system

Abstract

The system according to this embodiment aims to enable users to effectively learn language skills, including pronunciation. [Solution] The system according to the embodiment comprises a recognition unit, a feedback unit, a scenario unit, and a curriculum unit. The recognition unit recognizes the user's pronunciation. The feedback unit provides feedback based on the pronunciation recognized by the recognition unit. The scenario unit provides scenarios such as travel and shopping. The curriculum unit provides a curriculum tailored to the user's language level.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system for effectively learning a user's language skills including pronunciation has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to enable a user to effectively learn language skills including pronunciation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a recognition unit, a feedback unit, a scenario unit, and a curriculum unit. The recognition unit recognizes the user's pronunciation. The feedback unit provides feedback based on the pronunciation recognized by the recognition unit. The scenario unit provides scenarios such as travel and shopping. The curriculum unit provides a curriculum tailored to the user's language level. [Effects of the Invention]

[0007] The system according to this embodiment allows users to effectively learn language skills, including pronunciation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pronunciation instruction device according to an embodiment of the present invention is a device that uses generative AI to provide instruction up to a certain level of pronunciation. This device recreates scenarios such as travel and shopping in VR, allowing users to learn native pronunciation as if they were actually on a date. Furthermore, because it is offered on a subscription basis, it can overcome challenges such as "not having enough time" or "not having enough money." First, the user puts on the device and immerses themselves in the VR environment. Various scenarios are available, such as shopping at a travel destination or conversations at tourist spots. Through these scenarios, the user can learn native pronunciation. The generative AI recognizes the user's pronunciation and provides feedback in real time. For example, if the user pronounces "English," the generative AI analyzes the pronunciation and provides instruction on the correct pronunciation. In addition, a curriculum tailored to the user's language level is provided. As the language level increases, the scenarios develop into more intimate relationships. This transforms studying into enjoyable and active learning, increasing the motivation to continue learning. Furthermore, because this device can be used at home anytime, it can overcome challenges such as not being able to take long vacations, not having enough money, or anxiety about living abroad. For example, it can help learn a native foreign language without being separated from family for extended periods. Furthermore, it addresses the challenges of domestic language learning, such as the lack of opportunities for interaction outside of school. This device is offered on a monthly subscription basis, allowing users to keep costs down. Moreover, it offers the same specifications as regular VR equipment, enabling users to enjoy other VR content. For example, it allows for video streaming from services like Netflix. In this way, the AI-powered pronunciation instruction device provides an overwhelming sense of immersion, improving interest, engagement, and persistence. Users can easily experience authentic language learning at home. The pronunciation instruction device recognizes the user's pronunciation, provides feedback, and offers scenarios and curricula, enabling them to learn native-like pronunciation.

[0029] The pronunciation instruction device according to this embodiment comprises a recognition unit, a feedback unit, a scenario unit, and a curriculum unit. The recognition unit recognizes the user's pronunciation. The recognition unit analyzes the user's pronunciation using, for example, speech recognition technology. The recognition unit collects the user's pronunciation using, for example, a microphone and analyzes it using a speech recognition algorithm. The recognition unit extracts features of the user's pronunciation and evaluates the accuracy of the pronunciation. The feedback unit provides feedback based on the pronunciation recognized by the recognition unit. The feedback unit provides, for example, audio feedback. The feedback unit provides, for example, text feedback. The feedback unit provides, for example, visual feedback. The scenario unit provides scenarios such as travel and shopping. The scenario unit reproduces a travel scenario using, for example, VR technology. The scenario unit provides, for example, a shopping scenario. The scenario unit provides, for example, a conversation scenario in a tourist area. The curriculum unit provides a curriculum tailored to the user's language level. The curriculum unit provides, for example, beginner, intermediate, and advanced level curricula. The curriculum unit adjusts the curriculum according to the user's progress. The curriculum section provides, for example, curricula to strengthen specific skills. This allows the pronunciation instruction device to learn native pronunciation by recognizing the user's pronunciation, providing feedback, and offering scenarios and curricula.

[0030] The recognition unit recognizes the user's pronunciation. For example, it analyzes the user's pronunciation using speech recognition technology. Specifically, the recognition unit collects the user's pronunciation using a high-sensitivity microphone and analyzes the audio data using digital signal processing technology. The speech recognition algorithm decomposes the audio waveform into frequency components and extracts features. This allows for detailed analysis of features such as the pitch, rhythm, and intonation of the user's pronunciation. Furthermore, in addition to speech recognition technology, the recognition unit evaluates the accuracy of the pronunciation using a machine learning model. For example, it trains a deep learning-based speech model and identifies pronunciation errors by comparing the user's pronunciation with that of a native speaker. The recognition unit can analyze the characteristics of the user's pronunciation in real time and generate evaluation results immediately. This allows the user to quickly understand their pronunciation problems and receive specific guidance for improvement. Additionally, the recognition unit can accumulate user pronunciation data and track long-term changes and progress in pronunciation. This allows for continuous support in improving the user's pronunciation skills.

[0031] The feedback unit provides feedback based on the pronunciation recognized by the recognition unit. For example, the feedback unit provides audio feedback. Specifically, it plays the correct pronunciation of a native speaker in audio format, allowing the user to compare it to their own pronunciation. The feedback unit also provides text feedback. For example, it points out errors in the user's pronunciation in text and explains the correct pronunciation methods and points to note. Furthermore, the feedback unit provides visual feedback. For example, it displays the waveform or spectrogram of the user's pronunciation, visually showing which parts are incorrect. This allows the user to intuitively understand the problems with their pronunciation. The feedback unit can combine audio, text, and visual feedback according to the user's learning style and preferences. Additionally, the feedback unit can adjust the content and frequency of feedback according to the user's progress. For example, it can focus on basic pronunciation instruction for beginners and provide more advanced pronunciation fine-tuning instruction for intermediate and advanced learners. This allows the feedback unit to effectively support the improvement of the user's pronunciation skills.

[0032] The Scenario Department provides scenarios such as travel and shopping. For example, the Scenario Department recreates travel scenarios using VR technology. Specifically, it allows users to experience real-life situations such as checking into a hotel or ordering at a restaurant in a virtual reality environment. This allows users to practically learn the pronunciation skills necessary in real-life situations. The Scenario Department also provides shopping scenarios. For example, it allows users to experience situations such as searching for products in a virtual supermarket or conversing with store clerks. Furthermore, the Scenario Department provides conversation scenarios for tourist destinations. For example, it allows users to experience situations such as conversing with a guide or interacting with other tourists in a virtual tourist destination. Through these scenarios, the Scenario Department enables users to effectively learn the pronunciation skills necessary in real-life situations. In addition, the Scenario Department can adjust the content and difficulty level of the scenarios according to the user's language level and learning objectives. For example, it provides simple conversation scenarios for beginners and more complex conversation scenarios for intermediate and advanced learners. In this way, the Scenario Department can practically support the improvement of users' pronunciation skills.

[0033] The curriculum department provides curricula tailored to the user's language level. For example, it offers beginner, intermediate, and advanced level curricula. Specifically, beginners receive a curriculum focused on basic pronunciation practice and simple conversation practice, while intermediate learners receive more advanced pronunciation practice and practical conversation practice. Advanced learners receive a curriculum that includes advanced practice aimed at achieving near-native speaker pronunciation and specialized conversation situations. Furthermore, the curriculum department can adjust the curriculum according to the user's progress. For example, if a user has difficulty with a particular pronunciation, the curriculum will focus on practicing that pronunciation. Also, if a user wants to strengthen a specific skill, the curriculum will be tailored to that skill. This allows the curriculum department to provide flexible curricula that meet the individual needs of each user. In addition, the curriculum department can optimize the content and progression of the curriculum based on the user's learning history and feedback. This allows the curriculum department to effectively support the improvement of users' pronunciation skills.

[0034] The recognition unit can recognize the user's pronunciation in real time. The recognition unit analyzes the user's pronunciation in real time, for example, using speech recognition technology. The recognition unit collects the user's pronunciation using a microphone, for example, and analyzes it in real time using a speech recognition algorithm. The recognition unit extracts the characteristics of the user's pronunciation in real time and evaluates the accuracy of the pronunciation. This enables immediate feedback by recognizing the user's pronunciation in real time. The real-time processing may be performed using AI, for example, or without AI. For example, the recognition unit can input the audio data collected in real time into a generating AI and have the generating AI perform real-time pronunciation recognition.

[0035] The feedback unit can provide real-time feedback based on recognized pronunciation. For example, the feedback unit can provide real-time audio feedback. For example, the feedback unit can provide real-time text feedback. For example, the feedback unit can provide real-time visual feedback. This allows for rapid improvement of the user's pronunciation by providing real-time feedback. Real-time feedback may be provided using AI, or it may be provided without AI. For example, the feedback unit can input real-time recognized pronunciation data into a generating AI and have the generating AI provide real-time feedback.

[0036] The scenario unit can provide scenarios such as travel and shopping. For example, the scenario unit can recreate travel scenarios using VR technology. For example, the scenario unit can provide shopping scenarios. For example, the scenario unit can provide conversation scenarios in tourist destinations. By providing scenarios such as travel and shopping, it becomes possible to practice pronunciation in a way that is close to real-life situations. The provision of scenarios may be done using AI, for example, or without using AI. For example, the scenario unit can input the user's pronunciation data into a generating AI and have the generating AI select an appropriate scenario.

[0037] The curriculum unit can provide a curriculum tailored to the user's language level. For example, the curriculum unit can provide level-based curricula for beginner, intermediate, and advanced levels. For example, the curriculum unit can adjust the curriculum according to the user's progress. For example, the curriculum unit can provide a curriculum that strengthens specific skills. This enables effective learning by providing a curriculum tailored to the user's language level. Curriculum provision may be done using AI or not. For example, the curriculum unit can input the user's language level data into a generating AI and have the generating AI select an appropriate curriculum.

[0038] The scenario unit can develop scenarios into more intimate relationships as the user's language level improves. For example, the scenario unit can develop travel scenarios into more complex scenarios as the user's language level improves. For example, the scenario unit can develop shopping scenarios into more detailed scenarios as the user's language level improves. For example, the scenario unit can develop conversation scenarios at tourist destinations into more intimate scenarios as the user's language level improves. This allows the user's motivation to learn to be maintained by adjusting scenarios according to their language level. Scenario adjustments may be made using AI, or they may be made without AI. For example, the scenario unit can input the user's language level data into a generating AI and have the generating AI perform appropriate scenario adjustments.

[0039] The curriculum unit can adjust the curriculum according to the user's language level. For example, the curriculum unit can increase the difficulty level of the curriculum as the user's language level improves. For example, the curriculum unit can provide a curriculum that strengthens specific skills as the user's language level improves. For example, the curriculum unit can provide a curriculum that includes more advanced vocabulary and grammar as the user's language level improves. This allows for effective learning by adjusting the curriculum according to the user's language level. Curriculum adjustment may be performed using AI, or it may be performed without AI. For example, the curriculum unit can input the user's language level data into a generating AI and have the generating AI perform appropriate curriculum adjustments.

[0040] The system can be offered in a monthly subscription format. The system offers monthly plans such as a basic plan and a premium plan. The system adjusts the content of the services provided according to the plan selected by the user. The system can limit the number of scenarios or curricula available to the user based on the monthly plan. This allows for cost-effective use by offering the system in a monthly subscription format. The monthly subscription format may be implemented using AI or not. For example, the system can input user usage data into a generating AI and have the generating AI select the optimal subscription plan.

[0041] The system can also provide other VR content. For example, it can offer VR games, educational content, and entertainment content. The system can adjust the content of the services provided according to the VR content selected by the user. For example, the system can be used in conjunction with the user's pronunciation practice when using other VR content. This allows users to enjoy other VR content, thereby increasing user satisfaction. The provision of other VR content may be done using AI or not. For example, the system can input user usage data into a generating AI and have the generating AI select the most suitable VR content.

[0042] The recognition unit can analyze the user's past pronunciation data and select the optimal recognition algorithm. For example, the recognition unit can select an algorithm that improves the recognition accuracy for a specific phoneme based on data that the user has previously pronounced. For example, the recognition unit can analyze the user's pronunciation tendencies and apply an individually optimized recognition algorithm. For example, the recognition unit can cluster the user's pronunciation data and apply a common recognition algorithm to users with similar pronunciation patterns. This allows for the application of an individually optimized recognition algorithm by analyzing past pronunciation data. The selection of a recognition algorithm may be performed using AI, for example, or without using AI. For example, the recognition unit can input the user's past pronunciation data into a generating AI and have the generating AI select the optimal recognition algorithm.

[0043] The recognition unit can improve recognition accuracy by taking into account the speed and rhythm of the user's pronunciation during recognition. For example, if the user speaks quickly, the recognition unit adjusts the recognition algorithm to accommodate the speed of pronunciation. For example, if the user speaks slowly, the recognition unit improves recognition accuracy by emphasizing the rhythm of pronunciation. For example, the recognition unit analyzes the rhythm of the user's pronunciation and applies a recognition algorithm based on that rhythm. This improves recognition accuracy by taking into account the speed and rhythm of pronunciation. The adjustment of the recognition algorithm may be performed using AI, for example, or without using AI. For example, the recognition unit can input the user's pronunciation data into a generating AI and have the generating AI perform adjustments to the recognition algorithm based on the speed and rhythm of pronunciation.

[0044] The recognition unit can prioritize the recognition of region-specific pronunciations by considering the user's geographical location information during recognition. For example, if the user is in a specific region, the recognition unit will prioritize the recognition of the region-specific pronunciations of that region. For example, if the user is traveling, the recognition unit will recognize the region-specific pronunciations of the destination. For example, if the user moves to a different region, the recognition unit will prioritize the recognition of the pronunciations of the new region. In this way, by considering geographical location information, region-specific pronunciations can be accurately recognized. The consideration of geographical location information may be performed using AI, for example, or without using AI. For example, the recognition unit can input the user's geographical location information into a generating AI and have the generating AI perform the recognition of region-specific pronunciations.

[0045] The recognition unit can analyze the user's social media activity and recognize relevant pronunciations during recognition. For example, the recognition unit recognizes relevant pronunciations based on the language and expressions the user uses on social media. For example, if the user frequently posts about a particular topic, the recognition unit will prioritize recognizing pronunciations related to that topic. For example, the recognition unit recognizes pronunciations based on the user's interests from their social media activity. This allows for the recognition of pronunciations based on the user's interests by analyzing their social media activity. The analysis of social media activity may be performed using AI or not. For example, the recognition unit can input the user's social media data into a generating AI and have the generating AI perform the recognition of relevant pronunciations.

[0046] The feedback unit can adjust the level of detail in the feedback based on the importance of the pronunciation. For example, the feedback unit provides detailed feedback for important pronunciations. For example, the feedback unit provides concise feedback for general pronunciations. For example, the feedback unit provides particularly detailed feedback for pronunciations that are important in a specific scenario. This allows for detailed feedback to be provided for important pronunciations by adjusting the level of detail based on the importance of the pronunciation. The adjustment of the level of detail in the feedback may be performed using AI, or it may be performed without using AI. For example, the feedback unit can input pronunciation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the feedback.

[0047] The feedback unit can apply different feedback algorithms depending on the pronunciation category during feedback. For example, for noun pronunciation, the feedback unit provides feedback using concrete examples. For verb pronunciation, the feedback unit provides feedback that includes actions. For adjective pronunciation, the feedback unit provides feedback that conveys emotion. By applying a feedback algorithm according to the pronunciation category, more appropriate feedback can be provided. The application of the feedback algorithm may be performed using AI, for example, or without AI. For example, the feedback unit can input pronunciation category data into a generating AI and cause the generating AI to apply an appropriate feedback algorithm.

[0048] The feedback unit can prioritize feedback based on when the pronunciation was submitted. For example, the feedback unit may prioritize feedback for recently submitted pronunciations. For example, the feedback unit may also provide feedback for pronunciations that have not been submitted for a long time. For example, the feedback unit may prioritize feedback for pronunciations submitted within a specific time period. This ensures that the most recent pronunciations receive priority feedback by prioritizing feedback based on when they were submitted. The determination of feedback priority may be performed using AI or without AI. For example, the feedback unit can input pronunciation submission time data into a generating AI and have the generating AI determine the feedback priority.

[0049] The feedback unit can adjust the order of feedback based on the relevance of pronunciations during the feedback process. For example, the feedback unit may prioritize providing feedback to highly relevant pronunciations. For example, the feedback unit may also provide feedback to less relevant pronunciations. For example, the feedback unit may prioritize providing feedback to highly relevant pronunciations in a specific scenario. This allows for preferential feedback to be provided to highly relevant pronunciations by adjusting the order of feedback based on the relevance of pronunciations. The adjustment of the feedback order may be performed using AI, or it may be performed without using AI. For example, the feedback unit can input pronunciation relevance data into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0050] The scenario unit can optimize the current scenario by referring to past scenario data when providing a scenario. For example, the scenario unit optimizes the current scenario based on scenarios the user has experienced in the past. For example, the scenario unit provides scenarios based on the user's interests and preferences from the user's past scenario data. For example, the scenario unit analyzes the user's past scenario data and provides the most effective scenario. This allows the current scenario to be optimized by referring to past scenario data. Scenario optimization may be performed using AI, for example, or without AI. For example, the scenario unit can input past scenario data into a generating AI and have the generating AI perform the optimization of the current scenario.

[0051] The scenario unit can customize scenarios based on user interests when providing them. For example, the scenario unit can customize scenarios based on topics that the user is interested in. For example, the scenario unit can prioritize providing specific scenarios based on user interests. For example, the scenario unit can analyze user interests and provide the most suitable scenario. By customizing scenarios based on user interests, more effective scenario delivery becomes possible. Scenario customization may be performed using AI, or it may be performed without AI. For example, the scenario unit can input user interest data into a generating AI and have the generating AI perform scenario customization.

[0052] The scenario unit can provide the optimal scenario by considering the user's geographical location information when providing scenarios. For example, if the user is in a specific region, the scenario unit will provide a scenario related to that region. For example, if the user is traveling, the scenario unit will provide a scenario related to the region they are visiting. For example, if the user moves to a different region, the scenario unit will provide a scenario related to the new region. In this way, the optimal scenario can be provided by considering geographical location information. Consideration of geographical location information may be performed using AI, for example, or without using AI. For example, the scenario unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the optimal scenario.

[0053] The scenario unit can analyze a user's social media activity and provide relevant scenarios when providing them. For example, the scenario unit can provide scenarios based on topics the user shows interest in on social media. For example, if a user frequently posts about a particular topic, the scenario unit can provide scenarios related to that topic. For example, the scenario unit can provide scenarios based on the user's interests and concerns from their social media activity. In this way, by analyzing social media activity, it is possible to provide scenarios based on the user's interests and concerns. The analysis of social media activity may be performed using AI, for example, or without using AI. For example, the scenario unit can input the user's social media data into a generating AI and have the generating AI provide relevant scenarios.

[0054] The curriculum unit can provide an optimal curriculum by referring to the user's past learning history when providing a curriculum. For example, the curriculum unit can suggest what the user should learn next based on what the user has learned in the past. For example, the curriculum unit can provide a curriculum that strengthens areas where the user is particularly weak, based on the user's past learning history. For example, the curriculum unit can analyze the user's learning history and suggest the most effective learning sequence. In this way, an optimal curriculum can be provided by referring to past learning history. Referencing the learning history may be done using AI, for example, or without using AI. For example, the curriculum unit can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing an optimal curriculum.

[0055] The curriculum unit can customize the curriculum based on the user's current language level when providing it. For example, the curriculum unit can assess the user's language level and provide a curriculum of appropriate difficulty. For example, the curriculum unit can provide a curriculum that gradually increases in difficulty according to the user's language level. For example, the curriculum unit can provide a curriculum that strengthens specific skills based on the user's language level. This makes learning more effective by customizing the curriculum based on the current language level. Curriculum customization may be performed using AI or not. For example, the curriculum unit can input the user's language level data into a generating AI and have the generating AI perform the curriculum customization.

[0056] The curriculum unit can provide the optimal curriculum by considering the user's geographical location information when delivering the curriculum. For example, if the user is in a specific region, the curriculum unit will provide a curriculum related to that region. For example, if the user is traveling, the curriculum unit will provide a curriculum related to the region they are visiting. For example, if the user moves to a different region, the curriculum unit will provide a curriculum related to the new region. In this way, the optimal curriculum can be provided by considering geographical location information. The consideration of geographical location information may be performed using AI, for example, or without using AI. For example, the curriculum unit can input the user's geographical location information into a generating AI and have the generating AI execute the task of providing the optimal curriculum.

[0057] The curriculum department can analyze users' social media activity when providing curriculum and provide relevant curriculum. For example, the curriculum department can provide curriculum based on topics that users show interest in on social media. For example, if a user frequently posts about a particular topic, the curriculum department can provide curriculum related to that topic. For example, the curriculum department can provide curriculum based on users' interests and concerns from their social media activity. In this way, by analyzing social media activity, it is possible to provide curriculum based on users' interests and concerns. The analysis of social media activity may be performed using AI, for example, or without using AI. For example, the curriculum department can input user social media data into a generating AI and have the generating AI provide relevant curriculum.

[0058] A subscription model allows for the provision of the optimal plan by referencing the user's past usage history when providing a subscription. For example, a subscription model might suggest the optimal plan based on the plans the user has used in the past. For example, a subscription model might suggest plans that the user has used most frequently based on their past usage history. For example, a subscription model might analyze the user's usage history and suggest the most effective plan. In this way, the optimal plan can be provided by referring to past usage history. Referencing usage history may be done using AI, for example, or without using AI. For example, a subscription model could input the user's past usage history data into a generating AI and have the generating AI perform the task of providing the optimal plan.

[0059] A subscription model can provide the optimal plan by considering the user's geographical location when providing a subscription. For example, if the user is in a specific region, the subscription model will provide a plan relevant to that region. For example, if the user is traveling, the subscription model will provide a plan relevant to the region they are visiting. For example, if the user moves to a different region, the subscription model will provide a plan relevant to the new region. In this way, the optimal plan can be provided by considering geographical location. Consideration of geographical location may be done using AI, for example, or without using AI. For example, the subscription model can input the user's geographical location into a generating AI and have the generating AI perform the task of providing the optimal plan.

[0060] The feature that allows users to enjoy other VR content can provide optimal content by referencing the user's past viewing history when providing VR content. For example, this feature can suggest related content based on content the user has previously viewed. It can also suggest content in genres the user is particularly interested in based on their past viewing history. Furthermore, it can analyze the user's viewing history to suggest the most effective content. This allows for the provision of optimal content by referencing past viewing history. This reference to viewing history may be performed using AI or without AI. For example, the feature can input the user's past viewing history data into a generating AI, which can then perform the task of providing optimal content.

[0061] The feature that allows users to enjoy other VR content can provide optimal content by considering the user's geographical location when providing VR content. For example, if the user is in a specific region, the feature can provide content related to that region. For example, if the user is traveling, the feature can provide content related to the region they are visiting. For example, if the user moves to a different region, the feature can provide content related to the new region. In this way, the optimal content can be provided by considering geographical location. The consideration of geographical location may be done using AI, for example, or without using AI. For example, the feature that allows users to enjoy other VR content can input the user's geographical location into a generating AI and have the generating AI perform the task of providing the optimal content.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The pronunciation instruction device not only recognizes the user's pronunciation and provides feedback, but can also record the user's pronunciation progress and provide regular progress reports. For example, the recognition unit periodically collects the user's pronunciation data and evaluates their progress. The feedback unit generates and provides progress reports to the user. The scenario unit adjusts the scenario according to the user's progress to support more effective learning. The curriculum unit adjusts the curriculum based on progress to maximize the user's learning effectiveness. This allows the user to check their progress and continue learning while maintaining motivation.

[0064] The pronunciation instruction device not only recognizes the user's pronunciation but can also analyze the characteristics of the user's pronunciation and provide a individually optimized pronunciation practice plan. For example, the recognition unit extracts the characteristics of the user's pronunciation and identifies pronunciation problems for specific phonemes. The feedback unit generates and provides a practice plan for those specific phonemes. The scenario unit provides scenarios that focus on specific phonemes to support the user's pronunciation practice. The curriculum unit adjusts the curriculum based on the user's pronunciation characteristics to achieve effective pronunciation practice. As a result, the user can overcome their pronunciation problems and acquire more accurate pronunciation.

[0065] The pronunciation instruction device not only recognizes the user's pronunciation and provides feedback, but can also analyze the user's pronunciation practice history and suggest the optimal practice timing. For example, the recognition unit collects the user's pronunciation practice history and analyzes the frequency and timing of practice. The feedback unit suggests the optimal practice timing and notifies the user. The scenario unit provides a scenario based on the suggested practice timing to support the user's pronunciation practice. The curriculum unit adjusts the curriculum based on the practice timing to achieve effective pronunciation practice. As a result, the user can practice pronunciation at the optimal time and maximize learning effectiveness.

[0066] Pronunciation instruction devices not only recognize and provide feedback on a user's pronunciation, but can also enhance the enjoyment of learning by gamifying the user's pronunciation practice. For example, the recognition unit evaluates and scores the user's pronunciation. The feedback unit provides feedback based on the score, increasing the user's motivation. The scenario unit provides scenarios incorporating game elements to make pronunciation practice fun. The curriculum unit provides a curriculum incorporating game elements to improve the user's motivation to learn. As a result, users can practice pronunciation while having fun and improve their learning effectiveness.

[0067] Pronunciation instruction devices not only recognize and provide feedback on a user's pronunciation, but can also enhance learning motivation by integrating the user's pronunciation practice with social media. For example, the recognition unit evaluates and scores the user's pronunciation. The feedback unit provides feedback based on the score, increasing the user's motivation. The scenario unit provides scenarios integrated with social media to support the user's pronunciation practice. The curriculum unit provides a curriculum integrated with social media to improve the user's motivation to learn. As a result, users can practice pronunciation while competing with other users through social media, thereby enhancing the learning effect.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The recognition unit recognizes the user's pronunciation. The recognition unit analyzes the user's pronunciation using, for example, speech recognition technology, collects the user's pronunciation using a microphone, and analyzes it using a speech recognition algorithm. Furthermore, it extracts the characteristics of the user's pronunciation and evaluates the accuracy of the pronunciation. Step 2: The feedback unit provides feedback based on the pronunciation recognized by the recognition unit. The feedback unit provides, for example, audio feedback, text feedback, and visual feedback. Step 3: The scenario team provides scenarios such as travel and shopping. For example, the scenario team uses VR technology to recreate travel scenarios and provides shopping scenarios and conversation scenarios at tourist destinations. Step 4: The curriculum department provides a curriculum tailored to the user's language level. For example, the curriculum department provides level-based curricula for beginner, intermediate, and advanced levels, adjusting the curriculum according to the user's progress and providing a curriculum that strengthens specific skills.

[0070] (Example of form 2) The pronunciation instruction device according to an embodiment of the present invention is a device that uses generative AI to provide instruction up to a certain level of pronunciation. This device recreates scenarios such as travel and shopping in VR, allowing users to learn native pronunciation as if they were actually on a date. Furthermore, because it is offered on a subscription basis, it can overcome challenges such as "not having enough time" or "not having enough money." First, the user puts on the device and immerses themselves in the VR environment. Various scenarios are available, such as shopping at a travel destination or conversations at tourist spots. Through these scenarios, the user can learn native pronunciation. The generative AI recognizes the user's pronunciation and provides feedback in real time. For example, if the user pronounces "English," the generative AI analyzes the pronunciation and provides instruction on the correct pronunciation. In addition, a curriculum tailored to the user's language level is provided. As the language level increases, the scenarios develop into more intimate relationships. This transforms studying into enjoyable and active learning, increasing the motivation to continue learning. Furthermore, because this device can be used at home anytime, it can overcome challenges such as not being able to take long vacations, not having enough money, or anxiety about living abroad. For example, it can help learn a native foreign language without being separated from family for extended periods. Furthermore, it addresses the challenges of domestic language learning, such as the lack of opportunities for interaction outside of school. This device is offered on a monthly subscription basis, allowing users to keep costs down. Moreover, it offers the same specifications as regular VR equipment, enabling users to enjoy other VR content. For example, it allows for video streaming from services like Netflix. In this way, the AI-powered pronunciation instruction device provides an overwhelming sense of immersion, improving interest, engagement, and persistence. Users can easily experience authentic language learning at home. The pronunciation instruction device recognizes the user's pronunciation, provides feedback, and offers scenarios and curricula, enabling them to learn native-like pronunciation.

[0071] The pronunciation instruction device according to this embodiment comprises a recognition unit, a feedback unit, a scenario unit, and a curriculum unit. The recognition unit recognizes the user's pronunciation. The recognition unit analyzes the user's pronunciation using, for example, speech recognition technology. The recognition unit collects the user's pronunciation using, for example, a microphone and analyzes it using a speech recognition algorithm. The recognition unit extracts features of the user's pronunciation and evaluates the accuracy of the pronunciation. The feedback unit provides feedback based on the pronunciation recognized by the recognition unit. The feedback unit provides, for example, audio feedback. The feedback unit provides, for example, text feedback. The feedback unit provides, for example, visual feedback. The scenario unit provides scenarios such as travel and shopping. The scenario unit reproduces a travel scenario using, for example, VR technology. The scenario unit provides, for example, a shopping scenario. The scenario unit provides, for example, a conversation scenario in a tourist area. The curriculum unit provides a curriculum tailored to the user's language level. The curriculum unit provides, for example, beginner, intermediate, and advanced level curricula. The curriculum unit adjusts the curriculum according to the user's progress. The curriculum section provides, for example, curricula to strengthen specific skills. This allows the pronunciation instruction device to learn native pronunciation by recognizing the user's pronunciation, providing feedback, and offering scenarios and curricula.

[0072] The recognition unit recognizes the user's pronunciation. For example, it analyzes the user's pronunciation using speech recognition technology. Specifically, the recognition unit collects the user's pronunciation using a high-sensitivity microphone and analyzes the audio data using digital signal processing technology. The speech recognition algorithm decomposes the audio waveform into frequency components and extracts features. This allows for detailed analysis of features such as the pitch, rhythm, and intonation of the user's pronunciation. Furthermore, in addition to speech recognition technology, the recognition unit evaluates the accuracy of the pronunciation using a machine learning model. For example, it trains a deep learning-based speech model and identifies pronunciation errors by comparing the user's pronunciation with that of a native speaker. The recognition unit can analyze the characteristics of the user's pronunciation in real time and generate evaluation results immediately. This allows the user to quickly understand their pronunciation problems and receive specific guidance for improvement. Additionally, the recognition unit can accumulate user pronunciation data and track long-term changes and progress in pronunciation. This allows for continuous support in improving the user's pronunciation skills.

[0073] The feedback unit provides feedback based on the pronunciation recognized by the recognition unit. For example, the feedback unit provides audio feedback. Specifically, it plays the correct pronunciation of a native speaker in audio format, allowing the user to compare it to their own pronunciation. The feedback unit also provides text feedback. For example, it points out errors in the user's pronunciation in text and explains the correct pronunciation methods and points to note. Furthermore, the feedback unit provides visual feedback. For example, it displays the waveform or spectrogram of the user's pronunciation, visually showing which parts are incorrect. This allows the user to intuitively understand the problems with their pronunciation. The feedback unit can combine audio, text, and visual feedback according to the user's learning style and preferences. Additionally, the feedback unit can adjust the content and frequency of feedback according to the user's progress. For example, it can focus on basic pronunciation instruction for beginners and provide more advanced pronunciation fine-tuning instruction for intermediate and advanced learners. This allows the feedback unit to effectively support the improvement of the user's pronunciation skills.

[0074] The Scenario Department provides scenarios such as travel and shopping. For example, the Scenario Department recreates travel scenarios using VR technology. Specifically, it allows users to experience real-life situations such as checking into a hotel or ordering at a restaurant in a virtual reality environment. This allows users to practically learn the pronunciation skills necessary in real-life situations. The Scenario Department also provides shopping scenarios. For example, it allows users to experience situations such as searching for products in a virtual supermarket or conversing with store clerks. Furthermore, the Scenario Department provides conversation scenarios for tourist destinations. For example, it allows users to experience situations such as conversing with a guide or interacting with other tourists in a virtual tourist destination. Through these scenarios, the Scenario Department enables users to effectively learn the pronunciation skills necessary in real-life situations. In addition, the Scenario Department can adjust the content and difficulty level of the scenarios according to the user's language level and learning objectives. For example, it provides simple conversation scenarios for beginners and more complex conversation scenarios for intermediate and advanced learners. In this way, the Scenario Department can practically support the improvement of users' pronunciation skills.

[0075] The curriculum department provides curricula tailored to the user's language level. For example, it offers beginner, intermediate, and advanced level curricula. Specifically, beginners receive a curriculum focused on basic pronunciation practice and simple conversation practice, while intermediate learners receive more advanced pronunciation practice and practical conversation practice. Advanced learners receive a curriculum that includes advanced practice aimed at achieving near-native speaker pronunciation and specialized conversation situations. Furthermore, the curriculum department can adjust the curriculum according to the user's progress. For example, if a user has difficulty with a particular pronunciation, the curriculum will focus on practicing that pronunciation. Also, if a user wants to strengthen a specific skill, the curriculum will be tailored to that skill. This allows the curriculum department to provide flexible curricula that meet the individual needs of each user. In addition, the curriculum department can optimize the content and progression of the curriculum based on the user's learning history and feedback. This allows the curriculum department to effectively support the improvement of users' pronunciation skills.

[0076] The recognition unit can recognize the user's pronunciation in real time. The recognition unit analyzes the user's pronunciation in real time, for example, using speech recognition technology. The recognition unit collects the user's pronunciation using a microphone, for example, and analyzes it in real time using a speech recognition algorithm. The recognition unit extracts the characteristics of the user's pronunciation in real time and evaluates the accuracy of the pronunciation. This enables immediate feedback by recognizing the user's pronunciation in real time. The real-time processing may be performed using AI, for example, or without AI. For example, the recognition unit can input the audio data collected in real time into a generating AI and have the generating AI perform real-time pronunciation recognition.

[0077] The feedback unit can provide real-time feedback based on recognized pronunciation. For example, the feedback unit can provide real-time audio feedback. For example, the feedback unit can provide real-time text feedback. For example, the feedback unit can provide real-time visual feedback. This allows for rapid improvement of the user's pronunciation by providing real-time feedback. Real-time feedback may be provided using AI, or it may be provided without AI. For example, the feedback unit can input real-time recognized pronunciation data into a generating AI and have the generating AI provide real-time feedback.

[0078] The scenario unit can provide scenarios such as travel and shopping. For example, the scenario unit can recreate travel scenarios using VR technology. For example, the scenario unit can provide shopping scenarios. For example, the scenario unit can provide conversation scenarios in tourist destinations. By providing scenarios such as travel and shopping, it becomes possible to practice pronunciation in a way that is close to real-life situations. The provision of scenarios may be done using AI, for example, or without using AI. For example, the scenario unit can input the user's pronunciation data into a generating AI and have the generating AI select an appropriate scenario.

[0079] The curriculum unit can provide a curriculum tailored to the user's language level. For example, the curriculum unit can provide level-based curricula for beginner, intermediate, and advanced levels. For example, the curriculum unit can adjust the curriculum according to the user's progress. For example, the curriculum unit can provide a curriculum that strengthens specific skills. This enables effective learning by providing a curriculum tailored to the user's language level. Curriculum provision may be done using AI or not. For example, the curriculum unit can input the user's language level data into a generating AI and have the generating AI select an appropriate curriculum.

[0080] The scenario unit can develop scenarios into more intimate relationships as the user's language level improves. For example, the scenario unit can develop travel scenarios into more complex scenarios as the user's language level improves. For example, the scenario unit can develop shopping scenarios into more detailed scenarios as the user's language level improves. For example, the scenario unit can develop conversation scenarios at tourist destinations into more intimate scenarios as the user's language level improves. This allows the user's motivation to learn to be maintained by adjusting scenarios according to their language level. Scenario adjustments may be made using AI, or they may be made without AI. For example, the scenario unit can input the user's language level data into a generating AI and have the generating AI perform appropriate scenario adjustments.

[0081] The curriculum unit can adjust the curriculum according to the user's language level. For example, the curriculum unit can increase the difficulty level of the curriculum as the user's language level improves. For example, the curriculum unit can provide a curriculum that strengthens specific skills as the user's language level improves. For example, the curriculum unit can provide a curriculum that includes more advanced vocabulary and grammar as the user's language level improves. This allows for effective learning by adjusting the curriculum according to the user's language level. Curriculum adjustment may be performed using AI, or it may be performed without AI. For example, the curriculum unit can input the user's language level data into a generating AI and have the generating AI perform appropriate curriculum adjustments.

[0082] The system can be offered in a monthly subscription format. The system offers monthly plans such as a basic plan and a premium plan. The system adjusts the content of the services provided according to the plan selected by the user. The system can limit the number of scenarios or curricula available to the user based on the monthly plan. This allows for cost-effective use by offering the system in a monthly subscription format. The monthly subscription format may be implemented using AI or not. For example, the system can input user usage data into a generating AI and have the generating AI select the optimal subscription plan.

[0083] The system can also provide other VR content. For example, it can offer VR games, educational content, and entertainment content. The system can adjust the content of the services provided according to the VR content selected by the user. For example, the system can be used in conjunction with the user's pronunciation practice when using other VR content. This allows users to enjoy other VR content, thereby increasing user satisfaction. The provision of other VR content may be done using AI or not. For example, the system can input user usage data into a generating AI and have the generating AI select the most suitable VR content.

[0084] The recognition unit can estimate the user's emotions and adjust the accuracy of pronunciation recognition based on the estimated emotions. For example, if the user is nervous, the recognition unit emphasizes subtle differences in pronunciation to improve recognition accuracy. For example, if the user is relaxed, the recognition unit adjusts the recognition accuracy by prioritizing natural pronunciation. For example, if the user is excited, the recognition unit adjusts the recognition accuracy by considering the speed and rhythm of pronunciation. This allows for more accurate pronunciation recognition by adjusting the recognition accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of pronunciation recognition accuracy.

[0085] The recognition unit can analyze the user's past pronunciation data and select the optimal recognition algorithm. For example, the recognition unit can select an algorithm that improves the recognition accuracy for a specific phoneme based on data that the user has previously pronounced. For example, the recognition unit can analyze the user's pronunciation tendencies and apply an individually optimized recognition algorithm. For example, the recognition unit can cluster the user's pronunciation data and apply a common recognition algorithm to users with similar pronunciation patterns. This allows for the application of an individually optimized recognition algorithm by analyzing past pronunciation data. The selection of a recognition algorithm may be performed using AI, for example, or without using AI. For example, the recognition unit can input the user's past pronunciation data into a generating AI and have the generating AI select the optimal recognition algorithm.

[0086] The recognition unit can improve recognition accuracy by taking into account the speed and rhythm of the user's pronunciation during recognition. For example, if the user speaks quickly, the recognition unit adjusts the recognition algorithm to accommodate the speed of pronunciation. For example, if the user speaks slowly, the recognition unit improves recognition accuracy by emphasizing the rhythm of pronunciation. For example, the recognition unit analyzes the rhythm of the user's pronunciation and applies a recognition algorithm based on that rhythm. This improves recognition accuracy by taking into account the speed and rhythm of pronunciation. The adjustment of the recognition algorithm may be performed using AI, for example, or without using AI. For example, the recognition unit can input the user's pronunciation data into a generating AI and have the generating AI perform adjustments to the recognition algorithm based on the speed and rhythm of pronunciation.

[0087] The recognition unit can estimate the user's emotions and determine the priority of pronunciations to recognize based on the estimated emotions. For example, if the user is nervous, the recognition unit will prioritize the recognition of important pronunciations. If the user is relaxed, the recognition unit will recognize pronunciations in a balanced manner. If the user is excited, the recognition unit will prioritize the recognition of specific phonemes. This allows for the priority of pronunciations to be prioritized according to the user's emotions, thereby prioritizing the recognition of important pronunciations. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the recognition unit may be performed using AI, or not using AI. For example, the recognition unit can input user emotion data into a generative AI and have the generative AI determine the pronunciation priority.

[0088] The recognition unit can prioritize the recognition of region-specific pronunciations by considering the user's geographical location information during recognition. For example, if the user is in a specific region, the recognition unit will prioritize the recognition of the region-specific pronunciations of that region. For example, if the user is traveling, the recognition unit will recognize the region-specific pronunciations of the destination. For example, if the user moves to a different region, the recognition unit will prioritize the recognition of the pronunciations of the new region. In this way, by considering geographical location information, region-specific pronunciations can be accurately recognized. The consideration of geographical location information may be performed using AI, for example, or without using AI. For example, the recognition unit can input the user's geographical location information into a generating AI and have the generating AI perform the recognition of region-specific pronunciations.

[0089] The recognition unit can analyze the user's social media activity and recognize relevant pronunciations during recognition. For example, the recognition unit recognizes relevant pronunciations based on the language and expressions the user uses on social media. For example, if the user frequently posts about a particular topic, the recognition unit will prioritize recognizing pronunciations related to that topic. For example, the recognition unit recognizes pronunciations based on the user's interests from their social media activity. This allows for the recognition of pronunciations based on the user's interests by analyzing their social media activity. The analysis of social media activity may be performed using AI or not. For example, the recognition unit can input the user's social media data into a generating AI and have the generating AI perform the recognition of relevant pronunciations.

[0090] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit will provide feedback in gentle words. For example, if the user is relaxed, the feedback unit will provide detailed feedback. For example, if the user is excited, the feedback unit will provide feedback that includes words of encouragement. By adjusting the way it expresses the feedback according to the user's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way it expresses the feedback.

[0091] The feedback unit can adjust the level of detail in the feedback based on the importance of the pronunciation. For example, the feedback unit provides detailed feedback for important pronunciations. For example, the feedback unit provides concise feedback for general pronunciations. For example, the feedback unit provides particularly detailed feedback for pronunciations that are important in a specific scenario. This allows for detailed feedback to be provided for important pronunciations by adjusting the level of detail based on the importance of the pronunciation. The adjustment of the level of detail in the feedback may be performed using AI, or it may be performed without using AI. For example, the feedback unit can input pronunciation importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the feedback.

[0092] The feedback unit can apply different feedback algorithms depending on the pronunciation category during feedback. For example, for noun pronunciation, the feedback unit provides feedback using concrete examples. For verb pronunciation, the feedback unit provides feedback that includes actions. For adjective pronunciation, the feedback unit provides feedback that conveys emotion. By applying a feedback algorithm according to the pronunciation category, more appropriate feedback can be provided. The application of the feedback algorithm may be performed using AI, for example, or without AI. For example, the feedback unit can input pronunciation category data into a generating AI and cause the generating AI to apply an appropriate feedback algorithm.

[0093] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit provides short, concise feedback. For example, if the user is relaxed, the feedback unit provides detailed feedback. For example, if the user is excited, the feedback unit provides feedback that includes words of encouragement. By adjusting the length of the feedback according to the user's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the length of the feedback.

[0094] The feedback unit can prioritize feedback based on when the pronunciation was submitted. For example, the feedback unit may prioritize feedback for recently submitted pronunciations. For example, the feedback unit may also provide feedback for pronunciations that have not been submitted for a long time. For example, the feedback unit may prioritize feedback for pronunciations submitted within a specific time period. This ensures that the most recent pronunciations receive priority feedback by prioritizing feedback based on when they were submitted. The determination of feedback priority may be performed using AI or without AI. For example, the feedback unit can input pronunciation submission time data into a generating AI and have the generating AI determine the feedback priority.

[0095] The feedback unit can adjust the order of feedback based on the relevance of pronunciations during the feedback process. For example, the feedback unit may prioritize providing feedback to highly relevant pronunciations. For example, the feedback unit may also provide feedback to less relevant pronunciations. For example, the feedback unit may prioritize providing feedback to highly relevant pronunciations in a specific scenario. This allows for preferential feedback to be provided to highly relevant pronunciations by adjusting the order of feedback based on the relevance of pronunciations. The adjustment of the feedback order may be performed using AI, or it may be performed without using AI. For example, the feedback unit can input pronunciation relevance data into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0096] The scenario unit can estimate the user's emotions and adjust how the scenario is displayed based on those emotions. For example, if the user is tense, the scenario unit provides a simple and highly visible display method. If the user is relaxed, the scenario unit provides a display method that includes detailed information. If the user is excited, the scenario unit provides a visually stimulating display method. By adjusting how the scenario is displayed according to the user's emotions, a more effective scenario display becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the scenario unit may be performed using AI, or not using AI. For example, the scenario unit can input user emotion data into the generative AI and have the generative AI adjust how the scenario is displayed.

[0097] The scenario unit can optimize the current scenario by referring to past scenario data when providing a scenario. For example, the scenario unit optimizes the current scenario based on scenarios the user has experienced in the past. For example, the scenario unit provides scenarios based on the user's interests and preferences from the user's past scenario data. For example, the scenario unit analyzes the user's past scenario data and provides the most effective scenario. This allows the current scenario to be optimized by referring to past scenario data. Scenario optimization may be performed using AI, for example, or without AI. For example, the scenario unit can input past scenario data into a generating AI and have the generating AI perform the optimization of the current scenario.

[0098] The scenario unit can customize scenarios based on user interests when providing them. For example, the scenario unit can customize scenarios based on topics that the user is interested in. For example, the scenario unit can prioritize providing specific scenarios based on user interests. For example, the scenario unit can analyze user interests and provide the most suitable scenario. By customizing scenarios based on user interests, more effective scenario delivery becomes possible. Scenario customization may be performed using AI, or it may be performed without AI. For example, the scenario unit can input user interest data into a generating AI and have the generating AI perform scenario customization.

[0099] The scenario unit can estimate the user's emotions and determine the priority of scenarios based on the estimated emotions. For example, if the user is tense, the scenario unit will prioritize providing relaxing scenarios. If the user is relaxed, the scenario unit will provide challenging scenarios. If the user is excited, the scenario unit will provide visually stimulating scenarios. By prioritizing scenarios according to the user's emotions, more effective scenario delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scenario unit may be performed using AI, or not using AI. For example, the scenario unit can input user emotion data into the generative AI and have the generative AI determine the priority of scenarios.

[0100] The scenario unit can provide the optimal scenario by considering the user's geographical location information when providing scenarios. For example, if the user is in a specific region, the scenario unit will provide a scenario related to that region. For example, if the user is traveling, the scenario unit will provide a scenario related to the region they are visiting. For example, if the user moves to a different region, the scenario unit will provide a scenario related to the new region. In this way, the optimal scenario can be provided by considering geographical location information. Consideration of geographical location information may be performed using AI, for example, or without using AI. For example, the scenario unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing the optimal scenario.

[0101] The scenario unit can analyze a user's social media activity and provide relevant scenarios when providing them. For example, the scenario unit can provide scenarios based on topics the user shows interest in on social media. For example, if a user frequently posts about a particular topic, the scenario unit can provide scenarios related to that topic. For example, the scenario unit can provide scenarios based on the user's interests and concerns from their social media activity. In this way, by analyzing social media activity, it is possible to provide scenarios based on the user's interests and concerns. The analysis of social media activity may be performed using AI, for example, or without using AI. For example, the scenario unit can input the user's social media data into a generating AI and have the generating AI provide relevant scenarios.

[0102] The curriculum unit can estimate the user's emotions and adjust the curriculum content based on the estimated emotions. For example, if the user is tense, the curriculum unit can provide a relaxing curriculum. For example, if the user is relaxed, the curriculum unit can provide a challenging curriculum. For example, if the user is excited, the curriculum unit can provide a visually stimulating curriculum. By adjusting the curriculum content according to the user's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the curriculum unit may be performed using AI, for example, or without AI. For example, the curriculum unit can input user emotion data into a generative AI and have the generative AI adjust the curriculum content.

[0103] The curriculum unit can provide an optimal curriculum by referring to the user's past learning history when providing a curriculum. For example, the curriculum unit can suggest what the user should learn next based on what the user has learned in the past. For example, the curriculum unit can provide a curriculum that strengthens areas where the user is particularly weak, based on the user's past learning history. For example, the curriculum unit can analyze the user's learning history and suggest the most effective learning sequence. In this way, an optimal curriculum can be provided by referring to past learning history. Referencing the learning history may be done using AI, for example, or without using AI. For example, the curriculum unit can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing an optimal curriculum.

[0104] The curriculum unit can customize the curriculum based on the user's current language level when providing it. For example, the curriculum unit can assess the user's language level and provide a curriculum of appropriate difficulty. For example, the curriculum unit can provide a curriculum that gradually increases in difficulty according to the user's language level. For example, the curriculum unit can provide a curriculum that strengthens specific skills based on the user's language level. This makes learning more effective by customizing the curriculum based on the current language level. Curriculum customization may be performed using AI or not. For example, the curriculum unit can input the user's language level data into a generating AI and have the generating AI perform the curriculum customization.

[0105] The curriculum unit can estimate the user's emotions and determine the priority of the curriculum based on the estimated user emotions. For example, if the user is tense, the curriculum unit will prioritize providing relaxing curriculum content. For example, if the user is relaxed, the curriculum unit will provide challenging curriculum content. For example, if the user is excited, the curriculum unit will provide visually stimulating curriculum content. This allows for more effective learning by prioritizing the curriculum according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the curriculum unit may be performed using AI, for example, or not using AI. For example, the curriculum unit can input user emotion data into a generative AI and have the generative AI determine the priority of the curriculum.

[0106] The curriculum unit can provide the optimal curriculum by considering the user's geographical location information when delivering the curriculum. For example, if the user is in a specific region, the curriculum unit will provide a curriculum related to that region. For example, if the user is traveling, the curriculum unit will provide a curriculum related to the region they are visiting. For example, if the user moves to a different region, the curriculum unit will provide a curriculum related to the new region. In this way, the optimal curriculum can be provided by considering geographical location information. The consideration of geographical location information may be performed using AI, for example, or without using AI. For example, the curriculum unit can input the user's geographical location information into a generating AI and have the generating AI execute the task of providing the optimal curriculum.

[0107] The curriculum department can analyze users' social media activity when providing curriculum and provide relevant curriculum. For example, the curriculum department can provide curriculum based on topics that users show interest in on social media. For example, if a user frequently posts about a particular topic, the curriculum department can provide curriculum related to that topic. For example, the curriculum department can provide curriculum based on users' interests and concerns from their social media activity. In this way, by analyzing social media activity, it is possible to provide curriculum based on users' interests and concerns. The analysis of social media activity may be performed using AI, for example, or without using AI. For example, the curriculum department can input user social media data into a generating AI and have the generating AI provide relevant curriculum.

[0108] A subscription model can estimate a user's emotions and adjust the subscription plan based on those emotions. For example, if a user is stressed, the subscription model might suggest a short-term plan. If a user is relaxed, the subscription model might suggest a long-term plan. If a user is excited, the subscription model might suggest a plan with perks. This allows for the provision of a more appropriate plan by adjusting the subscription plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in a subscription model may be performed using AI or not. For example, a subscription model can input user emotion data into a generative AI and have the generative AI perform the adjustment of the subscription plan.

[0109] A subscription model allows for the provision of the optimal plan by referencing the user's past usage history when providing a subscription. For example, a subscription model might suggest the optimal plan based on the plans the user has used in the past. For example, a subscription model might suggest plans that the user has used most frequently based on their past usage history. For example, a subscription model might analyze the user's usage history and suggest the most effective plan. In this way, the optimal plan can be provided by referring to past usage history. Referencing usage history may be done using AI, for example, or without using AI. For example, a subscription model could input the user's past usage history data into a generating AI and have the generating AI perform the task of providing the optimal plan.

[0110] The subscription model can estimate the user's emotions and adjust the subscription renewal frequency based on those emotions. For example, if the user is stressed, the subscription model might set a lower renewal frequency. If the user is relaxed, the subscription model might set a higher renewal frequency. If the user is excited, the subscription model might suggest a renewal with special benefits. This allows for more appropriate renewals by adjusting the subscription renewal frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the subscription model may be performed using AI or not. For example, the subscription model can input user emotion data into a generative AI and have the generative AI adjust the subscription renewal frequency.

[0111] A subscription model can provide the optimal plan by considering the user's geographical location when providing a subscription. For example, if the user is in a specific region, the subscription model will provide a plan relevant to that region. For example, if the user is traveling, the subscription model will provide a plan relevant to the region they are visiting. For example, if the user moves to a different region, the subscription model will provide a plan relevant to the new region. In this way, the optimal plan can be provided by considering geographical location. Consideration of geographical location may be done using AI, for example, or without using AI. For example, the subscription model can input the user's geographical location into a generating AI and have the generating AI perform the task of providing the optimal plan.

[0112] The feature that allows users to enjoy other VR content can estimate the user's emotions and adjust the display method of the VR content based on the estimated emotions. For example, if the user is nervous, the feature can provide a simple and highly visible display method. If the user is relaxed, the feature can provide a display method that includes detailed information. If the user is excited, the feature can provide a visually stimulating display method. This allows for a more effective display by adjusting the display method of VR content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feature that allows users to enjoy other VR content may be performed using AI or not using AI. For example, the feature that allows users to enjoy other VR content can input user emotion data into a generative AI and have the generative AI adjust the display method of the VR content.

[0113] The feature that allows users to enjoy other VR content can provide optimal content by referencing the user's past viewing history when providing VR content. For example, this feature can suggest related content based on content the user has previously viewed. It can also suggest content in genres the user is particularly interested in based on their past viewing history. Furthermore, it can analyze the user's viewing history to suggest the most effective content. This allows for the provision of optimal content by referencing past viewing history. This reference to viewing history may be performed using AI or without AI. For example, the feature can input the user's past viewing history data into a generating AI, which can then perform the task of providing optimal content.

[0114] The feature that allows users to enjoy other VR content can estimate the user's emotions and prioritize VR content based on those emotions. For example, if the user is tense, the feature will prioritize relaxing content. If the user is relaxed, it will prioritize challenging content. If the user is excited, it will prioritize visually stimulating content. This allows for more effective content delivery by prioritizing VR content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feature that allows users to enjoy other VR content may be performed using AI or not. For example, the feature that allows users to enjoy other VR content can input user emotion data into a generative AI and have the generative AI determine the priority of VR content.

[0115] The feature that allows users to enjoy other VR content can provide optimal content by considering the user's geographical location when providing VR content. For example, if the user is in a specific region, the feature can provide content related to that region. For example, if the user is traveling, the feature can provide content related to the region they are visiting. For example, if the user moves to a different region, the feature can provide content related to the new region. In this way, the optimal content can be provided by considering geographical location. The consideration of geographical location may be done using AI, for example, or without using AI. For example, the feature that allows users to enjoy other VR content can input the user's geographical location into a generating AI and have the generating AI perform the task of providing the optimal content.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The pronunciation instruction device not only recognizes the user's pronunciation and provides feedback, but can also record the user's pronunciation progress and provide regular progress reports. For example, the recognition unit periodically collects the user's pronunciation data and evaluates their progress. The feedback unit generates and provides progress reports to the user. The scenario unit adjusts the scenario according to the user's progress to support more effective learning. The curriculum unit adjusts the curriculum based on progress to maximize the user's learning effectiveness. This allows the user to check their progress and continue learning while maintaining motivation.

[0118] The pronunciation instruction device not only recognizes the user's pronunciation but can also analyze the characteristics of the user's pronunciation and provide a individually optimized pronunciation practice plan. For example, the recognition unit extracts the characteristics of the user's pronunciation and identifies pronunciation problems for specific phonemes. The feedback unit generates and provides a practice plan for those specific phonemes. The scenario unit provides scenarios that focus on specific phonemes to support the user's pronunciation practice. The curriculum unit adjusts the curriculum based on the user's pronunciation characteristics to achieve effective pronunciation practice. As a result, the user can overcome their pronunciation problems and acquire more accurate pronunciation.

[0119] The pronunciation instruction device not only recognizes the user's pronunciation and provides feedback, but can also analyze the user's pronunciation practice history and suggest the optimal practice timing. For example, the recognition unit collects the user's pronunciation practice history and analyzes the frequency and timing of practice. The feedback unit suggests the optimal practice timing and notifies the user. The scenario unit provides a scenario based on the suggested practice timing to support the user's pronunciation practice. The curriculum unit adjusts the curriculum based on the practice timing to achieve effective pronunciation practice. As a result, the user can practice pronunciation at the optimal time and maximize learning effectiveness.

[0120] Pronunciation instruction devices not only recognize and provide feedback on a user's pronunciation, but can also enhance the enjoyment of learning by gamifying the user's pronunciation practice. For example, the recognition unit evaluates and scores the user's pronunciation. The feedback unit provides feedback based on the score, increasing the user's motivation. The scenario unit provides scenarios incorporating game elements to make pronunciation practice fun. The curriculum unit provides a curriculum incorporating game elements to improve the user's motivation to learn. As a result, users can practice pronunciation while having fun and improve their learning effectiveness.

[0121] Pronunciation instruction devices not only recognize and provide feedback on a user's pronunciation, but can also enhance learning motivation by integrating the user's pronunciation practice with social media. For example, the recognition unit evaluates and scores the user's pronunciation. The feedback unit provides feedback based on the score, increasing the user's motivation. The scenario unit provides scenarios integrated with social media to support the user's pronunciation practice. The curriculum unit provides a curriculum integrated with social media to improve the user's motivation to learn. As a result, users can practice pronunciation while competing with other users through social media, thereby enhancing the learning effect.

[0122] The pronunciation instruction device can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, the recognition unit estimates the user's emotions, and if the user is nervous, the feedback unit provides feedback in gentle words. If the user is relaxed, the feedback unit provides detailed feedback. If the user is excited, the feedback unit provides feedback that includes words of encouragement. In this way, by adjusting the content of the feedback according to the user's emotions, more effective feedback can be provided.

[0123] The pronunciation instruction device can estimate the user's emotions and adjust the difficulty of the scenario based on those emotions. For example, the recognition unit estimates the user's emotions, and if the user is nervous, the scenario unit provides an easy scenario. If the user is relaxed, the scenario unit provides a more difficult scenario. If the user is excited, the scenario unit provides a visually stimulating scenario. By adjusting the difficulty of the scenario according to the user's emotions, more effective learning becomes possible.

[0124] The pronunciation instruction device can estimate the user's emotions and adjust the pace of the curriculum based on those emotions. For example, the recognition unit estimates the user's emotions, and if the user is tense, the curriculum unit slows down the pace. If the user is relaxed, the curriculum unit speeds up the pace. If the user is excited, the curriculum unit provides a curriculum that includes visually stimulating content. By adjusting the pace of the curriculum according to the user's emotions, more effective learning becomes possible.

[0125] The pronunciation instruction device can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, the recognition unit estimates the user's emotions, and if the user is tense, the feedback unit provides immediate feedback. If the user is relaxed, the feedback unit provides feedback with a slight delay. If the user is excited, the feedback unit provides feedback that includes words of encouragement. This allows for more effective feedback by adjusting the timing of feedback according to the user's emotions.

[0126] The pronunciation instruction device can estimate the user's emotions and select a scenario based on those emotions. For example, the recognition unit estimates the user's emotions, and if the user is tense, the scenario unit provides a relaxing scenario. If the user is relaxed, the scenario unit provides a challenging scenario. If the user is excited, the scenario unit provides a visually stimulating scenario. By selecting a scenario according to the user's emotions, more effective learning becomes possible.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The recognition unit recognizes the user's pronunciation. The recognition unit analyzes the user's pronunciation using, for example, speech recognition technology, collects the user's pronunciation using a microphone, and analyzes it using a speech recognition algorithm. Furthermore, it extracts the characteristics of the user's pronunciation and evaluates the accuracy of the pronunciation. Step 2: The feedback unit provides feedback based on the pronunciation recognized by the recognition unit. The feedback unit provides, for example, audio feedback, text feedback, and visual feedback. Step 3: The scenario team provides scenarios such as travel and shopping. For example, the scenario team uses VR technology to recreate travel scenarios and provides shopping scenarios and conversation scenarios at tourist destinations. Step 4: The curriculum department provides a curriculum tailored to the user's language level. For example, the curriculum department provides level-based curricula for beginner, intermediate, and advanced levels, adjusting the curriculum according to the user's progress and providing a curriculum that strengthens specific skills.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0131] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the recognition unit, feedback unit, scenario unit, and curriculum unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit collects the user's pronunciation using the microphone 38B of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The feedback unit provides audio and visual feedback using the speaker 40B and display 40A of the smart device 14. The scenario unit provides a scenario using VR technology using the control unit 46A of the smart device 14. The curriculum unit provides a curriculum tailored to the user's language level using the specific processing unit 290 of the data processing unit 12. The recognition unit can input the user's emotional data into a generating AI and have the generating AI adjust the accuracy of pronunciation recognition. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the recognition unit, feedback unit, scenario unit, and curriculum unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit collects the user's pronunciation using the microphone 238 of the smart glasses 214 and analyzes it using the identification processing unit 290 of the data processing unit 12. The feedback unit provides audio and visual feedback using the speaker 240 and display of the smart glasses 214. The scenario unit provides a scenario using VR technology using the control unit 46A of the smart glasses 214. The curriculum unit provides a curriculum tailored to the user's language level using the identification processing unit 290 of the data processing unit 12. The recognition unit can input the user's emotional data into a generating AI and have the generating AI adjust the accuracy of pronunciation recognition. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the recognition unit, feedback unit, scenario unit, and curriculum unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit collects the user's pronunciation using the microphone 238 of the headset terminal 314 and analyzes it using the specific processing unit 290 of the data processing unit 12. The feedback unit provides audio and visual feedback using the speaker 240 and display 343 of the headset terminal 314. The scenario unit provides a scenario using VR technology using the control unit 46A of the headset terminal 314. The curriculum unit provides a curriculum tailored to the user's language level using the specific processing unit 290 of the data processing unit 12. The recognition unit can input the user's emotional data into a generating AI and have the generating AI adjust the accuracy of pronunciation recognition. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0167] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0169] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0171] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0172] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0173] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the recognition unit, feedback unit, scenario unit, and curriculum unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recognition unit collects the user's pronunciation using the microphone 238 of the robot 414 and analyzes it using the specific processing unit 290 of the data processing unit 12. The feedback unit provides audio and visual feedback using the speaker 240 and display of the robot 414. The scenario unit provides a scenario using VR technology using the control unit 46A of the robot 414. The curriculum unit provides a curriculum tailored to the user's language level using the specific processing unit 290 of the data processing unit 12. The recognition unit can input the user's emotional data into a generating AI and have the generating AI adjust the accuracy of pronunciation recognition. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0182] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0184] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0185] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0186] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0190] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0191] 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.

[0192] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0193] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0194] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0195] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0197] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0198] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0199] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0200] (Note 1) A recognition unit that recognizes the user's pronunciation, A feedback unit provides feedback based on the pronunciation recognized by the recognition unit, The scenario department provides scenarios for travel, shopping, etc. It includes a curriculum department that provides a curriculum tailored to the user's language level. A system characterized by the following features. (Note 2) The recognition unit, Recognizes the user's pronunciation in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is Provides real-time feedback based on recognized pronunciation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned scenario section is, Provides scenarios such as travel and shopping. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned curriculum department, We provide a curriculum tailored to the user's language level. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned scenario section is, As the user's language level improves, the scenario develops into a more intimate relationship. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned curriculum department, The curriculum is adjusted according to the user's language level. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned system, It is offered on a monthly subscription basis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned system, You can also enjoy other VR content. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recognition unit, It estimates the user's emotions and adjusts the accuracy of pronunciation recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recognition unit, The system analyzes the user's past pronunciation data and selects the optimal recognition algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recognition unit, During recognition, the system takes into account the user's pronunciation speed and rhythm to improve recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 13) The recognition unit, It estimates the user's emotions and determines the priority of pronunciations to recognize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recognition unit, During recognition, the system prioritizes recognizing region-specific pronunciations by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The recognition unit, During recognition, the system analyzes the user's social media activity and recognizes relevant pronunciations. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the pronunciation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback unit is During feedback, different feedback algorithms are applied depending on the pronunciation category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, we will prioritize the feedback based on when the pronunciation was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is During feedback, adjust the order of feedback based on the relevance of pronunciation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned scenario section is, It estimates the user's emotions and adjusts how the scenario is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned scenario section is, When providing a scenario, the current scenario is optimized by referencing past scenario data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned scenario section is, When providing scenarios, customize them based on the user's interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned scenario section is, It estimates user emotions and prioritizes scenarios based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned scenario section is, When providing scenarios, we will provide the optimal scenario considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned scenario section is, When providing scenarios, we analyze the user's social media activity and provide relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned curriculum department, The system estimates the user's emotions and adjusts the curriculum content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned curriculum department, When providing a curriculum, we refer to the user's past learning history to provide the most suitable curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned curriculum department, When providing the curriculum, customize it based on the user's current language level. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned curriculum department, It estimates user emotions and determines curriculum priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned curriculum department, When providing a curriculum, we take the user's geographical location into consideration to provide the most suitable curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned curriculum department, When providing curriculum, we analyze users' social media activity and provide relevant curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned subscription format is It estimates user sentiment and adjusts subscription plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned subscription format is When providing a subscription, we refer to the user's past usage history to provide the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned subscription format is It estimates the user's sentiment and adjusts the subscription renewal frequency based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned subscription format is When providing a subscription, we take the user's geographical location into consideration to offer the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 38) The function that allows you to enjoy other VR content mentioned above is, It estimates the user's emotions and adjusts how VR content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The function that allows you to enjoy other VR content mentioned above is, When providing VR content, we refer to the user's past viewing history to provide the most suitable content. The system described in Appendix 1, characterized by the features described herein. (Note 40) The function that allows you to enjoy other VR content mentioned above is, It estimates the user's emotions and prioritizes VR content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The function that allows you to enjoy other VR content mentioned above is, When providing VR content, we will provide the most suitable content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that recognizes the user's pronunciation, A feedback unit provides feedback based on the pronunciation recognized by the recognition unit, The scenario department provides scenarios for travel, shopping, etc. It includes a curriculum department that provides a curriculum tailored to the user's language level. A system characterized by the following features.

2. The recognition unit, Recognizes the user's pronunciation in real time. The system according to feature 1.

3. The aforementioned feedback unit is Provides real-time feedback based on recognized pronunciation. The system according to feature 1.

4. The aforementioned scenario section is, Provides scenarios such as travel and shopping. The system according to feature 1.

5. The aforementioned curriculum department, We provide a curriculum tailored to the user's language level. The system according to feature 1.

6. The aforementioned scenario section is, As the user's language level improves, the scenario develops into a more intimate relationship. The system according to feature 1.

7. The aforementioned curriculum department, The curriculum is adjusted according to the user's language level. The system according to feature 1.

8. The aforementioned system, It is offered on a monthly subscription basis. The system according to feature 1.

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