system

Abstract

The system according to this embodiment aims to instantly translate comics into multiple languages ​​without compromising their atmosphere. [Solution] The system according to this embodiment comprises a recognition unit, a translation unit, and a provision unit. The recognition unit reads a comic image and recognizes the printed text. The translation unit translates the printed text recognized by the recognition unit into the user's language. The provision unit makes the comic translated by the translation unit available for viewing.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to translate into multiple languages without impairing the atmosphere of the comic, and it takes a long time until translation and release.

[0005] The system according to the embodiment aims to instantaneously translate into multiple languages without impairing the atmosphere of the comic.

Means for Solving the Problems

[0006] The system according to the embodiment includes a recognition unit, a translation unit, and a provision unit. The recognition unit reads a comic image and recognizes the characters. The translation unit translates the characters recognized by the recognition unit into the user's language. The provision unit makes the comic translated by the translation unit viewable.

Effects of the Invention

[0007] The system according to this embodiment can instantly translate comics into multiple languages ​​without compromising their atmosphere. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. As an example of the data processing device 12, a server can be mentioned.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The comic translation system according to an embodiment of the present invention is a system that translates and distributes comics using generative AI. This comic translation system is configured to solve the language barrier, including the slang that creates the atmosphere of comics, and the problem that domestic merchants cannot use cards issued overseas. The comic translation system solves these problems with the following configuration. First, to eliminate the language barrier, the generative AI instantly translates comic images and text into multiple languages, including slang. The generative AI learns to leave comic-specific language, such as onomatopoeia, as is. Next, a Co server is set up overseas, making comics viewable at the same time as domestic content. This strongly assists payment merchants in expanding overseas and actively promotes the overseas expansion of payment businesses. The generative AI reads comic images, recognizes text, and translates it into the user's language. By feeding it a large number of comics, the accuracy of the translation is improved and the stylistic quirks of each comic author are incorporated. This makes it possible to reflect the slang of each comic and translate including onomatopoeia. This will allow the comic translation system to meet the global demand for comics, a powerful Japanese content, establish a successful model for Japan's growth industry, and eliminate the digital deficit. Furthermore, it will encourage Japanese people, who feel they have lost international competitiveness, to recognize that they continue to produce powerful and attractive content, and can serve as a model case that can be followed not only for comics but also for other digital content and software.

[0029] The comic translation system according to this embodiment comprises a recognition unit, a translation unit, and a provisioning unit. The recognition unit reads a comic image and recognizes the printed characters. The recognition unit can read comic images in various formats, such as digital comics and scanned paper comics. The recognition unit can recognize printed characters in various formats, such as handwritten characters and printed characters. The recognition unit extracts and recognizes printed characters from the comic image using a generation AI. For example, the recognition unit extracts character information from the comic image using OCR technology. The recognition unit can also improve the accuracy of character recognition by separating the background and characters of the comic image using image processing technology. The translation unit translates the printed characters recognized by the recognition unit into the user's language. The translation unit can translate into various languages, such as English, Japanese, and Spanish. The translation unit instantly translates the recognized printed characters using a generation AI. For example, the translation unit uses a text generation AI (e.g., LLM) to translate the recognized printed characters into the user's language. Furthermore, the translation unit can retain onomatopoeia specific to comics. For example, the translation unit translates onomatopoeia as is to avoid compromising the atmosphere of the comic. The translation unit can also learn the stylistic quirks of individual comic authors and reflect them in the translation. For example, the translation unit learns the unique expressions and writing styles of comic authors and reflects them in the translation. The distribution unit makes the translated comics available for viewing. For example, the distribution unit can display the translated comics using a web browser or a dedicated app. The distribution unit can use a generation AI to select the optimal display format according to the user's device. For example, the distribution unit provides display formats compatible with various devices such as smartphones, tablets, and PCs. The distribution unit can also make comics available for viewing at the same time as domestic content via a Co server located overseas. For example, the distribution unit allows overseas users to view comics at the same time as domestic users. As a result, the comic translation system according to this embodiment can instantly translate comic images and make them available for viewing by users.

[0030] The recognition unit reads comic images and recognizes printed characters. The recognition unit can read various formats of comic images, such as digital comics and scanned paper comics. Specifically, for digital comics, it supports ebook formats and image file formats (JPEG, PNG, TIFF, etc.), and for scanned paper comics, it can process image data directly captured from a scanner. The recognition unit can recognize various formats of printed characters, including handwritten and printed characters. For handwritten characters, it considers differences in handwriting and character distortion; for printed characters, it uses advanced algorithms to recognize differences in font type and size. The recognition unit uses generative AI to extract and recognize characters from comic images. For example, the recognition unit uses OCR technology to extract character information from comic images. OCR technology analyzes the shape and pattern of characters and converts the characters in the image into text data. Furthermore, the recognition unit can use image processing technology to separate the background and characters in comic images, improving the accuracy of character recognition. Specifically, it removes background noise, adjusts contrast, and emphasizes the character portion to enhance recognition accuracy. Furthermore, the recognition unit uses generation AI to analyze the position, size, and font style of characters, allowing it to extract characters while preserving the comic's layout. This enables the recognition unit to extract text information with high accuracy from various comic image formats and provide the data necessary for the next translation process.

[0031] The translation unit translates the text recognized by the recognition unit into the user's language. The translation unit can translate into various languages, such as English, Japanese, and Spanish. Specifically, the translation unit uses a multilingual generative AI to instantly translate the recognized text. The generative AI utilizes a large-scale language model (LLM) to provide natural translations that consider context and nuance. For example, the translation unit uses a text generation AI (e.g., an LLM) to translate recognized text into the user's language. The LLM has learned from a vast amount of text data and can provide appropriate translations based on context. Furthermore, the translation unit can preserve onomatopoeia specific to comics. For example, the translation unit translates onomatopoeia while preserving the comic's atmosphere. Onomatopoeia is an important element in comic expression, and the translation unit has a special algorithm to handle it appropriately. The translation unit can also learn the stylistic quirks of individual comic authors and reflect them in the translation. For example, the translation department learns the unique expressions and writing styles of comic authors and incorporates them into the translation. This ensures that the translated comic maintains the atmosphere and style of the original, providing a natural reading experience for readers. Furthermore, the translation department can collect user feedback and use it to improve translation accuracy. This allows the translation department to constantly utilize the latest technology and data to provide high-quality translations.

[0032] The service provider makes translated comics available for viewing. The service provider can display translated comics, for example, through a web browser or a dedicated app. Specifically, the service provider selects the optimal display format according to the user's device, providing a smooth viewing experience. The service provider can use generation AI to select the optimal display format according to the user's device. For example, the service provider provides display formats compatible with various devices such as smartphones, tablets, and PCs. For smartphones, it provides a layout optimized for vertical screens, while for tablets and PCs, it provides a layout suitable for larger screens. Furthermore, the service provider can adjust image resolution and loading speed according to the user's internet connection. This allows users to comfortably view comics. In addition, the service provider can make comics available at the same time as domestic content via Co servers located overseas. For example, the service provider allows overseas users to view comics at the same time as domestic users. This enables the service provider to provide high-quality service to global users. Furthermore, the service provider has a function to recommend relevant comics and new information based on the user's browsing history and preferences. This allows users to always have access to the latest information and enjoy comics they are interested in without missing anything.

[0033] The recognition unit can read comic images and recognize printed characters. The recognition unit can read comic images in various formats, such as digital comics and scanned paper comics. The recognition unit can recognize printed characters in various formats, such as handwritten characters and printed characters. The recognition unit uses a generation AI to extract and recognize printed characters from comic images. For example, the recognition unit uses OCR technology to extract character information from comic images. The recognition unit can also use image processing technology to separate the background and characters of comic images and improve the accuracy of character recognition. This allows for accurate recognition of printed characters from comic images. Some or all of the above-described processes in the recognition unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the recognition unit can input a comic image into a generation AI and have the generation AI perform character recognition.

[0034] The translation unit can translate recognized text into the user's language. The translation unit can translate into various languages, such as English, Japanese, and Spanish. The translation unit uses a generative AI to translate recognized text instantly. For example, the translation unit uses a text generation AI (e.g., LLM) to translate recognized text into the user's language. This allows for instant translation of recognized text. Some or all of the above-described processes in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input recognized text into a generative AI and have the generative AI perform the translation.

[0035] The service provider can make translated comics viewable. For example, the service provider can display translated comics using a web browser or a dedicated app. The service provider can use a generation AI to select the optimal display format for the user's device. For example, the service provider can provide display formats compatible with various devices such as smartphones, tablets, and PCs. This makes translated comics viewable by users. Some or all of the above-described processes in the service provider may be performed using a generation AI, or they may be performed without a generation AI. For example, the service provider can input a translated comic into a generation AI and have the generation AI select the optimal display format.

[0036] The translation unit can retain onomatopoeia specific to comics. For example, the translation unit will translate while preserving the onomatopoeia so as not to spoil the atmosphere of the comic. This makes it possible to create translations that retain the onomatopoeia specific to comics. Some or all of the above processing in the translation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the translation unit can have a generation AI perform translations that preserve onomatopoeia.

[0037] The translation unit can learn the stylistic quirks of individual comic authors and reflect them in the translation. For example, the translation unit can learn the unique expressions and writing styles of comic authors and reflect them in the translation. This makes it possible to produce translations that reflect the stylistic quirks of comic authors. Some or all of the above-described processes in the translation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the translation unit can learn the stylistic quirks of comic authors and have a generative AI perform translations that reflect them.

[0038] The service provider can make comics available for viewing at the same time as domestic content via a Co server located overseas. For example, the service provider can enable overseas users to view comics at the same time as domestic users. This makes it possible to view comics overseas at the same time as domestic users. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can have a generation AI perform the process of making comics available for viewing via the Co server.

[0039] The service provider can offer content that is popular overseas at a low cost by using a cashless payment app. For example, the service provider can offer specific content to overseas users at a discounted price using a cashless payment app. This allows the service provider to offer content that is popular overseas at a low cost by using a cashless payment app. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can have a generation AI perform the setting of discounted prices using a cashless payment app.

[0040] The recognition unit can separate the background and text from a comic image, thereby improving the accuracy of character recognition. For example, the recognition unit can automatically detect the background of a comic image and highlight the text portion. The recognition unit can also analyze the background color and pattern and extract the text portion. The recognition unit can also use image processing techniques to remove background noise and improve the accuracy of character recognition. This improves the accuracy of character recognition. Some or all of the above-described processes in the recognition unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the separation of the background and text from a comic image.

[0041] The recognition unit can automatically recognize the page order of a comic and read it in the correct order. For example, the recognition unit can automatically detect the page numbers of a comic and read them in the correct order. The recognition unit can also analyze the storyline of a comic and estimate the page order. The recognition unit can also suggest the correct page order by referring to the user's past browsing history. This allows the comic to be read in the correct page order. Some or all of the above processes in the recognition unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the recognition of the comic's page order.

[0042] The recognition unit can refer to the user's past browsing history and prioritize the recognition of relevant comics. For example, the recognition unit can analyze the genres of comics the user has read in the past and prioritize the recognition of related comics. The recognition unit can also prioritize the recognition of new works by authors of comics that the user has previously given high ratings to. The recognition unit can also prioritize the recognition of sequels to specific series based on the user's past browsing history. This allows the recognition unit to prioritize the recognition of relevant comics by referring to the user's past browsing history. Some or all of the above processing in the recognition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the recognition unit can input the user's past browsing history into a generation AI and have the generation AI perform the recognition of relevant comics.

[0043] The recognition unit can apply different recognition algorithms depending on the genre of the comic. For example, the recognition unit can apply a recognition algorithm that emphasizes dynamic scenes to action comics. The recognition unit can also apply a recognition algorithm that emphasizes emotional expression to romance comics. The recognition unit can also apply a recognition algorithm that emphasizes humorous elements to comedy comics. This allows for the application of a recognition algorithm appropriate to the genre of the comic. Some or all of the above processing in the recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the application of a recognition algorithm appropriate to the genre of the comic.

[0044] The translation unit can understand the context of the comic and select appropriate slang. For example, the translation unit can analyze the comic's storyline and select appropriate slang. The translation unit can also select appropriate slang considering the characters' personalities and backgrounds. The translation unit can also select appropriate slang depending on the genre of the comic. This allows for the selection of appropriate slang according to the context. Some or all of the above processes in the translation unit may be performed using a generative AI, or not. For example, the translation unit can have a generative AI understand the context of the comic and select appropriate slang.

[0045] The translation unit can apply different translation styles to each character in the comic. For example, the translation unit can apply a strong translation style to the protagonist. It can also apply a gentle translation style to the heroine. It can also apply a ruthless translation style to the villain. This allows for the application of different translation styles to each character. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can have a generative AI perform the application of different translation styles to each character.

[0046] The translation unit can learn regional slang specific to the user's area and incorporate it into translations. For example, the translation unit can add regional slang specific to the user's area to a database and incorporate it into translations. The translation unit can also select appropriate slang considering the regional culture and background of the user. The translation unit can also learn regional linguistic expressions specific to the user's area and incorporate them into translations. This makes it possible to perform translations that reflect regional slang. Some or all of the above processes in the translation unit may be performed using generative AI, or not. For example, the translation unit can learn regional slang specific to the user's area and have the generative AI perform translations that reflect it.

[0047] The translation unit can apply different translation algorithms depending on the genre of the comic. For example, the translation unit can apply a translation algorithm that emphasizes action scenes to action comics. It can also apply a translation algorithm that emphasizes emotional expression to romance comics. It can also apply a translation algorithm that emphasizes humor to comedy comics. This allows for the application of genre-appropriate translation algorithms. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can have a generative AI perform the application of a genre-appropriate translation algorithm to the comic.

[0048] The service provider can select the optimal display format according to the user's device. For example, if the user is using a smartphone, the service provider can provide a display format that matches the screen size. If the user is using a tablet, the service provider can also provide a display format optimized for a larger screen. If the user is using a desktop, the service provider can also provide a high-resolution display format. This allows the service provider to provide the optimal display format according to the device. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information into a generation AI and have the generation AI select the optimal display format.

[0049] The recommendation unit can refer to the user's past browsing history and recommend relevant comics. For example, the recommendation unit can analyze the genres of comics the user has read in the past and recommend related comics. The recommendation unit can also recommend new works by authors of comics that the user has previously given high ratings to. The recommendation unit can also recommend sequels to specific series based on the user's past browsing history. In this way, it can recommend relevant comics by referring to past browsing history. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit can input the user's past browsing history into a generative AI and have the generative AI perform the recommendation of relevant comics.

[0050] The distribution unit can prioritize providing comics that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the distribution unit can prioritize providing comics related to that region. If the user is traveling, the distribution unit can also prioritize providing comics related to the travel destination. If the user is participating in a specific event, the distribution unit can also prioritize providing comics related to that event. This allows for the provision of highly relevant comics based on geographical location information. Some or all of the above processing in the distribution unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the distribution unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of providing highly relevant comics.

[0051] The service provider can analyze a user's social media activity and provide relevant comics. For example, the service provider may prioritize providing comics that the user is talking about on social media. The service provider may also provide comics recommended by accounts that the user follows. The service provider may also analyze the content of a user's social media posts and provide relevant comics. This allows the service provider to provide relevant comics based on social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI perform the task of providing relevant comics.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The recognition unit can automatically recognize the page order of a comic and read it in the correct order. For example, the recognition unit can automatically detect the page numbers of a comic and read them in the correct order. The recognition unit can also analyze the storyline of a comic and estimate the page order. The recognition unit can also suggest the correct page order by referring to the user's past browsing history. This allows the comic to be read in the correct page order. Some or all of the above processes in the recognition unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the recognition of the comic's page order.

[0054] The translation unit can apply different translation styles to each character in the comic. For example, the translation unit can apply a strong translation style to the protagonist. It can also apply a gentle translation style to the heroine. It can also apply a ruthless translation style to the villain. This allows for the application of different translation styles to each character. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can have a generative AI perform the application of different translation styles to each character.

[0055] The distribution unit can prioritize providing comics that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the distribution unit can prioritize providing comics related to that region. If the user is traveling, the distribution unit can also prioritize providing comics related to the travel destination. If the user is participating in a specific event, the distribution unit can also prioritize providing comics related to that event. This allows for the provision of highly relevant comics based on geographical location information. Some or all of the above processing in the distribution unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the distribution unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of providing highly relevant comics.

[0056] The recognition unit can apply different recognition algorithms depending on the genre of the comic. For example, the recognition unit can apply a recognition algorithm that emphasizes dynamic scenes to action comics. The recognition unit can also apply a recognition algorithm that emphasizes emotional expression to romance comics. The recognition unit can also apply a recognition algorithm that emphasizes humorous elements to comedy comics. This allows for the application of a recognition algorithm appropriate to the genre of the comic. Some or all of the above processing in the recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the application of a recognition algorithm appropriate to the genre of the comic.

[0057] The service provider can analyze a user's social media activity and provide relevant comics. For example, the service provider may prioritize providing comics that the user is talking about on social media. The service provider may also provide comics recommended by accounts that the user follows. The service provider may also analyze the content of a user's social media posts and provide relevant comics. This allows the service provider to provide relevant comics based on social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI perform the task of providing relevant comics.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The recognition unit reads the comic image and recognizes the text. The recognition unit can read comic images in various formats, such as digital comics and scanned paper comics. The recognition unit can recognize text in various formats, such as handwritten characters and printed characters. The recognition unit uses generative AI to extract and recognize text from the comic image. For example, it uses OCR technology to extract text information from the comic image. In addition, it can use image processing technology to separate the background and text of the comic image and improve the accuracy of text recognition. Step 2: The translation unit translates the text recognized by the recognition unit into the user's language. The translation unit can translate into various languages, including English, Japanese, and Spanish. The translation unit uses generative AI to instantly translate the recognized text. For example, it uses text generation AI (e.g., LLM) to translate the recognized text into the user's language. The translation unit can also preserve onomatopoeia specific to comics. For example, it can translate onomatopoeia as is to avoid compromising the atmosphere of the comic. Furthermore, the translation unit can learn the stylistic quirks of individual comic authors and reflect them in the translation. For example, it can learn the unique expressions and writing styles of comic authors and reflect them in the translation. Step 3: The provider makes the translated comics available for viewing. The provider can display the translated comics via a web browser or a dedicated app. The provider can use a generation AI to select the optimal display format according to the user's device. For example, it can provide display formats compatible with various devices such as smartphones, tablets, and PCs. In addition, the provider can make the comics available for viewing at the same time as domestic content via a Co server located overseas. For example, it can allow overseas users to view the comics at the same time as domestic users.

[0060] (Example of form 2) The comic translation system according to an embodiment of the present invention is a system that translates and distributes comics using generative AI. This comic translation system is configured to solve the language barrier, including the slang that creates the atmosphere of comics, and the problem that domestic merchants cannot use cards issued overseas. The comic translation system solves these problems with the following configuration. First, to eliminate the language barrier, the generative AI instantly translates comic images and text into multiple languages, including slang. The generative AI learns to leave comic-specific language, such as onomatopoeia, as is. Next, a Co server is set up overseas, making comics viewable at the same time as domestic content. This strongly assists payment merchants in expanding overseas and actively promotes the overseas expansion of payment businesses. The generative AI reads comic images, recognizes text, and translates it into the user's language. By feeding it a large number of comics, the accuracy of the translation is improved and the stylistic quirks of each comic author are incorporated. This makes it possible to reflect the slang of each comic and translate including onomatopoeia. This will allow the comic translation system to meet the global demand for comics, a powerful Japanese content, establish a successful model for Japan's growth industry, and eliminate the digital deficit. Furthermore, it will encourage Japanese people, who feel they have lost international competitiveness, to recognize that they continue to produce powerful and attractive content, and can serve as a model case that can be followed not only for comics but also for other digital content and software.

[0061] The comic translation system according to this embodiment comprises a recognition unit, a translation unit, and a provisioning unit. The recognition unit reads a comic image and recognizes the printed characters. The recognition unit can read comic images in various formats, such as digital comics and scanned paper comics. The recognition unit can recognize printed characters in various formats, such as handwritten characters and printed characters. The recognition unit extracts and recognizes printed characters from the comic image using a generation AI. For example, the recognition unit extracts character information from the comic image using OCR technology. The recognition unit can also improve the accuracy of character recognition by separating the background and characters of the comic image using image processing technology. The translation unit translates the printed characters recognized by the recognition unit into the user's language. The translation unit can translate into various languages, such as English, Japanese, and Spanish. The translation unit instantly translates the recognized printed characters using a generation AI. For example, the translation unit uses a text generation AI (e.g., LLM) to translate the recognized printed characters into the user's language. Furthermore, the translation unit can retain onomatopoeia specific to comics. For example, the translation unit translates onomatopoeia as is to avoid compromising the atmosphere of the comic. The translation unit can also learn the stylistic quirks of individual comic authors and reflect them in the translation. For example, the translation unit learns the unique expressions and writing styles of comic authors and reflects them in the translation. The distribution unit makes the translated comics available for viewing. For example, the distribution unit can display the translated comics using a web browser or a dedicated app. The distribution unit can use a generation AI to select the optimal display format according to the user's device. For example, the distribution unit provides display formats compatible with various devices such as smartphones, tablets, and PCs. The distribution unit can also make comics available for viewing at the same time as domestic content via a Co server located overseas. For example, the distribution unit allows overseas users to view comics at the same time as domestic users. As a result, the comic translation system according to this embodiment can instantly translate comic images and make them available for viewing by users.

[0062] The recognition unit reads comic images and recognizes printed characters. The recognition unit can read various formats of comic images, such as digital comics and scanned paper comics. Specifically, for digital comics, it supports ebook formats and image file formats (JPEG, PNG, TIFF, etc.), and for scanned paper comics, it can process image data directly captured from a scanner. The recognition unit can recognize various formats of printed characters, including handwritten and printed characters. For handwritten characters, it considers differences in handwriting and character distortion; for printed characters, it uses advanced algorithms to recognize differences in font type and size. The recognition unit uses generative AI to extract and recognize characters from comic images. For example, the recognition unit uses OCR technology to extract character information from comic images. OCR technology analyzes the shape and pattern of characters and converts the characters in the image into text data. Furthermore, the recognition unit can use image processing technology to separate the background and characters in comic images, improving the accuracy of character recognition. Specifically, it removes background noise, adjusts contrast, and emphasizes the character portion to enhance recognition accuracy. Furthermore, the recognition unit uses generation AI to analyze the position, size, and font style of characters, allowing it to extract characters while preserving the comic's layout. This enables the recognition unit to extract text information with high accuracy from various comic image formats and provide the data necessary for the next translation process.

[0063] The translation unit translates the text recognized by the recognition unit into the user's language. The translation unit can translate into various languages, such as English, Japanese, and Spanish. Specifically, the translation unit uses a multilingual generative AI to instantly translate the recognized text. The generative AI utilizes a large-scale language model (LLM) to provide natural translations that consider context and nuance. For example, the translation unit uses a text generation AI (e.g., an LLM) to translate recognized text into the user's language. The LLM has learned from a vast amount of text data and can provide appropriate translations based on context. Furthermore, the translation unit can preserve onomatopoeia specific to comics. For example, the translation unit translates onomatopoeia while preserving the comic's atmosphere. Onomatopoeia is an important element in comic expression, and the translation unit has a special algorithm to handle it appropriately. The translation unit can also learn the stylistic quirks of individual comic authors and reflect them in the translation. For example, the translation department learns the unique expressions and writing styles of comic authors and incorporates them into the translation. This ensures that the translated comic maintains the atmosphere and style of the original, providing a natural reading experience for readers. Furthermore, the translation department can collect user feedback and use it to improve translation accuracy. This allows the translation department to constantly utilize the latest technology and data to provide high-quality translations.

[0064] The service provider makes translated comics available for viewing. The service provider can display translated comics, for example, through a web browser or a dedicated app. Specifically, the service provider selects the optimal display format according to the user's device, providing a smooth viewing experience. The service provider can use generation AI to select the optimal display format according to the user's device. For example, the service provider provides display formats compatible with various devices such as smartphones, tablets, and PCs. For smartphones, it provides a layout optimized for vertical screens, while for tablets and PCs, it provides a layout suitable for larger screens. Furthermore, the service provider can adjust image resolution and loading speed according to the user's internet connection. This allows users to comfortably view comics. In addition, the service provider can make comics available at the same time as domestic content via Co servers located overseas. For example, the service provider allows overseas users to view comics at the same time as domestic users. This enables the service provider to provide high-quality service to global users. Furthermore, the service provider has a function to recommend relevant comics and new information based on the user's browsing history and preferences. This allows users to always have access to the latest information and enjoy comics they are interested in without missing anything.

[0065] The recognition unit can read comic images and recognize printed characters. The recognition unit can read comic images in various formats, such as digital comics and scanned paper comics. The recognition unit can recognize printed characters in various formats, such as handwritten characters and printed characters. The recognition unit uses a generation AI to extract and recognize printed characters from comic images. For example, the recognition unit uses OCR technology to extract character information from comic images. The recognition unit can also use image processing technology to separate the background and characters of comic images and improve the accuracy of character recognition. This allows for accurate recognition of printed characters from comic images. Some or all of the above-described processes in the recognition unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the recognition unit can input a comic image into a generation AI and have the generation AI perform character recognition.

[0066] The translation unit can translate recognized text into the user's language. The translation unit can translate into various languages, such as English, Japanese, and Spanish. The translation unit uses a generative AI to translate recognized text instantly. For example, the translation unit uses a text generation AI (e.g., LLM) to translate recognized text into the user's language. This allows for instant translation of recognized text. Some or all of the above-described processes in the translation unit may be performed using a generative AI, or not. For example, the translation unit can input recognized text into a generative AI and have the generative AI perform the translation.

[0067] The service provider can make translated comics viewable. For example, the service provider can display translated comics using a web browser or a dedicated app. The service provider can use a generation AI to select the optimal display format for the user's device. For example, the service provider can provide display formats compatible with various devices such as smartphones, tablets, and PCs. This makes translated comics viewable by users. Some or all of the above-described processes in the service provider may be performed using a generation AI, or they may be performed without a generation AI. For example, the service provider can input a translated comic into a generation AI and have the generation AI select the optimal display format.

[0068] The translation unit can retain onomatopoeia specific to comics. For example, the translation unit will translate while preserving the onomatopoeia so as not to spoil the atmosphere of the comic. This makes it possible to create translations that retain the onomatopoeia specific to comics. Some or all of the above processing in the translation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the translation unit can have a generation AI perform translations that preserve onomatopoeia.

[0069] The translation unit can learn the stylistic quirks of individual comic authors and reflect them in the translation. For example, the translation unit can learn the unique expressions and writing styles of comic authors and reflect them in the translation. This makes it possible to produce translations that reflect the stylistic quirks of comic authors. Some or all of the above-described processes in the translation unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the translation unit can learn the stylistic quirks of comic authors and have a generative AI perform translations that reflect them.

[0070] The service provider can make comics available for viewing at the same time as domestic content via a Co server located overseas. For example, the service provider can enable overseas users to view comics at the same time as domestic users. This makes it possible to view comics overseas at the same time as domestic users. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can have a generation AI perform the process of making comics available for viewing via the Co server.

[0071] The service provider can offer content that is popular overseas at a low cost by using a cashless payment app. For example, the service provider can offer specific content to overseas users at a discounted price using a cashless payment app. This allows the service provider to offer content that is popular overseas at a low cost by using a cashless payment app. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can have a generation AI perform the setting of discounted prices using a cashless payment app.

[0072] The recognition unit can estimate the user's emotions and adjust the comic image loading speed based on the estimated emotions. For example, if the user is relaxed, the recognition unit will load the comic images at a slow pace. If the user is in a hurry, the recognition unit can load the comic images quickly. If the user is excited, the recognition unit can load the comic images at a faster pace than usual. This allows the comic image loading speed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using the generative AI or not. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI adjust the loading speed.

[0073] The recognition unit can separate the background and text from a comic image, thereby improving the accuracy of character recognition. For example, the recognition unit can automatically detect the background of a comic image and highlight the text portion. The recognition unit can also analyze the background color and pattern and extract the text portion. The recognition unit can also use image processing techniques to remove background noise and improve the accuracy of character recognition. This improves the accuracy of character recognition. Some or all of the above-described processes in the recognition unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the separation of the background and text from a comic image.

[0074] The recognition unit can automatically recognize the page order of a comic and read it in the correct order. For example, the recognition unit can automatically detect the page numbers of a comic and read them in the correct order. The recognition unit can also analyze the storyline of a comic and estimate the page order. The recognition unit can also suggest the correct page order by referring to the user's past browsing history. This allows the comic to be read in the correct page order. Some or all of the above processes in the recognition unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the recognition of the comic's page order.

[0075] The recognition unit can estimate the user's emotions and determine the priority of comics to recognize based on the estimated emotions. For example, if the user is relaxed, the recognition unit will prioritize recognizing comics with relaxing content. If the user is excited, the recognition unit may also prioritize recognizing comics with action or adventure. If the user is sad, the recognition unit may also prioritize recognizing comics with emotional stories. In this way, the priority of comics to recognize can be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using or without a generative AI. For example, the recognition unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0076] The recognition unit can refer to the user's past browsing history and prioritize the recognition of relevant comics. For example, the recognition unit can analyze the genres of comics the user has read in the past and prioritize the recognition of related comics. The recognition unit can also prioritize the recognition of new works by authors of comics that the user has previously given high ratings to. The recognition unit can also prioritize the recognition of sequels to specific series based on the user's past browsing history. This allows the recognition unit to prioritize the recognition of relevant comics by referring to the user's past browsing history. Some or all of the above processing in the recognition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the recognition unit can input the user's past browsing history into a generation AI and have the generation AI perform the recognition of relevant comics.

[0077] The recognition unit can apply different recognition algorithms depending on the genre of the comic. For example, the recognition unit can apply a recognition algorithm that emphasizes dynamic scenes to action comics. The recognition unit can also apply a recognition algorithm that emphasizes emotional expression to romance comics. The recognition unit can also apply a recognition algorithm that emphasizes humorous elements to comedy comics. This allows for the application of a recognition algorithm appropriate to the genre of the comic. Some or all of the above processing in the recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the application of a recognition algorithm appropriate to the genre of the comic.

[0078] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is relaxed, the translation unit will use softer language. If the user is excited, the translation unit may use stronger language. If the user is sad, the translation unit may use more emotionally charged language. This allows the translation's expression to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using or without a generative AI. For example, the translation unit can input user emotion data into a generative AI and have the generative AI adjust the translation's expression.

[0079] The translation unit can understand the context of the comic and select appropriate slang. For example, the translation unit can analyze the comic's storyline and select appropriate slang. The translation unit can also select appropriate slang considering the characters' personalities and backgrounds. The translation unit can also select appropriate slang depending on the genre of the comic. This allows for the selection of appropriate slang according to the context. Some or all of the above processes in the translation unit may be performed using a generative AI, or not. For example, the translation unit can have a generative AI understand the context of the comic and select appropriate slang.

[0080] The translation unit can apply different translation styles to each character in the comic. For example, the translation unit can apply a strong translation style to the protagonist. It can also apply a gentle translation style to the heroine. It can also apply a ruthless translation style to the villain. This allows for the application of different translation styles to each character. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can have a generative AI perform the application of different translation styles to each character.

[0081] The translation unit can estimate the user's emotions and adjust the translation length based on the estimated emotions. For example, if the user is in a hurry, the translation unit can produce a short, concise translation. If the user is relaxed, the translation unit can produce a longer translation that includes detailed explanations. If the user is excited, the translation unit can also produce a translation with visually stimulating effects. This allows the translation length to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using or without a generative AI. For example, the translation unit can input user emotion data into a generative AI and have the generative AI adjust the translation length.

[0082] The translation unit can learn regional slang specific to the user's area and incorporate it into translations. For example, the translation unit can add regional slang specific to the user's area to a database and incorporate it into translations. The translation unit can also select appropriate slang considering the regional culture and background of the user. The translation unit can also learn regional linguistic expressions specific to the user's area and incorporate them into translations. This makes it possible to perform translations that reflect regional slang. Some or all of the above processes in the translation unit may be performed using generative AI, or not. For example, the translation unit can learn regional slang specific to the user's area and have the generative AI perform translations that reflect it.

[0083] The translation unit can apply different translation algorithms depending on the genre of the comic. For example, the translation unit can apply a translation algorithm that emphasizes action scenes to action comics. It can also apply a translation algorithm that emphasizes emotional expression to romance comics. It can also apply a translation algorithm that emphasizes humor to comedy comics. This allows for the application of genre-appropriate translation algorithms. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can have a generative AI perform the application of a genre-appropriate translation algorithm to the comic.

[0084] The service provider can estimate the user's emotions and adjust the display method of the comics based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a display method with soft colors. If the user is excited, the service provider can also provide a display method with vibrant colors. If the user is sad, the service provider can also provide a display method with calm colors. This allows the service provider to provide a 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 a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0085] The service provider can select the optimal display format according to the user's device. For example, if the user is using a smartphone, the service provider can provide a display format that matches the screen size. If the user is using a tablet, the service provider can also provide a display format optimized for a larger screen. If the user is using a desktop, the service provider can also provide a high-resolution display format. This allows the service provider to provide the optimal display format according to the device. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information into a generation AI and have the generation AI select the optimal display format.

[0086] The recommendation unit can refer to the user's past browsing history and recommend relevant comics. For example, the recommendation unit can analyze the genres of comics the user has read in the past and recommend related comics. The recommendation unit can also recommend new works by authors of comics that the user has previously given high ratings to. The recommendation unit can also recommend sequels to specific series based on the user's past browsing history. In this way, it can recommend relevant comics by referring to past browsing history. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit can input the user's past browsing history into a generative AI and have the generative AI perform the recommendation of relevant comics.

[0087] The service provider can estimate the user's emotions and determine the priority of comics to offer based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize offering comics with relaxing content. If the user is excited, the service provider may also prioritize offering comics with action or adventure. If the user is sad, the service provider may also prioritize offering comics with emotional stories. This allows the service provider to offer comics with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0088] The distribution unit can prioritize providing comics that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the distribution unit can prioritize providing comics related to that region. If the user is traveling, the distribution unit can also prioritize providing comics related to the travel destination. If the user is participating in a specific event, the distribution unit can also prioritize providing comics related to that event. This allows for the provision of highly relevant comics based on geographical location information. Some or all of the above processing in the distribution unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the distribution unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of providing highly relevant comics.

[0089] The service provider can analyze a user's social media activity and provide relevant comics. For example, the service provider may prioritize providing comics that the user is talking about on social media. The service provider may also provide comics recommended by accounts that the user follows. The service provider may also analyze the content of a user's social media posts and provide relevant comics. This allows the service provider to provide relevant comics based on social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI perform the task of providing relevant comics.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The recognition unit can estimate the user's emotions and adjust the comic image loading speed based on the estimated emotions. For example, if the user is relaxed, the recognition unit will load the comic images at a slow pace. If the user is in a hurry, the recognition unit can load the comic images quickly. If the user is excited, the recognition unit can load the comic images at a faster pace than usual. This allows the comic image loading speed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using the generative AI or not. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI adjust the loading speed.

[0092] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is relaxed, the translation unit will use softer language. If the user is excited, the translation unit may use stronger language. If the user is sad, the translation unit may use more emotionally charged language. This allows the translation's expression to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using or without a generative AI. For example, the translation unit can input user emotion data into a generative AI and have the generative AI adjust the translation's expression.

[0093] The service provider can estimate the user's emotions and adjust the display method of the comics based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a display method with soft colors. If the user is excited, the service provider can also provide a display method with vibrant colors. If the user is sad, the service provider can also provide a display method with calm colors. This allows the service provider to provide a 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 a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0094] The recognition unit can estimate the user's emotions and determine the priority of comics to recognize based on the estimated emotions. For example, if the user is relaxed, the recognition unit will prioritize recognizing comics with relaxing content. If the user is excited, the recognition unit may also prioritize recognizing comics with action or adventure. If the user is sad, the recognition unit may also prioritize recognizing comics with emotional stories. In this way, the priority of comics to recognize can be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using or without a generative AI. For example, the recognition unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0095] The service provider can estimate the user's emotions and determine the priority of comics to offer based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize offering comics with relaxing content. If the user is excited, the service provider may also prioritize offering comics with action or adventure. If the user is sad, the service provider may also prioritize offering comics with emotional stories. This allows the service provider to offer comics with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0096] The recognition unit can automatically recognize the page order of a comic and read it in the correct order. For example, the recognition unit can automatically detect the page numbers of a comic and read them in the correct order. The recognition unit can also analyze the storyline of a comic and estimate the page order. The recognition unit can also suggest the correct page order by referring to the user's past browsing history. This allows the comic to be read in the correct page order. Some or all of the above processes in the recognition unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the recognition of the comic's page order.

[0097] The translation unit can apply different translation styles to each character in the comic. For example, the translation unit can apply a strong translation style to the protagonist. It can also apply a gentle translation style to the heroine. It can also apply a ruthless translation style to the villain. This allows for the application of different translation styles to each character. Some or all of the above processing in the translation unit may be performed using a generative AI, or not. For example, the translation unit can have a generative AI perform the application of different translation styles to each character.

[0098] The distribution unit can prioritize providing comics that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the distribution unit can prioritize providing comics related to that region. If the user is traveling, the distribution unit can also prioritize providing comics related to the travel destination. If the user is participating in a specific event, the distribution unit can also prioritize providing comics related to that event. This allows for the provision of highly relevant comics based on geographical location information. Some or all of the above processing in the distribution unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the distribution unit can input the user's geographical location information into a generation AI and have the generation AI perform the task of providing highly relevant comics.

[0099] The recognition unit can apply different recognition algorithms depending on the genre of the comic. For example, the recognition unit can apply a recognition algorithm that emphasizes dynamic scenes to action comics. The recognition unit can also apply a recognition algorithm that emphasizes emotional expression to romance comics. The recognition unit can also apply a recognition algorithm that emphasizes humorous elements to comedy comics. This allows for the application of a recognition algorithm appropriate to the genre of the comic. Some or all of the above processing in the recognition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recognition unit can have a generative AI perform the application of a recognition algorithm appropriate to the genre of the comic.

[0100] The service provider can analyze a user's social media activity and provide relevant comics. For example, the service provider may prioritize providing comics that the user is talking about on social media. The service provider may also provide comics recommended by accounts that the user follows. The service provider may also analyze the content of a user's social media posts and provide relevant comics. This allows the service provider to provide relevant comics based on social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's social media activity into a generative AI and have the generative AI perform the task of providing relevant comics.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The recognition unit reads the comic image and recognizes the text. The recognition unit can read comic images in various formats, such as digital comics and scanned paper comics. The recognition unit can recognize text in various formats, such as handwritten characters and printed characters. The recognition unit uses generative AI to extract and recognize text from the comic image. For example, it uses OCR technology to extract text information from the comic image. In addition, it can use image processing technology to separate the background and text of the comic image and improve the accuracy of text recognition. Step 2: The translation unit translates the text recognized by the recognition unit into the user's language. The translation unit can translate into various languages, including English, Japanese, and Spanish. The translation unit uses generative AI to instantly translate the recognized text. For example, it uses text generation AI (e.g., LLM) to translate the recognized text into the user's language. The translation unit can also preserve onomatopoeia specific to comics. For example, it can translate onomatopoeia as is to avoid compromising the atmosphere of the comic. Furthermore, the translation unit can learn the stylistic quirks of individual comic authors and reflect them in the translation. For example, it can learn the unique expressions and writing styles of comic authors and reflect them in the translation. Step 3: The provider makes the translated comics available for viewing. The provider can display the translated comics via a web browser or a dedicated app. The provider can use a generation AI to select the optimal display format according to the user's device. For example, it can provide display formats compatible with various devices such as smartphones, tablets, and PCs. In addition, the provider can make the comics available for viewing at the same time as domestic content via a Co server located overseas. For example, it can allow overseas users to view the comics at the same time as domestic users.

[0103] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0106] Each of the multiple elements described above, including the recognition unit, translation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit reads comic images and recognizes the characters using the camera 42 and processor 46 of the smart device 14. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and translates the recognized characters into the user's language. The provision unit is implemented by the control unit 46A of the smart device 14 and displays the translated comic on the user's device. Furthermore, the recognition unit can estimate the user's emotions and adjust the reading speed of the comic images based on the estimated emotions. Emotion estimation is implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the recognition unit, translation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit reads comic images and recognizes the characters using the camera 42 and processor 46 of the smart glasses 214. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and translates the recognized characters into the user's language. The provision unit is implemented by the control unit 46A of the smart glasses 214 and displays the translated comic on the user's device. Furthermore, the recognition unit can estimate the user's emotions and adjust the reading speed of the comic images based on the estimated emotions. Emotion estimation is implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the recognition unit, translation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit reads comic images and recognizes the characters using the camera 42 and processor 46 of the headset terminal 314. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and translates the recognized characters into the user's language. The provision unit is implemented by the control unit 46A of the headset terminal 314 and displays the translated comic on the user's device. Furthermore, the recognition unit can estimate the user's emotions and adjust the reading speed of the comic images based on the estimated emotions. Emotion estimation is implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 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 a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the recognition unit, translation unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the recognition unit reads comic images and recognizes the characters using the camera 42 and processor 46 of the robot 414. The translation unit is implemented by the identification processing unit 290 of the data processing unit 12 and translates the recognized characters into the user's language. The provision unit is implemented by the control unit 46A of the robot 414 and displays the translated comic on the user's device. Furthermore, the recognition unit can estimate the user's emotions and adjust the reading speed of the comic images based on the estimated emotions. Emotion estimation is implemented by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0156] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A recognition unit that reads comic images and recognizes printed text, A translation unit that translates the characters recognized by the recognition unit into the user's language, The system includes a provisioning unit that makes the comics translated by the aforementioned translation unit available for viewing. A system characterized by the following features. (Note 2) The recognition unit, Reads comic images and recognizes text. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned translation department, Translate recognized text into the user's language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Make translated comics available for viewing. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned translation department, Keep the onomatopoeia unique to comics as is. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned translation department, Learn the stylistic quirks of individual comic authors and incorporate them into translations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, By using a Co server located overseas, comics can be viewed at the same time as domestic content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, By using cashless payment apps, we can provide content with a strong international appeal at a low cost. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recognition unit, It estimates the user's emotions and adjusts the comic image loading speed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recognition unit, This tool separates the background and text from comic images to improve the accuracy of character recognition. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recognition unit, Automatically recognizes the page order of a comic and loads it in the correct order. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recognition unit, It estimates the user's emotions and determines the priority of comics to recognize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The recognition unit, Referencing the user's past browsing history prioritizes the recognition of related comics. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recognition unit, Apply different recognition algorithms depending on the genre of the comic. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned translation department, Understand the context of the comic and select appropriate slang according to that context. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned translation department, Apply different translation styles to each comic character. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned translation department, Learns regional slang specific to the user's area and incorporates it into translations. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned translation department, Apply different translation algorithms depending on the genre of the comic. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts how comics are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Select the optimal display format according to the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The system uses the user's past browsing history to recommend related comics. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of comics to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We prioritize providing relevant comics based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, Analyzes users' social media activity and provides relevant comics. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that reads comic images and recognizes printed text, A translation unit that translates the characters recognized by the recognition unit into the user's language, The system includes a provisioning unit that makes the comics translated by the aforementioned translation unit available for viewing. A system characterized by the following features.

2. The recognition unit, Reads comic images and recognizes text. The system according to feature 1.

3. The aforementioned translation department, Translate recognized text into the user's language. The system according to feature 1.

4. The aforementioned supply unit is, Make translated comics available for viewing. The system according to feature 1.

5. The aforementioned translation department, Keep the onomatopoeia unique to comics as is. The system according to feature 1.

6. The aforementioned translation department, Learn the stylistic quirks of individual comic authors and incorporate them into translations. The system according to feature 1.

7. The aforementioned supply unit is, By using a Co server located overseas, comics can be viewed at the same time as domestic content. The system according to feature 1.

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