System

Abstract

An object of a system according to an embodiment is to provide an opportunity to touch a foreign language on a daily basis and to support learning of the foreign language.SOLUTION: A system includes a mail conversion unit, a reply conversion unit, a voice utterance unit, and a voice recognition unit. The mail conversion unit converts the Japanese mail received by the user into a foreign language and displays the foreign language. When the user creates a reply in a foreign language, the reply conversion unit converts the reply into Japanese and transmits the reply to the other party. When the user speaks in Japanese, the voice utterance unit converts the content into a foreign language and utters the foreign language. When the user responds in a foreign language, the speech recognition unit converts the response into Japanese and displays it.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that there are few opportunities to come into contact with foreign languages ​​on a daily basis, making it difficult to learn foreign languages.

[0005] The system according to the embodiment aims to provide opportunities for daily exposure to foreign languages ​​and to support the acquisition of foreign languages. [Means for solving the problem]

[0006] The system according to the embodiment includes a mail conversion unit, a reply conversion unit, a voice utterance unit, and a voice recognition unit. The mail conversion unit converts Japanese emails received by a user into a foreign language and displays the foreign language. When a user creates a reply in a foreign language, the reply conversion unit converts the reply into Japanese and sends it to the recipient. When a user speaks in Japanese, the voice utterance unit converts the content of the message into the foreign language and speaks it. When a user responds in a foreign language, the voice recognition unit converts the response into Japanese and displays it. [Effects of the Invention]

[0007] The system according to the embodiment can provide opportunities for daily exposure to a foreign language and support the acquisition of the foreign language. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A foreign language learning platform according to an embodiment of the present invention is a system that utilizes generative AI to enable users to learn a foreign language on a daily basis, thereby providing users with opportunities to use the foreign language on a daily basis and promoting foreign language acquisition.

[0029] A foreign language learning platform according to an embodiment includes an email conversion unit, a reply conversion unit, a voice output unit, and a voice recognition unit. The email conversion unit converts Japanese emails received by a user into a foreign language and displays the foreign language. For example, the email conversion unit converts Japanese emails into English using a generation AI. The email conversion unit can also convert Japanese emails into Chinese using a generation AI. The email conversion unit can also convert Japanese emails into French using a generation AI. When a user creates a reply in a foreign language, the reply conversion unit converts the reply into Japanese and sends it to the recipient. For example, the reply conversion unit converts English replies into Japanese using a generation AI. The reply conversion unit can also convert Chinese replies into Japanese using a generation AI. The reply conversion unit can also convert French replies into Japanese using a generation AI. When a user speaks to the platform in Japanese, the voice output unit converts the content into a foreign language and outputs it. For example, the voice output unit converts Japanese speech into English using a generation AI and outputs it. The voice output unit can also convert Japanese speech into Chinese using a generation AI and output it. The voice utterance unit can also use the generation AI to convert Japanese speech into French and speak it. When the user responds in a foreign language, the voice recognition unit converts the response into Japanese and displays it. For example, the voice recognition unit can use the generation AI to convert an English response into Japanese and display it. The voice recognition unit can also use the generation AI to convert a Chinese response into Japanese and display it. The voice recognition unit can also use the generation AI to convert a French response into Japanese and display it. In this way, the foreign language learning platform according to the embodiment can provide users with opportunities to use foreign languages ​​on a daily basis and promote their acquisition of foreign languages.

[0030] The email conversion unit can perform translations that take into account appropriate cultural background and business etiquette based on the content of the email. For example, the email conversion unit analyzes the content of the email, and the generation AI performs translations that take into account the cultural background and business etiquette appropriate for that content. For example, in business emails, honorific language and polite expressions are used appropriately. The generation AI also understands the content of the email and provides a translation that is based on specific cultures and business etiquette. For example, it uses expressions based on American business etiquette. The generation AI also performs translations that take into account appropriate cultural backgrounds and business etiquette depending on the content of the email. For example, it uses expressions based on Japanese business etiquette. This ensures that the email translation is appropriate for the cultural background and business etiquette.

[0031] The reply conversion unit can make suggestions for improving grammar and expression in the content of the user's reply. In the reply conversion unit, for example, the generation AI analyzes the content of the user's reply and makes suggestions for improving grammar and expression. For example, it points out grammatical errors and suggests correct expressions. In addition, the generation AI suggests appropriate expressions and phrases for email replies, supporting the improvement of the user's foreign language skills. For example, it suggests more natural expressions. In addition, the generation AI analyzes the content of the user's reply and makes suggestions for improving grammar and expression. For example, it suggests more appropriate words and phrases. This can support the improvement of the user's foreign language skills.

[0032] The email conversion unit and reply conversion unit can also be extended to chat apps or social networking sites, enabling the use of foreign languages ​​in everyday communication in general. For example, the email conversion unit and reply conversion unit integrate the generative AI's translation function into a chat app, enabling the use of foreign languages ​​in everyday communication. For example, they translate messages on LINE or WhatsApp. They also add the generative AI's translation function to social networking platforms, allowing users to post and comment in foreign languages. For example, they translate posts on Facebook or Twitter. They also translate messages on chat apps and social networking sites in real time, providing an environment in which users can use foreign languages ​​on a daily basis. For example, they translate direct messages on Instagram. This allows the use of foreign languages ​​in everyday communication in general.

[0033] The email conversion unit and reply conversion unit can add a repository function that saves translation results and allows users to study later. The email conversion unit and reply conversion unit provide a repository function that saves the results of emails translated by the generation AI and allows users to study later. For example, it allows users to refer to the translation history. It also adds a function that saves translation results and allows users to review them later. For example, it allows users to search for specific phrases or expressions. It also saves email translation results in a repository so that users can use them for study. For example, it organizes translation results by category. This allows translation results to be saved and used for study later.

[0034] The speech output unit and speech recognition unit can analyze the user's pronunciation and intonation and provide individual pronunciation instruction. For example, the generation AI in the speech output unit and speech recognition unit analyzes the user's pronunciation and intonation and provides individual pronunciation instruction. For example, it provides advice to improve the pronunciation of specific phonemes. Furthermore, to improve the accuracy of speech recognition, the generation AI analyzes the user's pronunciation and intonation and provides specific pronunciation instruction. For example, it makes suggestions to correct the position of accents. Furthermore, the generation AI analyzes the user's pronunciation and intonation and provides individual pronunciation instruction. For example, it provides feedback to practice the pronunciation of specific words and phrases. This allows the user's pronunciation and intonation to improve.

[0035] The speech output unit and speech recognition unit can automatically generate conversation scenarios, allowing the user to practice conversations in multiple situations. For example, the generation AI of the speech output unit and speech recognition unit can automatically generate conversation scenarios for various situations, allowing the user to practice. For example, scenarios for business meetings and everyday conversations can be provided. The generation AI can also automatically generate conversation scenarios, providing an environment in which the user can practice conversations in different situations. For example, a conversation scenario for a travel destination can be provided. The generation AI can also automatically generate conversation scenarios, allowing the user to practice conversations in various situations. For example, scenarios for ordering at a restaurant and getting directions can be provided. This allows the user to practice conversations in various situations.

[0036] The voice output unit and voice recognition unit can be integrated into a video conferencing system or telephone system to promote use in actual work. For example, the voice output unit and voice recognition unit can integrate the voice output and voice recognition functions of the generative AI into a video conferencing system to promote use in actual work. For example, for use in meetings on Zoom or Microsoft Teams. The voice output and voice recognition functions of the generative AI can also be integrated into a telephone system to enable users to use foreign languages ​​in actual work. For example, to support business telephone conversations. The voice output and voice recognition functions of the generative AI can also be integrated into a video conferencing system or telephone system to provide an environment in which users can use foreign languages ​​in actual work. For example, to provide interpretation at international conferences. This provides an environment in which foreign languages ​​can be used in actual work.

[0037] Generative AI can automatically learn the technical and industry terminology used in everyday work and provide appropriate translations. Generative AI can automatically learn the technical and industry terminology used in everyday work and provide appropriate translations. For example, it can accurately translate technical and business terms. Generative AI can also automatically learn technical and industry terminology and provide appropriate translations when users use it in their everyday work. For example, it can accurately translate medical and legal terms. Generative AI can also automatically learn the technical and industry terminology used in everyday work and provide appropriate translations. For example, it can accurately translate financial and marketing terms. This provides appropriate translations of technical and industry terminology.

[0038] The generative AI can make suggestions for improving grammar and expressions in the translation results of documents and reports created by users. For example, the generative AI analyzes the translation results of documents and reports created by users and makes suggestions for improving grammar and expressions. For example, it may suggest more appropriate words and phrases. The generative AI can also suggest appropriate expressions and phrases in the translation results of documents and reports created by users, supporting the improvement of users' skills. For example, it may point out grammatical errors and suggest correct expressions. The generative AI can also analyze the translation results of documents and reports created by users and make suggestions for improving grammar and expressions. For example, it may suggest more natural expressions. This improves the translation results of users' documents and reports.

[0039] The generating AI can automatically record the frequency of foreign language use in daily work and provide feedback to the user. For example, the generating AI can automatically record the frequency of foreign language use in the user's daily work and provide feedback based on that data. For example, it can display a graph of usage frequency. The generating AI can also automatically record the frequency of foreign language use and provide regular feedback to the user. For example, it can give advice based on usage frequency. The generating AI can also record the frequency of foreign language use in daily work and provide feedback to the user based on that data. For example, it can suggest a learning plan based on usage frequency. This allows the generating AI to record the frequency of foreign language use in daily work and provide feedback to the user.

[0040] Generative AI can automatically generate templates and standard phrases to be used in business, allowing users to use foreign languages ​​efficiently. Generative AI can, for example, automatically generate templates and standard phrases to be used in business, allowing users to use foreign languages ​​efficiently. For example, it can provide templates for business emails. Generative AI can also automatically generate standard phrases to be used in business, providing an environment in which users can use foreign languages ​​efficiently. For example, it can provide standard phrases for reports. Generative AI can also automatically generate templates and standard phrases to be used in business, allowing users to use foreign languages ​​efficiently. For example, it can provide templates for meeting minutes. This allows templates and standard phrases to be used in business to be automatically generated, allowing users to use foreign languages ​​efficiently.

[0041] Generative AI can automatically generate an individual study plan based on the user's study history, supporting efficient study. For example, generative AI analyzes the user's study history and automatically generates an individual study plan. For example, it suggests the next study step based on past study content. Generative AI can also automatically generate an individual study plan based on the user's study history, supporting efficient study. For example, it can provide a study plan that focuses on weak areas. Generative AI can also analyze the user's study history and automatically generate an individual study plan. For example, it can suggest appropriate learning materials according to the user's study progress. This makes it possible to provide an efficient study plan based on the user's study history.

[0042] The generative AI can provide learning materials and practice problems at an appropriate level according to the progress of learning. For example, the generative AI analyzes the user's learning progress and provides learning materials and practice problems at an appropriate level. For example, it suggests learning materials according to beginner, intermediate, and advanced levels. The generative AI also provides learning materials and practice problems at an appropriate level according to the progress of learning. For example, it provides practice problems according to the user's level of understanding. The generative AI also analyzes the user's learning progress and provides learning materials and practice problems at an appropriate level. For example, it suggests learning materials that focus on specific skills. This makes it possible to provide learning materials and practice problems that are appropriate according to the progress of learning.

[0043] The generation AI can automatically generate graphs and charts to visualize learning progress and provide them to the user. For example, the generation AI analyzes the user's learning progress and automatically generates and provides graphs and charts. For example, it visually displays study time and achievement level. In addition, the generation AI automatically generates graphs and charts to visualize learning progress and provides them to the user. For example, it displays learning progress on a time axis. In addition, the generation AI analyzes the user's learning progress and automatically generates and provides graphs and charts. For example, it visually displays the acquisition status of a specific skill. In this way, learning progress can be visualized and provided to the user.

[0044] The generation AI can provide appropriate learning advice and supplementary materials to users according to their learning progress. For example, the generation AI analyzes the user's learning progress and provides appropriate learning advice and supplementary materials. For example, it provides advice to strengthen specific skills. The generation AI also provides appropriate learning advice and supplementary materials to users according to their learning progress. For example, it provides supplementary materials according to their level of understanding. The generation AI also analyzes the user's learning progress and provides appropriate learning advice and supplementary materials. For example, it suggests a learning plan based on their learning progress. This makes it possible to provide appropriate learning advice and supplementary materials according to their learning progress.

[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 foreign language learning platform may include a progress display unit for visualizing the user's learning progress. The progress display unit, for example, displays the user's learning content and achievement level in graphs or charts. For example, it visually displays the study time and the number of vocabulary words acquired. The progress display unit also displays the user's learning progress on a timeline, allowing the user to check the results of their learning at a glance. For example, it displays the study time and achievement level per week. The progress display unit also visually displays the acquisition status of specific skills, allowing the user to understand in which areas they are progressing. For example, it displays progress in listening skills and speaking skills. This allows the user to visualize their learning progress and maintain their motivation.

[0047] The foreign language learning platform may include a learning plan generation unit that automatically generates an individual learning plan based on the user's learning history. The learning plan generation unit, for example, analyzes the user's past learning content and suggests the next learning step. For example, it provides a learning plan that focuses on weak areas. The learning plan generation unit also provides appropriate learning materials and exercises according to the user's learning progress. For example, it suggests learning materials according to beginner, intermediate, and advanced levels. The learning plan generation unit also provides an appropriate learning plan according to the user's learning progress based on the user's learning history. For example, it suggests learning materials that focus on specific skills. This allows the user to progress with their studies efficiently.

[0048] The foreign language learning platform can be equipped with a document improvement unit that suggests improvements to grammar and expressions for the translation results of documents and reports created by users. For example, the document improvement unit analyzes the translation results of documents and reports created by users using a generative AI, points out grammatical errors, and suggests correct expressions. For example, it suggests more appropriate words and phrases. The document improvement unit also suggests appropriate expressions and phrases for the translation results of documents and reports created by users, supporting the improvement of users' skills. For example, it suggests more natural expressions. The document improvement unit also analyzes the translation results of documents and reports created by users, and suggests improvements to grammar and expressions. For example, it points out grammatical errors and suggests correct expressions. This improves the translation results of users' documents and reports.

[0049] Foreign language learning platforms can also be extended to chat apps and social media to increase users' opportunities to use foreign languages ​​on a daily basis. For example, generative AI translation functions can be integrated into LINE and WhatsApp, allowing users to use foreign languages ​​in everyday communication. Generative AI translation functions can also be added to social media platforms, allowing users to post and comment in foreign languages. For example, posts on Facebook and Twitter can be translated. Messages on chat apps and social media can also be translated in real time, providing an environment where users can use foreign languages ​​on a daily basis. For example, direct messages on Instagram can be translated. This allows foreign languages ​​to be used in general everyday communication.

[0050] The foreign language learning platform may include a pronunciation coaching unit that analyzes a user's pronunciation and intonation and provides individualized pronunciation guidance. The pronunciation coaching unit, for example, uses a generation AI to analyze the user's pronunciation and intonation and provide advice to improve the pronunciation of specific phonemes. For example, it may suggest correcting the position of an accent. The pronunciation coaching unit may also analyze the user's pronunciation and intonation and provide specific pronunciation guidance to improve the accuracy of speech recognition. For example, it may provide feedback to practice the pronunciation of specific words or phrases. The pronunciation coaching unit may also analyze the user's pronunciation and intonation and provide individualized pronunciation guidance. For example, it may provide advice to improve the pronunciation of specific phonemes. This allows the user's pronunciation and intonation to be improved.

[0051] The foreign language learning platform may include a scenario generation unit that automatically generates conversation scenarios so that users can practice conversations in various situations. The scenario generation unit may, for example, have a generation AI automatically generate scenarios for business meetings and everyday conversations to allow users to practice. For example, it may provide a conversation scenario for a travel destination. The scenario generation unit may also have a generation AI automatically generate conversation scenarios according to various situations to provide an environment in which users can practice conversations in different situations. For example, it may provide scenarios for ordering at a restaurant or giving directions. The scenario generation unit may also have a generation AI automatically generate conversation scenarios to allow users to practice conversations in various situations. For example, it may provide scenarios for business meetings and everyday conversations to allow users to practice conversations in various situations.

[0052] The foreign language learning platform may include a terminology learning unit that automatically learns technical terms and industry terms used by users in their daily work and provides appropriate translations. The terminology learning unit may, for example, have a generation AI that accurately translates technical terms and business terms. The terminology learning unit may also have a generation AI that accurately translates medical terms and legal terms. For example, it may accurately translate medical terms and legal terms. The terminology learning unit may also have a generation AI that accurately translates financial terms and marketing terms. This allows users to be provided with appropriate translations of technical terms and industry terms used in their daily work.

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

[0054] Step 1: The email conversion unit converts the Japanese email received by the user into a foreign language and displays it. For example, using a generation AI, the Japanese email is converted into English, Chinese, French, etc. Step 2: When a user writes a reply in a foreign language, the reply conversion unit converts the reply into Japanese and sends it to the recipient. For example, a generation AI is used to convert replies in English, Chinese, French, etc. into Japanese. Step 3: When the user speaks in Japanese, the voice output unit converts the content into a foreign language and outputs it. For example, using a generation AI, the Japanese speech can be converted into English, Chinese, French, etc. Step 4: When the user responds in a foreign language, the speech recognition unit converts the response into Japanese and displays it. For example, using a generation AI, responses in English, Chinese, French, etc. are converted into Japanese and displayed.

[0055] (Example 2) A foreign language learning platform according to an embodiment of the present invention is a system that utilizes generative AI to enable users to learn a foreign language on a daily basis, thereby providing users with opportunities to use the foreign language on a daily basis and promoting foreign language acquisition.

[0056] A foreign language learning platform according to an embodiment includes an email conversion unit, a reply conversion unit, a voice output unit, and a voice recognition unit. The email conversion unit converts Japanese emails received by a user into a foreign language and displays the foreign language. For example, the email conversion unit converts Japanese emails into English using a generation AI. The email conversion unit can also convert Japanese emails into Chinese using a generation AI. The email conversion unit can also convert Japanese emails into French using a generation AI. When a user creates a reply in a foreign language, the reply conversion unit converts the reply into Japanese and sends it to the recipient. For example, the reply conversion unit converts English replies into Japanese using a generation AI. The reply conversion unit can also convert Chinese replies into Japanese using a generation AI. The reply conversion unit can also convert French replies into Japanese using a generation AI. When a user speaks to the platform in Japanese, the voice output unit converts the content into a foreign language and outputs it. For example, the voice output unit converts Japanese speech into English using a generation AI and outputs it. The voice output unit can also convert Japanese speech into Chinese using a generation AI and output it. The voice utterance unit can also use the generation AI to convert Japanese speech into French and speak it. When the user responds in a foreign language, the voice recognition unit converts the response into Japanese and displays it. For example, the voice recognition unit can use the generation AI to convert an English response into Japanese and display it. The voice recognition unit can also use the generation AI to convert a Chinese response into Japanese and display it. The voice recognition unit can also use the generation AI to convert a French response into Japanese and display it. In this way, the foreign language learning platform according to the embodiment can provide users with opportunities to use foreign languages ​​on a daily basis and promote their acquisition of foreign languages.

[0057] The email conversion unit can perform translations that take into account appropriate cultural background and business etiquette based on the content of the email. For example, the email conversion unit analyzes the content of the email, and the generation AI performs translations that take into account the cultural background and business etiquette appropriate for that content. For example, in business emails, honorific language and polite expressions are used appropriately. The generation AI also understands the content of the email and provides a translation that is based on specific cultures and business etiquette. For example, it uses expressions based on American business etiquette. The generation AI also performs translations that take into account appropriate cultural backgrounds and business etiquette depending on the content of the email. For example, it uses expressions based on Japanese business etiquette. This ensures that the email translation is appropriate for the cultural background and business etiquette.

[0058] The reply conversion unit can make suggestions for improving grammar and expression in the content of the user's reply. In the reply conversion unit, for example, the generation AI analyzes the content of the user's reply and makes suggestions for improving grammar and expression. For example, it points out grammatical errors and suggests correct expressions. In addition, the generation AI suggests appropriate expressions and phrases for email replies, supporting the improvement of the user's foreign language skills. For example, it suggests more natural expressions. In addition, the generation AI analyzes the content of the user's reply and makes suggestions for improving grammar and expression. For example, it suggests more appropriate words and phrases. This can support the improvement of the user's foreign language skills.

[0059] The email conversion unit can use an emotion estimation function to analyze the emotional tone of an email and provide a translation that includes appropriate emotional expressions. In the email conversion unit, for example, the generation AI analyzes the emotional tone of an email and provides a translation that includes appropriate emotional expressions. For example, a translation that expresses positive emotions is provided. The content of the email is also analyzed, and the generation AI estimates the emotional tone and provides a translation that includes appropriate emotional expressions. For example, a translation that expresses gratitude is provided. The generation AI also uses the emotion estimation function to analyze the emotional tone of an email and provides a translation that includes appropriate emotional expressions. For example, polite expressions are used. This provides an appropriate translation that matches the emotional tone of the email.

[0060] The email conversion unit and reply conversion unit can also be extended to chat apps or social networking sites, enabling the use of foreign languages ​​in everyday communication in general. For example, the email conversion unit and reply conversion unit integrate the generative AI's translation function into a chat app, enabling the use of foreign languages ​​in everyday communication. For example, they translate messages on LINE or WhatsApp. They also add the generative AI's translation function to social networking platforms, allowing users to post and comment in foreign languages. For example, they translate posts on Facebook or Twitter. They also translate messages on chat apps and social networking sites in real time, providing an environment in which users can use foreign languages ​​on a daily basis. For example, they translate direct messages on Instagram. This allows the use of foreign languages ​​in everyday communication in general.

[0061] The email conversion unit and reply conversion unit can add a repository function that saves translation results and allows users to study later. The email conversion unit and reply conversion unit provide a repository function that saves the results of emails translated by the generation AI and allows users to study later. For example, it allows users to refer to the translation history. It also adds a function that saves translation results and allows users to review them later. For example, it allows users to search for specific phrases or expressions. It also saves email translation results in a repository so that users can use them for study. For example, it organizes translation results by category. This allows translation results to be saved and used for study later.

[0062] The speech output unit and speech recognition unit can analyze the user's pronunciation and intonation and provide individual pronunciation instruction. For example, the generation AI in the speech output unit and speech recognition unit analyzes the user's pronunciation and intonation and provides individual pronunciation instruction. For example, it provides advice to improve the pronunciation of specific phonemes. Furthermore, to improve the accuracy of speech recognition, the generation AI analyzes the user's pronunciation and intonation and provides specific pronunciation instruction. For example, it makes suggestions to correct the position of accents. Furthermore, the generation AI analyzes the user's pronunciation and intonation and provides individual pronunciation instruction. For example, it provides feedback to practice the pronunciation of specific words and phrases. This allows the user's pronunciation and intonation to improve.

[0063] The speech output unit and speech recognition unit can automatically generate conversation scenarios, allowing the user to practice conversations in multiple situations. For example, the generation AI of the speech output unit and speech recognition unit can automatically generate conversation scenarios for various situations, allowing the user to practice. For example, scenarios for business meetings and everyday conversations can be provided. The generation AI can also automatically generate conversation scenarios, providing an environment in which the user can practice conversations in different situations. For example, a conversation scenario for a travel destination can be provided. The generation AI can also automatically generate conversation scenarios, allowing the user to practice conversations in various situations. For example, scenarios for ordering at a restaurant and getting directions can be provided. This allows the user to practice conversations in various situations.

[0064] The voice output unit and the voice recognition unit can use the emotion estimation function to analyze the emotion of the user when speaking and provide feedback including appropriate emotional expressions. For example, the generation AI of the voice output unit and the voice recognition unit analyzes the emotion of the user when speaking and provides feedback including appropriate emotional expressions. For example, advice is given on how to express positive emotions. Furthermore, using the emotion estimation function, the generation AI analyzes the emotion of the user when speaking and provides feedback including appropriate emotional expressions. For example, phrases are suggested for expressing gratitude. Furthermore, the generation AI analyzes the emotion of the user when speaking and provides feedback including appropriate emotional expressions. For example, advice is given on how to use polite expressions. In this way, appropriate feedback is provided according to the emotion of the user when speaking.

[0065] The voice output unit and voice recognition unit can be integrated into a video conferencing system or telephone system to promote use in actual work. For example, the voice output unit and voice recognition unit can integrate the voice output and voice recognition functions of the generative AI into a video conferencing system to promote use in actual work. For example, for use in meetings on Zoom or Microsoft Teams. The voice output and voice recognition functions of the generative AI can also be integrated into a telephone system to enable users to use foreign languages ​​in actual work. For example, to support business telephone conversations. The voice output and voice recognition functions of the generative AI can also be integrated into a video conferencing system or telephone system to provide an environment in which users can use foreign languages ​​in actual work. For example, to provide interpretation at international conferences. This provides an environment in which foreign languages ​​can be used in actual work.

[0066] The voice output unit and voice recognition unit can use the emotion estimation function to analyze the stress and anxiety a user feels during a conversation in real time and make suggestions to help them relax. For example, the generation AI in the voice output unit and voice recognition unit can analyze the user's emotions in real time and make suggestions to help them relax if they feel stressed or anxious. For example, a message encouraging them to take a deep breath can be displayed. The emotion estimation function can also be used to analyze the stress and anxiety a user feels during a conversation and make suggestions to help them relax. For example, relaxing music can be played. The generation AI can also analyze the user's emotions in real time and make suggestions to help them relax if they feel stressed or anxious. For example, positive affirmations can be provided. This can reduce the stress and anxiety a user feels during a conversation.

[0067] Generative AI can automatically learn the technical and industry terminology used in everyday work and provide appropriate translations. Generative AI can automatically learn the technical and industry terminology used in everyday work and provide appropriate translations. For example, it can accurately translate technical and business terms. Generative AI can also automatically learn technical and industry terminology and provide appropriate translations when users use it in their everyday work. For example, it can accurately translate medical and legal terms. Generative AI can also automatically learn the technical and industry terminology used in everyday work and provide appropriate translations. For example, it can accurately translate financial and marketing terms. This provides appropriate translations of technical and industry terminology.

[0068] The generative AI can make suggestions for improving grammar and expressions in the translation results of documents and reports created by users. For example, the generative AI analyzes the translation results of documents and reports created by users and makes suggestions for improving grammar and expressions. For example, it may suggest more appropriate words and phrases. The generative AI can also suggest appropriate expressions and phrases in the translation results of documents and reports created by users, supporting the improvement of users' skills. For example, it may point out grammatical errors and suggest correct expressions. The generative AI can also analyze the translation results of documents and reports created by users and make suggestions for improving grammar and expressions. For example, it may suggest more natural expressions. This improves the translation results of users' documents and reports.

[0069] The generation AI can use the emotion estimation function to analyze the emotional tone of communication during work and provide a translation that includes appropriate emotional expressions. For example, the generation AI analyzes the emotional tone of communication during work and provides a translation that includes appropriate emotional expressions. For example, it provides a translation that expresses positive emotions. Furthermore, using the emotion estimation function, the generation AI analyzes the emotional tone of communication during work and provides a translation that includes appropriate emotional expressions. For example, it provides a translation that expresses gratitude. Furthermore, the generation AI analyzes the emotional tone of communication during work and provides a translation that includes appropriate emotional expressions. For example, it provides a translation that uses polite expressions. This provides a translation that includes appropriate emotional expressions for communication during work.

[0070] The generating AI can automatically record the frequency of foreign language use in daily work and provide feedback to the user. For example, the generating AI can automatically record the frequency of foreign language use in the user's daily work and provide feedback based on that data. For example, it can display a graph of usage frequency. The generating AI can also automatically record the frequency of foreign language use and provide regular feedback to the user. For example, it can give advice based on usage frequency. The generating AI can also record the frequency of foreign language use in daily work and provide feedback to the user based on that data. For example, it can suggest a learning plan based on usage frequency. This allows the generating AI to record the frequency of foreign language use in daily work and provide feedback to the user.

[0071] Generative AI can automatically generate templates and standard phrases to be used in business, allowing users to use foreign languages ​​efficiently. Generative AI can, for example, automatically generate templates and standard phrases to be used in business, allowing users to use foreign languages ​​efficiently. For example, it can provide templates for business emails. Generative AI can also automatically generate standard phrases to be used in business, providing an environment in which users can use foreign languages ​​efficiently. For example, it can provide standard phrases for reports. Generative AI can also automatically generate templates and standard phrases to be used in business, allowing users to use foreign languages ​​efficiently. For example, it can provide templates for meeting minutes. This allows templates and standard phrases to be used in business to be automatically generated, allowing users to use foreign languages ​​efficiently.

[0072] The generation AI can use its emotion estimation function to analyze the stress and anxiety a user feels while working in real time and make suggestions for relaxation. For example, the generation AI can analyze a user's emotions in real time and make suggestions for relaxation if the user feels stressed or anxious. For example, it can display a message encouraging deep breathing. The generation AI can also use its emotion estimation function to analyze the stress and anxiety a user feels while working and make suggestions for relaxation. For example, it can play relaxing music. The generation AI can also analyze a user's emotions in real time and make suggestions for relaxation if the user feels stressed or anxious. For example, it can provide positive affirmations. This can reduce the stress and anxiety the user feels while working.

[0073] Generative AI can automatically generate an individual study plan based on the user's study history, supporting efficient study. For example, generative AI analyzes the user's study history and automatically generates an individual study plan. For example, it suggests the next study step based on past study content. Generative AI can also automatically generate an individual study plan based on the user's study history, supporting efficient study. For example, it can provide a study plan that focuses on weak areas. Generative AI can also analyze the user's study history and automatically generate an individual study plan. For example, it can suggest appropriate learning materials according to the user's study progress. This makes it possible to provide an efficient study plan based on the user's study history.

[0074] The generative AI can provide learning materials and practice problems at an appropriate level according to the progress of learning. For example, the generative AI analyzes the user's learning progress and provides learning materials and practice problems at an appropriate level. For example, it suggests learning materials according to beginner, intermediate, and advanced levels. The generative AI also provides learning materials and practice problems at an appropriate level according to the progress of learning. For example, it provides practice problems according to the user's level of understanding. The generative AI also analyzes the user's learning progress and provides learning materials and practice problems at an appropriate level. For example, it suggests learning materials that focus on specific skills. This makes it possible to provide learning materials and practice problems that are appropriate according to the progress of learning.

[0075] The generative AI can use the emotion estimation function to analyze the user's emotions while studying and provide feedback to maintain motivation. For example, the generative AI can analyze the user's emotions while studying and provide feedback to maintain motivation. For example, it can display an encouraging message. The generative AI can also use the emotion estimation function to analyze the user's emotions while studying and provide feedback to maintain motivation. For example, it can provide advice to elicit positive emotions. The generative AI can also analyze the user's emotions while studying and provide feedback to maintain motivation. For example, it can visualize the user's learning progress and give them a sense of accomplishment. This makes it possible to provide feedback to maintain the user's motivation while studying.

[0076] The generation AI can automatically generate graphs and charts to visualize learning progress and provide them to the user. For example, the generation AI analyzes the user's learning progress and automatically generates and provides graphs and charts. For example, it visually displays study time and achievement level. In addition, the generation AI automatically generates graphs and charts to visualize learning progress and provides them to the user. For example, it displays learning progress on a time axis. In addition, the generation AI analyzes the user's learning progress and automatically generates and provides graphs and charts. For example, it visually displays the acquisition status of a specific skill. In this way, learning progress can be visualized and provided to the user.

[0077] The generation AI can provide appropriate learning advice and supplementary materials to users according to their learning progress. For example, the generation AI analyzes the user's learning progress and provides appropriate learning advice and supplementary materials. For example, it provides advice to strengthen specific skills. The generation AI also provides appropriate learning advice and supplementary materials to users according to their learning progress. For example, it provides supplementary materials according to their level of understanding. The generation AI also analyzes the user's learning progress and provides appropriate learning advice and supplementary materials. For example, it suggests a learning plan based on their learning progress. This makes it possible to provide appropriate learning advice and supplementary materials according to their learning progress.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The foreign language learning platform may include a progress display unit for visualizing the user's learning progress. The progress display unit, for example, displays the user's learning content and achievement level in graphs or charts. For example, it visually displays the study time and the number of vocabulary words acquired. The progress display unit also displays the user's learning progress on a timeline, allowing the user to check the results of their learning at a glance. For example, it displays the study time and achievement level per week. The progress display unit also visually displays the acquisition status of specific skills, allowing the user to understand in which areas they are progressing. For example, it displays progress in listening skills and speaking skills. This allows the user to visualize their learning progress and maintain their motivation.

[0080] The foreign language learning platform may include a learning plan generation unit that automatically generates an individual learning plan based on the user's learning history. The learning plan generation unit, for example, analyzes the user's past learning content and suggests the next learning step. For example, it provides a learning plan that focuses on weak areas. The learning plan generation unit also provides appropriate learning materials and exercises according to the user's learning progress. For example, it suggests learning materials according to beginner, intermediate, and advanced levels. The learning plan generation unit also provides an appropriate learning plan according to the user's learning progress based on the user's learning history. For example, it suggests learning materials that focus on specific skills. This allows the user to progress with their studies efficiently.

[0081] The foreign language learning platform can be equipped with a document improvement unit that suggests improvements to grammar and expressions for the translation results of documents and reports created by users. For example, the document improvement unit analyzes the translation results of documents and reports created by users using a generative AI, points out grammatical errors, and suggests correct expressions. For example, it suggests more appropriate words and phrases. The document improvement unit also suggests appropriate expressions and phrases for the translation results of documents and reports created by users, supporting the improvement of users' skills. For example, it suggests more natural expressions. The document improvement unit also analyzes the translation results of documents and reports created by users, and suggests improvements to grammar and expressions. For example, it points out grammatical errors and suggests correct expressions. This improves the translation results of users' documents and reports.

[0082] The foreign language learning platform can include a sentiment analysis unit that uses an emotion estimation function to analyze a user's emotions during learning and provides feedback to maintain motivation. The sentiment analysis unit, for example, uses a generative AI to analyze a user's emotions during learning and display encouraging messages. For example, it provides advice that elicits positive emotions. The sentiment analysis unit also analyzes a user's emotions during learning, visualizes learning progress, and gives the user a sense of accomplishment. For example, it displays learning progress in graphs or charts. The sentiment analysis unit also analyzes a user's emotions during learning and provides feedback to maintain motivation. For example, it provides advice according to learning progress. This makes it possible to maintain the user's motivation during learning.

[0083] Foreign language learning platforms can also be extended to chat apps and social media to increase users' opportunities to use foreign languages ​​on a daily basis. For example, generative AI translation functions can be integrated into LINE and WhatsApp, allowing users to use foreign languages ​​in everyday communication. Generative AI translation functions can also be added to social media platforms, allowing users to post and comment in foreign languages. For example, posts on Facebook and Twitter can be translated. Messages on chat apps and social media can also be translated in real time, providing an environment where users can use foreign languages ​​on a daily basis. For example, direct messages on Instagram can be translated. This allows foreign languages ​​to be used in general everyday communication.

[0084] The foreign language learning platform may include a pronunciation coaching unit that analyzes a user's pronunciation and intonation and provides individualized pronunciation guidance. The pronunciation coaching unit, for example, uses a generation AI to analyze the user's pronunciation and intonation and provide advice to improve the pronunciation of specific phonemes. For example, it may suggest correcting the position of an accent. The pronunciation coaching unit may also analyze the user's pronunciation and intonation and provide specific pronunciation guidance to improve the accuracy of speech recognition. For example, it may provide feedback to practice the pronunciation of specific words or phrases. The pronunciation coaching unit may also analyze the user's pronunciation and intonation and provide individualized pronunciation guidance. For example, it may provide advice to improve the pronunciation of specific phonemes. This allows the user's pronunciation and intonation to be improved.

[0085] The foreign language learning platform can be equipped with an emotion alleviation unit that uses an emotion estimation function to analyze the stress and anxiety a user feels during a conversation in real time and makes suggestions for relaxation. For example, the emotion alleviation unit uses a generation AI to analyze the user's emotions in real time and display a message encouraging the user to take a deep breath if the user feels stressed or anxious. The emotion alleviation unit also analyzes the stress and anxiety a user feels during a conversation and plays relaxing music. For example, it plays relaxing music. The emotion alleviation unit also uses a generation AI to analyze the user's emotions in real time and provides positive affirmations. For example, it provides positive affirmations. This reduces the stress and anxiety a user feels during a conversation.

[0086] The foreign language learning platform may include a scenario generation unit that automatically generates conversation scenarios so that users can practice conversations in various situations. The scenario generation unit may, for example, have a generation AI automatically generate scenarios for business meetings and everyday conversations to allow users to practice. For example, it may provide a conversation scenario for a travel destination. The scenario generation unit may also have a generation AI automatically generate conversation scenarios according to various situations to provide an environment in which users can practice conversations in different situations. For example, it may provide scenarios for ordering at a restaurant or giving directions. The scenario generation unit may also have a generation AI automatically generate conversation scenarios to allow users to practice conversations in various situations. For example, it may provide scenarios for business meetings and everyday conversations to allow users to practice conversations in various situations.

[0087] The foreign language learning platform can include an emotion feedback unit that uses an emotion estimation function to analyze the emotion a user expresses when speaking and provides feedback including appropriate emotional expressions. In the emotion feedback unit, for example, a generation AI analyzes the emotion a user expresses when speaking and provides advice on expressing positive emotions. For example, the emotion feedback unit suggests phrases for expressing gratitude. In addition, the emotion feedback unit analyzes the emotion a user expresses when speaking and provides advice on using polite expressions. For example, the emotion feedback unit suggests advice on using polite expressions. In addition, the emotion feedback unit analyzes the emotion a user expresses when speaking and provides feedback including appropriate emotional expressions. For example, the emotion feedback unit suggests advice on expressing positive emotions. In this way, appropriate feedback is provided according to the emotion a user expresses when speaking.

[0088] The foreign language learning platform may include a terminology learning unit that automatically learns technical terms and industry terms used by users in their daily work and provides appropriate translations. The terminology learning unit may, for example, have a generation AI that accurately translates technical terms and business terms. The terminology learning unit may also have a generation AI that accurately translates medical terms and legal terms. For example, it may accurately translate medical terms and legal terms. The terminology learning unit may also have a generation AI that accurately translates financial terms and marketing terms. This allows users to be provided with appropriate translations of technical terms and industry terms used in their daily work.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The email conversion unit converts the Japanese email received by the user into a foreign language and displays it. For example, using a generation AI, the Japanese email is converted into English, Chinese, French, etc. Step 2: When a user writes a reply in a foreign language, the reply conversion unit converts the reply into Japanese and sends it to the recipient. For example, a generation AI is used to convert replies in English, Chinese, French, etc. into Japanese. Step 3: When the user speaks in Japanese, the voice output unit converts the content into a foreign language and outputs it. For example, using a generation AI, the Japanese speech can be converted into English, Chinese, French, etc. Step 4: When the user responds in a foreign language, the speech recognition unit converts the response into Japanese and displays it. For example, using a generation AI, responses in English, Chinese, French, etc. are converted into Japanese and displayed.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] 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.

[0119] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the robot 414, 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 robot 414 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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. [Explanation of symbols]

[0158] 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 system equipped with a generative AI, an email conversion unit that converts Japanese emails received by a user into a foreign language and displays the converted emails; a reply conversion unit that converts a reply written in a foreign language by the user into Japanese and transmits the reply to the other party; a voice speaking unit that, when the user speaks in Japanese, converts the content into a foreign language and speaks it; and a voice recognition unit that converts a response in a foreign language from the user into Japanese and displays the response. A system characterized by:

2. The email conversion unit We translate emails based on their content, taking into account appropriate cultural background and business etiquette.

2. The system of claim 1.

3. The reply conversion unit Providing suggestions for improving grammar and expressions in user replies 2. The system of claim 1.

4. The email conversion unit Analyze the emotional tone of your email and provide a translation with appropriate emotional expressions 2. The system of claim 1.

5. The email conversion unit and the reply conversion unit It will also be expanded to chat apps and social media, allowing foreign languages ​​to be used in everyday communication.

2. The system of claim 1.

6. The email conversion unit and the reply conversion unit Add a repository feature to store translation results and allow users to study them later.

2. The system of claim 1.

7. The voice utterance unit and the voice recognition unit Analyzes the user's pronunciation and intonation and provides individual pronunciation guidance 2. The system of claim 1.

8. The voice utterance unit and the voice recognition unit Automatically generate conversation scenarios to allow users to practice conversations in multiple situations 2. The system of claim 1.

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