system

Abstract

The system according to the embodiment aims to efficiently provide content in Portuguese that a user wants to learn. [Solution] The system according to this embodiment comprises a selection unit, a translation unit, and a provision unit. The selection unit allows the user to select the content they wish to learn. The translation unit translates the content selected by the selection unit into Portuguese. The provision unit provides the content translated by the translation unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have presented a challenge in efficiently providing content that users want to learn in a specific language.

[0005] The system according to the embodiment aims to efficiently provide content in Portuguese that a user wants to learn. [Means for solving the problem]

[0006] The system according to the embodiment includes a selection unit, a translation unit, and a provision unit. The selection unit selects content that a user wants to learn. The translation unit translates the content selected by the selection unit into Portuguese. The provision unit provides the content translated by the translation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide the content that the user wants to learn in Portuguese. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An embodiment of the present invention is an online school that utilizes AI to enable users to learn Japanese pop culture (anime, manga, music, etc.) in Portuguese, even late at night. In this system, the user accesses an online platform and selects the content they wish to learn. The AI ​​then translates the selected content into Portuguese and provides it to the user. This mechanism allows users to learn Japanese pop culture in Portuguese, even late at night. For example, a user accesses the online platform and selects an anime episode, manga chapter, or song lyrics they wish to learn. This information is input into the AI. The AI ​​then analyzes the input information and translates it into Portuguese. The AI ​​understands the selected content and translates it into appropriate Portuguese. For example, an anime episode is translated into Portuguese and displayed as subtitles. Similarly, a manga chapter is translated into Portuguese and displayed as text. Song lyrics are also translated into Portuguese. The translated content is provided to the user. The user can then learn from the content displayed in Portuguese. For example, they can watch an anime episode with Portuguese subtitles or read a manga chapter in Portuguese. The song lyrics are also displayed in Portuguese, allowing users to enjoy the music while understanding the lyrics. This system enables users to learn about Japanese pop culture in Portuguese, even late at night. Furthermore, the service can be expanded to all Portuguese-speaking regions, including Brazil, through the online platform. This allows many users to learn about and enjoy Japanese pop culture. The system enables users to learn about Japanese pop culture in Portuguese, even late at night.

[0029] An online school system according to an embodiment includes a selection unit, a translation unit, and a provision unit. The selection unit selects content that a user wants to learn. Examples of content that a user wants to learn include, but are not limited to, anime episodes, manga chapters, and music lyrics. For example, the selection unit allows a user to access an online platform and select the content they want to learn. Next, the translation unit translates the content selected by the selection unit into Portuguese. The translation unit translates the selected content into Portuguese, for example, using AI. The AI ​​understands the selected content and translates it into appropriate Portuguese. For example, an anime episode is translated into Portuguese and displayed as subtitles. Also, a manga chapter is translated into Portuguese and displayed as text. Music lyrics are similarly translated into Portuguese. Some or all of the above-described processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit inputs the selected content into the generation AI and causes the generation AI to translate it into Portuguese. Next, the provision unit provides the content translated by the translation unit to the user. The providing unit, for example, provides translated content to a user. The user can study content displayed in Portuguese. For example, the user can watch an anime episode with Portuguese subtitles or read a manga chapter in Portuguese. Music lyrics are also displayed in Portuguese, allowing the user to enjoy the music while understanding the lyrics. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or without a generation AI. For example, the providing unit may input translated content into a generation AI and convert it into an optimal format for providing to the user. This allows the online school system according to the embodiment to allow users to learn Japanese pop culture in Portuguese, even late at night.

[0030] The system includes a feedback collection unit that collects user feedback and reflects it in the AI. The feedback collection unit collects user feedback and reflects it in the AI. The feedback collection unit can provide feedback, for example, for content provided by the user. The feedback includes, but is not limited to, text feedback, evaluation scores, and the like. The feedback collection unit can provide text feedback, for example, for content provided by the user. The feedback collection unit can also provide an evaluation score for the content provided by the user. Some or all of the above-described processing in the feedback collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback collection unit can input user feedback into the generation AI and cause the generation AI to analyze the feedback. In this way, the feedback collection unit collects user feedback and reflects it in the AI, thereby improving the accuracy of the system.

[0031] The selection unit can analyze the user's past selection history and automatically select optimal content. For example, the selection unit can analyze the user's past selection history and automatically select optimal content. For example, the selection unit can suggest sequels to anime that the user has previously watched. The selection unit can also suggest works related to manga that the user has previously read. The selection unit can also suggest new songs by artists whose music the user has previously listened to. In this way, the selection unit can automatically select optimal content by analyzing the user's past selection history. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the user's past selection history data into the generation AI and have the generation AI select optimal content.

[0032] The selection unit can perform filtering based on the user's current interests when selecting content. For example, the selection unit can perform filtering based on the user's current interests when selecting content. For example, the selection unit can prioritize suggesting anime genres in which the user is currently interested. The selection unit can also prioritize suggesting manga themes in which the user is currently interested. The selection unit can also prioritize suggesting music genres in which the user is currently interested. This allows the selection unit to provide more appropriate content by filtering based on the user's current interests. Some or all of the above-described processing in the selection unit can be performed using, or without, a generation AI. For example, the selection unit can input the user's current interests and interest data into the generation AI and have the generation AI perform filtering.

[0033] When selecting content, the selection unit can prioritize displaying highly relevant content by taking into account the user's geographical location information. For example, when selecting content, the selection unit prioritizes displaying highly relevant content by taking into account the user's geographical location information. For example, when the user is in Brazil, the selection unit can prioritize suggesting anime that is popular in Brazil. Furthermore, when the user is in Portugal, the selection unit can prioritize suggesting manga that is popular in Portugal. Furthermore, when the user is in Japan, the selection unit can prioritize suggesting music that is popular in Japan. In this way, the selection unit can provide highly relevant content by taking into account the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant content.

[0034] The selection unit can analyze the user's social media activity and suggest related content when selecting content. For example, the selection unit can analyze the user's social media activity and suggest related content when selecting content. For example, the selection unit can suggest anime that the user is talking about on social media. The selection unit can also suggest manga that the user has shared on social media. The selection unit can also suggest music by artists the user follows on social media. In this way, the selection unit can suggest related content by analyzing the user's social media activity. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related content.

[0035] The translation unit can adjust the level of detail of the translation based on the importance of the content during translation. For example, the translation unit adjusts the level of detail of the translation based on the importance of the content during translation. For example, the translation unit provides a detailed translation for important scenes. The translation unit can also provide a simplified translation for unimportant scenes. The translation unit can also provide a detailed translation for important lyrics. In this way, the translation unit can provide a more appropriate translation by adjusting the level of detail of the translation based on the importance of the content. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input content importance data into the generation AI and have the generation AI adjust the level of detail of the translation.

[0036] The translation unit can apply different translation algorithms depending on the content category during translation. For example, the translation unit applies different translation algorithms depending on the content category during translation. For example, the translation unit applies an algorithm dedicated to anime when translating anime. The translation unit can also apply an algorithm dedicated to manga when translating manga. The translation unit can also apply an algorithm dedicated to music when translating music. In this way, the translation unit can provide more appropriate translations by applying different translation algorithms depending on the content category. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI, for example. For example, the translation unit can input content category data into the generation AI and have the generation AI select an appropriate translation algorithm.

[0037] The translation unit can determine the priority of translations based on the time of submission of content during translation. For example, the translation unit determines the priority of translations based on the time of submission of content during translation. For example, the translation unit prioritizes translating new anime episodes. The translation unit can also prioritize translating new manga chapters. The translation unit can also prioritize translating new music lyrics. In this way, the translation unit can provide more appropriate translations by determining the priority of translations based on the time of submission of content. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input content submission time data into the generation AI and have the generation AI determine the priority of translations.

[0038] The translation unit can adjust the order of translations based on the relevance of the content during the translation process. For example, the translation unit can prioritize translating highly relevant anime episodes. It can also prioritize translating highly relevant manga chapters. It can also prioritize translating highly relevant song lyrics. By doing so, the translation unit can provide more appropriate translations by adjusting the order of translations based on the relevance of the content. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input content relevance data into a generative AI and have the generative AI perform the adjustment of the translation order.

[0039] The service provider can select the optimal display method by referring to the user's past viewing history at the time of service provision. For example, the service provider can select the optimal display method by referring to the user's past viewing history at the time of service provision. For example, the service provider can refer to the display methods of anime that the user has watched in the past. The service provider can also refer to the display methods of manga that the user has read in the past. The service provider can also refer to the display methods of music that the user has listened to in the past. In this way, the service provider can select the optimal display method by referring to the user's past viewing history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past viewing history data into a generation AI and have the generation AI perform the selection of the optimal display method.

[0040] The providing unit can adjust the content provision order based on the user's current learning progress when providing the content. For example, the providing unit adjusts the content provision order based on the user's current learning progress when providing the content. For example, the providing unit can prioritize providing an anime episode that the user is studying. The providing unit can also prioritize providing a manga chapter that the user is studying. The providing unit can also prioritize providing lyrics of music that the user is studying. In this way, the providing unit can provide more appropriate content by adjusting the content provision order based on the user's current learning progress. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's learning progress data into the generation AI and cause the generation AI to adjust the content provision order.

[0041] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the data. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the data. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. In this way, the providing unit can provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0042] The providing unit can analyze the user's social media activity and provide related content at the time of providing. For example, the providing unit can analyze the user's social media activity and provide related content at the time of providing. For example, the providing unit can provide anime that the user is talking about on social media. The providing unit can also provide manga that the user has shared on social media. The providing unit can also provide music by artists that the user follows on social media. In this way, the providing unit can provide related content by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related content.

[0043] The feedback collection unit can select an optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit selects an optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, the feedback collection unit may request detailed feedback. Furthermore, if the user has provided brief feedback in the past, the feedback collection unit may request brief feedback. Furthermore, if the user has provided emotional feedback in the past, the feedback collection unit may request emotional feedback. In this way, the feedback collection unit can select an optimal collection method by referring to the user's past feedback history. Some or all of the above-described processing in the feedback collection unit may be performed using, or without, a generation AI. For example, the feedback collection unit may input the user's past feedback history data into the generation AI and cause the generation AI to select an optimal collection method.

[0044] The feedback collection unit can select the optimal collection method by taking into account the user's device information when collecting feedback. For example, the feedback collection unit selects the optimal collection method by taking into account the user's device information when collecting feedback. For example, if the user is using a smartphone, the feedback collection unit provides a feedback collection method that matches the screen size. Furthermore, if the user is using a tablet, the feedback collection unit can provide a feedback collection method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the feedback collection unit can provide a feedback collection method that is concise and highly visible. In this way, the feedback collection unit can provide the optimal collection method by taking into account the user's device information. Some or all of the above-described processing in the feedback collection unit may be performed using, or without, a generation AI. For example, the feedback collection unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal collection method.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The selection unit can analyze the user's learning style and suggest the most suitable content format. For example, if the user is a visual learner, anime or manga can be preferentially suggested. If the user is an auditory learner, music or podcast content can be suggested. Furthermore, if the user is a hands-on learner, interactive quiz or game content can be suggested. In this way, the selection unit can improve learning effectiveness by suggesting the most suitable content format based on the user's learning style.

[0047] The feedback collection unit can collect user feedback in real time and immediately reflect it in the AI. For example, if a user is dissatisfied with the translation of content, that feedback is immediately sent to the AI, and the translation algorithm is adjusted. Also, if a user requests new content, that request can be immediately reflected in the system and reflected in the next content provision. Furthermore, if a user reports difficulties they experienced while studying, that information can be immediately sent to the AI, and the learning support function can be enhanced. In this way, the feedback collection unit can collect user feedback in real time and immediately reflect it in the AI, thereby improving the flexibility and responsiveness of the system.

[0048] The selection unit can analyze the user's past selection history and suggest content on related topics. For example, based on the genre of anime the user has watched in the past, it can suggest new anime of the same genre. It can also suggest manga on related themes based on the themes of manga the user has read in the past. Furthermore, based on the artists of music the user has listened to in the past, it can suggest new songs by the same artists or music by related artists. In this way, the selection unit can suggest content on related topics by analyzing the user's past selection history and continue to attract the user's interest.

[0049] The selection unit can set learning goals based on the user's current interests. For example, based on the anime genre in which the user is currently interested, the selection unit can set learning goals related to that genre. Also, based on the theme of a manga in which the user is currently interested, the selection unit can set learning goals related to that theme. Furthermore, based on the genre of music in which the user is currently interested, the selection unit can set learning goals related to that genre. In this way, the selection unit can increase learning motivation by setting learning goals based on the user's current interests.

[0050] The selection unit can suggest content related to local cultures and events by taking into account the user's geographical location information. For example, if the user is in Brazil, anime and manga related to Brazilian festivals and events can be suggested. If the user is in Portugal, content related to Portuguese history and culture can be suggested. Furthermore, if the user is in Japan, music and videos related to Japanese seasons and events can be suggested. In this way, the selection unit can provide content related to local cultures and events by taking into account the user's geographical location information, thereby continuing to attract the user's interest.

[0051] The selection unit can analyze the user's social media activity and suggest content that the user's friends and followers are interested in. For example, it can suggest anime that the user's friends have shared. It can also suggest manga that the user's followers have commented on. It can also suggest music that the user's friends have retweeted. In this way, the selection unit can analyze the user's social media activity to suggest content that the user's friends and followers are interested in, thereby keeping the user interested.

[0052] During translation, the translation unit can adjust the translation style based on the importance of the content. For example, important scenes can be translated in a formal style, while unimportant scenes can be translated in a casual style. Furthermore, important lyrics can be translated in a poetic style. This allows the translation unit to provide more appropriate translations by adjusting the translation style based on the importance of the content.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The selection unit selects the content that the user wants to learn. The content that the user wants to learn may include, for example, an anime episode, a manga chapter, music lyrics, etc. The selection unit allows the user to access the online platform and select the content that they want to learn. Step 2: The translation unit translates the content selected by the selection unit into Portuguese. The translation unit translates the selected content into Portuguese, for example, using AI. The AI ​​understands the selected content and translates it into appropriate Portuguese. For example, it translates an anime episode into Portuguese and displays it as subtitles. It also translates a manga chapter into Portuguese and displays it as text. Song lyrics are also translated into Portuguese in a similar manner. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or without a generative AI. Step 3: The provider unit provides the user with the translated content. The provider unit provides the user with the translated content, for example. The user can learn from the content displayed in Portuguese. For example, they can watch an anime episode with Portuguese subtitles. They can also read a manga chapter in Portuguese. Since the lyrics of a song are also displayed in Portuguese, the user can enjoy the music while understanding the lyrics. Some or all of the above processing in the provider unit may be performed using, for example, a generative AI, or without a generative AI.

[0055] (Example 2) An embodiment of the present invention is an online school that utilizes AI to enable users to learn Japanese pop culture (anime, manga, music, etc.) in Portuguese, even late at night. In this system, the user accesses an online platform and selects the content they wish to learn. The AI ​​then translates the selected content into Portuguese and provides it to the user. This mechanism allows users to learn Japanese pop culture in Portuguese, even late at night. For example, a user accesses the online platform and selects an anime episode, manga chapter, or song lyrics they wish to learn. This information is input into the AI. The AI ​​then analyzes the input information and translates it into Portuguese. The AI ​​understands the selected content and translates it into appropriate Portuguese. For example, an anime episode is translated into Portuguese and displayed as subtitles. Similarly, a manga chapter is translated into Portuguese and displayed as text. Song lyrics are also translated into Portuguese. The translated content is provided to the user. The user can then learn from the content displayed in Portuguese. For example, they can watch an anime episode with Portuguese subtitles or read a manga chapter in Portuguese. The song lyrics are also displayed in Portuguese, allowing users to enjoy the music while understanding the lyrics. This system enables users to learn about Japanese pop culture in Portuguese, even late at night. Furthermore, the service can be expanded to all Portuguese-speaking regions, including Brazil, through the online platform. This allows many users to learn about and enjoy Japanese pop culture. The system enables users to learn about Japanese pop culture in Portuguese, even late at night.

[0056] An online school system according to an embodiment includes a selection unit, a translation unit, and a provision unit. The selection unit selects content that a user wants to learn. Examples of content that a user wants to learn include, but are not limited to, anime episodes, manga chapters, and music lyrics. For example, the selection unit allows a user to access an online platform and select the content they want to learn. Next, the translation unit translates the content selected by the selection unit into Portuguese. The translation unit translates the selected content into Portuguese, for example, using AI. The AI ​​understands the selected content and translates it into appropriate Portuguese. For example, an anime episode is translated into Portuguese and displayed as subtitles. Also, a manga chapter is translated into Portuguese and displayed as text. Music lyrics are similarly translated into Portuguese. Some or all of the above-described processing in the translation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the translation unit inputs the selected content into the generation AI and causes the generation AI to translate it into Portuguese. Next, the provision unit provides the content translated by the translation unit to the user. The providing unit, for example, provides translated content to a user. The user can study content displayed in Portuguese. For example, the user can watch an anime episode with Portuguese subtitles or read a manga chapter in Portuguese. Music lyrics are also displayed in Portuguese, allowing the user to enjoy the music while understanding the lyrics. Some or all of the above-described processing in the providing unit may be performed, for example, using a generation AI or without a generation AI. For example, the providing unit may input translated content into a generation AI and convert it into an optimal format for providing to the user. This allows the online school system according to the embodiment to allow users to learn Japanese pop culture in Portuguese, even late at night.

[0057] The system includes a feedback collection unit that collects user feedback and reflects it in the AI. The feedback collection unit collects user feedback and reflects it in the AI. The feedback collection unit can provide feedback, for example, for content provided by the user. The feedback includes, but is not limited to, text feedback, evaluation scores, and the like. The feedback collection unit can provide text feedback, for example, for content provided by the user. The feedback collection unit can also provide an evaluation score for the content provided by the user. Some or all of the above-described processing in the feedback collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback collection unit can input user feedback into the generation AI and cause the generation AI to analyze the feedback. In this way, the feedback collection unit collects user feedback and reflects it in the AI, thereby improving the accuracy of the system.

[0058] The selection unit can estimate the user's emotions and suggest content they might want to learn based on those emotions. For example, if the user is tired, the selection unit might suggest a relaxing anime episode. If the user is excited, it might suggest an action-packed manga chapter. If the user is sad, it might suggest uplifting song lyrics. In this way, the selection unit improves the user's learning experience by suggesting the most suitable content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 selection unit may be performed using a generative AI, or not. For example, the selection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0059] The selection unit can analyze the user's past selection history and automatically select optimal content. For example, the selection unit can analyze the user's past selection history and automatically select optimal content. For example, the selection unit can suggest sequels to anime that the user has previously watched. The selection unit can also suggest works related to manga that the user has previously read. The selection unit can also suggest new songs by artists whose music the user has previously listened to. In this way, the selection unit can automatically select optimal content by analyzing the user's past selection history. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the user's past selection history data into the generation AI and have the generation AI select optimal content.

[0060] The selection unit can perform filtering based on the user's current interests when selecting content. For example, the selection unit can perform filtering based on the user's current interests when selecting content. For example, the selection unit can prioritize suggesting anime genres in which the user is currently interested. The selection unit can also prioritize suggesting manga themes in which the user is currently interested. The selection unit can also prioritize suggesting music genres in which the user is currently interested. This allows the selection unit to provide more appropriate content by filtering based on the user's current interests. Some or all of the above-described processing in the selection unit can be performed using, or without, a generation AI. For example, the selection unit can input the user's current interests and interest data into the generation AI and have the generation AI perform filtering.

[0061] The selection unit can estimate the user's emotions and prioritize the content to be selected based on the estimated user emotions. For example, the selection unit can estimate the user's emotions and prioritize the content to be selected based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can prioritize relaxing content to suggest. Alternatively, if the user is excited, the selection unit can prioritize action-packed content to suggest. Alternatively, if the user is sad, the selection unit can prioritize uplifting content to suggest. This allows the selection unit to prioritize content based on the user's emotions and provide more appropriate content. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit can be performed using, for example, the generation AI. For example, the selection unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0062] When selecting content, the selection unit can prioritize displaying highly relevant content by taking into account the user's geographical location information. For example, when selecting content, the selection unit prioritizes displaying highly relevant content by taking into account the user's geographical location information. For example, when the user is in Brazil, the selection unit can prioritize suggesting anime that is popular in Brazil. Furthermore, when the user is in Portugal, the selection unit can prioritize suggesting manga that is popular in Portugal. Furthermore, when the user is in Japan, the selection unit can prioritize suggesting music that is popular in Japan. In this way, the selection unit can provide highly relevant content by taking into account the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant content.

[0063] The selection unit can analyze the user's social media activity and suggest related content when selecting content. For example, the selection unit can analyze the user's social media activity and suggest related content when selecting content. For example, the selection unit can suggest anime that the user is talking about on social media. The selection unit can also suggest manga that the user has shared on social media. The selection unit can also suggest music by artists the user follows on social media. In this way, the selection unit can suggest related content by analyzing the user's social media activity. Some or all of the above-mentioned processing in the selection unit may be performed using, or without, a generation AI. For example, the selection unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest related content.

[0064] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, the translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is relaxed, the translation unit can perform a translation using softer expressions. If the user is excited, the translation unit can perform a translation using stronger expressions. If the user is sad, the translation unit can perform a translation using gentler expressions. This allows the translation unit to adjust the translation expression based on the user's emotions and provide a more appropriate translation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression.

[0065] The translation unit can adjust the level of detail of the translation based on the importance of the content during translation. For example, the translation unit adjusts the level of detail of the translation based on the importance of the content during translation. For example, the translation unit provides a detailed translation for important scenes. The translation unit can also provide a simplified translation for unimportant scenes. The translation unit can also provide a detailed translation for important lyrics. In this way, the translation unit can provide a more appropriate translation by adjusting the level of detail of the translation based on the importance of the content. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input content importance data into the generation AI and have the generation AI adjust the level of detail of the translation.

[0066] The translation unit can apply different translation algorithms depending on the content category during translation. For example, the translation unit applies different translation algorithms depending on the content category during translation. For example, the translation unit applies an algorithm dedicated to anime when translating anime. The translation unit can also apply an algorithm dedicated to manga when translating manga. The translation unit can also apply an algorithm dedicated to music when translating music. In this way, the translation unit can provide more appropriate translations by applying different translation algorithms depending on the content category. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, a generation AI, for example. For example, the translation unit can input content category data into the generation AI and have the generation AI select an appropriate translation algorithm.

[0067] The translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. For example, the translation unit can estimate the user's emotions and adjust the length of the translation based on the estimated user emotions. For example, if the user is in a hurry, the translation unit can provide a short, concise translation. If the user is relaxed, the translation unit can provide a detailed translation. If the user is excited, the translation unit can provide a translation that adds visually stimulating effects. This allows the translation unit to adjust the length of the translation based on the user's emotions and provide a more appropriate translation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the translation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the translation.

[0068] The translation unit can determine the priority of translations based on the time of submission of content during translation. For example, the translation unit determines the priority of translations based on the time of submission of content during translation. For example, the translation unit prioritizes translating new anime episodes. The translation unit can also prioritize translating new manga chapters. The translation unit can also prioritize translating new music lyrics. In this way, the translation unit can provide more appropriate translations by determining the priority of translations based on the time of submission of content. Some or all of the above-described processing in the translation unit may be performed using, or without, a generation AI. For example, the translation unit can input content submission time data into the generation AI and have the generation AI determine the priority of translations.

[0069] The translation unit can adjust the order of translations based on the relevance of the content during the translation process. For example, the translation unit can prioritize translating highly relevant anime episodes. It can also prioritize translating highly relevant manga chapters. It can also prioritize translating highly relevant song lyrics. By doing so, the translation unit can provide more appropriate translations by adjusting the order of translations based on the relevance of the content. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the translation unit can input content relevance data into a generative AI and have the generative AI perform the adjustment of the translation order.

[0070] The service provider can estimate the user's emotions and adjust how the content is displayed based on the estimated emotions. For example, the service provider can estimate the user's emotions and adjust how the content is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. In this way, the service provider can provide a more appropriate display by adjusting how the content is displayed based on 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 service provider may be performed using a generative AI, for example, or not using a generative AI. 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.

[0071] The service provider can select the optimal display method by referring to the user's past viewing history at the time of service provision. For example, the service provider can select the optimal display method by referring to the user's past viewing history at the time of service provision. For example, the service provider can refer to the display methods of anime that the user has watched in the past. The service provider can also refer to the display methods of manga that the user has read in the past. The service provider can also refer to the display methods of music that the user has listened to in the past. In this way, the service provider can select the optimal display method by referring to the user's past viewing history. Some or all of the above processing in the service provider may be performed using, for example, a generation AI, or without a generation AI. For example, the service provider can input the user's past viewing history data into a generation AI and have the generation AI perform the selection of the optimal display method.

[0072] The providing unit can adjust the content provision order based on the user's current learning progress when providing the content. For example, the providing unit adjusts the content provision order based on the user's current learning progress when providing the content. For example, the providing unit can prioritize providing an anime episode that the user is studying. The providing unit can also prioritize providing a manga chapter that the user is studying. The providing unit can also prioritize providing lyrics of music that the user is studying. In this way, the providing unit can provide more appropriate content by adjusting the content provision order based on the user's current learning progress. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's learning progress data into the generation AI and cause the generation AI to adjust the content provision order.

[0073] The providing unit can estimate the user's emotions and prioritize the content to be provided based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and prioritize the content to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize relaxing content to be provided. Also, if the user is excited, the providing unit can prioritize action-packed content to be provided. Also, if the user is sad, the providing unit can prioritize uplifting content to be provided. This allows the providing unit to prioritize content based on the user's emotions and provide more appropriate content. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input user emotion data into the generation AI and have the generation AI determine the priority of the content.

[0074] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the data. For example, the providing unit selects the optimal display method by taking into consideration the user's device information when providing the data. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. In this way, the providing unit can provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0075] The providing unit can analyze the user's social media activity and provide related content at the time of providing. For example, the providing unit can analyze the user's social media activity and provide related content at the time of providing. For example, the providing unit can provide anime that the user is talking about on social media. The providing unit can also provide manga that the user has shared on social media. The providing unit can also provide music by artists that the user follows on social media. In this way, the providing unit can provide related content by analyzing the user's social media activity. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the user's social media activity data into the generation AI and cause the generation AI to provide related content.

[0076] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, the feedback collection unit estimates the user's emotions and adjusts the feedback collection method based on the estimated user's emotions. For example, the feedback collection unit requests detailed feedback when the user is relaxed. The feedback collection unit can also request brief feedback when the user is in a hurry. The feedback collection unit can also request emotional feedback when the user is excited. This allows the feedback collection unit to collect more appropriate feedback by adjusting the feedback collection method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the feedback collection method.

[0077] The feedback collection unit can select an optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit selects an optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, the feedback collection unit may request detailed feedback. Furthermore, if the user has provided brief feedback in the past, the feedback collection unit may request brief feedback. Furthermore, if the user has provided emotional feedback in the past, the feedback collection unit may request emotional feedback. In this way, the feedback collection unit can select an optimal collection method by referring to the user's past feedback history. Some or all of the above-described processing in the feedback collection unit may be performed using, or without, a generation AI. For example, the feedback collection unit may input the user's past feedback history data into the generation AI and cause the generation AI to select an optimal collection method.

[0078] The feedback collection unit can estimate the user's emotions and prioritize the feedback based on the estimated user's emotions. For example, the feedback collection unit can estimate the user's emotions and prioritize the feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the feedback collection unit can prioritize collecting relaxing feedback. Also, if the user is excited, the feedback collection unit can prioritize collecting action-packed feedback. Also, if the user is sad, the feedback collection unit can prioritize collecting encouraging feedback. In this way, the feedback collection unit can prioritize the feedback based on the user's emotions and collect more appropriate feedback. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the feedback.

[0079] The feedback collection unit can select the optimal collection method by taking into account the user's device information when collecting feedback. For example, the feedback collection unit selects the optimal collection method by taking into account the user's device information when collecting feedback. For example, if the user is using a smartphone, the feedback collection unit provides a feedback collection method that matches the screen size. Furthermore, if the user is using a tablet, the feedback collection unit can provide a feedback collection method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the feedback collection unit can provide a feedback collection method that is concise and highly visible. In this way, the feedback collection unit can provide the optimal collection method by taking into account the user's device information. Some or all of the above-described processing in the feedback collection unit may be performed using, or without, a generation AI. For example, the feedback collection unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal collection method. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, translation unit, provision unit, and feedback collection unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart device 14 and allows the user to select content they want to learn. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the selected content into Portuguese. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the translated content to the user. The feedback collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects user feedback and reflects it in the AI. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned selection unit, translation unit, provision unit, and feedback collection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the smart glasses 214 and allows the user to select content they want to learn. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the selected content into Portuguese. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the translated content to the user. The feedback collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects user feedback and reflects it in the AI. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned selection unit, translation unit, provision unit, and feedback collection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the headset type terminal 314 and allows the user to select content they want to learn. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the selected content into Portuguese. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the translated content to the user. The feedback collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects user feedback and reflects it in the AI. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned selection unit, translation unit, provision unit, and feedback collection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit is realized by the control unit 46A of the robot 414 and allows the user to select content they want to learn. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and translates the selected content into Portuguese. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the translated content to the user. The feedback collection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collects user feedback and reflects it in the AI.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The selection unit can analyze the user's learning style and suggest the most suitable content format. For example, if the user is a visual learner, anime or manga can be preferentially suggested. If the user is an auditory learner, music or podcast content can be suggested. Furthermore, if the user is a hands-on learner, interactive quiz or game content can be suggested. In this way, the selection unit can improve learning effectiveness by suggesting the most suitable content format based on the user's learning style.

[0082] The feedback collection unit can collect user feedback in real time and immediately reflect it in the AI. For example, if a user is dissatisfied with the translation of content, that feedback is immediately sent to the AI, and the translation algorithm is adjusted. Also, if a user requests new content, that request can be immediately reflected in the system and reflected in the next content provision. Furthermore, if a user reports difficulties they experienced while studying, that information can be immediately sent to the AI, and the learning support function can be enhanced. In this way, the feedback collection unit can collect user feedback in real time and immediately reflect it in the AI, thereby improving the flexibility and responsiveness of the system.

[0083] The selection unit can estimate the user's emotions and adjust the learning pace based on the estimated user's emotions. For example, if the user is tired, the selection unit can slow down the learning pace and provide relaxing content. Alternatively, if the user is concentrating, the selection unit can speed up the learning pace and provide challenging content. Furthermore, if the user is feeling stressed, the selection unit can adjust the learning pace and provide content to reduce stress. In this way, the selection unit can optimize the user's learning experience by adjusting the learning pace based on the user's emotions.

[0084] The selection unit can analyze the user's past selection history and suggest content on related topics. For example, based on the genre of anime the user has watched in the past, it can suggest new anime of the same genre. It can also suggest manga on related themes based on the themes of manga the user has read in the past. Furthermore, based on the artists of music the user has listened to in the past, it can suggest new songs by the same artists or music by related artists. In this way, the selection unit can suggest content on related topics by analyzing the user's past selection history and continue to attract the user's interest.

[0085] The selection unit can set learning goals based on the user's current interests. For example, based on the anime genre in which the user is currently interested, the selection unit can set learning goals related to that genre. Also, based on the theme of a manga in which the user is currently interested, the selection unit can set learning goals related to that theme. Furthermore, based on the genre of music in which the user is currently interested, the selection unit can set learning goals related to that genre. In this way, the selection unit can increase learning motivation by setting learning goals based on the user's current interests.

[0086] The selection unit can estimate the user's emotion and adjust the learning environment based on the estimated user's emotion. For example, if the user is relaxed, a quiet learning environment can be provided. If the user is excited, a lively learning environment can be provided. Furthermore, if the user is sad, a comforting learning environment can be provided. In this way, the selection unit can improve the user's learning experience by adjusting the learning environment based on the user's emotion.

[0087] The selection unit can suggest content related to local cultures and events by taking into account the user's geographical location information. For example, if the user is in Brazil, anime and manga related to Brazilian festivals and events can be suggested. If the user is in Portugal, content related to Portuguese history and culture can be suggested. Furthermore, if the user is in Japan, music and videos related to Japanese seasons and events can be suggested. In this way, the selection unit can provide content related to local cultures and events by taking into account the user's geographical location information, thereby continuing to attract the user's interest.

[0088] The selection unit can analyze the user's social media activity and suggest content that the user's friends and followers are interested in. For example, it can suggest anime that the user's friends have shared. It can also suggest manga that the user's followers have commented on. It can also suggest music that the user's friends have retweeted. In this way, the selection unit can analyze the user's social media activity to suggest content that the user's friends and followers are interested in, thereby keeping the user interested.

[0089] The translation unit can estimate the user's emotions and adjust the tone of the translation based on that estimation. For example, if the user is relaxed, the translation will be done in a calm tone. If the user is excited, the translation can be done in an energetic tone. Furthermore, if the user is sad, the translation can be done in a comforting tone. In this way, the translation unit can provide more appropriate translations by adjusting the tone of the translation based on the user's emotions.

[0090] During translation, the translation unit can adjust the translation style based on the importance of the content. For example, important scenes can be translated in a formal style, while unimportant scenes can be translated in a casual style. Furthermore, important lyrics can be translated in a poetic style. This allows the translation unit to provide more appropriate translations by adjusting the translation style based on the importance of the content.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The selection unit selects the content that the user wants to learn. The content that the user wants to learn may include, for example, an anime episode, a manga chapter, music lyrics, etc. The selection unit allows the user to access the online platform and select the content that they want to learn. Step 2: The translation unit translates the content selected by the selection unit into Portuguese. The translation unit translates the selected content into Portuguese, for example, using AI. The AI ​​understands the selected content and translates it into appropriate Portuguese. For example, it translates an anime episode into Portuguese and displays it as subtitles. It also translates a manga chapter into Portuguese and displays it as text. Song lyrics are also translated into Portuguese in a similar manner. Some or all of the above processing in the translation unit may be performed using, for example, a generative AI, or without a generative AI. Step 3: The provider unit provides the user with the translated content. The provider unit provides the user with the translated content, for example. The user can learn from the content displayed in Portuguese. For example, they can watch an anime episode with Portuguese subtitles. They can also read a manga chapter in Portuguese. Since the lyrics of a song are also displayed in Portuguese, the user can enjoy the music while understanding the lyrics. Some or all of the above processing in the provider unit may be performed using, for example, a generative AI, or without a generative AI.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a selection unit for selecting content that the user wants to learn; a translation unit that translates the content selected by the selection unit into Portuguese; a providing unit that provides the content translated by the translation unit to a user; Equipped with A system characterized by:

2. Equipped with a feedback collection unit that collects user feedback and reflects it in AI 2. The system of claim 1.

3. The selection unit Estimate the user's emotions and suggest learning content based on the estimated user emotions 2. The system of claim 1.

4. The selection unit Analyzes the user's past selection history and automatically selects the most suitable content 2. The system of claim 1.

5. The selection unit Filter content selection based on the user's current interests 2. The system of claim 1.

6. The selection unit Estimate the user's emotions and determine the priority of the content to be selected based on the estimated user emotions.

2. The system of claim 1.

7. The selection unit When selecting content, the app takes into account the user's geographic location information and prioritizes the display of highly relevant content.

2. The system of claim 1.

8. The selection unit When selecting content, analyze users' social media activity and suggest relevant content 2. The system of claim 1.

9. The translation unit Estimate the user's emotions and adjust the translation style based on the estimated user emotions.

2. The system of claim 1.

Images