System

Abstract

An object of the system according to the embodiment is to efficiently learn English through daily conversation.SOLUTION: A system includes a translation unit, a pronunciation conversion unit, and a level adjustment unit. The translation unit translates the daily conversation. The pronunciation conversion unit converts the English translated by the translation unit into a native pronunciation. A level adjustment part converts the English converted by the pronunciation conversion part according to the level of the learner.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to know the optimal method for learning English, making it difficult to continue learning.

[0005] The system according to the embodiment aims to efficiently learn English through everyday conversation. [Means for solving the problem]

[0006] The system according to the embodiment includes a translation unit, a pronunciation conversion unit, and a level adjustment unit. The translation unit translates everyday conversations. The pronunciation conversion unit converts the English translated by the translation unit into native pronunciation. The level adjustment unit converts the English converted by the pronunciation conversion unit to match the level of the learner. [Effects of the Invention]

[0007] The system according to the embodiment allows users to efficiently learn English through everyday conversation. [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) The English language education system according to the embodiment of the present invention is a system that translates everyday conversations into English, converts them into native pronunciation, and provides English appropriate to the learner's level, thereby supporting effective English learning through everyday conversations.

[0029] The English language education system according to the embodiment includes a translation unit, a pronunciation conversion unit, and a level adjustment unit. The translation unit translates everyday conversations. For example, it translates conversations between family and friends into English in real time. The translation unit can also translate business and casual conversations. For example, it appropriately translates technical terms in business conversations and translates slang and colloquial expressions in casual conversations. The pronunciation conversion unit converts the English translated by the translation unit into native pronunciation. For example, it converts into American English or British English pronunciation. The pronunciation conversion unit can also select different accents according to a user's preferences. For example, it can provide a Southern accent for American English or a Cockney accent for British English. The level adjustment unit converts the English converted by the pronunciation conversion unit to match the learner's level. For example, it uses simple words and phrases for beginners and more complex grammar and expressions for intermediate learners. The level adjustment unit automatically adjusts the difficulty level according to the learner's progress. For example, when a learner has mastered a specific word or phrase, it presents content for the next level. This enables the English language education system according to the embodiment to support effective English learning through everyday conversation. For example, learners can learn English naturally in their daily lives, acquiring correct pronunciation and easy-to-understand English. Furthermore, by making learning progress visible, parents and teachers can provide appropriate support. This is expected to make children's English learning more effective and sustainable.

[0030] The translation unit can automatically record translated conversation content so that it can be reviewed later. For example, the translation unit can automatically save translated conversation content in the cloud so that the user can access it later. For example, past conversation history can be checked and reviewed through a dedicated app. The translation unit can also save translated conversation content in text format so that the user can read it again later. For example, important parts of the conversation can be highlighted to make review easier. The translation unit can also save translated conversation content as an audio file so that the user can play it back and review it later. For example, listening to specific phrases or words repeatedly can strengthen memory. In this way, the ability to record translated conversation content and review it later improves learning effectiveness.

[0031] The translation unit can display grammatical explanations and example sentences for the translated conversation in real time. For example, the translation unit displays grammatical explanations for the translated conversation in real time. For example, it briefly explains grammatical rules and exceptions to make it easier for the user to understand. The translation unit also displays example sentences related to the translated conversation in real time to make it easier for the user to understand the context. For example, it presents different example sentences using the same words or phrases. The translation unit also points out grammatical errors in the translated conversation and displays correct expressions in real time. For example, it displays incorrect grammar in red and correct grammar in green. In this way, displaying grammatical explanations and example sentences in real time deepens the learner's understanding.

[0032] The level adjustment unit can add learning modes for different languages, allowing users to learn multiple languages ​​simultaneously. For example, the level adjustment unit can add multiple language learning modes to the translation earphones, allowing users to learn multiple languages ​​simultaneously. For example, a mode that alternates between English and Spanish can be provided. The level adjustment unit can also provide an interface that allows users to select different language learning modes, allowing users to freely switch between the languages ​​they want to learn. For example, a menu for selecting languages ​​can be provided within the app. The level adjustment unit can also add a function to the translation earphones to manage the learning progress of multiple languages, allowing users to grasp the learning status of each language at a glance. For example, the learning time and level of acquisition for each language can be displayed in a graph. This allows users to learn multiple languages ​​simultaneously, broadening the scope of their learning.

[0033] The translation unit can be linked to smart glasses for visually displaying the translated conversation content. The translation unit, for example, links translation earphones with smart glasses to visually display the translated conversation content. For example, the translated text is displayed in real time on the smart glasses display. The translation unit also displays the translated conversation content like subtitles through the smart glasses to make it easier for users to visually understand. For example, it provides a function that scrolls the subtitles in accordance with the flow of the conversation. The translation unit also links translation earphones with smart glasses to visually highlight the translated conversation content. For example, it displays important words and phrases in different colors. This visual display of the translated conversation content helps learners understand.

[0034] The pronunciation conversion unit can compare the audio data of native pronunciation with the user's pronunciation and provide feedback on areas for improvement in pronunciation in real time. For example, the pronunciation conversion unit compares the audio data of native pronunciation with the user's pronunciation in real time and provides feedback on areas for improvement in pronunciation. For example, it visually displays differences in pronunciation and indicates correct pronunciation. The pronunciation conversion unit also provides a function for recording the user's pronunciation and comparing it with the native pronunciation to point out areas for improvement. For example, it specifically indicates differences in pronunciation stress and intonation. The pronunciation conversion unit also evaluates the user's pronunciation in real time based on the audio data of native pronunciation and provides feedback on areas for improvement. For example, it scores the accuracy of pronunciation and provides specific advice. In this way, by providing feedback on areas for improvement in pronunciation in real time, the user's pronunciation is effectively improved.

[0035] The pronunciation conversion unit can provide native pronunciation audio data with different accents. For example, the pronunciation conversion unit adds a function to provide native pronunciation audio data with different accents. For example, it allows the user to select American English and British English pronunciation. The pronunciation conversion unit also provides an interface that allows the user to select the accent they want to learn and switch between native pronunciation audio data. For example, it provides a menu for selecting accents within the app. The pronunciation conversion unit also provides a function that allows the user to compare native pronunciations with different accents and select the pronunciation that suits them. For example, it plays American English and British English pronunciations alternately. This allows the user to learn a variety of pronunciations by providing native pronunciations with different accents.

[0036] The pronunciation conversion unit can be linked to an application for visually displaying native pronunciation audio data. The pronunciation conversion unit, for example, links with an application that visually displays native pronunciation audio data, allowing the user to visually understand pronunciation key points. For example, it displays audio waveforms and mouth movements. The pronunciation conversion unit also visually displays native pronunciation audio data through the application, allowing the user to understand differences in pronunciation at a glance. For example, it displays pronunciation stress and intonation using different colors. The pronunciation conversion unit also links with an application that visually displays native pronunciation audio data, allowing the user to effectively practice pronunciation. For example, it provides a function for evaluating pronunciation accuracy in real time. This visual display makes it easier for the user to understand pronunciation key points.

[0037] The pronunciation conversion unit can provide native pronunciation audio data in the form of songs or poems, allowing users to learn in an enjoyable way. For example, the pronunciation conversion unit provides native pronunciation audio data in the form of songs or poems, allowing users to learn in an enjoyable way. For example, pronunciation practice is performed using popular English songs. The pronunciation conversion unit also allows users to practice pronunciation in time with the rhythm and melody through native pronunciation audio data provided in the form of songs or poems. For example, pronunciation practice is performed through poetry recitation. The pronunciation conversion unit also provides native pronunciation audio data in the form of songs or poems, allowing users to learn pronunciation while having fun. For example, pronunciation key points are highlighted in time with the lyrics. In this way, providing the data in the form of songs or poems allows users to learn pronunciation in an enjoyable way.

[0038] The level adjustment unit can automatically adjust the difficulty level according to the learner's progress. The level adjustment unit, for example, provides a function that analyzes the learner's progress in real time and automatically adjusts the difficulty level. For example, when a learner has mastered a specific word or phrase, the level adjustment unit presents content at the next level. The level adjustment unit also builds a system that gradually adjusts the difficulty level based on the learner's progress data. For example, when a learner achieves a certain score, it presents a more difficult task. The level adjustment unit also adds a function that monitors the learner's progress in real time and dynamically adjusts the difficulty level. For example, it adjusts the difficulty level to allow the learner to focus on reviewing areas that the learner is weak at. In this way, the difficulty level can be adjusted according to the learner's progress, thereby supporting effective learning.

[0039] The level adjustment unit can provide customized learning content based on the learner's interests. For example, the level adjustment unit analyzes the learner's interests and provides customized learning content based on the results. For example, it provides English sentences and conversations related to the learner's favorite topics. The level adjustment unit also analyzes the learner's interests in real time and builds a system that provides customized learning content based on the results. For example, it automatically generates learning materials related to topics that interest the learner. The level adjustment unit also adds a function to provide customized learning content based on the learner's interests and concerns. For example, it provides news articles and videos in areas that interest the learner as learning materials. In this way, providing customized learning content based on the learner's interests and concerns increases motivation to learn.

[0040] The level adjustment unit can provide game-style learning content that matches the learner's level, allowing them to learn in a fun way. The level adjustment unit can, for example, provide game-style learning content that matches the learner's level, allowing them to learn in a fun way. For example, it can provide learning content in the form of a word matching game or quiz. The level adjustment unit can also build a system that automatically generates game-style learning content that matches the learner's level based on the learner's progress. For example, it can provide a game that uses words that the learner has mastered. The level adjustment unit can also provide game-style learning content that matches the learner's level, increasing their motivation to learn. For example, it can introduce a system that allows the learner to receive a reward each time they complete a game. In this way, providing game-style learning content allows the learner to learn in a fun way.

[0041] The level adjustment unit can provide a platform for sharing the learner's progress with parents and teachers. For example, the level adjustment unit provides a platform for recording the learner's progress in real time and sharing it with parents and teachers. For example, it displays study time and acquired vocabulary in a graph. The level adjustment unit also provides an interface for sharing the learner's progress data with parents and teachers, thereby enhancing learning support. For example, it provides a dashboard that allows parents and teachers to check the learner's progress. The level adjustment unit also builds a platform for sharing the learner's progress with parents and teachers, thereby improving the effectiveness of learning. For example, it adds a function that allows parents and teachers to provide advice based on the learner's progress. As a result, sharing the learner's progress with parents and teachers enables effective learning support.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The English language education system can also be equipped with customization functions according to the user's learning style. For example, for users who prefer visual learning, learning materials that make extensive use of illustrations and videos can be provided. For users who prefer auditory learning, audio learning materials and podcast-style content can be provided. Furthermore, for users who prefer tactile learning, learning can be advanced through interactive quizzes and games. By providing customization functions according to the user's learning style, it is expected that learning effectiveness will be maximized.

[0044] The translation unit can have a social function for sharing translated conversation content with other users. For example, users can share what they have learned on social media with friends and family. The translation unit can also provide a community function for users to share what they have learned and give each other feedback. Furthermore, the translation unit can have a ranking function for users to compete with other users on what they have learned. This allows users to share what they have learned and interact with other users, thereby increasing their motivation to learn.

[0045] The level adjustment unit can automatically generate an individual learning plan according to the learner's progress. For example, if a learner has mastered a specific word or phrase, the next learning content will be suggested based on that progress. The level adjustment unit can also customize an individual learning plan based on the learner's progress data. For example, it can provide a plan that focuses on reviewing areas in which the learner is weak. Furthermore, the level adjustment unit can monitor the learner's progress in real time and dynamically adjust the learning plan based on the results. This makes it possible to support effective learning by providing an individual learning plan according to the learner's progress.

[0046] The level adjustment unit can have a function for setting learning goals according to the learner's progress. For example, if a learner has mastered a specific word or phrase, the next learning goal is set based on that progress. The level adjustment unit can also customize learning goals based on the learner's progress data. For example, it can set goals to focus on overcoming areas in which the learner is weak. Furthermore, the level adjustment unit can monitor the learner's progress in real time and dynamically adjust learning goals based on the results. This makes it possible to support effective learning by setting learning goals according to the learner's progress.

[0047] The level adjustment unit can have a function to visualize the learning progress according to the learner's progress. For example, it can display the words and phrases that the learner has acquired in a graph or chart. The level adjustment unit can also provide an interface that visualizes the learning progress based on the learner's progress data. For example, it can display the goals and progress status that the learner has achieved on a dashboard. Furthermore, the level adjustment unit can monitor the learner's progress in real time and dynamically visualize the learning progress based on the results. This makes it possible to increase the learner's motivation by visualizing the learner's progress.

[0048] The level adjustment unit can provide a platform for sharing learning progress with parents and teachers according to the learner's progress. For example, it can record the learner's progress data in real time so that parents and teachers can check it. The level adjustment unit can also provide an interface that allows parents and teachers to provide appropriate support based on the learner's progress. For example, it can add a function that provides advice according to the learner's progress. Furthermore, the level adjustment unit can monitor the learner's progress in real time and dynamically adjust the platform for sharing with parents and teachers based on the results. This enables effective learning support by sharing the learner's progress with parents and teachers.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The translation department translates everyday conversations. For example, conversations between family and friends are translated into English in real time. It can also translate business and casual conversations. In business conversations, it translates technical terms appropriately, and in casual conversations, it translates slang and colloquial expressions. Step 2: The pronunciation converter converts the English translated by the translation unit into native pronunciation, for example, American English or British English. It also allows users to select different accents according to their preferences, for example, providing a Southern American accent or a Cockney British accent. Step 3: The level adjustment unit converts the English converted by the pronunciation conversion unit to match the learner's level. For example, it uses simple words and phrases for beginners and more complex grammar and expressions for intermediate learners. It also automatically adjusts the difficulty level according to the learner's progress. For example, if a learner has mastered a certain word or phrase, it will present content at the next level.

[0051] (Example 2) The English language education system according to the embodiment of the present invention is a system that translates everyday conversations into English, converts them into native pronunciation, and provides English appropriate to the learner's level, thereby supporting effective English learning through everyday conversations.

[0052] The English language education system according to the embodiment includes a translation unit, a pronunciation conversion unit, and a level adjustment unit. The translation unit translates everyday conversations. For example, it translates conversations between family and friends into English in real time. The translation unit can also translate business and casual conversations. For example, it appropriately translates technical terms in business conversations and translates slang and colloquial expressions in casual conversations. The pronunciation conversion unit converts the English translated by the translation unit into native pronunciation. For example, it converts into American English or British English pronunciation. The pronunciation conversion unit can also select different accents according to a user's preferences. For example, it can provide a Southern accent for American English or a Cockney accent for British English. The level adjustment unit converts the English converted by the pronunciation conversion unit to match the learner's level. For example, it uses simple words and phrases for beginners and more complex grammar and expressions for intermediate learners. The level adjustment unit automatically adjusts the difficulty level according to the learner's progress. For example, when a learner has mastered a specific word or phrase, it presents content for the next level. This enables the English language education system according to the embodiment to support effective English learning through everyday conversation. For example, learners can learn English naturally in their daily lives, acquiring correct pronunciation and easy-to-understand English. Furthermore, by making learning progress visible, parents and teachers can provide appropriate support. This is expected to make children's English learning more effective and sustainable.

[0053] The translation unit can automatically record translated conversation content so that it can be reviewed later. For example, the translation unit can automatically save translated conversation content in the cloud so that the user can access it later. For example, past conversation history can be checked and reviewed through a dedicated app. The translation unit can also save translated conversation content in text format so that the user can read it again later. For example, important parts of the conversation can be highlighted to make review easier. The translation unit can also save translated conversation content as an audio file so that the user can play it back and review it later. For example, listening to specific phrases or words repeatedly can strengthen memory. In this way, the ability to record translated conversation content and review it later improves learning effectiveness.

[0054] The translation unit can display grammatical explanations and example sentences for the translated conversation in real time. For example, the translation unit displays grammatical explanations for the translated conversation in real time. For example, it briefly explains grammatical rules and exceptions to make it easier for the user to understand. The translation unit also displays example sentences related to the translated conversation in real time to make it easier for the user to understand the context. For example, it presents different example sentences using the same words or phrases. The translation unit also points out grammatical errors in the translated conversation and displays correct expressions in real time. For example, it displays incorrect grammar in red and correct grammar in green. In this way, displaying grammatical explanations and example sentences in real time deepens the learner's understanding.

[0055] The translation unit can use the emotion estimation function to analyze emotions during a conversation and provide feedback to elicit positive emotions. For example, the translation unit uses the emotion estimation function to analyze emotions during a conversation in real time and provide feedback to elicit positive emotions. For example, if the user is feeling nervous, the translation unit displays advice to help the user relax. The translation unit also analyzes emotions during a conversation and suggests specific phrases and expressions to elicit positive emotions. For example, it presents words of praise or encouragement. The translation unit also uses the emotion estimation function to monitor changes in emotions during a conversation in real time and provide feedback to maintain positive emotions. For example, if the user is feeling down, it displays an encouraging message. In this way, the learner's motivation is maintained by analyzing emotions and providing feedback to elicit positive emotions.

[0056] The level adjustment unit can add learning modes for different languages, allowing users to learn multiple languages ​​simultaneously. For example, the level adjustment unit can add multiple language learning modes to the translation earphones, allowing users to learn multiple languages ​​simultaneously. For example, a mode that alternates between English and Spanish can be provided. The level adjustment unit can also provide an interface that allows users to select different language learning modes, allowing users to freely switch between the languages ​​they want to learn. For example, a menu for selecting languages ​​can be provided within the app. The level adjustment unit can also add a function to the translation earphones to manage the learning progress of multiple languages, allowing users to grasp the learning status of each language at a glance. For example, the learning time and level of acquisition for each language can be displayed in a graph. This allows users to learn multiple languages ​​simultaneously, broadening the scope of their learning.

[0057] The translation unit can be linked to smart glasses for visually displaying the translated conversation content. The translation unit, for example, links translation earphones with smart glasses to visually display the translated conversation content. For example, the translated text is displayed in real time on the smart glasses display. The translation unit also displays the translated conversation content like subtitles through the smart glasses to make it easier for users to visually understand. For example, it provides a function that scrolls the subtitles in accordance with the flow of the conversation. The translation unit also links translation earphones with smart glasses to visually highlight the translated conversation content. For example, it displays important words and phrases in different colors. This visual display of the translated conversation content helps learners understand.

[0058] The translation unit uses the emotion estimation function to automatically play music and sound effects according to the emotion expressed during the conversation, making the learning environment more enjoyable. For example, the translation unit uses the emotion estimation function to automatically play music according to the emotion expressed during the conversation. For example, calm music is played when the user is relaxed. The translation unit also automatically plays sound effects according to the emotion expressed during the conversation, making the learning environment more enjoyable. For example, the translation unit plays the sound of applause when the user is happy. The translation unit also uses the emotion estimation function to adjust the music and sound effects according to the emotion expressed during the conversation in real time. For example, the tempo and volume of the music are adjusted each time the user's emotion changes. In this way, the learning environment is made more enjoyable by playing music and sound effects according to the emotion.

[0059] The pronunciation conversion unit can compare the audio data of native pronunciation with the user's pronunciation and provide feedback on areas for improvement in pronunciation in real time. For example, the pronunciation conversion unit compares the audio data of native pronunciation with the user's pronunciation in real time and provides feedback on areas for improvement in pronunciation. For example, it visually displays differences in pronunciation and indicates correct pronunciation. The pronunciation conversion unit also provides a function for recording the user's pronunciation and comparing it with the native pronunciation to point out areas for improvement. For example, it specifically indicates differences in pronunciation stress and intonation. The pronunciation conversion unit also evaluates the user's pronunciation in real time based on the audio data of native pronunciation and provides feedback on areas for improvement. For example, it scores the accuracy of pronunciation and provides specific advice. In this way, by providing feedback on areas for improvement in pronunciation in real time, the user's pronunciation is effectively improved.

[0060] The pronunciation conversion unit can provide native pronunciation audio data with different accents. For example, the pronunciation conversion unit adds a function to provide native pronunciation audio data with different accents. For example, it allows the user to select American English and British English pronunciation. The pronunciation conversion unit also provides an interface that allows the user to select the accent they want to learn and switch between native pronunciation audio data. For example, it provides a menu for selecting accents within the app. The pronunciation conversion unit also provides a function that allows the user to compare native pronunciations with different accents and select the pronunciation that suits them. For example, it plays American English and British English pronunciations alternately. This allows the user to learn a variety of pronunciations by providing native pronunciations with different accents.

[0061] The pronunciation conversion unit can use the emotion estimation function to provide pronunciation practice advice according to the user's emotions. The pronunciation conversion unit, for example, uses the emotion estimation function to provide pronunciation practice advice according to the user's emotions. For example, if the user is nervous, the pronunciation conversion unit suggests pronunciation practice to help the user relax. The pronunciation conversion unit also analyzes the user's emotions in real time and provides pronunciation practice advice based on the results. For example, if the user has strong positive emotions, the pronunciation conversion unit suggests practice that includes many compliments. The pronunciation conversion unit also uses the emotion estimation function to provide pronunciation practice advice according to the user's emotions in real time. For example, if the user is feeling down, the pronunciation conversion unit displays an encouraging message. In this way, by providing pronunciation practice advice according to the user's emotions, the pronunciation conversion unit effectively supports the user's pronunciation practice.

[0062] The pronunciation conversion unit can be linked to an application for visually displaying native pronunciation audio data. The pronunciation conversion unit, for example, links with an application that visually displays native pronunciation audio data, allowing the user to visually understand pronunciation key points. For example, it displays audio waveforms and mouth movements. The pronunciation conversion unit also visually displays native pronunciation audio data through the application, allowing the user to understand differences in pronunciation at a glance. For example, it displays pronunciation stress and intonation using different colors. The pronunciation conversion unit also links with an application that visually displays native pronunciation audio data, allowing the user to effectively practice pronunciation. For example, it provides a function for evaluating pronunciation accuracy in real time. This visual display makes it easier for the user to understand pronunciation key points.

[0063] The pronunciation conversion unit can provide native pronunciation audio data in the form of songs or poems, allowing users to learn in an enjoyable way. For example, the pronunciation conversion unit provides native pronunciation audio data in the form of songs or poems, allowing users to learn in an enjoyable way. For example, pronunciation practice is performed using popular English songs. The pronunciation conversion unit also allows users to practice pronunciation in time with the rhythm and melody through native pronunciation audio data provided in the form of songs or poems. For example, pronunciation practice is performed through poetry recitation. The pronunciation conversion unit also provides native pronunciation audio data in the form of songs or poems, allowing users to learn pronunciation while having fun. For example, pronunciation key points are highlighted in time with the lyrics. In this way, providing the data in the form of songs or poems allows users to learn pronunciation in an enjoyable way.

[0064] The pronunciation conversion unit can use the emotion estimation function to provide native pronunciation audio data related to the topic in which the user is most interested. The pronunciation conversion unit, for example, uses the emotion estimation function to provide native pronunciation audio data related to the topic in which the user is most interested. For example, pronunciation practice is performed using lines from a movie that the user likes. The pronunciation conversion unit also analyzes the user's emotions in real time and provides native pronunciation audio data related to the topic in which the user is interested based on the results. For example, pronunciation practice is performed using news articles that the user is interested in. The pronunciation conversion unit also uses the emotion estimation function to provide native pronunciation audio data related to the topic in which the user is most interested in in real time. For example, pronunciation practice is performed using commentary on a sport that the user is interested in. In this way, providing audio data related to the topic in which the user is interested increases motivation for learning.

[0065] The level adjustment unit can automatically adjust the difficulty level according to the learner's progress. The level adjustment unit, for example, provides a function that analyzes the learner's progress in real time and automatically adjusts the difficulty level. For example, when a learner has mastered a specific word or phrase, the level adjustment unit presents content at the next level. The level adjustment unit also builds a system that gradually adjusts the difficulty level based on the learner's progress data. For example, when a learner achieves a certain score, it presents a more difficult task. The level adjustment unit also adds a function that monitors the learner's progress in real time and dynamically adjusts the difficulty level. For example, it adjusts the difficulty level to allow the learner to focus on reviewing areas that the learner is weak at. In this way, the difficulty level can be adjusted according to the learner's progress, thereby supporting effective learning.

[0066] The level adjustment unit can provide customized learning content based on the learner's interests. For example, the level adjustment unit analyzes the learner's interests and provides customized learning content based on the results. For example, it provides English sentences and conversations related to the learner's favorite topics. The level adjustment unit also analyzes the learner's interests in real time and builds a system that provides customized learning content based on the results. For example, it automatically generates learning materials related to topics that interest the learner. The level adjustment unit also adds a function to provide customized learning content based on the learner's interests and concerns. For example, it provides news articles and videos in areas that interest the learner as learning materials. In this way, providing customized learning content based on the learner's interests and concerns increases motivation to learn.

[0067] The level adjustment unit can use the emotion estimation function to provide feedback to maintain the learner's motivation. For example, the level adjustment unit uses the emotion estimation function to analyze the learner's emotions in real time and provide feedback to maintain motivation. For example, if the learner is tired, it displays an encouraging message. The level adjustment unit also analyzes the learner's emotions and provides specific advice to maintain motivation based on the results of the analysis. For example, if the learner is feeling down, it displays an encouraging message. The level adjustment unit also uses the emotion estimation function to provide feedback to maintain the learner's motivation in real time. For example, if the learner is successful, it displays words of praise. In this way, feedback to maintain the learner's motivation is provided, thereby supporting continuation of learning.

[0068] The level adjustment unit can provide game-style learning content that matches the learner's level, allowing them to learn in a fun way. The level adjustment unit can, for example, provide game-style learning content that matches the learner's level, allowing them to learn in a fun way. For example, it can provide learning content in the form of a word matching game or quiz. The level adjustment unit can also build a system that automatically generates game-style learning content that matches the learner's level based on the learner's progress. For example, it can provide a game that uses words that the learner has mastered. The level adjustment unit can also provide game-style learning content that matches the learner's level, increasing their motivation to learn. For example, it can introduce a system that allows the learner to receive a reward each time they complete a game. In this way, providing game-style learning content allows the learner to learn in a fun way.

[0069] The level adjustment unit can provide a platform for sharing the learner's progress with parents and teachers. For example, the level adjustment unit provides a platform for recording the learner's progress in real time and sharing it with parents and teachers. For example, it displays study time and acquired vocabulary in a graph. The level adjustment unit also provides an interface for sharing the learner's progress data with parents and teachers, thereby enhancing learning support. For example, it provides a dashboard that allows parents and teachers to check the learner's progress. The level adjustment unit also builds a platform for sharing the learner's progress with parents and teachers, thereby improving the effectiveness of learning. For example, it adds a function that allows parents and teachers to provide advice based on the learner's progress. As a result, sharing the learner's progress with parents and teachers enables effective learning support.

[0070] The level adjustment unit can use the emotion estimation function to provide learning content related to topics that interest the learner most. For example, the level adjustment unit uses the emotion estimation function to provide learning content related to topics that interest the learner most. For example, it provides English sentences and conversations related to a sport that the learner likes. The level adjustment unit also analyzes the learner's emotions in real time and, based on the results, builds a system that provides learning content related to topics of interest. For example, it provides news articles that interest the learner as learning materials. The level adjustment unit also uses the emotion estimation function to provide learning content related to topics that interest the learner most in real time. For example, it uses movie scenes that interest the learner to study English. In this way, providing learning content related to topics that interest the learner increases their motivation to learn.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The English language education system can also be equipped with customization functions according to the user's learning style. For example, for users who prefer visual learning, learning materials that make extensive use of illustrations and videos can be provided. For users who prefer auditory learning, audio learning materials and podcast-style content can be provided. Furthermore, for users who prefer tactile learning, learning can be advanced through interactive quizzes and games. By providing customization functions according to the user's learning style, it is expected that learning effectiveness will be maximized.

[0073] The translation unit can have a social function for sharing translated conversation content with other users. For example, users can share what they have learned on social media with friends and family. The translation unit can also provide a community function for users to share what they have learned and give each other feedback. Furthermore, the translation unit can have a ranking function for users to compete with other users on what they have learned. This allows users to share what they have learned and interact with other users, thereby increasing their motivation to learn.

[0074] The translation unit can use its emotion estimation function to adjust the learning pace according to the user's emotions. For example, if the user is tired, it can provide advice to slow down the learning pace. Also, if the user is concentrating, it can provide advice to speed up the learning pace. Furthermore, the translation unit can analyze the user's emotions in real time and dynamically adjust the learning pace based on the results. For example, if the user is feeling stressed, it can suggest taking a break to relax. In this way, adjusting the learning pace according to the user's emotions can support effective learning.

[0075] The level adjustment unit can automatically generate an individual learning plan according to the learner's progress. For example, if a learner has mastered a specific word or phrase, the next learning content will be suggested based on that progress. The level adjustment unit can also customize an individual learning plan based on the learner's progress data. For example, it can provide a plan that focuses on reviewing areas in which the learner is weak. Furthermore, the level adjustment unit can monitor the learner's progress in real time and dynamically adjust the learning plan based on the results. This makes it possible to support effective learning by providing an individual learning plan according to the learner's progress.

[0076] The translation unit can use the emotion estimation function to provide learning content that corresponds to the user's emotions. For example, if the user is relaxed, it can provide content that allows the user to study in a relaxed state. Also, if the user is concentrating, it can provide content that helps the user maintain concentration. Furthermore, the translation unit can analyze the user's emotions in real time and dynamically adjust the learning content based on the results. For example, if the user is feeling stressed, it can provide content that helps the user relax. In this way, by providing learning content that corresponds to the user's emotions, it is possible to support effective learning.

[0077] The level adjustment unit can have a function for setting learning goals according to the learner's progress. For example, if a learner has mastered a specific word or phrase, the next learning goal is set based on that progress. The level adjustment unit can also customize learning goals based on the learner's progress data. For example, it can set goals to focus on overcoming areas in which the learner is weak. Furthermore, the level adjustment unit can monitor the learner's progress in real time and dynamically adjust learning goals based on the results. This makes it possible to support effective learning by setting learning goals according to the learner's progress.

[0078] The translation unit can use the emotion estimation function to provide study reminders according to the user's emotions. For example, if the user is tired, it can remind the user to take a break. Also, if the user is concentrating, it can remind the user to continue studying. Furthermore, the translation unit can analyze the user's emotions in real time and dynamically adjust study reminders based on the results. For example, if the user is feeling stressed, it can provide a reminder to relax. This allows for effective study support by providing study reminders according to the user's emotions.

[0079] The level adjustment unit can have a function to visualize the learning progress according to the learner's progress. For example, it can display the words and phrases that the learner has acquired in a graph or chart. The level adjustment unit can also provide an interface that visualizes the learning progress based on the learner's progress data. For example, it can display the goals and progress status that the learner has achieved on a dashboard. Furthermore, the level adjustment unit can monitor the learner's progress in real time and dynamically visualize the learning progress based on the results. This makes it possible to increase the learner's motivation by visualizing the learner's progress.

[0080] The translation unit can use the emotion estimation function to provide learning feedback according to the user's emotions. For example, if the user is relaxed, it can provide feedback that allows the user to study in a relaxed state. Also, if the user is concentrating, it can provide feedback to help the user maintain concentration. Furthermore, the translation unit can analyze the user's emotions in real time and dynamically adjust the learning feedback based on the results. For example, if the user is feeling stressed, it can provide feedback to help the user relax. In this way, by providing learning feedback according to the user's emotions, it is possible to support effective learning.

[0081] The level adjustment unit can provide a platform for sharing learning progress with parents and teachers according to the learner's progress. For example, it can record the learner's progress data in real time so that parents and teachers can check it. The level adjustment unit can also provide an interface that allows parents and teachers to provide appropriate support based on the learner's progress. For example, it can add a function that provides advice according to the learner's progress. Furthermore, the level adjustment unit can monitor the learner's progress in real time and dynamically adjust the platform for sharing with parents and teachers based on the results. This enables effective learning support by sharing the learner's progress with parents and teachers.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The translation department translates everyday conversations. For example, conversations between family and friends are translated into English in real time. It can also translate business and casual conversations. In business conversations, it translates technical terms appropriately, and in casual conversations, it translates slang and colloquial expressions. Step 2: The pronunciation converter converts the English translated by the translation unit into native pronunciation, for example, American English or British English. It also allows users to select different accents according to their preferences, for example, providing a Southern American accent or a Cockney British accent. Step 3: The level adjustment unit converts the English converted by the pronunciation conversion unit to match the learner's level. For example, it uses simple words and phrases for beginners and more complex grammar and expressions for intermediate learners. It also automatically adjusts the difficulty level according to the learner's progress. For example, if a learner has mastered a certain word or phrase, it will present content at the next level.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 translation department that translates everyday conversations, a pronunciation conversion unit that converts the English translated by the translation unit into native pronunciation; a level adjustment unit that converts the English converted by the pronunciation conversion unit to match the learner's level. A system characterized by:

2. The translation unit Automatically record translated conversations for later review 2. The system of claim 1.

3. The translation unit Displays grammatical explanations and example sentences in real time for translated conversations 2. The system of claim 1.

4. The translation unit Analyze emotions during conversations and provide feedback to elicit positive emotions 2. The system of claim 1.

5. The level adjustment unit Add different language learning modes, allowing you to learn multiple languages ​​at the same time 2. The system of claim 1.

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