System

Abstract

To provide a system for easily converting text of a blog into a cartoon form.SOLUTION: A system includes a reception part, a generation part, a generation part including a character generation part and a background generation part, and an output part. The reception part inputs a text of a blog. The generation unit analyzes the text input by the reception unit and generates a storyboard. The generation unit including the character generation unit and the background generation unit generates the character and the background based on the storyboard generated by the generation unit. The output unit outputs the comic-style blog content generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback that the process of converting blog text into manga format is time-consuming and laborious, and difficult to carry out easily.

[0005] The system according to the embodiment aims to easily convert blog text into a comic format. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, a generation unit, a generation unit including a character generation unit and a background generation unit, and an output unit. The reception unit inputs blog text. The generation unit analyzes the text input by the reception unit and generates a storyboard. The generation unit including the character generation unit and the background generation unit generates characters and backgrounds based on the storyboard generated by the generation unit. The output unit outputs the blog content in manga format generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily convert blog text into comic format. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention converts blogs into manga and easily converts them into easy-to-read blogs. In this system, a user inputs blog text, and a generation AI analyzes the text and generates a storyboard. Based on the generated storyboard, manga elements such as characters and backgrounds are automatically generated, ultimately outputting a manga-style blog. This mechanism makes the blog content visually easy to understand and attracts readers' interest. By converting blogs based on specific themes, such as travelogues or cooking recipes, into manga format, the content becomes more familiar to readers. Furthermore, by automatically generating manga elements such as characters and backgrounds, users can easily create high-quality manga-style blogs.

[0029] A system according to an embodiment includes a reception unit, a generation unit, a character generation unit, a background generation unit, and an output unit. The reception unit receives blog text input from a user. For example, information about the content and theme of the blog can also be input. The generation unit uses a generation AI to analyze the text input by the reception unit and generate a storyboard. For example, the generation AI understands the content of the text and generates a storyboard. The character generation unit and background generation unit generate characters and backgrounds based on the storyboard generated by the generation unit. For example, the generation AI selects characters and backgrounds appropriate for each scene in the storyboard and converts them into a manga format. The output unit outputs the blog content in manga format generated by the generation unit. For example, a user can review the generated manga and make corrections or additional edits as necessary. This allows the system according to an embodiment to convert blog text into a manga format and generate a visually easy-to-understand blog.

[0030] The reception unit can input information about the theme and style of the blog. Examples of themes and styles of blogs include, but are not limited to, travelogues, cooking recipes, and technical articles. The reception unit can, for example, allow the user to select the theme and style of the blog and input text based on that. This allows input according to the theme and style of the blog.

[0031] The generation unit can provide templates and styles that the user can select. Examples of templates and styles include, but are not limited to, design templates, font styles, layouts, etc. The generation unit can, for example, allow the user to select a preferred template or style and generate a storyboard based on the template or style. This allows the user to select a preferred template or style.

[0032] The character generation unit and the background generation unit may provide customizable options for the user. Customizable options include, but are not limited to, for example, character clothing, background color, and scene layout. The character generation unit and the background generation unit may, for example, allow the user to customize characters and backgrounds and generate a manga based on the customized characters and backgrounds. This allows the user to customize the characters and backgrounds.

[0033] The output unit may provide an interface that allows the user to easily make corrections and additional edits. Examples of corrections and additional edits include, but are not limited to, correcting text, rearranging characters, and changing backgrounds. The output unit may provide an interface that allows the user to check the generated comic and make corrections or additional edits as necessary. This allows the user to easily correct and edit the generated comic.

[0034] The generation unit can generate a storyboard by using natural language processing technology to understand the meaning of the text in detail. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The generation unit can generate a storyboard by analyzing the content of the text in detail using, for example, a generation AI. This makes it possible to generate a storyboard with a deep understanding of the meaning of the text.

[0035] The reception unit can analyze the user's past blog entry history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze the themes and styles of blog entries the user has made in the past and suggest the optimal input method. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. This makes it possible to provide the optimal input method based on the user's past input history.

[0036] The reception unit can filter blog entries based on the user's current areas of interest when the blog entries are submitted. For example, the reception unit prioritizes blog entries related to topics that the user is currently interested in. The reception unit can also suggest blog entries related to the user's areas of interest based on the user's recent search history. The reception unit can also analyze the user's social media activity and filter the blog entries based on the user's areas of interest. This allows blog entries based on the user's areas of interest.

[0037] When a user inputs a blog entry, the reception unit can select an appropriate input means according to the user's input method. For example, if the user desires voice input, the reception unit can support the input using voice recognition technology. Furthermore, if the user desires text input, the reception unit can also optimize keyboard input. Furthermore, if the user desires image input, the reception unit can also support the input using image recognition technology. This makes it possible to provide the optimal input means according to the user's input method.

[0038] When inputting a blog, the reception unit can prioritize input of highly relevant blogs based on the user's geographical location information. For example, if the user is traveling, the reception unit can prioritize input of blogs related to the user's current location. Furthermore, if the user is in a specific area, the reception unit can also prioritize input of blogs related to that area. Furthermore, the reception unit can also prioritize input of blogs related to local events and news based on the user's location information. This makes it possible to input blogs based on the user's geographical location information.

[0039] When a blog entry is made, the reception unit can analyze the user's social media activity and input related blog entries. For example, the reception unit prioritizes the input of blog entries related to content the user has shared on social media. The reception unit can also analyze the content posted by the user on social media and suggest the input of related blog entries. The reception unit can also suggest the input of related blog entries by taking into account the activity of the user's friends on social media. This makes it possible to input blog entries based on the user's social media activity.

[0040] The reception unit can customize the input method based on the user's past feedback when inputting a blog. For example, the reception unit suggests the optimal input method based on the user's past feedback. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by reflecting the user's feedback. This makes it possible to provide an input method based on the user's past feedback.

[0041] When generating a storyboard, the generator can adjust the level of detail of the storyboard according to the importance of the blog. For example, the generator generates a detailed storyboard for an important blog. The generator can also generate a concise storyboard for a general blog. The generator can also generate a detailed storyboard for a part that the user wants to particularly emphasize. This makes it possible to provide a storyboard with a level of detail according to the importance of the blog.

[0042] The generator can apply different generation algorithms to generate storyboards based on the blog category. For example, in the case of a travelogue, the generator can sequentially organize the places visited and the events experienced in the storyboard. In the case of a cooking recipe, the generator can also visually organize the cooking steps and ingredients in the storyboard. In the case of a news article, the generator can also emphasize important information and organize it in the storyboard. This makes it possible to provide an optimal storyboard according to the blog category.

[0043] When generating a storyboard, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of generation by referring to storyboards generated by the user in the past. The generation unit can also learn the user's preferences and style from the user's past generation results and generate an optimal storyboard. The generation unit can also improve the accuracy of generation by reflecting user feedback. This makes it possible to provide an accurate storyboard based on the user's past generation results.

[0044] When generating a storyboard, the generation unit can determine the priority of generation based on the submission date of the blog. For example, the generation unit generates a storyboard preferentially for a blog whose deadline is approaching. The generation unit can also postpone generating a storyboard for a blog whose submission date is far away. The generation unit can also generate a storyboard preferentially for a blog for which the user is particularly in a hurry. This makes it possible to provide a priority of generation of storyboards according to the submission date of the blog.

[0045] When generating a storyboard, the generation unit can adjust the order of generation according to the relevance of the blogs. For example, the generation unit prioritizes highly relevant blogs in the storyboard. The generation unit can also prioritize less relevant blogs in the storyboard. The generation unit can also prioritize generating storyboards for blogs for which the user places particular importance on relevance. This makes it possible to provide a storyboard generation order according to the relevance of the blogs.

[0046] The generator may adjust the use of technical terminology when generating a storyboard based on the user's level of expertise. For example, if the user has technical expertise, the generator may generate a storyboard that uses a lot of technical terminology. Alternatively, if the user is a beginner, the generator may generate an easy-to-understand storyboard that avoids technical terminology. Alternatively, the generator may generate a storyboard that uses appropriate technical terminology according to the user's level of expertise. This allows the use of technical terminology according to the user's level of expertise.

[0047] When generating characters and backgrounds, the character generation unit and background generation unit can select appropriate characters and backgrounds based on the content of the blog. For example, in the case of a travelogue, the character generation unit and background generation unit generate characters and backgrounds that visually represent the scenery of places visited and events experienced. In the case of a cooking recipe, the character generation unit and background generation unit can also generate characters and backgrounds that visually represent cooking steps and ingredients in an easy-to-understand manner. In the case of a news article, the character generation unit and background generation unit can also generate characters and backgrounds that emphasize important information. This makes it possible to provide optimal characters and backgrounds according to the content of the blog.

[0048] When generating characters and backgrounds, the character generation unit and background generation unit can improve the accuracy of generation by referring to the user's past generation results. The character generation unit and background generation unit can improve the accuracy of generation by referring to, for example, characters and backgrounds generated by the user in the past. The character generation unit and background generation unit can also learn the user's preferences and style from the user's past generation results and generate optimal characters and backgrounds. The character generation unit and background generation unit can also improve the accuracy of generation by reflecting user feedback. This makes it possible to provide accurate characters and backgrounds based on the user's past generation results.

[0049] The character generation unit and the background generation unit can improve the generation method based on user feedback when generating characters and backgrounds. For example, the character generation unit and the background generation unit improve the character and background generation method based on feedback provided by the user. The character generation unit and the background generation unit can also adjust algorithms to improve generation accuracy based on user feedback. The character generation unit and the background generation unit can also improve the quality of the generated characters and backgrounds by reflecting user feedback. This makes it possible to provide a character and background generation method based on user feedback.

[0050] When generating characters and backgrounds, the character generation unit and background generation unit can select appropriate characters and backgrounds based on the geographical information of the blog. For example, if a user is writing a travelogue, the character generation unit and background generation unit can generate characters and backgrounds based on the geographical information of the places visited. Also, if a user is writing a blog related to a specific region, the character generation unit and background generation unit can generate characters and backgrounds based on the geographical information of the region. Also, the character generation unit and background generation unit can generate characters and backgrounds that reflect the local scenery and culture based on the user's location information. This makes it possible to provide characters and backgrounds based on the geographical information of the blog.

[0051] The character generation unit and background generation unit can improve the accuracy of generation by referring to literature related to the blog when generating characters and backgrounds. For example, the character generation unit and background generation unit refer to literature related to the content of the blog entered by the user and reflects the information in the generation of the characters and backgrounds. The character generation unit and background generation unit can also improve the accuracy of generation of characters and backgrounds based on information obtained from the related literature. The character generation unit and background generation unit can also optimize the generation of characters and backgrounds based on reference materials provided by the user. This makes it possible to provide accurate generation of characters and backgrounds based on literature related to the blog.

[0052] The character generation unit and background generation unit can generate characters and backgrounds based on the market value of the blog. For example, if a user is writing a commercial blog, the character generation unit and background generation unit can generate characters and backgrounds taking market value into consideration. Furthermore, if a user is writing a blog aimed at generating advertising revenue, the character generation unit and background generation unit can also generate characters and backgrounds that increase market value. Furthermore, if a user is writing a blog aimed at a specific target demographic, the character generation unit and background generation unit can also generate characters and backgrounds taking market value into consideration. This makes it possible to provide characters and backgrounds based on the market value of the blog.

[0053] At the time of output, the output unit can select an appropriate display method based on the user's past output results. For example, the output unit selects the optimal display method based on the user's past preferred display methods. The output unit can also learn the user's preferences and style from the user's past output results and suggest the optimal display method. The output unit can also optimize the display method by reflecting user feedback. This makes it possible to provide the optimal display method based on the user's past output results.

[0054] The output unit can adjust the display content according to the user's current task when outputting. For example, when the user is in a hurry, the output unit can provide display content that is concise and to the point. On the other hand, when the user is relaxed, the output unit can provide display content that includes detailed information. On the other hand, when the user is performing a specific task, the output unit can prioritize displaying information related to that task. In this way, it is possible to provide display content according to the user's current task.

[0055] The output unit can improve the display method based on user feedback at the time of output. For example, the output unit improves the display method based on feedback provided by the user. The output unit can also adjust an algorithm for optimizing the display content based on the user feedback. The output unit can also improve the quality of the displayed manga by reflecting the user feedback. This makes it possible to provide a display method based on user feedback.

[0056] The output unit can select an appropriate display method based on the user's device information at the time of output. For example, if the user is using a smartphone, the output unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the output unit can also provide a simple and highly visible display method. This makes it possible to provide an optimal display method based on the user's device information.

[0057] The output unit can make the display content multilingual based on the user's language setting at the time of output. The output unit automatically sets the display content based on, for example, the language setting of the user's device. The output unit can also provide a language switching function when the user uses multiple languages. The output unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide multilingual display content based on the user's language setting.

[0058] The output unit can improve the display method based on user feedback at the time of output. For example, the output unit improves the display method based on feedback provided by the user. The output unit can also adjust an algorithm for optimizing the display content based on the user feedback. The output unit can also improve the quality of the displayed manga by reflecting the user feedback. This makes it possible to provide a display method based on user feedback.

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

[0060] The reception unit can analyze the user's past blog browsing history and suggest themes and styles that the user is likely to be interested in. For example, if the user has frequently viewed travelogues in the past, the reception unit can suggest themes and styles related to travelogues. Also, if the user frequently views technical articles, the reception unit can suggest templates and styles suitable for technical articles. Furthermore, if the user shows interest in a specific genre, the reception unit can preferentially suggest blog entries related to that genre. This makes it possible to provide the optimal themes and styles based on the user's past browsing history.

[0061] The reception unit can analyze the user's past blog posting history and suggest the optimal posting time. For example, if the user has posted many blog posts in the past during a specific time period, the reception unit can suggest that time period. Also, if the user has posted many blog posts on a specific day of the week, the reception unit can suggest that day of the week. Furthermore, the reception unit can suggest posting times related to specific seasons or events based on the user's past posting history. This makes it possible to provide the optimal posting time based on the user's past posting history.

[0062] The generation unit can analyze the content of a user's past blog and reflect it in the generation of a storyboard. For example, a storyboard for a new travelogue can be generated by referring to the content of a travelogue written by the user in the past. A storyboard for a new cooking recipe can also be generated by referring to the content of a cooking recipe written by the user in the past. Furthermore, a storyboard for a new technical article can also be generated by referring to the content of a technical article written by the user in the past. This makes it possible to provide a storyboard based on the content of a user's past blog.

[0063] The character generation unit and background generation unit can analyze the user's past character and background selection history and propose optimal characters and backgrounds. For example, a new character can be proposed by referring to the style of a character previously selected by the user. A new background can also be proposed by referring to the color tone of a background previously selected by the user. Furthermore, the layout of a new scene can be proposed by referring to the layout of a scene previously selected by the user. This makes it possible to provide optimal characters and backgrounds based on the user's past selection history.

[0064] The output unit can adjust the display method based on the remaining battery level of the user's device. For example, when the remaining battery level is low, the display content can be simplified to reduce battery consumption. When the remaining battery level is sufficient, the output unit can provide detailed display content. When the remaining battery level is medium, the output unit can provide balanced display content. This allows the display unit to provide an optimal display method based on the remaining battery level of the user's device.

[0065] The output unit can adjust the display method based on the user's Internet connection status. For example, if the Internet connection is unstable, the output unit can simplify the display content to reduce data traffic. If the Internet connection is stable, the output unit can provide detailed display content. If the Internet connection is moderate, the output unit can provide balanced display content. This allows the optimal display method to be provided based on the user's Internet connection status.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The user inputs the blog text into the reception unit. For example, information about the content and theme of the blog can also be input. Step 2: The generation unit uses the generation AI to analyze the text input by the reception unit and generate a storyboard. For example, the generation AI understands the content of the text and generates a storyboard. Step 3: The character generation and background generation units generate characters and backgrounds based on the storyboard generated by the generation unit. For example, the generation AI selects characters and backgrounds suitable for each scene in the storyboard and converts them into a manga format. Step 4: The output unit outputs the blog content in the form of a comic book generated by the generation unit. For example, a user can check the generated comic book and make corrections or additional edits as necessary.

[0068] (Example 2) A system according to an embodiment of the present invention converts blogs into manga and easily converts them into easy-to-read blogs. In this system, a user inputs blog text, and a generation AI analyzes the text and generates a storyboard. Based on the generated storyboard, manga elements such as characters and backgrounds are automatically generated, ultimately outputting a manga-style blog. This mechanism makes the blog content visually easy to understand and attracts readers' interest. By converting blogs based on specific themes, such as travelogues or cooking recipes, into manga format, the content becomes more familiar to readers. Furthermore, by automatically generating manga elements such as characters and backgrounds, users can easily create high-quality manga-style blogs.

[0069] A system according to an embodiment includes a reception unit, a generation unit, a character generation unit, a background generation unit, and an output unit. The reception unit receives blog text input from a user. For example, information about the content and theme of the blog can also be input. The generation unit uses a generation AI to analyze the text input by the reception unit and generate a storyboard. For example, the generation AI understands the content of the text and generates a storyboard. The character generation unit and background generation unit generate characters and backgrounds based on the storyboard generated by the generation unit. For example, the generation AI selects characters and backgrounds appropriate for each scene in the storyboard and converts them into a manga format. The output unit outputs the blog content in manga format generated by the generation unit. For example, a user can review the generated manga and make corrections or additional edits as necessary. This allows the system according to an embodiment to convert blog text into a manga format and generate a visually easy-to-understand blog.

[0070] The reception unit can input information about the theme and style of the blog. Examples of themes and styles of blogs include, but are not limited to, travelogues, cooking recipes, and technical articles. The reception unit can, for example, allow the user to select the theme and style of the blog and input text based on that. This allows input according to the theme and style of the blog.

[0071] The generation unit can provide templates and styles that the user can select. Examples of templates and styles include, but are not limited to, design templates, font styles, layouts, etc. The generation unit can, for example, allow the user to select a preferred template or style and generate a storyboard based on the template or style. This allows the user to select a preferred template or style.

[0072] The character generation unit and the background generation unit may provide customizable options for the user. Customizable options include, but are not limited to, for example, character clothing, background color, and scene layout. The character generation unit and the background generation unit may, for example, allow the user to customize characters and backgrounds and generate a manga based on the customized characters and backgrounds. This allows the user to customize the characters and backgrounds.

[0073] The output unit may provide an interface that allows the user to easily make corrections and additional edits. Examples of corrections and additional edits include, but are not limited to, correcting text, rearranging characters, and changing backgrounds. The output unit may provide an interface that allows the user to check the generated comic and make corrections or additional edits as necessary. This allows the user to easily correct and edit the generated comic.

[0074] The generation unit can generate a storyboard by using natural language processing technology to understand the meaning of the text in detail. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The generation unit can generate a storyboard by analyzing the content of the text in detail using, for example, a generation AI. This makes it possible to generate a storyboard with a deep understanding of the meaning of the text.

[0075] The reception unit can estimate the user's emotions and adjust the timing of blog entry based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to provide time for the user to relax. Furthermore, if the user is relaxed, the reception unit can also accelerate the input timing to encourage the user to enter content while they are highly focused. Furthermore, if the user is in a hurry, the reception unit can optimize the input timing to enable quick entry. This makes it possible to provide the optimal input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The reception unit can analyze the user's past blog entry history and select the optimal input method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze the themes and styles of blog entries the user has made in the past and suggest the optimal input method. The reception unit can also suggest the optimal input method for a specific time period based on the user's past input history. This makes it possible to provide the optimal input method based on the user's past input history.

[0077] The reception unit can filter blog entries based on the user's current areas of interest when the blog entries are submitted. For example, the reception unit prioritizes blog entries related to topics that the user is currently interested in. The reception unit can also suggest blog entries related to the user's areas of interest based on the user's recent search history. The reception unit can also analyze the user's social media activity and filter the blog entries based on the user's areas of interest. This allows blog entries based on the user's areas of interest.

[0078] When a user inputs a blog entry, the reception unit can select an appropriate input means according to the user's input method. For example, if the user desires voice input, the reception unit can support the input using voice recognition technology. Furthermore, if the user desires text input, the reception unit can also optimize keyboard input. Furthermore, if the user desires image input, the reception unit can also support the input using image recognition technology. This makes it possible to provide the optimal input means according to the user's input method.

[0079] The reception unit can estimate the user's emotions and determine the priority of blog entries to be entered based on the estimated user emotions. For example, if the user is excited, the reception unit can prioritize blog entries that match the user's emotions. Furthermore, if the user is relaxed, the reception unit can prioritize blog entries that can be entered in a relaxed state. Furthermore, if the user is stressed, the reception unit can prioritize blog entries with content that will reduce stress. This makes it possible to provide a blog priority order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0080] When inputting a blog, the reception unit can prioritize input of highly relevant blogs based on the user's geographical location information. For example, if the user is traveling, the reception unit can prioritize input of blogs related to the user's current location. Furthermore, if the user is in a specific area, the reception unit can also prioritize input of blogs related to that area. Furthermore, the reception unit can also prioritize input of blogs related to local events and news based on the user's location information. This makes it possible to input blogs based on the user's geographical location information.

[0081] When a blog entry is made, the reception unit can analyze the user's social media activity and input related blog entries. For example, the reception unit prioritizes the input of blog entries related to content the user has shared on social media. The reception unit can also analyze the content posted by the user on social media and suggest the input of related blog entries. The reception unit can also suggest the input of related blog entries by taking into account the activity of the user's friends on social media. This makes it possible to input blog entries based on the user's social media activity.

[0082] The reception unit can customize the input method based on the user's past feedback when inputting a blog. For example, the reception unit suggests the optimal input method based on the user's past feedback. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also optimize the input procedure by reflecting the user's feedback. This makes it possible to provide an input method based on the user's past feedback.

[0083] The generation unit can estimate the user's emotions and adjust the storyboard expression method based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a storyboard using a calm expression method. Furthermore, if the user is excited, the generation unit can generate a storyboard using a dynamic and visually stimulating expression method. Furthermore, if the user is sad, the generation unit can generate a storyboard using an expression method that is closer to the user's emotions. This makes it possible to provide a storyboard expression method that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0084] When generating a storyboard, the generator can adjust the level of detail of the storyboard according to the importance of the blog. For example, the generator generates a detailed storyboard for an important blog. The generator can also generate a concise storyboard for a general blog. The generator can also generate a detailed storyboard for a part that the user wants to particularly emphasize. This makes it possible to provide a storyboard with a level of detail according to the importance of the blog.

[0085] The generator can apply different generation algorithms to generate storyboards based on the blog category. For example, in the case of a travelogue, the generator can sequentially organize the places visited and the events experienced in the storyboard. In the case of a cooking recipe, the generator can also visually organize the cooking steps and ingredients in the storyboard. In the case of a news article, the generator can also emphasize important information and organize it in the storyboard. This makes it possible to provide an optimal storyboard according to the blog category.

[0086] When generating a storyboard, the generation unit can improve the accuracy of generation by referring to the user's past generation results. For example, the generation unit improves the accuracy of generation by referring to storyboards generated by the user in the past. The generation unit can also learn the user's preferences and style from the user's past generation results and generate an optimal storyboard. The generation unit can also improve the accuracy of generation by reflecting user feedback. This makes it possible to provide an accurate storyboard based on the user's past generation results.

[0087] The generation unit can estimate the user's emotions and adjust the length of the storyboard 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 storyboard. If the user is relaxed, the generation unit can also generate a longer storyboard with detailed explanations. If the user is excited, the generation unit can also generate a storyboard with visually stimulating effects. This makes it possible to provide a storyboard length that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] When generating a storyboard, the generation unit can determine the priority of generation based on the submission date of the blog. For example, the generation unit generates a storyboard preferentially for a blog whose deadline is approaching. The generation unit can also postpone generating a storyboard for a blog whose submission date is far away. The generation unit can also generate a storyboard preferentially for a blog for which the user is particularly in a hurry. This makes it possible to provide a priority of generation of storyboards according to the submission date of the blog.

[0089] When generating a storyboard, the generation unit can adjust the order of generation according to the relevance of the blogs. For example, the generation unit prioritizes highly relevant blogs in the storyboard. The generation unit can also prioritize less relevant blogs in the storyboard. The generation unit can also prioritize generating storyboards for blogs for which the user places particular importance on relevance. This makes it possible to provide a storyboard generation order according to the relevance of the blogs.

[0090] The generator may adjust the use of technical terminology when generating a storyboard based on the user's level of expertise. For example, if the user has technical expertise, the generator may generate a storyboard that uses a lot of technical terminology. Alternatively, if the user is a beginner, the generator may generate an easy-to-understand storyboard that avoids technical terminology. Alternatively, the generator may generate a storyboard that uses appropriate technical terminology according to the user's level of expertise. This allows the use of technical terminology according to the user's level of expertise.

[0091] The character generation unit and background generation unit can estimate the user's emotions and adjust the character and background generation method based on the estimated user's emotions. For example, if the user is relaxed, the character generation unit and background generation unit can generate a character with a calm expression and a calm background. Furthermore, if the user is excited, the character generation unit and background generation unit can generate a dynamic and visually stimulating character and background. Furthermore, if the user is sad, the character generation unit and background generation unit can generate a character and background that are in line with the user's emotions. This makes it possible to provide a method for generating characters and backgrounds that correspond to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] When generating characters and backgrounds, the character generation unit and background generation unit can select appropriate characters and backgrounds based on the content of the blog. For example, in the case of a travelogue, the character generation unit and background generation unit generate characters and backgrounds that visually represent the scenery of places visited and events experienced. In the case of a cooking recipe, the character generation unit and background generation unit can also generate characters and backgrounds that visually represent cooking steps and ingredients in an easy-to-understand manner. In the case of a news article, the character generation unit and background generation unit can also generate characters and backgrounds that emphasize important information. This makes it possible to provide optimal characters and backgrounds according to the content of the blog.

[0093] When generating characters and backgrounds, the character generation unit and background generation unit can improve the accuracy of generation by referring to the user's past generation results. The character generation unit and background generation unit can improve the accuracy of generation by referring to, for example, characters and backgrounds generated by the user in the past. The character generation unit and background generation unit can also learn the user's preferences and style from the user's past generation results and generate optimal characters and backgrounds. The character generation unit and background generation unit can also improve the accuracy of generation by reflecting user feedback. This makes it possible to provide accurate characters and backgrounds based on the user's past generation results.

[0094] The character generation unit and the background generation unit can improve the generation method based on user feedback when generating characters and backgrounds. For example, the character generation unit and the background generation unit improve the character and background generation method based on feedback provided by the user. The character generation unit and the background generation unit can also adjust algorithms to improve generation accuracy based on user feedback. The character generation unit and the background generation unit can also improve the quality of the generated characters and backgrounds by reflecting user feedback. This makes it possible to provide a character and background generation method based on user feedback.

[0095] The character generation unit and background generation unit can estimate the user's emotions and determine the priority of characters and backgrounds to be generated based on the estimated user emotions. For example, if the user is excited, the character generation unit and background generation unit can prioritize generating characters and backgrounds that match the user's emotions. Furthermore, if the user is relaxed, the character generation unit and background generation unit can prioritize characters and backgrounds that can be generated in a relaxed state. Furthermore, if the user is stressed, the character generation unit and background generation unit can prioritize characters and backgrounds that reduce stress. This makes it possible to provide a priority of characters and backgrounds according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0096] When generating characters and backgrounds, the character generation unit and background generation unit can select appropriate characters and backgrounds based on the geographical information of the blog. For example, if a user is writing a travelogue, the character generation unit and background generation unit can generate characters and backgrounds based on the geographical information of the places visited. Also, if a user is writing a blog related to a specific region, the character generation unit and background generation unit can generate characters and backgrounds based on the geographical information of the region. Also, the character generation unit and background generation unit can generate characters and backgrounds that reflect the local scenery and culture based on the user's location information. This makes it possible to provide characters and backgrounds based on the geographical information of the blog.

[0097] The character generation unit and background generation unit can improve the accuracy of generation by referring to literature related to the blog when generating characters and backgrounds. For example, the character generation unit and background generation unit refer to literature related to the content of the blog entered by the user and reflects the information in the generation of the characters and backgrounds. The character generation unit and background generation unit can also improve the accuracy of generation of characters and backgrounds based on information obtained from the related literature. The character generation unit and background generation unit can also optimize the generation of characters and backgrounds based on reference materials provided by the user. This makes it possible to provide accurate generation of characters and backgrounds based on literature related to the blog.

[0098] The character generation unit and background generation unit can generate characters and backgrounds based on the market value of the blog. For example, if a user is writing a commercial blog, the character generation unit and background generation unit can generate characters and backgrounds taking market value into consideration. Furthermore, if a user is writing a blog aimed at generating advertising revenue, the character generation unit and background generation unit can also generate characters and backgrounds that increase market value. Furthermore, if a user is writing a blog aimed at a specific target demographic, the character generation unit and background generation unit can also generate characters and backgrounds taking market value into consideration. This makes it possible to provide characters and backgrounds based on the market value of the blog.

[0099] The output unit can estimate the user's emotions and adjust the display method of the output cartoon based on the estimated user's emotions. For example, if the user is relaxed, the output unit can display the cartoon in calm colors. If the user is excited, the output unit can also display the cartoon in vibrant colors. If the user is sad, the output unit can also display the cartoon in subdued colors. This makes it possible to provide a cartoon display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] At the time of output, the output unit can select an appropriate display method based on the user's past output results. For example, the output unit selects the optimal display method based on the user's past preferred display methods. The output unit can also learn the user's preferences and style from the user's past output results and suggest the optimal display method. The output unit can also optimize the display method by reflecting user feedback. This makes it possible to provide the optimal display method based on the user's past output results.

[0101] The output unit can adjust the display content according to the user's current task when outputting. For example, when the user is in a hurry, the output unit can provide display content that is concise and to the point. On the other hand, when the user is relaxed, the output unit can provide display content that includes detailed information. On the other hand, when the user is performing a specific task, the output unit can prioritize displaying information related to that task. In this way, it is possible to provide display content according to the user's current task.

[0102] The output unit can improve the display method based on user feedback at the time of output. For example, the output unit improves the display method based on feedback provided by the user. The output unit can also adjust an algorithm for optimizing the display content based on the user feedback. The output unit can also improve the quality of the displayed manga by reflecting the user feedback. This makes it possible to provide a display method based on user feedback.

[0103] The output unit can estimate the user's emotions and adjust the operation procedures for the cartoon to be output based on the estimated user's emotions. For example, if the user is nervous, the output unit can provide simple and intuitive operation procedures. The output unit can also provide detailed operation procedures if the user is relaxed. The output unit can also provide quick operation procedures if the user is in a hurry. This makes it possible to provide operation procedures for the cartoon according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0104] The output unit can select an appropriate display method based on the user's device information at the time of output. For example, if the user is using a smartphone, the output unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the output unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the output unit can also provide a simple and highly visible display method. This makes it possible to provide an optimal display method based on the user's device information.

[0105] The output unit can make the display content multilingual based on the user's language setting at the time of output. The output unit automatically sets the display content based on, for example, the language setting of the user's device. The output unit can also provide a language switching function when the user uses multiple languages. The output unit can also provide the display content in a specific language when the user selects that language. This makes it possible to provide multilingual display content based on the user's language setting.

[0106] The output unit can improve the display method based on user feedback at the time of output. For example, the output unit improves the display method based on feedback provided by the user. The output unit can also adjust an algorithm for optimizing the display content based on the user feedback. The output unit can also improve the quality of the displayed manga by reflecting the user feedback. This makes it possible to provide a display method based on user feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, character generation unit, background generation unit, and output unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing a user to input blog text. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the input text using a generation AI and generating a storyboard. The character generation unit and background generation unit are implemented, for example, by the specific processing unit 290 of the data processing device 12, generating characters and backgrounds based on the generated storyboard. The output unit is implemented, for example, by the output device 40 of the smart device 14, providing the generated blog content in manga format to a user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, character generation unit, background generation unit, and output unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, allowing the user to input blog text by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the input text using a generation AI and generating a storyboard. The character generation unit and background generation unit are realized, for example, by the specific processing unit 290 of the data processing device 12, generating characters and backgrounds based on the generated storyboard. The output unit is realized, for example, by the speaker 240 of the smart glasses 214, providing the generated manga-style blog content to the user by voice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, character generation unit, background generation unit, and output unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314, allowing the user to input blog text by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input text using a generation AI to generate a storyboard. The character generation unit and background generation unit are realized, for example, by the specific processing unit 290 of the data processing device 12, and generate characters and backgrounds based on the generated storyboard. The output unit is realized, for example, by the display 343 of the headset-type terminal 314, and provides the generated manga-style blog content to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, character generation unit, background generation unit, and output 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 of the robot 414, allowing the user to input blog text by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzing the input text using a generation AI and generating a storyboard. The character generation unit and background generation unit are realized, for example, by the specific processing unit 290 of the data processing device 12, generating characters and backgrounds based on the generated storyboard. The output unit is realized, for example, by the speaker 240 of the robot 414, providing the generated manga-style blog content to the user by voice.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's past blog browsing history and suggest themes and styles that the user is likely to be interested in. For example, if the user has frequently viewed travelogues in the past, the reception unit can suggest themes and styles related to travelogues. Also, if the user frequently views technical articles, the reception unit can suggest templates and styles suitable for technical articles. Furthermore, if the user shows interest in a specific genre, the reception unit can preferentially suggest blog entries related to that genre. This makes it possible to provide the optimal themes and styles based on the user's past browsing history.

[0109] The generation unit can estimate the user's emotion and adjust the tempo of the storyboard based on the estimated user's emotion. For example, if the user is relaxed, a storyboard with a slow tempo can be generated. If the user is excited, a storyboard with a fast tempo can be generated. Furthermore, if the user is sad, a storyboard with a tempo that is in line with the user's emotion can be generated. In this way, it is possible to provide a storyboard tempo that corresponds to the user's emotion.

[0110] The character generation unit and background generation unit can estimate the user's emotions and adjust the character's facial expression and background color tone based on the estimated user's emotions. For example, if the user is relaxed, a character with a calm expression and a background with a muted color tone can be generated. If the user is excited, a character with a dynamic and visually stimulating expression and a background with a vivid color tone can be generated. Furthermore, if the user is sad, a character with an expression that matches the user's emotions and a background with a muted color tone can be generated. This makes it possible to provide characters and background expressions that correspond to the user's emotions.

[0111] The output unit can estimate the user's emotion and adjust the font style of the cartoon to be output based on the estimated user's emotion. For example, if the user is relaxed, an easy-to-read font style can be used. If the user is excited, a visually stimulating font style can be used. Furthermore, if the user is sad, a font style that is closer to the emotion can be used. In this way, a cartoon font style according to the user's emotion can be provided.

[0112] The generation unit can estimate the user's emotion and adjust the length of a scene in the storyboard based on the estimated user's emotion. For example, if the user is relaxed, the length of the scene can be set longer. Also, if the user is excited, the length of the scene can be set shorter. Furthermore, if the user is sad, the length of the scene can be set to match the emotion. In this way, it is possible to provide a scene length that matches the user's emotion.

[0113] The reception unit can analyze the user's past blog posting history and suggest the optimal posting time. For example, if the user has posted many blog posts in the past during a specific time period, the reception unit can suggest that time period. Also, if the user has posted many blog posts on a specific day of the week, the reception unit can suggest that day of the week. Furthermore, the reception unit can suggest posting times related to specific seasons or events based on the user's past posting history. This makes it possible to provide the optimal posting time based on the user's past posting history.

[0114] The generation unit can analyze the content of a user's past blog and reflect it in the generation of a storyboard. For example, a storyboard for a new travelogue can be generated by referring to the content of a travelogue written by the user in the past. A storyboard for a new cooking recipe can also be generated by referring to the content of a cooking recipe written by the user in the past. Furthermore, a storyboard for a new technical article can also be generated by referring to the content of a technical article written by the user in the past. This makes it possible to provide a storyboard based on the content of a user's past blog.

[0115] The character generation unit and background generation unit can analyze the user's past character and background selection history and propose optimal characters and backgrounds. For example, a new character can be proposed by referring to the style of a character previously selected by the user. A new background can also be proposed by referring to the color tone of a background previously selected by the user. Furthermore, the layout of a new scene can be proposed by referring to the layout of a scene previously selected by the user. This makes it possible to provide optimal characters and backgrounds based on the user's past selection history.

[0116] The output unit can adjust the display method based on the remaining battery level of the user's device. For example, when the remaining battery level is low, the display content can be simplified to reduce battery consumption. When the remaining battery level is sufficient, the output unit can provide detailed display content. When the remaining battery level is medium, the output unit can provide balanced display content. This allows the display unit to provide an optimal display method based on the remaining battery level of the user's device.

[0117] The output unit can adjust the display method based on the user's Internet connection status. For example, if the Internet connection is unstable, the output unit can simplify the display content to reduce data traffic. If the Internet connection is stable, the output unit can provide detailed display content. If the Internet connection is moderate, the output unit can provide balanced display content. This allows the optimal display method to be provided based on the user's Internet connection status.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The user inputs the blog text into the reception unit. For example, information about the content and theme of the blog can also be input. Step 2: The generation unit uses the generation AI to analyze the text input by the reception unit and generate a storyboard. For example, the generation AI understands the content of the text and generates a storyboard. Step 3: The character generation and background generation units generate characters and backgrounds based on the storyboard generated by the generation unit. For example, the generation AI selects characters and backgrounds suitable for each scene in the storyboard and converts them into a manga format. Step 4: The output unit outputs the blog content in the form of a comic book generated by the generation unit. For example, a user can check the generated comic book and make corrections or additional edits as necessary.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 area where you enter blog text; a generation unit that analyzes the text input by the reception unit and generates a storyboard; a generation unit including a character generation unit and a background generation unit that generates characters and backgrounds based on the storyboard generated by the generation unit; an output unit that outputs the blog content in the comic format generated by the generation unit; A system characterized by:

2. The reception unit You can enter information about the theme and style of your blog.

2. The system of claim 1.

3. The generation unit Providing templates and styles for users to choose from 2. The system of claim 1.

4. The character generation unit and the background generation unit Providing user-customizable options 2. The system of claim 1.

5. The output unit Provide an interface that allows users to easily make corrections and further edits 2. The system of claim 1.

6. The generation unit Natural language processing technology is used to understand the meaning of text in detail and generate storyboards 2. The system of claim 1.

7. The reception unit Estimate user emotions and adjust blog entry timing based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past blog entry history and select the appropriate entry method 2. The system of claim 1.

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