system

Abstract

The system according to this embodiment aims to support effective language learning by providing practice problems and conversation sessions tailored to the user's learning progress. [Solution] The system according to the embodiment comprises a generation unit, an analysis unit, and a feedback unit. The generation unit generates practice problems and conversation sessions tailored to the user's learning progress. The analysis unit analyzes the practice problems and conversation sessions generated by the generation unit and points out errors in pronunciation and grammar. The feedback unit provides immediate feedback to the user based on the errors pointed out by the analysis unit.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, it is difficult to provide appropriate practice problems and conversation sessions according to the learning progress of users, and there is room for improvement in supporting effective language learning.

[0005] The system according to the embodiment aims to provide practice problems and conversation sessions according to the learning progress of users and support effective language learning.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, an analysis unit, and a feedback unit. The generation unit generates practice problems and conversation sessions tailored to the user's learning progress. The analysis unit analyzes the practice problems and conversation sessions generated by the generation unit and points out errors in pronunciation and grammar. The feedback unit provides immediate feedback to the user based on the errors pointed out by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can support effective language learning by providing practice problems and conversation sessions tailored to the user's learning progress. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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 reception 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 reception 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 generative AI agent according to an embodiment of the present invention is a system that provides support for users to effectively learn a new language through interactive conversations and practice exercises. This generative AI agent generates practice exercises and conversation sessions tailored to the user's learning progress when the user selects and inputs the language they wish to learn. This allows the user to learn the language effectively at their own pace. For example, the user selects the language they wish to learn and inputs it into the generative AI agent. At this time, the user can set their level and learning goals. For example, settings can be adjusted according to the user's needs, such as wanting to learn everyday conversation at a beginner level or specialized terminology at a business level. Next, the generative AI agent generates practice exercises and conversation sessions tailored to the user's learning progress. The generative AI agent uses natural language processing (NLP) to analyze the interaction with the user and point out pronunciation and grammatical errors. It also generates personalized lessons and practice exercises according to the user's learning progress. For example, if a user has difficulty with a particular grammatical item, the agent will provide many practice exercises related to that grammatical item to support effective learning. Furthermore, the generative AI agent provides the user with immediate feedback. As users complete practice exercises or participate in conversation sessions, the generative AI agent provides immediate feedback to check their understanding. This allows users to grasp their learning progress and make necessary corrections. This system enables users to learn a new language effectively at their own pace. The generative AI agent supports effective and continuous language learning by providing practice exercises and conversation sessions tailored to the user's learning progress and offering immediate feedback. Furthermore, because the generative AI agent provides personalized lessons that meet the user's needs, it can reduce the cost of individual tutoring and provide appropriate learning resources. For example, if a student wants to learn English, the generative AI agent will provide English practice exercises and conversation sessions tailored to the student's learning progress.Students can learn English at their own pace and effectively progress through their studies by receiving immediate feedback from a generative AI agent. Furthermore, for business professionals who wish to learn a new language, the generative AI agent provides practice exercises and conversation sessions specifically tailored to business and technical terms. This allows business professionals to effectively acquire the language skills necessary for their work. In this way, the generative AI agent supports effective and continuous language learning by providing practice exercises and conversation sessions tailored to the user's learning progress and offering immediate feedback. This enables users to learn a new language at their own pace and overcome communication barriers to succeed internationally. Thus, the generative AI agent supports effective and continuous language learning by providing practice exercises and conversation sessions tailored to the user's learning progress and offering immediate feedback.

[0029] The generative AI agent according to this embodiment comprises a generation unit, an analysis unit, and a feedback unit. The generation unit generates practice problems and conversation sessions tailored to the user's learning progress. For example, the generation unit generates practice problems and conversation sessions tailored to the user's learning progress when the user selects a language they want to learn and inputs it into the generative AI agent. The generation unit can be configured according to the user's needs, for example, whether the user wants to learn everyday conversation at a beginner level or learn specialized terminology at a business level. The generation unit analyzes the interaction with the user using natural language processing (NLP) and points out errors in pronunciation and grammar. For example, if the user has difficulty with a particular grammatical item, the generation unit supports effective learning by providing many practice problems related to that grammatical item. The generation unit can provide personalized lessons tailored to the user's learning progress using generative AI. For example, the generation unit provides customized lessons based on the user's past learning history and learning goals. The analysis unit analyzes the practice problems and conversation sessions generated by the generation unit and points out errors in pronunciation and grammar. The analysis unit analyzes user interactions and identifies pronunciation and grammatical errors. The analysis unit uses natural language processing (NLP) to analyze user interactions and identify pronunciation and grammatical errors. The analysis unit can provide learning resources tailored to user needs using generative AI. For example, the analysis unit generates personalized lessons and practice exercises based on the user's learning progress. The feedback unit provides immediate feedback to the user based on the errors identified by the analysis unit. For example, the feedback unit provides feedback as the user solves practice exercises or participates in conversation sessions to check the user's understanding. The feedback unit can use generative AI to check the user's understanding and make necessary corrections. For example, if a user has difficulty with a particular grammatical item, the feedback unit can support effective learning by providing more practice exercises related to that grammatical item.As a result, the AI ​​agent according to the embodiment can generate practice problems and conversation sessions tailored to the user's learning progress, point out pronunciation and grammatical errors, and provide immediate feedback, thereby supporting effective language learning.

[0030] The generation unit generates practice exercises and conversation sessions tailored to the user's learning progress. For example, the user selects the language they want to learn and inputs it into the generation AI agent, which then generates practice exercises and conversation sessions that match the user's learning progress. Specifically, the generation unit considers the grammatical structure and vocabulary of the language selected by the user and automatically creates practice exercises at an appropriate level. For example, if a user is a beginner and wants to learn everyday conversation, the generation unit generates basic greetings, self-introductions, and simple conversational sentences related to daily life. On the other hand, if a user wants to learn specialized terminology at a business level, the generation unit generates practice exercises that include specialized terminology and phrases used in business settings. The generation unit uses natural language processing (NLP) technology to analyze the interaction with the user and point out pronunciation and grammatical errors. For example, if a user has difficulty with a particular grammatical item, the generation unit supports effective learning by presenting many practice exercises related to that grammatical item. The generation unit can provide customized lessons based on the user's past learning history and learning goals. For example, it considers what the user has learned in the past and the goals they have achieved, and automatically suggests what they should learn next. This allows the generation unit to provide personalized lessons tailored to the user's learning progress, supporting effective learning. Furthermore, the generation unit can adjust the format and difficulty level of practice exercises according to the user's learning style and preferences. For example, if the user prefers visual learning, it can generate practice exercises that include images and videos. Similarly, if the user prefers auditory learning, it can generate practice exercises that include audio. This allows the generation unit to flexibly adapt to the user's learning style and preferences, supporting effective learning.

[0031] The analysis unit analyzes the practice exercises and conversation sessions generated by the generation unit and points out pronunciation and grammatical errors. For example, the analysis unit analyzes user interactions and points out pronunciation and grammatical errors. Specifically, the analysis unit analyzes the audio data spoken by the user and detects errors at the phoneme level. For example, if a particular phoneme is not pronounced correctly, it emphasizes that phoneme to provide feedback to the user. Regarding grammatical errors, it analyzes the text data entered by the user and detects errors in grammatical structure. For example, it points out errors such as subject-verb agreement and tense errors and presents the correct grammatical structure. The analysis unit uses natural language processing (NLP) technology to analyze user interactions and point out pronunciation and grammatical errors. For example, if a user has difficulty with a particular grammatical item, it supports effective learning by presenting many practice exercises related to that grammatical item. The analysis unit can use generational AI to provide learning resources tailored to the user's needs. For example, it can generate personalized lessons and practice exercises according to the user's learning progress. This allows the analysis unit to provide appropriate feedback tailored to the user's learning progress, supporting effective learning. Furthermore, the analysis unit can analyze the user's learning history and develop long-term learning plans. For example, based on what the user has learned in the past and the goals they have achieved, it can suggest what they should learn next and provide a long-term learning plan. This allows the analysis unit to continuously monitor the user's learning progress and support effective learning.

[0032] The feedback unit provides immediate feedback to the user based on errors identified by the analysis unit. For example, the feedback unit provides real-time feedback as the user solves practice problems or participates in conversation sessions, confirming the user's understanding. Specifically, the feedback unit instantly determines the correctness of the user's answers to practice problems and provides the correct answers and explanations. For instance, if the user solves a grammar problem, it presents the correct grammatical structure and explains the cause of the error. In conversation sessions, it points out the user's pronunciation and grammatical errors in real time and presents the correct pronunciation and grammatical structure. The feedback unit can use generative AI to confirm the user's understanding and make necessary corrections. For example, if a user has difficulty with a particular grammatical item, it can support effective learning by presenting many practice problems related to that grammatical item. The feedback unit can analyze the user's learning history and develop long-term learning plans. For example, based on what the user has learned in the past and the goals they have achieved, it suggests what they should learn next and provides a long-term learning plan. This allows the feedback unit to continuously monitor the user's learning progress and support effective learning. Furthermore, the feedback unit can adjust the format and content of feedback according to the user's learning style and preferences. For example, if the user prefers visual learning, feedback including images and videos can be provided. Similarly, if the user prefers auditory learning, feedback including audio can be provided. This allows the feedback unit to flexibly respond to the user's learning style and preferences, thereby supporting effective learning.

[0033] The generation unit can provide personalized lessons tailored to the user's learning progress. For example, the generation unit provides customized lessons based on the user's past learning history and learning goals. The generation unit can use generation AI to provide personalized lessons tailored to the user's learning progress. For example, the generation unit provides customized lessons based on the user's past learning history and learning goals. This enhances the effectiveness of individualized instruction by providing personalized lessons tailored to the user's learning progress.

[0034] The analysis unit can provide learning resources tailored to the user's needs. For example, the analysis unit generates personalized lessons and practice problems according to the user's learning progress. The analysis unit can provide learning resources tailored to the user's needs using generation AI. For example, the analysis unit generates personalized lessons and practice problems according to the user's learning progress. This supports effective learning by providing learning resources tailored to the user's needs.

[0035] The feedback unit can check the user's understanding and make necessary corrections. For example, the feedback unit provides immediate feedback to check the user's understanding when the user solves practice problems or participates in conversation sessions. The feedback unit can use generative AI to check the user's understanding and make necessary corrections. For example, if the user has difficulty with a particular grammar point, the feedback unit will support effective learning by providing many practice problems related to that grammar point. This allows the system to check the user's understanding and make necessary corrections, thereby improving the effectiveness of learning.

[0036] The generation unit can analyze the user's past learning history and determine the optimal order of practice questions. For example, the generation unit can prioritize questions the user has answered incorrectly in the past. The generation unit can also postpone questions in areas the user excels at and present questions in areas the user struggles with first. The generation unit can also identify specific patterns from the user's learning history and present questions based on those patterns. In this way, by analyzing the user's past learning history, the optimal order of practice questions can be provided, thereby improving learning effectiveness. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past learning history data into a generation AI and have the generation AI determine the optimal order of practice questions.

[0037] The generation unit can provide different question formats depending on the user's learning objectives when generating practice questions. For example, if the user wants to learn everyday conversation, the generation unit can provide conversation-style questions. If the user wants to learn business terminology, the generation unit can also provide questions that include specialized terminology. If the user wants to prepare for an exam, the generation unit can also provide exam-style questions. In this way, by providing question formats that match the user's learning objectives, effective learning can be supported. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's learning objective data into a generation AI and have the generation AI determine an appropriate question format.

[0038] The generation unit can provide highly relevant practice questions by considering the user's geographical location. For example, if the user is in a specific region, the generation unit can provide questions related to the culture and history of that region. If the user is traveling, the generation unit can also provide questions related to the language and culture of the travel destination. The generation unit can also provide questions related to everyday conversations in the area where the user lives. By considering the user's geographical location, the generation unit can provide highly relevant questions and enhance learning effectiveness. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate highly relevant questions.

[0039] The generation unit can analyze the user's social media activity and incorporate relevant topics when generating practice questions. For example, the generation unit can provide questions related to topics the user has shown interest in on social media. The generation unit can also provide questions related to the content of accounts the user follows. The generation unit can also provide questions related to content the user has posted. In this way, by analyzing the user's social media activity, relevant topics can be incorporated and learning can be made more engaging. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI select relevant topics.

[0040] The analysis unit can analyze the user's past dialogue history and identify and point out specific error patterns. For example, the analysis unit can identify errors that the user has frequently made in the past and point out their patterns. The analysis unit can also identify errors related to specific grammatical items from the user's dialogue history. The analysis unit can also identify and point out error patterns in the user's pronunciation. This allows for the identification of specific error patterns by analyzing the user's past dialogue history, thereby improving the effectiveness of learning. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past dialogue history data into a generating AI and have the generating AI perform the identification of error patterns.

[0041] The analysis unit can apply different analysis algorithms depending on the user's learning style during analysis. For example, if the user is a visual learner, the analysis unit can provide visual feedback. If the user is an auditory learner, the analysis unit can also provide audio feedback. If the user is a haptic learner, the analysis unit can also provide interactive feedback. This allows for improved learning effectiveness by applying analysis algorithms tailored to the user's learning style. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning style data into a generating AI and have the generating AI apply an appropriate analysis algorithm.

[0042] The analysis unit can identify region-specific pronunciation and grammatical errors by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit can identify region-specific pronunciation errors. The analysis unit can also identify region-specific grammatical errors if the user is in a specific region. If the user is traveling, the analysis unit can also identify region-specific pronunciation and grammatical errors in the destination region. By considering the user's geographical location, the analysis unit can identify region-specific pronunciation and grammatical errors and improve the learning effect. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI identify region-specific pronunciation and grammatical errors.

[0043] The analysis unit can improve the accuracy of error detection by referring to the user's relevant literature during analysis. For example, the analysis unit can improve the accuracy of error detection by referring to literature in the language the user is learning. The analysis unit can also improve the accuracy of error detection by referring to textbooks and learning materials the user is using. The analysis unit can also improve the accuracy of error detection by referring to online resources the user is using. In this way, by referring to the user's relevant literature, the accuracy of error detection can be improved and the effectiveness of learning can be enhanced. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the error detection accuracy improvement.

[0044] The feedback unit can provide optimal feedback by referring to the user's past learning history. For example, the feedback unit can provide detailed feedback on problems the user has previously answered incorrectly. The feedback unit can also identify specific patterns from the user's learning history and provide feedback based on those patterns. The feedback unit can also refer to the user's past learning history and provide feedback according to the user's progress. This allows for the provision of optimal feedback by referring to the user's past learning history, thereby enhancing the effectiveness of learning. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0045] The feedback unit can provide different feedback formats depending on the user's learning objectives. For example, if the user wants to learn everyday conversation, the feedback unit can provide conversational feedback. If the user wants to learn business terminology, the feedback unit can also provide feedback that includes technical terms. If the user wants to prepare for an exam, the feedback unit can also provide exam-style feedback. By providing feedback formats that match the user's learning objectives, the effectiveness of learning can be enhanced. 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 the user's learning objective data into a generating AI and have the generating AI perform the task of providing an appropriate feedback format.

[0046] The feedback unit can provide region-specific feedback by taking into account the user's geographical location information during the feedback process. For example, if the user is in a specific region, the feedback unit can provide region-specific pronunciation and grammar feedback. If the user is traveling, the feedback unit can also provide region-specific feedback for the destination region. The feedback unit can also provide feedback related to the region where the user lives. By considering the user's geographical location information, region-specific feedback can be provided, thereby enhancing the effectiveness of learning. 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 the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific feedback.

[0047] The feedback unit can analyze the user's social media activity and provide relevant feedback when providing feedback. For example, the feedback unit can provide feedback related to topics the user is interested in on social media. The feedback unit can also provide feedback related to the content of accounts the user follows. The feedback unit can also provide feedback related to content the user has posted. In this way, by analyzing the user's social media activity, relevant feedback can be provided and learning can be stimulated. 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 the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant feedback.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The generation unit not only generates practice problems and conversation sessions tailored to the user's learning progress, but can also provide learning materials in formats suited to the user's learning style. For example, it can provide visually-oriented learners with materials that heavily utilize images and videos, and auditory learners with audio materials and podcasts. Furthermore, it can provide tactile learners with interactive simulations and practical exercises. This allows for the provision of optimal learning materials tailored to the user's learning style, thereby enhancing learning effectiveness.

[0050] The feedback system can customize the content of feedback according to the user's learning progress. For example, it can provide basic feedback to beginners and more detailed feedback to intermediate learners. Furthermore, it can provide expert feedback to advanced learners, ensuring appropriate feedback for each level. It can also adjust the content of feedback according to the user's learning goals. This allows for optimal feedback tailored to the user's learning progress and goals, thereby enhancing learning effectiveness.

[0051] The generation unit can select topics based on the user's interests when generating practice problems and conversation sessions tailored to the user's learning progress. For example, if the user is interested in sports, it can select sports-related topics; if the user is interested in music, it can select music-related topics. Furthermore, if the user is interested in a particular culture or region, it can select topics related to that culture or region. By providing topics that match the user's interests, it can stimulate their interest in learning and support effective learning.

[0052] The generation unit, when generating practice questions and conversation sessions tailored to the user's learning progress, can consider the user's learning history and focus on questions they have previously answered incorrectly or areas they struggle with. For example, it can present many questions related to grammar points the user has answered incorrectly in the past, and provide practice questions related to pronunciation, which the user struggles with. Furthermore, it can postpone questions in areas the user excels at and present questions in areas they struggle with first. By providing optimal practice questions that take the user's learning history into account, the learning effect can be enhanced.

[0053] The generation unit can generate practice questions and conversation sessions tailored to the user's learning progress, taking into account the user's geographical location to provide questions related to the culture and history specific to that region. For example, if the user is in a particular region, it can provide questions related to the culture and history of that region; if the user is traveling, it can provide questions related to the language and culture of the travel destination. Furthermore, it can also provide everyday conversation questions related to the area where the user lives. By providing highly relevant questions that take the user's geographical location into account, the learning effect can be enhanced.

[0054] The feedback section can customize the format of feedback according to the user's learning progress. For example, it can provide basic feedback to beginners and more detailed feedback to intermediate learners. Furthermore, it can provide expert feedback to advanced learners, ensuring appropriate feedback for each level. It can also adjust the format of feedback according to the user's learning goals. This allows for optimal feedback tailored to the user's learning progress and goals, thereby enhancing learning effectiveness.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The generation unit generates practice exercises and conversation sessions tailored to the user's learning progress. For example, by selecting the language the user wants to learn and inputting it into the generation AI agent, the unit generates practice exercises and conversation sessions that match the user's learning progress. The generation unit can be configured according to the user's needs and uses natural language processing (NLP) to analyze the interaction with the user and point out pronunciation and grammatical errors. The generation unit can also provide customized lessons based on the user's past learning history and learning goals. Step 2: The analysis unit analyzes the practice exercises and conversation sessions generated by the generation unit and points out pronunciation and grammatical errors. The analysis unit uses natural language processing (NLP) to analyze the interaction with the user and point out pronunciation and grammatical errors. Furthermore, the analysis unit can use the generation AI to provide learning resources tailored to the user's needs. Step 3: The feedback unit provides immediate feedback to the user based on the errors identified by the analysis unit. For example, it provides feedback on the spot as the user solves practice problems or participates in conversation sessions to check the user's understanding. The feedback unit can use generative AI to check the user's understanding and make necessary corrections.

[0057] (Example of form 2) The generative AI agent according to an embodiment of the present invention is a system that provides support for users to effectively learn a new language through interactive conversations and practice exercises. This generative AI agent generates practice exercises and conversation sessions tailored to the user's learning progress when the user selects and inputs the language they wish to learn. This allows the user to learn the language effectively at their own pace. For example, the user selects the language they wish to learn and inputs it into the generative AI agent. At this time, the user can set their level and learning goals. For example, settings can be adjusted according to the user's needs, such as wanting to learn everyday conversation at a beginner level or specialized terminology at a business level. Next, the generative AI agent generates practice exercises and conversation sessions tailored to the user's learning progress. The generative AI agent uses natural language processing (NLP) to analyze the interaction with the user and point out pronunciation and grammatical errors. It also generates personalized lessons and practice exercises according to the user's learning progress. For example, if a user has difficulty with a particular grammatical item, the agent will provide many practice exercises related to that grammatical item to support effective learning. Furthermore, the generative AI agent provides the user with immediate feedback. As users complete practice exercises or participate in conversation sessions, the generative AI agent provides immediate feedback to check their understanding. This allows users to grasp their learning progress and make necessary corrections. This system enables users to learn a new language effectively at their own pace. The generative AI agent supports effective and continuous language learning by providing practice exercises and conversation sessions tailored to the user's learning progress and offering immediate feedback. Furthermore, because the generative AI agent provides personalized lessons that meet the user's needs, it can reduce the cost of individual tutoring and provide appropriate learning resources. For example, if a student wants to learn English, the generative AI agent will provide English practice exercises and conversation sessions tailored to the student's learning progress.Students can learn English at their own pace and effectively progress through their studies by receiving immediate feedback from a generative AI agent. Furthermore, for business professionals who wish to learn a new language, the generative AI agent provides practice exercises and conversation sessions specifically tailored to business and technical terms. This allows business professionals to effectively acquire the language skills necessary for their work. In this way, the generative AI agent supports effective and continuous language learning by providing practice exercises and conversation sessions tailored to the user's learning progress and offering immediate feedback. This enables users to learn a new language at their own pace and overcome communication barriers to succeed internationally. Thus, the generative AI agent supports effective and continuous language learning by providing practice exercises and conversation sessions tailored to the user's learning progress and offering immediate feedback.

[0058] The generative AI agent according to this embodiment comprises a generation unit, an analysis unit, and a feedback unit. The generation unit generates practice problems and conversation sessions tailored to the user's learning progress. For example, the generation unit generates practice problems and conversation sessions tailored to the user's learning progress when the user selects a language they want to learn and inputs it into the generative AI agent. The generation unit can be configured according to the user's needs, for example, whether the user wants to learn everyday conversation at a beginner level or learn specialized terminology at a business level. The generation unit analyzes the interaction with the user using natural language processing (NLP) and points out errors in pronunciation and grammar. For example, if the user has difficulty with a particular grammatical item, the generation unit supports effective learning by providing many practice problems related to that grammatical item. The generation unit can provide personalized lessons tailored to the user's learning progress using generative AI. For example, the generation unit provides customized lessons based on the user's past learning history and learning goals. The analysis unit analyzes the practice problems and conversation sessions generated by the generation unit and points out errors in pronunciation and grammar. The analysis unit analyzes user interactions and identifies pronunciation and grammatical errors. The analysis unit uses natural language processing (NLP) to analyze user interactions and identify pronunciation and grammatical errors. The analysis unit can provide learning resources tailored to user needs using generative AI. For example, the analysis unit generates personalized lessons and practice exercises based on the user's learning progress. The feedback unit provides immediate feedback to the user based on the errors identified by the analysis unit. For example, the feedback unit provides feedback as the user solves practice exercises or participates in conversation sessions to check the user's understanding. The feedback unit can use generative AI to check the user's understanding and make necessary corrections. For example, if a user has difficulty with a particular grammatical item, the feedback unit can support effective learning by providing more practice exercises related to that grammatical item.As a result, the AI ​​agent according to the embodiment can generate practice problems and conversation sessions tailored to the user's learning progress, point out pronunciation and grammatical errors, and provide immediate feedback, thereby supporting effective language learning.

[0059] The generation unit generates practice exercises and conversation sessions tailored to the user's learning progress. For example, the user selects the language they want to learn and inputs it into the generation AI agent, which then generates practice exercises and conversation sessions that match the user's learning progress. Specifically, the generation unit considers the grammatical structure and vocabulary of the language selected by the user and automatically creates practice exercises at an appropriate level. For example, if a user is a beginner and wants to learn everyday conversation, the generation unit generates basic greetings, self-introductions, and simple conversational sentences related to daily life. On the other hand, if a user wants to learn specialized terminology at a business level, the generation unit generates practice exercises that include specialized terminology and phrases used in business settings. The generation unit uses natural language processing (NLP) technology to analyze the interaction with the user and point out pronunciation and grammatical errors. For example, if a user has difficulty with a particular grammatical item, the generation unit supports effective learning by presenting many practice exercises related to that grammatical item. The generation unit can provide customized lessons based on the user's past learning history and learning goals. For example, it considers what the user has learned in the past and the goals they have achieved, and automatically suggests what they should learn next. This allows the generation unit to provide personalized lessons tailored to the user's learning progress, supporting effective learning. Furthermore, the generation unit can adjust the format and difficulty level of practice exercises according to the user's learning style and preferences. For example, if the user prefers visual learning, it can generate practice exercises that include images and videos. Similarly, if the user prefers auditory learning, it can generate practice exercises that include audio. This allows the generation unit to flexibly adapt to the user's learning style and preferences, supporting effective learning.

[0060] The analysis unit analyzes the practice exercises and conversation sessions generated by the generation unit and points out pronunciation and grammatical errors. For example, the analysis unit analyzes user interactions and points out pronunciation and grammatical errors. Specifically, the analysis unit analyzes the audio data spoken by the user and detects errors at the phoneme level. For example, if a particular phoneme is not pronounced correctly, it emphasizes that phoneme to provide feedback to the user. Regarding grammatical errors, it analyzes the text data entered by the user and detects errors in grammatical structure. For example, it points out errors such as subject-verb agreement and tense errors and presents the correct grammatical structure. The analysis unit uses natural language processing (NLP) technology to analyze user interactions and point out pronunciation and grammatical errors. For example, if a user has difficulty with a particular grammatical item, it supports effective learning by presenting many practice exercises related to that grammatical item. The analysis unit can use generational AI to provide learning resources tailored to the user's needs. For example, it can generate personalized lessons and practice exercises according to the user's learning progress. This allows the analysis unit to provide appropriate feedback tailored to the user's learning progress, supporting effective learning. Furthermore, the analysis unit can analyze the user's learning history and develop long-term learning plans. For example, based on what the user has learned in the past and the goals they have achieved, it can suggest what they should learn next and provide a long-term learning plan. This allows the analysis unit to continuously monitor the user's learning progress and support effective learning.

[0061] The feedback unit provides immediate feedback to the user based on errors identified by the analysis unit. For example, the feedback unit provides real-time feedback as the user solves practice problems or participates in conversation sessions, confirming the user's understanding. Specifically, the feedback unit instantly determines the correctness of the user's answers to practice problems and provides the correct answers and explanations. For instance, if the user solves a grammar problem, it presents the correct grammatical structure and explains the cause of the error. In conversation sessions, it points out the user's pronunciation and grammatical errors in real time and presents the correct pronunciation and grammatical structure. The feedback unit can use generative AI to confirm the user's understanding and make necessary corrections. For example, if a user has difficulty with a particular grammatical item, it can support effective learning by presenting many practice problems related to that grammatical item. The feedback unit can analyze the user's learning history and develop long-term learning plans. For example, based on what the user has learned in the past and the goals they have achieved, it suggests what they should learn next and provides a long-term learning plan. This allows the feedback unit to continuously monitor the user's learning progress and support effective learning. Furthermore, the feedback unit can adjust the format and content of feedback according to the user's learning style and preferences. For example, if the user prefers visual learning, feedback including images and videos can be provided. Similarly, if the user prefers auditory learning, feedback including audio can be provided. This allows the feedback unit to flexibly respond to the user's learning style and preferences, thereby supporting effective learning.

[0062] The generation unit can provide personalized lessons tailored to the user's learning progress. For example, the generation unit provides customized lessons based on the user's past learning history and learning goals. The generation unit can use generation AI to provide personalized lessons tailored to the user's learning progress. For example, the generation unit provides customized lessons based on the user's past learning history and learning goals. This enhances the effectiveness of individualized instruction by providing personalized lessons tailored to the user's learning progress.

[0063] The analysis unit can provide learning resources tailored to the user's needs. For example, the analysis unit generates personalized lessons and practice problems according to the user's learning progress. The analysis unit can provide learning resources tailored to the user's needs using generation AI. For example, the analysis unit generates personalized lessons and practice problems according to the user's learning progress. This supports effective learning by providing learning resources tailored to the user's needs.

[0064] The feedback unit can check the user's understanding and make necessary corrections. For example, the feedback unit provides immediate feedback to check the user's understanding when the user solves practice problems or participates in conversation sessions. The feedback unit can use generative AI to check the user's understanding and make necessary corrections. For example, if the user has difficulty with a particular grammar point, the feedback unit will support effective learning by providing many practice problems related to that grammar point. This allows the system to check the user's understanding and make necessary corrections, thereby improving the effectiveness of learning.

[0065] The generation unit can estimate the user's emotions and adjust the difficulty of practice problems based on the estimated emotions. For example, if the user is stressed, the generation unit can provide easy problems to build confidence. If the user is relaxed, the generation unit can also provide slightly more difficult problems to challenge them. If the user is excited, the generation unit can provide problems that can be enjoyed like a game. In this way, by adjusting the difficulty of practice problems according to the user's emotions, learning motivation can be maintained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0066] The generation unit can analyze the user's past learning history and determine the optimal order of practice questions. For example, the generation unit can prioritize questions the user has answered incorrectly in the past. The generation unit can also postpone questions in areas the user excels at and present questions in areas the user struggles with first. The generation unit can also identify specific patterns from the user's learning history and present questions based on those patterns. In this way, by analyzing the user's past learning history, the optimal order of practice questions can be provided, thereby improving learning effectiveness. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past learning history data into a generation AI and have the generation AI determine the optimal order of practice questions.

[0067] The generation unit can provide different question formats depending on the user's learning objectives when generating practice questions. For example, if the user wants to learn everyday conversation, the generation unit can provide conversation-style questions. If the user wants to learn business terminology, the generation unit can also provide questions that include specialized terminology. If the user wants to prepare for an exam, the generation unit can also provide exam-style questions. In this way, by providing question formats that match the user's learning objectives, effective learning can be supported. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's learning objective data into a generation AI and have the generation AI determine an appropriate question format.

[0068] The generation unit can estimate the user's emotions and select conversation session topics based on the estimated emotions. For example, if the user is relaxed, the generation unit can select topics related to hobbies and interests. If the user is stressed, the generation unit can also select relaxing topics. If the user is excited, the generation unit can also select energetic topics. By selecting topics that match the user's emotions, the effectiveness of the conversation session can be enhanced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI select conversation session topics.

[0069] The generation unit can provide highly relevant practice questions by considering the user's geographical location. For example, if the user is in a specific region, the generation unit can provide questions related to the culture and history of that region. If the user is traveling, the generation unit can also provide questions related to the language and culture of the travel destination. The generation unit can also provide questions related to everyday conversations in the area where the user lives. By considering the user's geographical location, the generation unit can provide highly relevant questions and enhance learning effectiveness. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate highly relevant questions.

[0070] The generation unit can analyze the user's social media activity and incorporate relevant topics when generating practice questions. For example, the generation unit can provide questions related to topics the user has shown interest in on social media. The generation unit can also provide questions related to the content of accounts the user follows. The generation unit can also provide questions related to content the user has posted. In this way, by analyzing the user's social media activity, relevant topics can be incorporated and learning can be made more engaging. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI select relevant topics.

[0071] The analysis unit can estimate the user's emotions and adjust how it points out pronunciation and grammatical errors based on the estimated emotions. For example, if the user is nervous, the analysis unit will point out errors in a gentle tone. If the user is relaxed, the analysis unit may also point out errors with more detailed explanations. If the user is in a hurry, the analysis unit may also point out errors concisely. This allows for improved learning effectiveness by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the feedback method.

[0072] The analysis unit can analyze the user's past dialogue history and identify and point out specific error patterns. For example, the analysis unit can identify errors that the user has frequently made in the past and point out their patterns. The analysis unit can also identify errors related to specific grammatical items from the user's dialogue history. The analysis unit can also identify and point out error patterns in the user's pronunciation. This allows for the identification of specific error patterns by analyzing the user's past dialogue history, thereby improving the effectiveness of learning. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past dialogue history data into a generating AI and have the generating AI perform the identification of error patterns.

[0073] The analysis unit can apply different analysis algorithms depending on the user's learning style during analysis. For example, if the user is a visual learner, the analysis unit can provide visual feedback. If the user is an auditory learner, the analysis unit can also provide audio feedback. If the user is a haptic learner, the analysis unit can also provide interactive feedback. This allows for improved learning effectiveness by applying analysis algorithms tailored to the user's learning style. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's learning style data into a generating AI and have the generating AI apply an appropriate analysis algorithm.

[0074] The analysis unit can estimate the user's emotions and adjust the timing of error correction based on the estimated emotions. For example, if the user is nervous, the analysis unit may delay error correction. If the user is relaxed, the analysis unit may also correct errors immediately. If the user is in a hurry, the analysis unit may also correct only the most important errors. This allows for improved learning effectiveness by adjusting the timing of corrections according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the timing of corrections.

[0075] The analysis unit can identify region-specific pronunciation and grammatical errors by considering the user's geographical location during analysis. For example, if the user is in a specific region, the analysis unit can identify region-specific pronunciation errors. The analysis unit can also identify region-specific grammatical errors if the user is in a specific region. If the user is traveling, the analysis unit can also identify region-specific pronunciation and grammatical errors in the destination region. By considering the user's geographical location, the analysis unit can identify region-specific pronunciation and grammatical errors and improve the learning effect. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI identify region-specific pronunciation and grammatical errors.

[0076] The analysis unit can improve the accuracy of error detection by referring to the user's relevant literature during analysis. For example, the analysis unit can improve the accuracy of error detection by referring to literature in the language the user is learning. The analysis unit can also improve the accuracy of error detection by referring to textbooks and learning materials the user is using. The analysis unit can also improve the accuracy of error detection by referring to online resources the user is using. In this way, by referring to the user's relevant literature, the accuracy of error detection can be improved and the effectiveness of learning can be enhanced. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature data into a generating AI and have the generating AI perform the error detection accuracy improvement.

[0077] The feedback unit can estimate the user's emotions and adjust the way it expresses feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit will provide feedback in a gentle tone. If the user is relaxed, the feedback unit may also provide feedback with more detailed explanations. If the user is in a hurry, the feedback unit may provide feedback concisely. By adjusting the way feedback is expressed according to the user's emotions, the learning effect can be enhanced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, 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 feedback is expressed.

[0078] The feedback unit can provide optimal feedback by referring to the user's past learning history. For example, the feedback unit can provide detailed feedback on problems the user has previously answered incorrectly. The feedback unit can also identify specific patterns from the user's learning history and provide feedback based on those patterns. The feedback unit can also refer to the user's past learning history and provide feedback according to the user's progress. This allows for the provision of optimal feedback by referring to the user's past learning history, thereby enhancing the effectiveness of learning. Some or all of the above processes in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past learning history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0079] The feedback unit can provide different feedback formats depending on the user's learning objectives. For example, if the user wants to learn everyday conversation, the feedback unit can provide conversational feedback. If the user wants to learn business terminology, the feedback unit can also provide feedback that includes technical terms. If the user wants to prepare for an exam, the feedback unit can also provide exam-style feedback. By providing feedback formats that match the user's learning objectives, the effectiveness of learning can be enhanced. 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 the user's learning objective data into a generating AI and have the generating AI perform the task of providing an appropriate feedback format.

[0080] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit may delay the timing of feedback. If the user is relaxed, the feedback unit may provide immediate feedback. If the user is in a hurry, the feedback unit may provide only important feedback immediately. This allows for improved learning effectiveness by adjusting the timing of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the timing of feedback.

[0081] The feedback unit can provide region-specific feedback by taking into account the user's geographical location information during the feedback process. For example, if the user is in a specific region, the feedback unit can provide region-specific pronunciation and grammar feedback. If the user is traveling, the feedback unit can also provide region-specific feedback for the destination region. The feedback unit can also provide feedback related to the region where the user lives. By considering the user's geographical location information, region-specific feedback can be provided, thereby enhancing the effectiveness of learning. 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 the user's geographical location data into a generating AI and have the generating AI perform the provision of region-specific feedback.

[0082] The feedback unit can analyze the user's social media activity and provide relevant feedback when providing feedback. For example, the feedback unit can provide feedback related to topics the user is interested in on social media. The feedback unit can also provide feedback related to the content of accounts the user follows. The feedback unit can also provide feedback related to content the user has posted. In this way, by analyzing the user's social media activity, relevant feedback can be provided and learning can be stimulated. 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 the user's social media activity data into a generating AI and have the generating AI perform the task of providing relevant feedback.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The generation unit not only generates practice problems and conversation sessions tailored to the user's learning progress, but can also provide learning materials in formats suited to the user's learning style. For example, it can provide visually-oriented learners with materials that heavily utilize images and videos, and auditory learners with audio materials and podcasts. Furthermore, it can provide tactile learners with interactive simulations and practical exercises. This allows for the provision of optimal learning materials tailored to the user's learning style, thereby enhancing learning effectiveness.

[0085] The analysis unit can estimate the user's emotions when analyzing the user's learning progress and adjust the analysis results based on those emotions. For example, if the user is stressed, it can provide a concise summary of the analysis results, while if the user is relaxed, it can provide a more detailed analysis. Furthermore, if the user is excited, it can enhance their motivation to learn by emphasizing positive feedback. In this way, by providing analysis results that match the user's emotions, it is possible to maintain their motivation to learn.

[0086] The feedback system can customize the content of feedback according to the user's learning progress. For example, it can provide basic feedback to beginners and more detailed feedback to intermediate learners. Furthermore, it can provide expert feedback to advanced learners, ensuring appropriate feedback for each level. It can also adjust the content of feedback according to the user's learning goals. This allows for optimal feedback tailored to the user's learning progress and goals, thereby enhancing learning effectiveness.

[0087] The generation unit can select topics based on the user's interests when generating practice problems and conversation sessions tailored to the user's learning progress. For example, if the user is interested in sports, it can select sports-related topics; if the user is interested in music, it can select music-related topics. Furthermore, if the user is interested in a particular culture or region, it can select topics related to that culture or region. By providing topics that match the user's interests, it can stimulate their interest in learning and support effective learning.

[0088] The analysis unit can estimate the user's emotions and adjust how the analysis results are presented based on those emotions. For example, if the user is nervous, the analysis results are presented in a gentle tone; if the user is relaxed, detailed explanations are added. Furthermore, if the user is in a hurry, only the important points are presented concisely. By adjusting how the analysis results are presented according to the user's emotions, the learning effect can be enhanced.

[0089] The generation unit, when generating practice questions and conversation sessions tailored to the user's learning progress, can consider the user's learning history and focus on questions they have previously answered incorrectly or areas they struggle with. For example, it can present many questions related to grammar points the user has answered incorrectly in the past, and provide practice questions related to pronunciation, which the user struggles with. Furthermore, it can postpone questions in areas the user excels at and present questions in areas they struggle with first. By providing optimal practice questions that take the user's learning history into account, the learning effect can be enhanced.

[0090] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, it emphasizes positive feedback; if the user is relaxed, it provides detailed feedback. Furthermore, if the user is excited, it can provide challenging feedback to increase their motivation to learn. In this way, by adjusting the content of feedback according to the user's emotions, it is possible to maintain their motivation to learn.

[0091] The generation unit can generate practice questions and conversation sessions tailored to the user's learning progress, taking into account the user's geographical location to provide questions related to the culture and history specific to that region. For example, if the user is in a particular region, it can provide questions related to the culture and history of that region; if the user is traveling, it can provide questions related to the language and culture of the travel destination. Furthermore, it can also provide everyday conversation questions related to the area where the user lives. By providing highly relevant questions that take the user's geographical location into account, the learning effect can be enhanced.

[0092] The analysis unit can estimate the user's emotions and adjust the timing of error detection based on those emotions. For example, if the user is nervous, error detection can be delayed; if the user is relaxed, errors can be detected immediately. Furthermore, if the user is in a hurry, only important errors can be detected. By adjusting the timing of error detection according to the user's emotions, the learning effect can be enhanced.

[0093] The feedback section can customize the format of feedback according to the user's learning progress. For example, it can provide basic feedback to beginners and more detailed feedback to intermediate learners. Furthermore, it can provide expert feedback to advanced learners, ensuring appropriate feedback for each level. It can also adjust the format of feedback according to the user's learning goals. This allows for optimal feedback tailored to the user's learning progress and goals, thereby enhancing learning effectiveness.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The generation unit generates practice exercises and conversation sessions tailored to the user's learning progress. For example, by selecting the language the user wants to learn and inputting it into the generation AI agent, the unit generates practice exercises and conversation sessions that match the user's learning progress. The generation unit can be configured according to the user's needs and uses natural language processing (NLP) to analyze the interaction with the user and point out pronunciation and grammatical errors. The generation unit can also provide customized lessons based on the user's past learning history and learning goals. Step 2: The analysis unit analyzes the practice exercises and conversation sessions generated by the generation unit and points out pronunciation and grammatical errors. The analysis unit uses natural language processing (NLP) to analyze the interaction with the user and point out pronunciation and grammatical errors. Furthermore, the analysis unit can use the generation AI to provide learning resources tailored to the user's needs. Step 3: The feedback unit provides immediate feedback to the user based on the errors identified by the analysis unit. For example, it provides feedback on the spot as the user solves practice problems or participates in conversation sessions to check the user's understanding. The feedback unit can use generative AI to check the user's understanding and make necessary corrections.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the generation unit, analysis unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates practice problems and conversation sessions tailored to the user's learning progress. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the generated practice problems and conversation sessions, pointing out pronunciation and grammatical errors. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides immediate feedback to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the generation unit, analysis unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates practice problems and conversation sessions tailored to the user's learning progress. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the generated practice problems and conversation sessions, pointing out pronunciation and grammatical errors. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides immediate feedback to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements, including the generation unit, analysis unit, and feedback unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates practice problems and conversation sessions tailored to the user's learning progress. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the generated practice problems and conversation sessions, pointing out pronunciation and grammatical errors. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides immediate feedback to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[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 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.

[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 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.

[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 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.

[0148] Each of the multiple elements, including the generation unit, analysis unit, and feedback unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates practice problems and conversation sessions tailored to the user's learning progress. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the generated practice problems and conversation sessions, pointing out pronunciation and grammatical errors. The feedback unit is implemented by the control unit 46A of the robot 414 and provides immediate feedback to the user based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A generation unit that generates practice problems and conversation sessions tailored to the user's learning progress, The analysis unit analyzes the practice problems and conversation sessions generated by the generation unit and points out errors in pronunciation and grammar, The system includes a feedback unit that provides immediate feedback to the user based on errors identified by the analysis unit. A system characterized by the following features. (Note 2) The generating unit is Provides personalized lessons tailored to the user's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We provide learning resources tailored to user needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Check the user's understanding and make any necessary corrections. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The system estimates the user's emotions and adjusts the difficulty level of the practice exercises based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system analyzes the user's past learning history to determine the optimal order of practice problems. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When generating practice problems, provide different problem formats according to the user's learning objectives. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and selects conversation session topics based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating practice problems, the system takes the user's geographical location into consideration to provide highly relevant problems. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating practice problems, analyze users' social media activity and incorporate relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the user's emotions and adjusts how it points out pronunciation and grammatical errors based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Analyze the user's past conversation history to identify and point out specific error patterns. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the timing of error detection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the system takes the user's geographical location into account to identify region-specific pronunciation and grammatical errors. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, we improve the accuracy of error detection by referencing relevant literature from the user. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned feedback unit is When providing feedback, the system refers to the user's past learning history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is When providing feedback, offer different feedback formats depending on the user's learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is It estimates the user's emotions and adjusts the timing of feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, the system takes the user's geographical location into account to provide region-specific feedback. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and provide relevant feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0168] 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 generation unit that generates practice problems and conversation sessions tailored to the user's learning progress, The analysis unit analyzes the practice problems and conversation sessions generated by the generation unit and points out errors in pronunciation and grammar, The system includes a feedback unit that provides immediate feedback to the user based on errors identified by the analysis unit. A system characterized by the following features.

2. The generating unit is Provides personalized lessons tailored to the user's learning progress. The system according to feature 1.

3. The aforementioned analysis unit, We provide learning resources tailored to user needs. The system according to feature 1.

4. The aforementioned feedback unit is Check the user's understanding and make any necessary corrections. The system according to feature 1.

5. The generating unit is The system estimates the user's emotions and adjusts the difficulty level of the practice exercises based on those emotions. The system according to feature 1.

6. The generating unit is The system analyzes the user's past learning history to determine the optimal order of practice problems. The system according to feature 1.

7. The generating unit is When generating practice problems, provide different problem formats according to the user's learning objectives. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and selects conversation session topics based on the estimated user emotions. The system according to feature 1.

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