system

Abstract

The system according to this embodiment aims to enable users to learn English expressions in a fun and natural way. [Solution] The system according to the embodiment comprises a reading unit, a selection unit, a playback unit, and a provision unit. The reading unit reads the content of movies and dramas. The selection unit analyzes the content read by the reading unit and selects scenes according to the user's learning level. The playback unit plays scenes in which the user speaks lines based on the scenes selected by the selection unit. The provision unit analyzes the user's pronunciation and intonation based on the scenes played by the playback unit and provides feedback.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Conventional technologies have problems such as the inability to continue English conversation learning, the lack of a sense of improvement, and the difficulty in learning natural English expressions and cultural backgrounds.

[0005] The system according to the embodiment aims to enable a user to learn natural English expressions in an enjoyable manner.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, a selection unit, a playback unit, and a provision unit. The reading unit reads the content of movies and dramas. The selection unit analyzes the content read by the reading unit and selects scenes according to the user's learning level. The playback unit plays scenes in which the user speaks lines based on the scenes selected by the selection unit. The provision unit analyzes the user's pronunciation and intonation based on the scenes played by the playback unit and provides feedback. [Effects of the Invention]

[0007] The system according to this embodiment can enable users to learn English expressions in a fun and natural way. [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 controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The English conversation learning system according to an embodiment of the present invention is a system that allows users to learn natural English expressions through their favorite movies and dramas and continue learning English conversation in an enjoyable way. In this system, the user selects their favorite English movie or drama, and the content is fed into a generating AI to generate learning content. The user selects a character they want to embody, and scenes appropriate to their learning level are played. By speaking lines according to the scenes, the user can learn while receiving feedback on whether the conversation in the scene is coherent, as well as on pronunciation and intonation. In this way, the system proposes a new form of English conversation learning that can be continued naturally and enjoyably by having simulated conversations with actors and actresses from their favorite movies and dramas. First, the user selects their favorite English movie or drama and the content is fed into the generating AI. The generating AI analyzes scenes from the movie or drama and selects scenes appropriate to the user's learning level. For example, simple everyday conversation scenes are selected for beginners, slightly more complex conversation scenes for intermediate learners, and business or specialized conversation scenes for advanced learners. Next, the user selects a character they want to embody. For example, users can role-play as their favorite character, such as the protagonist of a movie or a main cast member of a drama. Based on the selected character, the generating AI plays scenes appropriate to the user's learning level. As a scene plays, the user speaks the lines accordingly. The generating AI analyzes the user's pronunciation and intonation and provides feedback. For example, if the pronunciation is inaccurate, it points out what is wrong and teaches the correct pronunciation. Also, if the intonation is unnatural, it tells the user what should be corrected. In this way, users can learn natural English expressions and cultural backgrounds while enjoying conversations as characters from their favorite movies or dramas. Furthermore, the entertainment aspect helps maintain motivation for learning. For example, by introducing a system where users accumulate points when they succeed in movie scenes, they can continue learning in a game-like manner. This service can solve problems such as not being able to continue learning English conversation, not feeling like they are improving, and the difficulty of learning natural English expressions and cultural backgrounds with existing materials.Users can learn English conversation in an enjoyable way through their favorite movies and TV shows, and understand natural English expressions and cultural backgrounds. This allows the English conversation learning system to help users learn natural English expressions through their favorite movies and TV shows, making learning English conversation fun and engaging.

[0029] The English conversation learning system according to this embodiment comprises a reading unit, a selection unit, a playback unit, and a provision unit. The reading unit reads the content of movies and dramas. For example, the reading unit reads the content of movies and dramas selected by the user using a generating AI. The generating AI analyzes scenes from movies and dramas and selects scenes according to the user's learning level. The selection unit analyzes the content read by the reading unit and selects scenes according to the user's learning level. For example, the selection unit selects simple everyday conversation scenes for beginners, slightly more complex conversation scenes for intermediate learners, and business or specialized conversation scenes for advanced learners. The playback unit plays scenes in which the user speaks lines based on the scenes selected by the selection unit. For example, the playback unit plays scenes based on the cast selected by the user. The provision unit analyzes the user's pronunciation and intonation based on the scenes played by the playback unit and provides feedback. For example, if the pronunciation is inaccurate, the provision unit points out which part is wrong and teaches the correct pronunciation. Furthermore, if the intonation is unnatural, the system will tell the user which part needs to be corrected. This allows the English conversation learning system according to this embodiment to enable users to learn natural English expressions through their favorite movies and dramas, and to continue learning English conversation in an enjoyable way.

[0030] The loading unit reads the content of movies and dramas. Specifically, it retrieves the movie or drama file selected by the user and analyzes its content. The generating AI analyzes the movie or drama scenes frame by frame and extracts audio and subtitle data. This allows for a detailed understanding of the dialogue, background sounds, and character movements in each scene of the movie or drama. Furthermore, the generating AI considers the user's learning history and current learning level to provide foundational data for selecting appropriate scenes. For example, it analyzes the content the user has learned in the past, their strengths and weaknesses, and provides information to select the optimal scene based on that. This allows the loading unit to efficiently load movie and drama scenes that meet the user's learning needs. In addition, the loading unit can analyze the content of movies and dramas in real time and immediately provide the scenes selected by the user. This allows the user to start learning without waiting time, improving the efficiency of learning.

[0031] The selection unit analyzes the content loaded by the loading unit and selects scenes appropriate to the user's learning level. Specifically, it evaluates the difficulty and content of scenes based on data provided by the generating AI and selects the most suitable scene for the user. For example, beginners are selected for simple everyday conversation scenes, including basic greetings, self-introductions, and simple question-and-answer sessions. Intermediate learners are selected for slightly more complex conversation scenes, including specific situations in daily life and problem-solving conversations. Advanced learners are selected for business and specialized conversation scenes, including meetings, presentations, and discussions involving technical terms. The selection unit uses an algorithm to select the most suitable scene, taking into account the user's learning history and current learning level. This allows users to learn scenes that match their level and effectively improve their English conversation skills. Furthermore, the selection unit can collect user feedback and continuously improve its selection algorithm. For example, if a user finds a particular scene difficult, the selection unit adjusts the next scene selection based on that feedback. This allows the selection unit to provide the most suitable scenes according to the user's learning needs and maximize learning effectiveness.

[0032] The playback section plays scenes in which the user speaks lines, based on scenes selected by the selection section. Specifically, it plays scenes based on the cast selected by the user, allowing the user to imitate the lines in those scenes. The playback section plays movie and drama scenes with high-quality audio and video, providing an environment where users can learn natural English pronunciation and intonation. Furthermore, the playback section provides visual and audio cues to guide the user on when to speak their lines. For example, it highlights the parts of the lines the user should speak or provides an audio countdown, allowing the user to speak their lines smoothly. The playback section also provides a recording function for when the user speaks their lines, allowing them to check their own pronunciation. This allows the user to objectively evaluate their pronunciation and find areas for improvement. In addition, the playback section provides a scene repeat playback function so that users can repeatedly practice specific scenes. This allows users to learn at their own pace and effectively improve their English conversation skills.

[0033] The service provider analyzes the user's pronunciation and intonation based on the scenes played by the playback service provider and provides feedback. Specifically, it records the lines spoken by the user and uses a generative AI to analyze the pronunciation and intonation. The generative AI compares the user's pronunciation to the original pronunciation in the movie or drama and identifies which parts are inaccurate. For example, if a particular sound is unclear or the intonation is unnatural, it points out the issue and teaches the user the correct pronunciation and intonation. The service provider also provides visual feedback to specifically show areas for improvement in the user's pronunciation. For example, it displays audio waveforms and pitch changes in graphs so that the user can visually understand which parts need correction. Furthermore, the service provider records the user's pronunciation progress and visualizes the user's growth by comparing it with past feedback. This allows the user to see how much their pronunciation has improved and maintain their motivation. Based on user feedback, the service provider can continuously improve the pronunciation analysis algorithm and provide more accurate feedback. This allows the service provider to help users effectively improve their pronunciation and intonation and acquire natural English conversation skills.

[0034] The loading unit can analyze the user's past viewing history and select the optimal loading method. For example, the loading unit can prioritize loading content of the same genre based on the genres of movies and dramas the user has watched in the past. It can also prioritize loading scenes that the user found particularly interesting from scenes they have watched in the past. Furthermore, the loading unit can prioritize loading scenes featuring specific actors or actresses from the user's viewing history. This allows the loading of optimal content based on the user's past viewing history. Some or all of the above processing in the loading unit may be performed using AI, for example, or without AI. For example, the loading unit can input the user's viewing history data into a generating AI and have the generating AI select the optimal loading method.

[0035] The loading unit can filter the content of movies and dramas based on the user's current interests. For example, the loading unit can prioritize loading scenes related to themes the user is currently interested in. It can also prioritize loading scenes related to keywords the user has recently searched for. Furthermore, it can prioritize loading scenes related to content the user has mentioned on social media. This allows the system to provide content that is optimally suited to the user's current interests. Some or all of the above processing in the loading unit may be performed using AI, for example, or not. For example, the loading unit can input user interest data into a generating AI and have the generating AI perform the filtering.

[0036] The selection unit can select the optimal scene by referring to the user's past learning history when selecting a scene. For example, the selection unit can re-select scenes that were particularly effective among the scenes the user has learned in the past. The selection unit can also re-select scenes that the user found particularly interesting among the scenes the user has learned in the past. Furthermore, the selection unit can select scenes related to a specific theme from the user's learning history. This allows the selection of the optimal scene based on the user's past learning history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's learning history data into a generating AI and have the generating AI perform the selection of the optimal scene.

[0037] The selection unit can customize scenes based on the user's current learning goals when selecting scenes. For example, if the user wants to learn everyday conversation, the selection unit will select a scene of everyday conversation. It can also select a scene of business conversation if the user wants to learn business English. Furthermore, if the user is interested in a specific theme, the selection unit can select a scene related to that theme. This allows the system to provide the most suitable scenes based on the user's current learning goals. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's learning goal data into a generating AI and have the generating AI perform the scene customization.

[0038] The playback unit can select the optimal playback method by referring to the user's past viewing history when playing scenes. For example, the playback unit can reapply playback methods that were particularly effective among scenes the user has watched in the past. It can also reapply playback methods that the user found particularly interesting among scenes they have watched in the past. Furthermore, the playback unit can select playback methods related to specific themes from the user's viewing history. This allows the playback unit to provide the optimal playback method based on the user's past viewing history. Some or all of the above processing in the playback unit may be performed using AI, for example, or not using AI. For example, the playback unit can input the user's viewing history data into a generating AI and have the generating AI select the optimal playback method.

[0039] The playback unit can customize scenes based on the user's current learning goals when playing them. For example, if the user wants to learn everyday conversation, the playback unit will play scenes of everyday conversation. It can also play scenes of business conversation if the user wants to learn business English. Furthermore, if the user is interested in a specific topic, the playback unit can play scenes related to that topic. This allows the system to provide the most suitable scenes based on the user's current learning goals. Some or all of the above processing in the playback unit may be performed using AI, for example, or not. For example, the playback unit can input the user's learning goal data into a generating AI and have the generating AI perform the scene customization.

[0040] The feedback provider can provide optimal feedback by referring to the user's past learning history. For example, the provider can provide progress-appropriate feedback based on what the user has learned in the past. The provider can also highlight areas where the user has struggled in the past when providing feedback. Furthermore, the provider can provide feedback related to specific themes from the user's learning history. This allows for the provision of optimal feedback based on the user's past learning history. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the provider can input the user's learning history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0041] The service provider can customize the feedback based on the user's current learning goals when providing it. For example, if the user wants to learn everyday conversation, the service provider will provide feedback related to everyday conversation. It can also provide feedback related to business conversation if the user wants to learn business English. Furthermore, if the user is interested in a specific topic, the service provider can provide feedback related to that topic. This allows the service provider to provide optimal feedback based on the user's current learning goals. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning goal data into a generating AI and have the generating AI customize the feedback.

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

[0043] The English conversation learning system can monitor the user's learning progress in real time and dynamically adjust the learning content according to that progress. For example, if a user is struggling with a particular pronunciation or intonation, it can prioritize providing scenes that reinforce that area. Also, if a user has mastered a particular conversation pattern, it can broaden the scope of learning by introducing new patterns. Furthermore, it can insert review scenes at appropriate times based on the user's learning progress. This allows users to learn efficiently.

[0044] English conversation learning systems can provide customized feedback tailored to the user's learning style. For example, visual learners can receive easily understandable feedback by displaying pronunciation waveforms and intonation graphs. Auditory learners can receive easily understandable feedback by providing audio samples of correct pronunciation and intonation. Furthermore, tactile learners can be provided with an interactive touchscreen to encourage pronunciation and intonation practice. This allows for the provision of optimal feedback tailored to the user's learning style.

[0045] The English conversation learning system can analyze a user's learning history and automatically generate a personalized learning plan. For example, it can suggest what the user should learn next based on what they have learned in the past. Furthermore, if the user is interested in a particular field, it can prioritize providing scenes related to that field. In addition, it can dynamically adjust the learning plan according to the user's learning pace, ensuring that learning progresses smoothly. This allows the system to provide an optimal learning plan based on the user's learning history.

[0046] An English conversation learning system can provide customized learning content according to the user's learning goals. For example, if a user wants to learn travel English, it can provide travel-related scenarios. Similarly, if a user wants to learn business English, it can provide business-related scenarios. Furthermore, if a user is interested in a specific culture or region, it can provide scenarios related to that culture or region. This allows for the provision of optimal learning content tailored to the user's learning objectives.

[0047] The English conversation learning system can automatically generate a review plan to maximize learning effectiveness based on the user's learning history. For example, it can identify areas that need review based on what the user has learned in the past and provide review scenes at the appropriate time. Furthermore, if the user has difficulty in a particular area, it can prioritize reviewing scenes related to that area. In addition, it can dynamically adjust the review plan according to the user's learning pace, allowing for smooth and manageable review. This enables the system to provide an optimal review plan based on the user's learning history.

[0048] The following briefly describes the processing flow for example form 1.

[0049] Step 1: The loading unit loads the content of movies and dramas. For example, it uses a generating AI to load the content of movies and dramas selected by the user. Step 2: The selection unit analyzes the content read by the reading unit and selects scenes appropriate to the user's learning level. For example, beginners will be shown simple everyday conversation scenes, intermediate users will be shown slightly more complex conversation scenes, and advanced users will be shown business or specialized conversation scenes. Step 3: The playback unit plays scenes in which the user speaks lines, based on the scenes selected by the selection unit. For example, it plays scenes based on the cast selected by the user. Step 4: The delivery unit analyzes the user's pronunciation and intonation based on the scenes played by the playback unit and provides feedback. For example, if the pronunciation is inaccurate, it points out which part is wrong and teaches the correct pronunciation. Also, if the intonation is unnatural, it tells the user which part should be corrected.

[0050] (Example of form 2) The English conversation learning system according to an embodiment of the present invention is a system that allows users to learn natural English expressions through their favorite movies and dramas and continue learning English conversation in an enjoyable way. In this system, the user selects their favorite English movie or drama, and the content is fed into a generating AI to generate learning content. The user selects a character they want to embody, and scenes appropriate to their learning level are played. By speaking lines according to the scenes, the user can learn while receiving feedback on whether the conversation in the scene is coherent, as well as on pronunciation and intonation. In this way, the system proposes a new form of English conversation learning that can be continued naturally and enjoyably by having simulated conversations with actors and actresses from their favorite movies and dramas. First, the user selects their favorite English movie or drama and the content is fed into the generating AI. The generating AI analyzes scenes from the movie or drama and selects scenes appropriate to the user's learning level. For example, simple everyday conversation scenes are selected for beginners, slightly more complex conversation scenes for intermediate learners, and business or specialized conversation scenes for advanced learners. Next, the user selects a character they want to embody. For example, users can role-play as their favorite character, such as the protagonist of a movie or a main cast member of a drama. Based on the selected character, the generating AI plays scenes appropriate to the user's learning level. As a scene plays, the user speaks the lines accordingly. The generating AI analyzes the user's pronunciation and intonation and provides feedback. For example, if the pronunciation is inaccurate, it points out what is wrong and teaches the correct pronunciation. Also, if the intonation is unnatural, it tells the user what should be corrected. In this way, users can learn natural English expressions and cultural backgrounds while enjoying conversations as characters from their favorite movies or dramas. Furthermore, the entertainment aspect helps maintain motivation for learning. For example, by introducing a system where users accumulate points when they succeed in movie scenes, they can continue learning in a game-like manner. This service can solve problems such as not being able to continue learning English conversation, not feeling like they are improving, and the difficulty of learning natural English expressions and cultural backgrounds with existing materials.Users can learn English conversation in an enjoyable way through their favorite movies and TV shows, and understand natural English expressions and cultural backgrounds. This allows the English conversation learning system to help users learn natural English expressions through their favorite movies and TV shows, making learning English conversation fun and engaging.

[0051] The English conversation learning system according to this embodiment comprises a reading unit, a selection unit, a playback unit, and a provision unit. The reading unit reads the content of movies and dramas. For example, the reading unit reads the content of movies and dramas selected by the user using a generating AI. The generating AI analyzes scenes from movies and dramas and selects scenes according to the user's learning level. The selection unit analyzes the content read by the reading unit and selects scenes according to the user's learning level. For example, the selection unit selects simple everyday conversation scenes for beginners, slightly more complex conversation scenes for intermediate learners, and business or specialized conversation scenes for advanced learners. The playback unit plays scenes in which the user speaks lines based on the scenes selected by the selection unit. For example, the playback unit plays scenes based on the cast selected by the user. The provision unit analyzes the user's pronunciation and intonation based on the scenes played by the playback unit and provides feedback. For example, if the pronunciation is inaccurate, the provision unit points out which part is wrong and teaches the correct pronunciation. Furthermore, if the intonation is unnatural, the system will tell the user which part needs to be corrected. This allows the English conversation learning system according to this embodiment to enable users to learn natural English expressions through their favorite movies and dramas, and to continue learning English conversation in an enjoyable way.

[0052] The loading unit reads the content of movies and dramas. Specifically, it retrieves the movie or drama file selected by the user and analyzes its content. The generating AI analyzes the movie or drama scenes frame by frame and extracts audio and subtitle data. This allows for a detailed understanding of the dialogue, background sounds, and character movements in each scene of the movie or drama. Furthermore, the generating AI considers the user's learning history and current learning level to provide foundational data for selecting appropriate scenes. For example, it analyzes the content the user has learned in the past, their strengths and weaknesses, and provides information to select the optimal scene based on that. This allows the loading unit to efficiently load movie and drama scenes that meet the user's learning needs. In addition, the loading unit can analyze the content of movies and dramas in real time and immediately provide the scenes selected by the user. This allows the user to start learning without waiting time, improving the efficiency of learning.

[0053] The selection unit analyzes the content loaded by the loading unit and selects scenes appropriate to the user's learning level. Specifically, it evaluates the difficulty and content of scenes based on data provided by the generating AI and selects the most suitable scene for the user. For example, beginners are selected for simple everyday conversation scenes, including basic greetings, self-introductions, and simple question-and-answer sessions. Intermediate learners are selected for slightly more complex conversation scenes, including specific situations in daily life and problem-solving conversations. Advanced learners are selected for business and specialized conversation scenes, including meetings, presentations, and discussions involving technical terms. The selection unit uses an algorithm to select the most suitable scene, taking into account the user's learning history and current learning level. This allows users to learn scenes that match their level and effectively improve their English conversation skills. Furthermore, the selection unit can collect user feedback and continuously improve its selection algorithm. For example, if a user finds a particular scene difficult, the selection unit adjusts the next scene selection based on that feedback. This allows the selection unit to provide the most suitable scenes according to the user's learning needs and maximize learning effectiveness.

[0054] The playback section plays scenes in which the user speaks lines, based on scenes selected by the selection section. Specifically, it plays scenes based on the cast selected by the user, allowing the user to imitate the lines in those scenes. The playback section plays movie and drama scenes with high-quality audio and video, providing an environment where users can learn natural English pronunciation and intonation. Furthermore, the playback section provides visual and audio cues to guide the user on when to speak their lines. For example, it highlights the parts of the lines the user should speak or provides an audio countdown, allowing the user to speak their lines smoothly. The playback section also provides a recording function for when the user speaks their lines, allowing them to check their own pronunciation. This allows the user to objectively evaluate their pronunciation and find areas for improvement. In addition, the playback section provides a scene repeat playback function so that users can repeatedly practice specific scenes. This allows users to learn at their own pace and effectively improve their English conversation skills.

[0055] The service provider analyzes the user's pronunciation and intonation based on the scenes played by the playback service provider and provides feedback. Specifically, it records the lines spoken by the user and uses a generative AI to analyze the pronunciation and intonation. The generative AI compares the user's pronunciation to the original pronunciation in the movie or drama and identifies which parts are inaccurate. For example, if a particular sound is unclear or the intonation is unnatural, it points out the issue and teaches the user the correct pronunciation and intonation. The service provider also provides visual feedback to specifically show areas for improvement in the user's pronunciation. For example, it displays audio waveforms and pitch changes in graphs so that the user can visually understand which parts need correction. Furthermore, the service provider records the user's pronunciation progress and visualizes the user's growth by comparing it with past feedback. This allows the user to see how much their pronunciation has improved and maintain their motivation. Based on user feedback, the service provider can continuously improve the pronunciation analysis algorithm and provide more accurate feedback. This allows the service provider to help users effectively improve their pronunciation and intonation and acquire natural English conversation skills.

[0056] The loading unit can estimate the user's emotions and adjust the timing of loading movie or drama content based on the estimated emotions. For example, if the user is relaxed, the loading unit can immediately load movie or drama content. If the user is stressed, the loading unit can prioritize loading relaxing scenes. Furthermore, if the user is excited, the loading unit can prioritize loading action scenes or emotional scenes. This allows the loading of movie or drama content to occur at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the loading unit may be performed using AI, or not using AI. For example, the loading unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0057] The loading unit can analyze the user's past viewing history and select the optimal loading method. For example, the loading unit can prioritize loading content of the same genre based on the genres of movies and dramas the user has watched in the past. It can also prioritize loading scenes that the user found particularly interesting from scenes they have watched in the past. Furthermore, the loading unit can prioritize loading scenes featuring specific actors or actresses from the user's viewing history. This allows the loading of optimal content based on the user's past viewing history. Some or all of the above processing in the loading unit may be performed using AI, for example, or without AI. For example, the loading unit can input the user's viewing history data into a generating AI and have the generating AI select the optimal loading method.

[0058] The loading unit can filter the content of movies and dramas based on the user's current interests. For example, the loading unit can prioritize loading scenes related to themes the user is currently interested in. It can also prioritize loading scenes related to keywords the user has recently searched for. Furthermore, it can prioritize loading scenes related to content the user has mentioned on social media. This allows the system to provide content that is optimally suited to the user's current interests. Some or all of the above processing in the loading unit may be performed using AI, for example, or not. For example, the loading unit can input user interest data into a generating AI and have the generating AI perform the filtering.

[0059] The selection unit can estimate the user's emotions and adjust the scene selection criteria based on the estimated user emotions. For example, if the user is relaxed, the selection unit will select a relaxing scene. If the user is stressed, the selection unit can also select a scene that reduces stress. Furthermore, if the user is excited, the selection unit can select a scene that maintains excitement. This allows the system to select the optimal scene according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using AI, or not using AI. For example, the selection unit can input user emotion data into a generative AI and have the generative AI adjust the scene selection criteria.

[0060] The selection unit can select the optimal scene by referring to the user's past learning history when selecting a scene. For example, the selection unit can re-select scenes that were particularly effective among the scenes the user has learned in the past. The selection unit can also re-select scenes that the user found particularly interesting among the scenes the user has learned in the past. Furthermore, the selection unit can select scenes related to a specific theme from the user's learning history. This allows the selection of the optimal scene based on the user's past learning history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's learning history data into a generating AI and have the generating AI perform the selection of the optimal scene.

[0061] The selection unit can customize scenes based on the user's current learning goals when selecting scenes. For example, if the user wants to learn everyday conversation, the selection unit will select a scene of everyday conversation. It can also select a scene of business conversation if the user wants to learn business English. Furthermore, if the user is interested in a specific theme, the selection unit can select a scene related to that theme. This allows the system to provide the most suitable scenes based on the user's current learning goals. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's learning goal data into a generating AI and have the generating AI perform the scene customization.

[0062] The playback unit can estimate the user's emotions and adjust the way scenes are played based on the estimated emotions. For example, if the user is relaxed, the playback unit will play scenes at a relaxed pace. If the user is stressed, the playback unit can also play scenes at a pace that reduces stress. Furthermore, if the user is excited, the playback unit can play scenes at a pace that maintains excitement. This allows scenes to be played at an optimal pace according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the playback unit may be performed using AI, or not using AI. For example, the playback unit can input user emotion data into a generative AI and have the generative AI adjust the way scenes are played.

[0063] The playback unit can select the optimal playback method by referring to the user's past viewing history when playing scenes. For example, the playback unit can reapply playback methods that were particularly effective among scenes the user has watched in the past. It can also reapply playback methods that the user found particularly interesting among scenes they have watched in the past. Furthermore, the playback unit can select playback methods related to specific themes from the user's viewing history. This allows the playback unit to provide the optimal playback method based on the user's past viewing history. Some or all of the above processing in the playback unit may be performed using AI, for example, or not using AI. For example, the playback unit can input the user's viewing history data into a generating AI and have the generating AI select the optimal playback method.

[0064] The playback unit can customize scenes based on the user's current learning goals when playing them. For example, if the user wants to learn everyday conversation, the playback unit will play scenes of everyday conversation. It can also play scenes of business conversation if the user wants to learn business English. Furthermore, if the user is interested in a specific topic, the playback unit can play scenes related to that topic. This allows the system to provide the most suitable scenes based on the user's current learning goals. Some or all of the above processing in the playback unit may be performed using AI, for example, or not. For example, the playback unit can input the user's learning goal data into a generating AI and have the generating AI perform the scene customization.

[0065] The service provider can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed feedback. If the user is stressed, the service provider can also provide concise and to-the-point feedback. Furthermore, if the user is excited, the service provider can emphasize positive feedback. This allows the service provider to provide optimal feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0066] The feedback provider can provide optimal feedback by referring to the user's past learning history. For example, the provider can provide progress-appropriate feedback based on what the user has learned in the past. The provider can also highlight areas where the user has struggled in the past when providing feedback. Furthermore, the provider can provide feedback related to specific themes from the user's learning history. This allows for the provision of optimal feedback based on the user's past learning history. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the provider can input the user's learning history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0067] The service provider can customize the feedback based on the user's current learning goals when providing it. For example, if the user wants to learn everyday conversation, the service provider will provide feedback related to everyday conversation. It can also provide feedback related to business conversation if the user wants to learn business English. Furthermore, if the user is interested in a specific topic, the service provider can provide feedback related to that topic. This allows the service provider to provide optimal feedback based on the user's current learning goals. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning goal data into a generating AI and have the generating AI customize the feedback.

[0068] The service provider can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the service provider may prioritize detailed feedback. If the user is stressed, the service provider may prioritize concise and to-the-point feedback. Furthermore, if the user is excited, the service provider may prioritize positive feedback. This allows for the provision of optimal feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of feedback.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] The English conversation learning system can monitor the user's learning progress in real time and dynamically adjust the learning content according to that progress. For example, if a user is struggling with a particular pronunciation or intonation, it can prioritize providing scenes that reinforce that area. Also, if a user has mastered a particular conversation pattern, it can broaden the scope of learning by introducing new patterns. Furthermore, it can insert review scenes at appropriate times based on the user's learning progress. This allows users to learn efficiently.

[0071] An English conversation learning system can estimate the user's emotions and provide incentives to maintain learning motivation based on those estimated emotions. For example, if the user is relaxed, a system can be implemented that allows them to earn badges or points according to their learning progress. If the user is stressed, the learning environment can be improved by providing relaxing music or videos. Furthermore, if the user is excited, a challenge mode can be introduced, providing more difficult scenes to increase their motivation to learn. In this way, the system can provide optimal incentives according to the user's emotions and maintain their learning motivation.

[0072] English conversation learning systems can provide customized feedback tailored to the user's learning style. For example, visual learners can receive easily understandable feedback by displaying pronunciation waveforms and intonation graphs. Auditory learners can receive easily understandable feedback by providing audio samples of correct pronunciation and intonation. Furthermore, tactile learners can be provided with an interactive touchscreen to encourage pronunciation and intonation practice. This allows for the provision of optimal feedback tailored to the user's learning style.

[0073] An English conversation learning system can estimate the user's emotions and visualize learning progress based on those emotions. For example, if the user is relaxed, learning progress can be displayed in graphs or charts to give them a sense of accomplishment. If the user is stressed, progress can be displayed with simple icons or messages to avoid putting excessive pressure on them. Furthermore, if the user is excited, progress can be displayed with a game-like interface to make learning enjoyable and encourage continued learning. This allows for the visualization of learning progress in the most optimal way according to the user's emotions.

[0074] The English conversation learning system can analyze a user's learning history and automatically generate a personalized learning plan. For example, it can suggest what the user should learn next based on what they have learned in the past. Furthermore, if the user is interested in a particular field, it can prioritize providing scenes related to that field. In addition, it can dynamically adjust the learning plan according to the user's learning pace, ensuring that learning progresses smoothly. This allows the system to provide an optimal learning plan based on the user's learning history.

[0075] An English conversation learning system can estimate the user's emotions and personalize learning feedback based on those emotions. For example, if the user is relaxed, it can provide detailed feedback to deepen their understanding of the material. If the user is stressed, it can provide concise and to-the-point feedback, avoiding overwhelming the user with information. Furthermore, if the user is excited, it can emphasize positive feedback to boost their motivation to learn. This allows the system to provide optimal feedback tailored to the user's emotions.

[0076] An English conversation learning system can provide customized learning content according to the user's learning goals. For example, if a user wants to learn travel English, it can provide travel-related scenarios. Similarly, if a user wants to learn business English, it can provide business-related scenarios. Furthermore, if a user is interested in a specific culture or region, it can provide scenarios related to that culture or region. This allows for the provision of optimal learning content tailored to the user's learning objectives.

[0077] An English conversation learning system can estimate the user's emotions and adjust how it reports learning progress based on those emotions. For example, if the user is relaxed, it can provide detailed progress reports to help them feel a sense of accomplishment. If the user is stressed, it can provide concise progress reports to avoid putting excessive pressure on them. Furthermore, if the user is excited, it can provide progress reports with a game-like interface to make learning more enjoyable. This allows the system to report learning progress in the most optimal way according to the user's emotions.

[0078] The English conversation learning system can automatically generate a review plan to maximize learning effectiveness based on the user's learning history. For example, it can identify areas that need review based on what the user has learned in the past and provide review scenes at the appropriate time. Furthermore, if the user has difficulty in a particular area, it can prioritize reviewing scenes related to that area. In addition, it can dynamically adjust the review plan according to the user's learning pace, allowing for smooth and manageable review. This enables the system to provide an optimal review plan based on the user's learning history.

[0079] An English conversation learning system can estimate the user's emotions and customize the learning interface based on those emotions. For example, if the user is relaxed, it can provide a simple and calming interface design. If the user is stressed, it can provide an interface incorporating relaxing colors and music. Furthermore, if the user is excited, it can provide an interface incorporating energetic designs and music. This allows the system to provide the optimal interface according to the user's emotions.

[0080] The following briefly describes the processing flow for example form 2.

[0081] Step 1: The loading unit loads the content of movies and dramas. For example, it uses a generating AI to load the content of movies and dramas selected by the user. Step 2: The selection unit analyzes the content read by the reading unit and selects scenes appropriate to the user's learning level. For example, beginners will be shown simple everyday conversation scenes, intermediate users will be shown slightly more complex conversation scenes, and advanced users will be shown business or specialized conversation scenes. Step 3: The playback unit plays scenes in which the user speaks lines, based on the scenes selected by the selection unit. For example, it plays scenes based on the cast selected by the user. Step 4: The delivery unit analyzes the user's pronunciation and intonation based on the scenes played by the playback unit and provides feedback. For example, if the pronunciation is inaccurate, it points out which part is wrong and teaches the correct pronunciation. Also, if the intonation is unnatural, it tells the user which part should be corrected.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] Each of the multiple elements described above, including the reading unit, selection unit, playback unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the smart device 14 and reads the content of a movie or drama selected by the user using a generating AI. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read content to select scenes according to the user's learning level. The playback unit is implemented by the control unit 46A of the smart device 14 and plays scenes in which the user speaks lines based on the selected scenes. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's pronunciation and intonation and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0086] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the reading unit, selection unit, playback unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the smart glasses 214, which uses a generating AI to read the content of a movie or drama selected by the user. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the read content and selects scenes according to the user's learning level. The playback unit is implemented by the control unit 46A of the smart glasses 214, which plays scenes in which the user speaks lines based on the selected scenes. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's pronunciation and intonation and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0117] Each of the multiple elements described above, including the reading unit, selection unit, playback unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the headset terminal 314, which uses a generating AI to read the content of a movie or drama selected by the user. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the read content and selects scenes according to the user's learning level. The playback unit is implemented by the control unit 46A of the headset terminal 314, which plays scenes in which the user speaks lines based on the selected scenes. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the user's pronunciation and intonation and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0118] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reading unit, selection unit, playback unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the robot 414 and reads the content of a movie or drama selected by the user using a generating AI. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the read content to select scenes according to the user's learning level. The playback unit is implemented by the control unit 46A of the robot 414 and plays scenes in which the user speaks lines based on the selected scenes. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's pronunciation and intonation and provides feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] (Note 1) A loading unit that reads the content of movies and dramas, A selection unit analyzes the content read by the aforementioned reading unit and selects a scene according to the user's learning level, A playback unit plays scenes in which the user speaks lines based on the scenes selected by the aforementioned selection unit, The system includes a providing unit that analyzes the user's pronunciation and intonation based on the scenes played by the aforementioned playback unit and provides feedback. A system characterized by the following features. (Note 2) The aforementioned reading unit, It estimates the user's emotions and adjusts the timing of loading movie or TV show content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reading unit, Analyze the user's past viewing history and select the optimal loading method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reading unit, When loading movie and TV show content, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is It estimates the user's emotions and adjusts the scene selection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is When selecting a scene, the system will refer to the user's past learning history to select the most suitable scene. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is When selecting a scene, customize the scene based on the user's current learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sink section is, It estimates the user's emotions and adjusts the way the scene unfolds based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sink section is, When playing a scene, the system selects the optimal way to play it by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sink section is, When playing a scene, customize the scene based on the user's current learning objectives. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing feedback, we refer 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 13) The aforementioned supply unit is, When providing feedback, customize the feedback based on the user's current learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0154] 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 loading unit that reads the content of movies and dramas, A selection unit analyzes the content read by the aforementioned reading unit and selects a scene according to the user's learning level, A playback unit plays scenes in which the user speaks lines based on the scenes selected by the aforementioned selection unit, The system includes a providing unit that analyzes the user's pronunciation and intonation based on the scenes played by the aforementioned playback unit and provides feedback. A system characterized by the following features.

2. The aforementioned reading unit, It estimates the user's emotions and adjusts the timing of loading movie or TV show content based on those estimated emotions. The system according to feature 1.

3. The aforementioned reading unit, Analyze the user's past viewing history and select the optimal loading method. The system according to feature 1.

4. The aforementioned reading unit, When loading movie and TV show content, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

5. The aforementioned selection unit is It estimates the user's emotions and adjusts the scene selection criteria based on the estimated user emotions. The system according to feature 1.

6. The aforementioned selection unit is When selecting a scene, the system will refer to the user's past learning history to select the most suitable scene. The system according to feature 1.

7. The aforementioned selection unit is When selecting a scene, customize the scene based on the user's current learning goals. The system according to feature 1.

8. The aforementioned sink section is, It estimates the user's emotions and adjusts the way the scene unfolds based on those estimated emotions. The system according to feature 1.

Images