoint selected by the user. The generation unit can also generate a manga based on a third-person viewpoint selected by the user. The generation unit can also generate a manga based on a viewpoint selected by the user. The intensity of emotion includes, for example, a method of expressing the intensity of emotion numerically, but is not limited to, such an example. The generation unit generates a manga based on, for example, the intensity of emotion set by the user. The generation unit can also adjust the emotional flow of the story based on the intensity of emotion set by the user. The generation unit can also adjust the facial expressions of the characters based on the intensity of emotion set by the user. The target includes, for example, an age group, gender, hobbies and preferences, but is not limited to such an example. The generation unit generates, for example, a manga for children specified by the user. The generation unit can also generate a manga for women specified by the user. The generation unit can also generate a manga tailored to specific hobbies and preferences specified by the user. This makes it possible to generate a manga based on the user's detailed settings.

[0031] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display content that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest content that will be used in a specific time period based on the user's past input history. This improves user convenience 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. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0032] When receiving input content, the reception unit can filter the input content based on the user's current interests and trends. For example, the reception unit can preferentially receive related input content based on topics recently searched by the user. The reception unit can also analyze the user's social media activity and suggest input content that matches the trends. The reception unit can also filter the input content based on news and events in which the user is interested. In this way, by filtering the input content based on the user's interests and trends, more relevant content can be received. 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. For example, the reception unit can input the user's social media data to a generation AI and have the generation AI filter relevant input content.

[0033] When accepting input content, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the input content using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the input content using a keyboard or touch panel. Furthermore, if the user selects image input, the acceptance unit can also accept the input content using image analysis technology. This improves input efficiency by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data to a generation AI and have the generation AI select the optimal acceptance means.

[0034] When accepting input content, the reception unit can prioritize accepting highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize accepting content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize accepting content related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize accepting content related to information around the user's home. In this way, by taking the user's geographical location information into account, highly relevant content can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to prioritize related input content.

[0035] The reception unit can analyze the user's social media activity and receive related content when receiving input content. The reception unit can receive related input content based on, for example, content shared by the user on social media. The reception unit can also receive related content by referring to the activity of the user's friends on social media. The reception unit can also analyze the content posted by the user on social media and receive related content. In this way, related content can be received by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to analyze the related input content.

[0036] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input content. The reception unit customizes the input interface, for example, based on feedback provided by the user in the past. The reception unit can also preferentially provide a preferred input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest an optimal input procedure. This makes it possible to provide a more appropriate reception method by reflecting the user's past feedback. 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. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the reception method.

[0037] During analysis, the analysis unit can adjust the level of detail of the storyline based on the importance of the input content. For example, the analysis unit generates a detailed storyline if the content is important. The analysis unit can also generate a concise storyline if the content is general. The analysis unit can also adjust the level of detail of the storyline based on the importance specified by the user. This makes it possible to generate a more appropriate storyline by adjusting the level of detail of the storyline based on the importance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and have the generation AI adjust the level of detail of the storyline.

[0038] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input content. For example, in the entertainment category, the analysis unit can apply an analysis algorithm that emphasizes humor. In addition, in the education category, the analysis unit can apply an analysis algorithm that emphasizes accuracy of information. In addition, in the advertising category, the analysis unit can apply an analysis algorithm that emphasizes visual impact. In this way, by applying different analysis algorithms depending on the category of the input content, a more appropriate storyline can be generated. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0039] During analysis, the analysis unit can improve the accuracy of the storyline by referring to the user's past analysis results. The analysis unit can improve the accuracy by, for example, referring to storylines generated by the user in the past. The analysis unit can also extract preferred storyline patterns from the user's past analysis results. The analysis unit can also adjust the accuracy of the storyline based on the user's past feedback. In this way, the accuracy of the storyline can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the storyline.

[0040] During analysis, the analysis unit can determine the priority of storylines based on the submission time of the input content. For example, in the case of urgent content, the analysis unit can generate storylines with priority. In addition, in the case of general content, the analysis unit can also generate storylines with normal priority. In addition, the analysis unit can adjust the priority of storylines based on the submission time specified by the user. In this way, by determining the priority of storylines based on the submission time of the input content, more appropriate storylines can be generated. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's submission time data into the generation AI and have the generation AI determine the priority of storylines.

[0041] During analysis, the analysis unit can adjust the order of storylines based on the relevance of the input content. For example, the analysis unit places important content first and generates storylines in order of relevance. The analysis unit can also generate storylines based on an order specified by the user. The analysis unit can also analyze the relevance of the input content and generate storylines in an optimal order. This allows for the generation of more appropriate storylines by adjusting the order of storylines based on the relevance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and have the generation AI adjust the order of the storylines.

[0042] During analysis, the analysis unit can adjust the use of technical terms in the storyline according to the user's level of expertise. For example, the analysis unit generates a storyline that uses a lot of technical terms for users with high levels of expertise. The analysis unit can also generate a storyline that avoids technical terms for general users. The analysis unit can also analyze the user's level of expertise and adjust the use of technical terms to an optimal level. This allows for the generation of a more appropriate storyline by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's technical knowledge data into a generation AI and cause the generation AI to adjust the use of technical terms.

[0043] The generation unit can adjust the level of detail of the manga based on the importance of the storyline during generation. For example, the generation unit generates a detailed manga if the content is important. The generation unit can also generate a concise manga if the content is general. The generation unit can also adjust the level of detail of the manga based on the importance specified by the user. This makes it possible to generate a more appropriate manga by adjusting the level of detail of the manga based on the importance of the storyline. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user input data into the generation AI and have the generation AI adjust the level of detail of the manga.

[0044] During generation, the generation unit can apply different generation algorithms depending on the storyline category. For example, in the entertainment category, the generation unit can apply a generation algorithm that emphasizes humor. In addition, in the education category, the generation unit can apply a generation algorithm that emphasizes accuracy of information. In addition, in the advertising category, the generation unit can apply a generation algorithm that emphasizes visual impact. In this way, by applying different generation algorithms depending on the storyline category, more appropriate manga can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data into the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0045] The generation unit can improve the accuracy of the manga by referring to the user's past generation results when generating the manga. The generation unit can improve the accuracy by, for example, referring to manga that the user has generated in the past. The generation unit can also extract preferred manga patterns from the user's past generation results. The generation unit can also adjust the accuracy of the manga based on the user's past feedback. In this way, the accuracy of the manga can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation data into the generation AI and cause the generation AI to improve the accuracy of the manga.

[0046] The generation unit can determine the priority of manga based on the submission time of the storyline during generation. For example, the generation unit generates manga with priority in the case of urgent content. The generation unit can also generate manga with normal priority in the case of general content. The generation unit can also adjust the priority of manga based on the submission time specified by the user. This makes it possible to generate more appropriate manga by determining the priority of manga based on the submission time of the storyline. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's submission time data into the generation AI and have the generation AI determine the priority of manga.

[0047] The generation unit can adjust the order of the manga based on the relevance of the storyline during generation. For example, the generation unit places important content first and generates the manga in order of relevance. The generation unit can also generate the manga based on an order specified by the user. The generation unit can also analyze the relevance of the input content and generate the manga in the optimal order. This makes it possible to generate a more appropriate manga by adjusting the order of the manga based on the relevance of the storyline. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data into the generation AI and have the generation AI adjust the order of the manga.

[0048] During generation, the generation unit can adjust the use of technical terms in the manga according to the user's level of expertise. For example, the generation unit generates a manga that uses a lot of technical terms for users with high levels of expertise. The generation unit can also generate a manga that avoids technical terms for general users. The generation unit can also analyze the user's level of expertise and adjust the use of technical terms to an optimal level. This makes it possible to generate a more appropriate manga by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's expert knowledge data into the generation AI and have the generation AI adjust the use of technical terms.

[0049] At the time of provision, the providing unit can select the optimal provision method by referring to the user's past usage history. For example, the providing unit selects the optimal method based on the provision methods used by the user in the past. The providing unit can also extract a preferred provision method from the user's past usage history. The providing unit can also adjust the optimal provision method based on the user's past feedback. In this way, the optimal provision method can be selected by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past usage data into the generation AI and cause the generation AI to select the optimal provision method.

[0050] The providing unit can customize the content to be provided according to the user's current task when providing the content. For example, when the user is working, the providing unit can prioritize providing work-related content. Furthermore, when the user is taking a break, the providing unit can also provide relaxing content. Furthermore, when the user is performing a specific task, the providing unit can also provide content related to that task. In this way, by customizing the content to be provided according to the user's current task, more appropriate content can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generating AI and cause the generating AI to customize the content to be provided.

[0051] The providing unit can select the optimal delivery method by taking into account the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a delivery method that is simple and highly visible. This makes it possible to select the optimal delivery method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal delivery method.

[0052] The providing unit can make the provided content multilingual based on the user's language setting when providing the content. The providing unit can automatically set the language of the provided content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the provided content in a specific language when the user selects that language. This makes it possible to accommodate a larger number of users by making the provided content multilingual based on the user's language setting. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to execute the multilingual provided content.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can automatically present related reference materials and similar past cases based on the user's input. For example, if the user inputs information about a specific technology, papers and articles related to that technology will be presented. If the user inputs a travel story, travelogues and tourist information for the same area can be provided. Furthermore, if the user inputs a successful work experience, similar success stories and information on business strategies can be presented. This allows the user to easily obtain information related to the input content and use it as reference for creating more fulfilling manga.

[0055] The generator can generate manga that reflects a specific culture or historical background based on a theme specified by the user. For example, if a user desires a manga themed on medieval Europe, the generator can generate designs that reflect the costumes and architecture of that era. If a user desires a manga themed on Japan's Edo period, the generator can generate a storyline that reflects the customs and habits of that era. Furthermore, if a user desires a manga themed on a futuristic city, the generator can generate designs that reflect futuristic architecture and technology. This allows users to create their own original manga based on a specific theme.

[0056] The accepting unit can analyze the user's past input history and provide an auto-completion function for the input content. For example, it can automatically complete phrases and words that the user has frequently used in the past. It can also predict and suggest content that the user is likely to input next based on content that the user has input in the past. It can also automatically apply specific styles and formats that the user has used in the past. This allows the user to streamline input work and input content more smoothly.

[0057] When accepting input content, the accepting unit can adjust the input method taking into account the user's current health condition. For example, if the user is tired, a simple input method can be provided to minimize the input steps. Also, if the user is concentrating, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is feeling stressed, a relaxing interface can be provided. In this way, input tasks can be supported by providing an appropriate input method according to the user's health condition.

[0058] During analysis, the analysis unit can evaluate the reliability of the input content and prioritize highly reliable information to be reflected in the storyline. For example, data from highly reliable information sources can be used preferentially. The analysis unit can also evaluate the reliability of information provided by the user and eliminate unreliable information. Furthermore, the analysis unit can cross-check data from multiple information sources and extract highly reliable information. This makes it possible to generate a storyline based on highly reliable information.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives input from the user. The input from the user includes text, voice, images, etc. For example, the reception unit receives text data entered by the user. It can also receive voice input and convert it into text data using voice recognition technology. It can also receive image input and analyze the content using image analysis technology. Step 2: The analysis unit uses the generation AI to analyze the input received by the reception unit and generate a storyline. The storyline is generated based on the plot structure and character settings. For example, the analysis unit analyzes the text data, audio data, and image data input by the generation AI and generates a storyline. Step 3: The generator uses the generation AI to generate each page of the manga based on the storyline generated by the analysis unit. The generator generates the manga based on the number of pages, art style, perspective, emotional intensity, and target specified by the user. For example, the generator can generate a 10-page manga specified by the user and adjust the character and background designs to match a realistic art style. It can also generate manga based on a first-person perspective. Step 4: The providing unit provides the generated comic to the user. The providing unit displays the generated comic to the user through a web application or a mobile application. The providing unit can also send the generated comic by email. Furthermore, the generated comic can also be provided in print.

[0061] (Example 2) A manga generation system according to an embodiment of the present invention automatically converts user-input content into a manga. This manga generation system analyzes the user's input, generates a storyline, generates each page of the manga, and ultimately provides it to the user. For example, a user can input a story about a mishap that occurred during a trip, a success story at work, or an explanation of a specific technology. The generation AI analyzes the content and generates a storyline. The user can then specify detailed settings, such as the number of pages, art style, perspective, emotional intensity, and target audience. The generation AI then generates each page of the manga based on these settings and ultimately provides it to the user. This allows users to easily create high-quality manga that can be used for entertainment, self-promotion, advertising, seminar materials, and more. The manga generation system automatically generates and provides manga based on user input and settings. For example, users can show the manga to friends and family, share it on social media, and use it as a means of self-promotion. For example, users can turn their own success stories into manga and use them in presentations or interviews. Furthermore, they can also be used as materials for advertising and seminars. For example, explaining a product's features and usage through manga can be visually and easily conveyed. In addition, turning the seminar content into manga can help deepen participants' understanding.

[0062] A manga generation system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, and a providing unit. The receiving unit receives input from a user. The input from the user includes, but is not limited to, text, audio, and images. The receiving unit receives, for example, text data input by the user. The receiving unit can also receive audio input and convert it into text data using audio recognition technology. The receiving unit can also receive image input and analyze the content using image analysis technology. The analysis unit uses a generation AI to analyze the input received by the receiving unit and generate a storyline. The storyline is generated based on, for example, a plot structure and character settings, but is not limited to, examples. For example, the analysis unit analyzes input text data and generates a storyline using the generation AI. The analysis unit can also analyze audio data and generate a storyline using the generation AI. The analysis unit can also analyze image data and generate a storyline using the generation AI. The generation unit uses the generation AI to generate each page of the manga based on the storyline generated by the analysis unit. The generation unit generates a manga based on, for example, the number of pages, art style, viewpoint, emotional intensity, and target specified by the user. For example, the generation unit generates a 10-page manga specified by the user. The generation unit can also adjust the character and background designs to match the realistic art style selected by the user. The generation unit can also generate a manga based on a first-person viewpoint selected by the user. The providing unit provides the generated manga to the user. The providing unit can display the generated manga to the user through, for example, a web application or a mobile application. The providing unit can also send the generated manga by email. The providing unit can also print and provide the generated manga. In this way, the manga generation system according to the embodiment can automatically generate and provide a manga based on user input.

[0063] The generation unit can generate a manga based on the number of pages, art style, viewpoint, emotional intensity, and target specified by the user. The number of pages includes, but is not limited to, a minimum number of pages and a maximum number of pages. For example, the generation unit generates a 10-page manga specified by the user. The generation unit can also generate a 20-page manga specified by the user. The generation unit can also generate a 5-page manga specified by the user. The art style includes, but is not limited to, realistic, deformed, anime-style, etc. For example, the generation unit adjusts the character and background designs to match the realistic art style selected by the user. The generation unit can also adjust the character and background designs to match the deformed art style selected by the user. The generation unit can also adjust the character and background designs to match the anime-style art style selected by the user. The viewpoint includes, but is not limited to, a first-person viewpoint, a third-person viewpoint, etc. For example, the generation unit generates a manga based on a first-person viewpoint selected by the user. The generation unit can also generate a manga based on a third-person viewpoint selected by the user. The generation unit can also generate a manga based on a viewpoint selected by the user. The intensity of emotion includes, for example, a method of expressing the intensity of emotion numerically, but is not limited to, such an example. The generation unit generates a manga based on, for example, the intensity of emotion set by the user. The generation unit can also adjust the emotional flow of the story based on the intensity of emotion set by the user. The generation unit can also adjust the facial expressions of the characters based on the intensity of emotion set by the user. The target includes, for example, an age group, gender, hobbies and preferences, but is not limited to such an example. The generation unit generates, for example, a manga for children specified by the user. The generation unit can also generate a manga for women specified by the user. The generation unit can also generate a manga tailored to specific hobbies and preferences specified by the user. This makes it possible to generate a manga based on the user's detailed settings.

[0064] The reception unit can estimate the user's emotions and adjust the method for receiving input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept input content. This allows for more appropriate input reception by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display content that the user has frequently input in the past as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest content that will be used in a specific time period based on the user's past input history. This improves user convenience 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. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0066] When receiving input content, the reception unit can filter the input content based on the user's current interests and trends. For example, the reception unit can preferentially receive related input content based on topics recently searched by the user. The reception unit can also analyze the user's social media activity and suggest input content that matches the trends. The reception unit can also filter the input content based on news and events in which the user is interested. In this way, by filtering the input content based on the user's interests and trends, more relevant content can be received. 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. For example, the reception unit can input the user's social media data to a generation AI and have the generation AI filter relevant input content.

[0067] When accepting input content, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the input content using voice recognition technology. Furthermore, if the user selects text input, the acceptance unit can also accept the input content using a keyboard or touch panel. Furthermore, if the user selects image input, the acceptance unit can also accept the input content using image analysis technology. This improves input efficiency by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's input data to a generation AI and have the generation AI select the optimal acceptance means.

[0068] The reception unit can estimate the user's emotions and determine the priority of input content to be accepted based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize emotional content. Furthermore, if the user is calm, the reception unit can prioritize logical content. Furthermore, if the user is tired, the reception unit can prioritize simple content. By determining the priority of input content according to the user's emotions, more appropriate content can be preferentially accepted. The emotion estimation is realized 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 AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0069] When accepting input content, the reception unit can prioritize accepting highly relevant content by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize accepting content related to that area. Furthermore, if the user is traveling, the reception unit can prioritize accepting content related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize accepting content related to information around the user's home. In this way, by taking the user's geographical location information into account, highly relevant content can be prioritized. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to prioritize related input content.

[0070] The reception unit can analyze the user's social media activity and receive related content when receiving input content. The reception unit can receive related input content based on, for example, content shared by the user on social media. The reception unit can also receive related content by referring to the activity of the user's friends on social media. The reception unit can also analyze the content posted by the user on social media and receive related content. In this way, related content can be received by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to analyze the related input content.

[0071] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input content. The reception unit customizes the input interface, for example, based on feedback provided by the user in the past. The reception unit can also preferentially provide a preferred input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and suggest an optimal input procedure. This makes it possible to provide a more appropriate reception method by reflecting the user's past feedback. 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. For example, the reception unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the reception method.

[0072] The analysis unit can estimate the user's emotions and adjust the storyline generation method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can generate a storyline that progresses at a leisurely pace. If the user is in a hurry, the analysis unit can generate a storyline that emphasizes the shortest route. If the user is excited, the analysis unit can generate a storyline that adds visually stimulating effects. This allows for adjusting the storyline generation method according to the user's emotions to generate a more appropriate storyline. 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] During analysis, the analysis unit can adjust the level of detail of the storyline based on the importance of the input content. For example, the analysis unit generates a detailed storyline if the content is important. The analysis unit can also generate a concise storyline if the content is general. The analysis unit can also adjust the level of detail of the storyline based on the importance specified by the user. This makes it possible to generate a more appropriate storyline by adjusting the level of detail of the storyline based on the importance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and have the generation AI adjust the level of detail of the storyline.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input content. For example, in the entertainment category, the analysis unit can apply an analysis algorithm that emphasizes humor. In addition, in the education category, the analysis unit can apply an analysis algorithm that emphasizes accuracy of information. In addition, in the advertising category, the analysis unit can apply an analysis algorithm that emphasizes visual impact. In this way, by applying different analysis algorithms depending on the category of the input content, a more appropriate storyline can be generated. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and cause the generation AI to apply an analysis algorithm depending on the category.

[0075] During analysis, the analysis unit can improve the accuracy of the storyline by referring to the user's past analysis results. The analysis unit can improve the accuracy by, for example, referring to storylines generated by the user in the past. The analysis unit can also extract preferred storyline patterns from the user's past analysis results. The analysis unit can also adjust the accuracy of the storyline based on the user's past feedback. In this way, the accuracy of the storyline can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the storyline.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the storyline based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can generate a short, to-the-point storyline. If the user is relaxed, the analysis unit can generate a longer storyline with detailed explanations. If the user is excited, the analysis unit can generate a storyline with visually stimulating effects. This allows for adjusting the length of the storyline according to the user's emotions, thereby generating a more appropriate storyline. 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 analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] During analysis, the analysis unit can determine the priority of storylines based on the submission time of the input content. For example, in the case of urgent content, the analysis unit can generate storylines with priority. In addition, in the case of general content, the analysis unit can also generate storylines with normal priority. In addition, the analysis unit can adjust the priority of storylines based on the submission time specified by the user. In this way, by determining the priority of storylines based on the submission time of the input content, more appropriate storylines can be generated. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's submission time data into the generation AI and have the generation AI determine the priority of storylines.

[0078] During analysis, the analysis unit can adjust the order of storylines based on the relevance of the input content. For example, the analysis unit places important content first and generates storylines in order of relevance. The analysis unit can also generate storylines based on an order specified by the user. The analysis unit can also analyze the relevance of the input content and generate storylines in an optimal order. This allows for the generation of more appropriate storylines by adjusting the order of storylines based on the relevance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's input data to a generation AI and have the generation AI adjust the order of the storylines.

[0079] During analysis, the analysis unit can adjust the use of technical terms in the storyline according to the user's level of expertise. For example, the analysis unit generates a storyline that uses a lot of technical terms for users with high levels of expertise. The analysis unit can also generate a storyline that avoids technical terms for general users. The analysis unit can also analyze the user's level of expertise and adjust the use of technical terms to an optimal level. This allows for the generation of a more appropriate storyline by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's technical knowledge data into a generation AI and cause the generation AI to adjust the use of technical terms.

[0080] The generation unit can estimate the user's emotions and adjust the cartoon generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a cartoon that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a cartoon that emphasizes the shortest route. If the user is excited, the generation unit can also generate a cartoon that adds visually stimulating effects. By adjusting the cartoon generation method according to the user's emotions, a more appropriate cartoon can be generated. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0081] The generation unit can adjust the level of detail of the manga based on the importance of the storyline during generation. For example, the generation unit generates a detailed manga if the content is important. The generation unit can also generate a concise manga if the content is general. The generation unit can also adjust the level of detail of the manga based on the importance specified by the user. This makes it possible to generate a more appropriate manga by adjusting the level of detail of the manga based on the importance of the storyline. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input user input data into the generation AI and have the generation AI adjust the level of detail of the manga.

[0082] During generation, the generation unit can apply different generation algorithms depending on the storyline category. For example, in the entertainment category, the generation unit can apply a generation algorithm that emphasizes humor. In addition, in the education category, the generation unit can apply a generation algorithm that emphasizes accuracy of information. In addition, in the advertising category, the generation unit can apply a generation algorithm that emphasizes visual impact. In this way, by applying different generation algorithms depending on the storyline category, more appropriate manga can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data into the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0083] The generation unit can improve the accuracy of the manga by referring to the user's past generation results when generating the manga. The generation unit can improve the accuracy by, for example, referring to manga that the user has generated in the past. The generation unit can also extract preferred manga patterns from the user's past generation results. The generation unit can also adjust the accuracy of the manga based on the user's past feedback. In this way, the accuracy of the manga can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past generation data into the generation AI and cause the generation AI to improve the accuracy of the manga.

[0084] The generation unit can estimate the user's emotions and adjust the number of pages of the manga based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point manga. If the user is relaxed, the generation unit can generate a longer manga with detailed explanations. If the user is excited, the generation unit can generate a manga with visually stimulating effects. This allows for the generation of a more appropriate manga by adjusting the number of pages of the manga according to the user's emotions. The 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 generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] The generation unit can determine the priority of manga based on the submission time of the storyline during generation. For example, the generation unit generates manga with priority in the case of urgent content. The generation unit can also generate manga with normal priority in the case of general content. The generation unit can also adjust the priority of manga based on the submission time specified by the user. This makes it possible to generate more appropriate manga by determining the priority of manga based on the submission time of the storyline. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's submission time data into the generation AI and have the generation AI determine the priority of manga.

[0086] The generation unit can adjust the order of the manga based on the relevance of the storyline during generation. For example, the generation unit places important content first and generates the manga in order of relevance. The generation unit can also generate the manga based on an order specified by the user. The generation unit can also analyze the relevance of the input content and generate the manga in the optimal order. This makes it possible to generate a more appropriate manga by adjusting the order of the manga based on the relevance of the storyline. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user input data into the generation AI and have the generation AI adjust the order of the manga.

[0087] During generation, the generation unit can adjust the use of technical terms in the manga according to the user's level of expertise. For example, the generation unit generates a manga that uses a lot of technical terms for users with high levels of expertise. The generation unit can also generate a manga that avoids technical terms for general users. The generation unit can also analyze the user's level of expertise and adjust the use of technical terms to an optimal level. This makes it possible to generate a more appropriate manga by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's expert knowledge data into the generation AI and have the generation AI adjust the use of technical terms.

[0088] The providing unit can estimate the user's emotions and adjust the providing method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the manga at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can provide the manga quickly. Furthermore, if the user is excited, the providing unit can provide the manga with visually stimulating effects. By adjusting the providing method according to the user's emotions, the manga can be provided in a more appropriate manner. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] At the time of provision, the providing unit can select the optimal provision method by referring to the user's past usage history. For example, the providing unit selects the optimal method based on the provision methods used by the user in the past. The providing unit can also extract a preferred provision method from the user's past usage history. The providing unit can also adjust the optimal provision method based on the user's past feedback. In this way, the optimal provision method can be selected by referring to the user's past usage history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past usage data into the generation AI and cause the generation AI to select the optimal provision method.

[0090] The providing unit can customize the content to be provided according to the user's current task when providing the content. For example, when the user is working, the providing unit can prioritize providing work-related content. Furthermore, when the user is taking a break, the providing unit can also provide relaxing content. Furthermore, when the user is performing a specific task, the providing unit can also provide content related to that task. In this way, by customizing the content to be provided according to the user's current task, more appropriate content can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's task data into a generating AI and cause the generating AI to customize the content to be provided.

[0091] The providing unit can estimate the user's emotions and determine the priority of the comics to be provided based on the estimated user emotions. For example, if the user is excited, the providing unit can prioritize providing emotional content. Furthermore, if the user is calm, the providing unit can prioritize providing logical content. Furthermore, if the user is tired, the providing unit can prioritize providing simple content. This allows for more appropriate content to be provided by determining the priority of the comics to be provided according to the user's emotions. Emotion estimation is realized 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 providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0092] The providing unit can select the optimal delivery method by taking into account the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit can provide a delivery method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a delivery method that is simple and highly visible. This makes it possible to select the optimal delivery method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal delivery method.

[0093] The providing unit can make the provided content multilingual based on the user's language setting when providing the content. The providing unit can automatically set the language of the provided content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the provided content in a specific language when the user selects that language. This makes it possible to accommodate a larger number of users by making the provided content multilingual based on the user's language setting. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's language setting data into a generation AI and cause the generation AI to execute the multilingual provided content. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives input such as text, voice, and image from a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input using a generation AI to generate a storyline. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates each page of the manga based on the storyline generated by the analysis unit. For example, the provision unit is realized by the output device 40 of the smart device 14 and provides the generated manga to a user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision 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 or camera 42 of the smart glasses 214 and receives input such as text, voice, and image from a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input using a generation AI to generate a storyline. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates each page of a manga based on the storyline generated by the analysis unit. For example, the provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated manga to a user. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision 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 or camera 42 of the headset-type terminal 314 and receives input such as text, voice, and image from the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input using a generation AI to generate a storyline. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates each page of the manga based on the storyline generated by the analysis unit. For example, the provision unit is realized by the display 343 of the headset-type terminal 314 and provides the generated manga to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision unit, 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 or camera 42 of the robot 414 and receives input such as text, voice, and image from a user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input using a generation AI to generate a storyline. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates each page of the manga based on the storyline generated by the analysis unit. For example, the provision unit is realized by the speaker 240 or display device of the robot 414 and provides the generated manga to a user.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The reception unit can automatically present related reference materials and similar past cases based on the user's input. For example, if the user inputs information about a specific technology, papers and articles related to that technology will be presented. If the user inputs a travel story, travelogues and tourist information for the same area can be provided. Furthermore, if the user inputs a successful work experience, similar success stories and information on business strategies can be presented. This allows the user to easily obtain information related to the input content and use it as reference for creating more fulfilling manga.

[0096] The generator can generate manga that reflects a specific culture or historical background based on a theme specified by the user. For example, if a user desires a manga themed on medieval Europe, the generator can generate designs that reflect the costumes and architecture of that era. If a user desires a manga themed on Japan's Edo period, the generator can generate a storyline that reflects the customs and habits of that era. Furthermore, if a user desires a manga themed on a futuristic city, the generator can generate designs that reflect futuristic architecture and technology. This allows users to create their own original manga based on a specific theme.

[0097] The reception unit can estimate the user's emotions and provide real-time feedback on the input content based on the estimated user emotions. For example, if the user feels anxious, an encouraging message can be displayed to encourage the user to continue inputting. Also, if the user feels excited, positive feedback reflecting the user's emotions can be provided. Furthermore, if the user feels tired, a message suggesting that the user take a break can be displayed. In this way, appropriate feedback according to the user's emotions can be provided to support the input work.

[0098] The accepting unit can analyze the user's past input history and provide an auto-completion function for the input content. For example, it can automatically complete phrases and words that the user has frequently used in the past. It can also predict and suggest content that the user is likely to input next based on content that the user has input in the past. It can also automatically apply specific styles and formats that the user has used in the past. This allows the user to streamline input work and input content more smoothly.

[0099] When accepting input content, the accepting unit can adjust the input method taking into account the user's current health condition. For example, if the user is tired, a simple input method can be provided to minimize the input steps. Also, if the user is concentrating, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is feeling stressed, a relaxing interface can be provided. In this way, input tasks can be supported by providing an appropriate input method according to the user's health condition.

[0100] The reception unit can estimate the user's emotions and provide appropriate music or sound effects when receiving input content based on the estimated user emotions. For example, if the user is relaxed, calm music can be played to support the input work. If the user is excited, energetic music can be played. Furthermore, if the user is concentrating, environmental sounds can be played to enhance concentration. In this way, music and sound effects according to the user's emotions can be provided, making the input work more comfortable.

[0101] The analysis unit can estimate the user's emotions and adjust the tempo of the storyline based on the estimated user emotions. For example, if the user is relaxed, a storyline that progresses at a leisurely tempo can be generated. If the user is in a hurry, a fast-paced storyline can be generated. Furthermore, if the user is excited, a fast-paced storyline with visually stimulating effects can be generated. In this way, a storyline can be generated at an appropriate tempo according to the user's emotions.

[0102] During analysis, the analysis unit can evaluate the reliability of the input content and prioritize highly reliable information to be reflected in the storyline. For example, data from highly reliable information sources can be used preferentially. The analysis unit can also evaluate the reliability of information provided by the user and eliminate unreliable information. Furthermore, the analysis unit can cross-check data from multiple information sources and extract highly reliable information. This makes it possible to generate a storyline based on highly reliable information.

[0103] The generation unit can estimate the user's emotions and adjust the colors and tones of the cartoon based on the estimated user's emotions. For example, if the user is relaxed, a cartoon using calm colors and tones can be generated. If the user is excited, a cartoon using vivid colors and strong contrasts can be generated. Furthermore, if the user is sad, a cartoon using calm colors and soft tones can be generated. This makes it possible to generate a cartoon with appropriate colors and tones according to the user's emotions.

[0104] The providing unit can estimate the user's emotions and adjust the format of the manga to be provided based on the estimated user's emotions. For example, if the user is relaxed, the manga can be provided in an e-book format so that the user can enjoy it at their own pace. If the user is in a hurry, the manga can be provided in a digest format that summarizes the main points. Furthermore, if the user is excited, the manga can be provided in an interactive format so that the user can enjoy it visually. This makes it possible to provide manga in an appropriate format according to the user's emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The reception unit receives input from the user. The input from the user includes text, voice, images, etc. For example, the reception unit receives text data entered by the user. It can also receive voice input and convert it into text data using voice recognition technology. It can also receive image input and analyze the content using image analysis technology. Step 2: The analysis unit uses the generation AI to analyze the input received by the reception unit and generate a storyline. The storyline is generated based on the plot structure and character settings. For example, the analysis unit analyzes the text data, audio data, and image data input by the generation AI and generates a storyline. Step 3: The generator uses the generation AI to generate each page of the manga based on the storyline generated by the analysis unit. The generator generates the manga based on the number of pages, art style, perspective, emotional intensity, and target specified by the user. For example, the generator can generate a 10-page manga specified by the user and adjust the character and background designs to match a realistic art style. It can also generate manga based on a first-person perspective. Step 4: The providing unit provides the generated comic to the user. The providing unit displays the generated comic to the user through a web application or a mobile application. The providing unit can also send the generated comic by email. Furthermore, the generated comic can also be provided in print.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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 AI 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 AI 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 AI 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.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 input from a user; an analysis unit that analyzes the input received by the reception unit and generates a storyline; a generation unit that generates each page of the manga based on the storyline generated by the analysis unit; a providing unit that provides the comic book generated by the generating unit to a user. A system characterized by:

2. The generation unit Generates manga based on user-specified page count, art style, perspective, emotional intensity, and target audience.

2. The system of claim 1.

3. The reception unit Estimate the user's emotions and adjust the way input is accepted based on the estimated user emotions.

2. The system of claim 1.

4. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

5. The reception unit As input is accepted, filter it based on the user's current interests and trends 2. The system of claim 1.

6. The reception unit When accepting input content, select the most appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and prioritize the inputs to be accepted based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit When accepting input, the system takes into account the user's geographic location information and prioritizes relevant content.

2. The system of claim 1.

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