system

Abstract

The system according to this embodiment aims to provide a customized English learning plan that meets the individual needs of learners. [Solution] The system according to the embodiment comprises a generation unit, a practice unit, an evaluation unit, and a tracking unit. The generation unit creates a customized learning plan based on the learner's current English level, goals, and interests. The practice unit provides real-time conversation practice based on the learning plan created by the generation unit. The evaluation unit evaluates the learner's pronunciation based on the conversation practice provided by the practice unit and provides feedback on corrections and areas for improvement. The tracking unit tracks the learner's progress based on the information provided by the evaluation unit, analyzes weaknesses, and suggests learning content.

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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide an English learning plan according to the individual needs of learners, and effective learning is difficult.

[0005] The system according to the embodiment aims to provide a customized English learning plan according to the individual needs of learners.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a practice unit, an evaluation unit, and a tracking unit. The generation unit creates a customized learning plan based on the learner's current English level, goals, and interests. The practice unit provides real-time conversation practice based on the learning plan created by the generation unit. The evaluation unit evaluates the learner's pronunciation based on the conversation practice provided by the practice unit and provides feedback on corrections and areas for improvement. The tracking unit tracks the learner's progress based on the information provided by the evaluation unit, analyzes weaknesses, and suggests learning content. [Effects of the Invention]

[0007] The system according to this embodiment can provide a customized English learning plan that meets the individual needs of each learner. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 learning support system according to an embodiment of the present invention is a system that provides learners with a personalized learning experience using generative AI. The learning support system creates a customized learning plan based on the learner's current English level, goals, and interests. Next, the learning support system provides real-time conversation practice using generative AI. The robot can practice various scenarios, from everyday conversation to business English, with native-speaker-like pronunciation. Furthermore, the learning support system evaluates the learner's pronunciation in real time and provides specific feedback on corrections and areas for improvement. It can also provide unique exercises for pronunciation practice. Learners can improve their English skills while having fun through interactive learning activities such as games, quizzes, and role-playing. The learning support system tracks the learner's progress, analyzes their weaknesses, and suggests appropriate learning content. It also provides progress reports and a sense of accomplishment to maintain motivation. In addition to the robot, it can also be linked with smartphones and tablets to enable learning anytime, anywhere. This system provides low-cost opportunities for practical conversation practice to a variety of target groups, including schools, language schools, individual home study, corporate training, international exchange, and the tourism industry. It maintains learners' motivation and provides an environment where they can learn anytime, anywhere. As a result, the learning support system can provide learners with a personalized learning experience, enabling efficient English language learning.

[0029] The learning support system according to this embodiment comprises a generation unit, a practice unit, an evaluation unit, and a tracking unit. The generation unit creates a customized learning plan based on the learner's current English level, goals, and interests. The generation unit evaluates the learner's English level based on test results, self-assessments, teacher evaluations, etc. The generation unit can also set the learner's goals to TOEFL scores, mastering everyday conversation, mastering business English, etc. Furthermore, the generation unit can identify the learner's interests from questionnaires, past learning history, hobbies, etc. Based on this information, the generation unit customizes the learning plan and selects learning content, allocates learning time, and selects learning materials. The practice unit provides real-time conversation practice based on the learning plan created by the generation unit. The practice unit conducts real-time conversation practice using, for example, online chat, video calls, and speech recognition technology. The practice unit can set various scenarios, from everyday conversation to business English, and allow practice with native-like pronunciation. The practice unit uses a generation AI to evaluate the learner's pronunciation in real time and provides specific feedback on pronunciation corrections and areas for improvement. The evaluation unit evaluates the learner's pronunciation based on the conversation practice provided by the practice unit and provides feedback on corrections and areas for improvement. For example, the evaluation unit provides voice analysis results, specific pronunciation guidance, and suggestions for practice methods. The evaluation unit evaluates the learner's pronunciation in real time and provides specific feedback on corrections and areas for improvement. The tracking unit tracks the learner's progress based on the information provided by the evaluation unit, analyzes weaknesses, and suggests learning content. For example, the tracking unit records learning history, analyzes test results, and records learning time. The tracking unit tracks the learner's progress, analyzes weaknesses, and suggests learning content accordingly. Furthermore, the tracking unit provides progress reports and a sense of accomplishment to maintain motivation. As a result, the learning support system according to this embodiment can provide learners with a personalized learning experience and achieve efficient English learning.

[0030] The generation unit creates a customized learning plan based on the learner's current English level, goals, and interests. Specifically, the generation unit utilizes multiple data sources to comprehensively evaluate the learner's English level. For example, it collects results from past English tests, self-assessment sheets, and teacher evaluation comments, and integrates this information to calculate an overall English level. Furthermore, the generation unit conducts interviews and questionnaires with learners to clarify specific goals in order to set detailed objectives. For example, it sets goals that meet the learner's needs, such as improving TOEFL scores, mastering everyday conversation, or mastering business English. The generation unit also collects information on the learner's past learning history, hobbies, and interests to identify their interests. For example, if a learner is interested in movies or music, it incorporates materials and topics related to those interests into the learning plan. Based on this information, the generation unit selects learning content, allocates learning time, and chooses materials to provide the learner with the optimal learning plan. In addition, the generation unit utilizes AI technology to evaluate the effectiveness of the learning plan in real time and revise the plan as needed. For example, the learning plan can be dynamically adjusted based on the learner's progress and feedback, providing an optimal learning environment at all times. This allows the generation unit to provide learners with a personalized learning experience, supporting efficient English language learning.

[0031] The practice section provides real-time conversation practice based on learning plans created by the generation section. Specifically, the practice section uses online chat, video calls, and speech recognition technology to allow learners to experience real-life conversation situations. For example, online chat allows learners to practice English conversation in a text-based format, while video calls provide an environment where learners can practice conversation face-to-face with native speakers. By using speech recognition technology, learners' pronunciation and intonation can be evaluated in real time, and feedback can be provided immediately. The practice section utilizes generative AI to evaluate learners' pronunciation in real time and provide specific feedback on corrections and areas for improvement. For example, when a learner pronounces a specific word or phrase, the generative AI analyzes the pronunciation and suggests the correct pronunciation method and areas for improvement. In addition, the practice section allows learners to practice with a variety of scenarios, from everyday conversation to business English. For example, in everyday conversation scenarios, learners can learn practical English by recreating real-life situations such as shopping, ordering at a restaurant, and conversations with friends. Business English scenarios allow learners to practice essential English skills needed in business settings, such as meetings, presentations, and email correspondence. This provides practical conversation practice for learners, improving their English communication abilities.

[0032] The evaluation unit assesses learners' pronunciation based on conversation practice provided by the practice unit and provides feedback on corrections and areas for improvement. Specifically, the evaluation unit uses speech analysis technology to analyze learners' pronunciation in detail. For example, it analyzes the audio data produced by learners and evaluates the accuracy of pronunciation at the phoneme level, as well as intonation and rhythm. Based on the analysis results, the evaluation unit proposes specific pronunciation guidance and practice methods. For example, if the pronunciation of a particular phoneme is inaccurate, it will show the correct way to pronounce that phoneme and suggest practice methods. Furthermore, the evaluation unit can evaluate learners' pronunciation in real time and provide immediate feedback. For example, while a learner is practicing pronunciation, the evaluation unit can immediately point out areas for correction and suggest ways to improve. In addition, the evaluation unit evaluates the progress of learners' pronunciation based on past pronunciation data and practice history and provides advice for long-term pronunciation improvement. For example, it evaluates the degree of improvement compared to past pronunciation data and proposes future practice strategies. In this way, the evaluation unit can provide learners with specific and effective pronunciation guidance and support their pronunciation improvement.

[0033] The tracking unit tracks learners' progress based on feedback from the evaluation unit, analyzes their weaknesses, and proposes learning content. Specifically, the tracking unit centrally manages learners' learning history, test results, and practice data, and records their progress in detail. For example, it collects information such as which materials learners used, how long they spent studying, and what test results they obtained, and stores this information in a database. Based on this data, the tracking unit analyzes learners' weaknesses and proposes specific improvement measures. For example, if a learner has weaknesses in specific pronunciation or grammar, it proposes practice methods and materials to overcome those weaknesses. The tracking unit also monitors learners' progress in real time and modifies the learning plan as needed. For example, if a learner is behind schedule towards their goal, it reviews the learning plan and proposes more effective learning methods. Furthermore, the tracking unit provides progress reports and a sense of accomplishment to maintain learners' motivation. For example, when a learner achieves a certain goal, it provides a progress report to help them feel a sense of accomplishment. It also provides incentives to encourage learners to continue learning and increases their motivation. This allows the tracking unit to closely monitor the learner's progress, analyze their weaknesses, suggest effective learning content, and support the learner's English language learning.

[0034] The generation unit can analyze the learner's past learning history and select the optimal learning plan. For example, the generation unit's AI can create a learning plan that prioritizes topics the learner has struggled with in the past. The generation unit can also have the AI ​​create a learning plan that strengthens topics the learner has excelled at in the past. Furthermore, the generation unit can have the AI ​​create a learning plan with an appropriate pace based on the learner's past learning speed. This allows the generation unit to provide the learner with the optimal learning plan based on their past learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learner's past learning history data into the generation AI and have the generation AI select the optimal learning plan.

[0035] The generation unit can filter learning plans based on the learner's current lifestyle and areas of interest. For example, the generation unit can have the AI ​​create a learning plan that includes topics related to the learner's current job. The generation unit can also have the AI ​​create a learning plan that includes topics related to the learner's hobbies. Furthermore, the generation unit can have the AI ​​create a learning plan that is tailored to the learner's lifestyle. This allows the generation unit to provide learning plans that are appropriate to the learner's lifestyle and areas of interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the learner's lifestyle and areas of interest into the generation AI and have the generation AI perform the filtering.

[0036] The generation unit can prioritize and incorporate highly relevant content when creating a learning plan, taking into account the learner's geographical location. For example, the generation unit can provide topics related to the culture and history of the area where the learner lives using its generating AI. The generation unit can also have its generating AI create a learning plan that includes information about the learner's travel destinations. Furthermore, based on the learner's geographical location, the generation unit can also provide topics related to local news and events using its generating AI. This allows for the provision of learning plans tailored to the learner's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learner's geographical location information into the generating AI and have the generating AI select highly relevant content.

[0037] The generation unit can analyze the learner's social media activity and incorporate relevant content when creating a learning plan. For example, the generation unit's AI can create a learning plan that includes topics the learner has shown interest in on social media. The generation unit can also provide topics related to the posts of influencers the learner follows. Furthermore, the generation unit can create an optimal learning plan based on the learner's social media activity patterns. This allows the generation unit to provide a learning plan tailored to the learner's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learner's social media activity data into the generation AI and have the generation AI select relevant content.

[0038] The practice unit can select the most suitable scenario during conversation practice by referring to the learner's past practice history. For example, the practice unit can prioritize providing scenarios that the learner has struggled with in the past. It can also provide practice to reinforce scenarios that the learner has excelled at in the past. Furthermore, the practice unit can provide scenarios at an appropriate pace based on the learner's past practice speed. This allows the practice unit to provide the learner with the most suitable scenario based on their past practice history. Some or all of the above processing in the practice unit may be performed using AI, for example, or not. For example, the practice unit can input the learner's past practice history data into a generating AI and have the generating AI select the most suitable scenario.

[0039] The practice unit can customize scenarios during conversation practice based on the learner's current life circumstances. For example, the practice unit can provide scenarios related to the learner's current job. It can also provide scenarios related to the learner's hobbies. Furthermore, the practice unit can provide scenarios that match the learner's daily routine. This allows for conversation practice with scenarios tailored to the learner's life circumstances. Some or all of the above processing in the practice unit may be performed using AI, for example, or without AI. For example, the practice unit can input the learner's life circumstances data into a generating AI and have the generating AI perform the scenario customization.

[0040] The practice unit can prioritize providing highly relevant scenarios during conversation practice, taking into account the learner's geographical location. For example, the practice unit can provide scenarios related to the culture and history of the area where the learner lives. It can also provide scenarios that include information about the learner's travel destinations. Furthermore, based on the learner's geographical location, the practice unit can provide scenarios related to local news and events. This allows for conversation practice with scenarios tailored to the learner's geographical location. Some or all of the above processing in the practice unit may be performed using AI, for example, or without AI. For example, the practice unit can input the learner's geographical location information into a generating AI and have the generating AI select highly relevant scenarios.

[0041] The practice unit can analyze the learner's social media activity during conversation practice and provide relevant scenarios. For example, the practice unit can provide scenarios that include topics the learner has shown interest in on social media. It can also provide scenarios related to the content of posts by influencers the learner follows. Furthermore, the practice unit can provide optimal scenarios based on the learner's social media activity patterns. This allows for conversation practice with scenarios based on the learner's social media activity. Some or all of the above processing in the practice unit may be performed using AI, for example, or not. For example, the practice unit can input the learner's social media activity data into a generating AI and have the generating AI select relevant scenarios.

[0042] The evaluation unit can improve the accuracy of its evaluation by referring to the learner's past pronunciation history during pronunciation evaluation. For example, the evaluation unit can focus on evaluating pronunciations that the learner has struggled with in the past. The evaluation unit can also perform evaluations to reinforce pronunciations that the learner has excelled at in the past. Furthermore, the evaluation unit can apply appropriate evaluation criteria based on the learner's past pronunciation history. This allows the evaluation unit to provide the learner with the most optimal pronunciation evaluation based on their past pronunciation history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's past pronunciation history data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0043] The evaluation unit can customize the evaluation criteria based on the learner's current life circumstances when evaluating pronunciation. For example, the evaluation unit may focus on evaluating pronunciation related to the learner's current job. It can also evaluate pronunciation related to the learner's hobbies. Furthermore, the evaluation unit can apply evaluation criteria that are tailored to the learner's daily routine. This allows for pronunciation evaluation based on criteria that are appropriate to the learner's life circumstances. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input learner life circumstances data into a generating AI and have the generating AI perform the customization of evaluation criteria.

[0044] The evaluation unit can apply highly relevant evaluation criteria when evaluating pronunciation, taking into account the learner's geographical location. For example, the evaluation unit can apply evaluation criteria that take into account the accent and dialect of the region where the learner lives. It can also apply evaluation criteria that take into account the accent and dialect of the place the learner is traveling to. Furthermore, the evaluation unit can apply evaluation criteria related to local pronunciation based on the learner's geographical location. This allows pronunciation evaluation to be performed using criteria based on the learner's geographical location. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's geographical location information into a generating AI and have the generating AI perform the application of highly relevant evaluation criteria.

[0045] The evaluation unit can analyze the learner's social media activity and apply relevant evaluation criteria during pronunciation evaluation. For example, the evaluation unit may focus on evaluating pronunciations that the learner has shown interest in on social media. It can also apply evaluation criteria related to the pronunciations of influencers the learner follows. Furthermore, the evaluation unit can apply the most appropriate evaluation criteria based on the learner's social media activity patterns. This allows for pronunciation evaluation based on criteria derived from the learner's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's social media activity data into a generating AI and have the generating AI apply the relevant evaluation criteria.

[0046] The tracking unit can select the optimal tracking method by referring to the learner's past learning history when tracking progress. For example, the tracking unit can focus on tracking topics that the learner has struggled with in the past. It can also track topics that the learner has excelled at in the past to reinforce those areas. Furthermore, the tracking unit can track at an appropriate pace based on the learner's past learning pace. This allows the tracking unit to provide the learner with optimal progress tracking based on their past learning history. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the learner's past learning history data into a generating AI and have the generating AI select the optimal tracking method.

[0047] The tracking unit can customize the tracking method based on the learner's current life circumstances when tracking progress. For example, the tracking unit can focus on tracking topics related to the learner's current work. It can also track topics related to the learner's hobbies. Furthermore, the tracking unit can provide a tracking method that matches the learner's daily rhythm. This allows progress tracking to be performed in a way that suits the learner's life circumstances. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input learner life circumstances data into a generating AI and have the generating AI perform the customization of the tracking method.

[0048] The tracking unit can select the optimal tracking method when tracking progress, taking into account the learner's geographical location. For example, the tracking unit can track topics related to the culture and history of the area where the learner lives. It can also track topics that include information about the learner's travel destinations. Furthermore, based on the learner's geographical location, the tracking unit can track topics related to local news and events. This allows for progress tracking in a way that is based on the learner's geographical location. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the learner's geographical location information into a generating AI and have the generating AI select the optimal tracking method.

[0049] The tracking unit can analyze the learner's social media activity and apply relevant tracking methods when tracking progress. For example, the tracking unit can track topics that the learner has shown interest in on social media. It can also track topics related to the content of posts by influencers that the learner follows. Furthermore, the tracking unit can provide the optimal tracking method based on the learner's social media activity patterns. This allows progress tracking to be performed in a way that is based on the learner's social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the learner's social media activity data into a generating AI and have the generating AI apply relevant tracking methods.

[0050] The system can select the optimal connection method by referring to the learner's past device usage history during connection. For example, the system may prioritize connection to devices that the learner has frequently used in the past. The system can also select a connection method based on the learner's past device usage patterns. Furthermore, the system can provide the optimal connection method based on the learner's past device usage history. This allows the system to provide the learner with the most suitable connection method based on their past device usage history. Some or all of the above processes in the system may be performed using AI, for example, or without AI. For example, the system can input the learner's past device usage history data into a generating AI and have the generating AI select the optimal connection method.

[0051] The system can select the optimal collaboration method when collaborating, taking into account the learner's device information. For example, if the learner is using a smartphone, the system can provide a collaboration method optimized for smartphones. The system can also provide a collaboration method optimized for tablets if the learner is using a tablet. Furthermore, if the learner is using a robot, the system can provide a collaboration method optimized for robots. This allows the system to provide the optimal collaboration method based on the learner's device information. Some or all of the above-described processes in the system may be performed using AI, for example, or without AI. For example, the system can input the learner's device information into a generating AI and have the generating AI select the optimal collaboration method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The learning support system can analyze a learner's past learning history and select the optimal learning plan. For example, the generating AI can create a learning plan that prioritizes topics the learner has struggled with in the past. It can also create a learning plan that strengthens topics the learner has excelled at in the past. Furthermore, the generating AI can create a learning plan with an appropriate pace based on the learner's past learning speed. This allows the system to provide the learner with the most suitable learning plan based on their past learning history. Some or all of the above processing in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the learner's past learning history data into the generating AI and have the generating AI select the optimal learning plan.

[0054] The learning support system can filter learning materials based on the learner's current living situation and areas of interest. For example, the generating AI can create a learning plan that includes topics related to the learner's current job. It can also create a learning plan that includes topics related to the learner's hobbies. Furthermore, the generating AI can create a learning plan that matches the learner's daily routine. This allows the system to provide learning plans tailored to the learner's living situation and areas of interest. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the learner's living situation and areas of interest into the generating AI and have the generating AI perform the filtering.

[0055] The learning support system can prioritize and incorporate highly relevant content by considering the learner's geographical location. For example, the generating AI can provide topics related to the culture and history of the area where the learner lives. The generating AI can also create learning plans that include information about the learner's travel destinations. Furthermore, based on the learner's geographical location, the generating AI can provide topics related to local news and events. This allows for the provision of learning plans tailored to the learner's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learner's geographical location information into the generating AI and have the generating AI select highly relevant content.

[0056] The learning support system can analyze learners' social media activity and incorporate relevant content. For example, the generating AI can create a learning plan that includes topics the learner has shown interest in on social media. The generating AI can also provide topics related to the posts of influencers the learner follows. Furthermore, the generating AI can create an optimal learning plan based on the learner's social media activity patterns. This allows the system to provide learning plans tailored to the learner's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input learner's social media activity data into the generating AI and have the generating AI select relevant content.

[0057] The learning support system can select the optimal scenario during conversation practice by referring to the learner's past practice history. For example, it can prioritize providing scenarios that the learner has struggled with in the past. It can also provide practice to reinforce scenarios that the learner has excelled at in the past. Furthermore, it can provide scenarios with an appropriate pace based on the learner's past practice speed. This allows the system to provide the optimal scenario for the learner based on their past practice history. Some or all of the above processing in the practice section may be performed using AI, for example, or without AI. For example, the practice section can input the learner's past practice history data into a generating AI and have the generating AI select the optimal scenario.

[0058] The learning support system can customize scenarios during conversation practice based on the learner's current life circumstances. For example, the learner can provide scenarios related to their current job. They can also provide scenarios related to their hobbies. Furthermore, it can provide scenarios that match the learner's daily routine. This allows for conversation practice with scenarios tailored to the learner's life circumstances. Some or all of the above processing in the practice section may be performed using AI, for example, or without AI. For example, the practice section can input the learner's life circumstances data into a generating AI and have the generating AI perform the scenario customization.

[0059] The learning support system can improve the accuracy of pronunciation evaluations by referring to the learner's past pronunciation history. For example, it can focus on evaluating pronunciations that the learner has struggled with in the past. It can also perform evaluations to reinforce pronunciations that the learner has excelled at in the past. Furthermore, it can apply appropriate evaluation criteria based on the learner's past pronunciation history. This allows the system to provide learners with optimal pronunciation evaluations based on their past pronunciation history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's past pronunciation history data into a generating AI and have the generating AI perform improvements to the evaluation accuracy.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The generation unit creates a customized learning plan based on the learner's current English level, goals, and interests. For example, it assesses the learner's English level based on test results, self-assessments, and teacher evaluations, and sets goals such as achieving a TOEFL score, mastering everyday conversation, or mastering business English. It also identifies the learner's interests from questionnaires, past learning history, hobbies, etc., and customizes the learning plan based on this information. Step 2: The practice unit provides real-time conversation practice based on the learning plan created by the generation unit. For example, it uses online chat, video calls, and speech recognition technology to conduct real-time conversation practice, setting up various scenarios from everyday conversation to business English, with pronunciation like a native speaker. Step 3: The evaluation department assesses the learner's pronunciation based on the conversation practice provided by the practice department and provides feedback on corrections and areas for improvement. This may include voice analysis results, specific pronunciation guidance, and suggestions for practice methods. Step 4: The tracking unit tracks the learner's progress based on the information provided by the evaluation unit, analyzes weaknesses, and suggests learning content. For example, it tracks progress by recording learning history, analyzing test results, and recording study time, analyzes weaknesses, and suggests appropriate learning content. It also provides progress reports and a sense of accomplishment to maintain motivation.

[0062] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that provides learners with a personalized learning experience using generative AI. The learning support system creates a customized learning plan based on the learner's current English level, goals, and interests. Next, the learning support system provides real-time conversation practice using generative AI. The robot can practice various scenarios, from everyday conversation to business English, with native-speaker-like pronunciation. Furthermore, the learning support system evaluates the learner's pronunciation in real time and provides specific feedback on corrections and areas for improvement. It can also provide unique exercises for pronunciation practice. Learners can improve their English skills while having fun through interactive learning activities such as games, quizzes, and role-playing. The learning support system tracks the learner's progress, analyzes their weaknesses, and suggests appropriate learning content. It also provides progress reports and a sense of accomplishment to maintain motivation. In addition to the robot, it can also be linked with smartphones and tablets to enable learning anytime, anywhere. This system provides low-cost opportunities for practical conversation practice to a variety of target groups, including schools, language schools, individual home study, corporate training, international exchange, and the tourism industry. It maintains learners' motivation and provides an environment where they can learn anytime, anywhere. As a result, the learning support system can provide learners with a personalized learning experience, enabling efficient English language learning.

[0063] The learning support system according to this embodiment comprises a generation unit, a practice unit, an evaluation unit, and a tracking unit. The generation unit creates a customized learning plan based on the learner's current English level, goals, and interests. The generation unit evaluates the learner's English level based on test results, self-assessments, teacher evaluations, etc. The generation unit can also set the learner's goals to TOEFL scores, mastering everyday conversation, mastering business English, etc. Furthermore, the generation unit can identify the learner's interests from questionnaires, past learning history, hobbies, etc. Based on this information, the generation unit customizes the learning plan and selects learning content, allocates learning time, and selects learning materials. The practice unit provides real-time conversation practice based on the learning plan created by the generation unit. The practice unit conducts real-time conversation practice using, for example, online chat, video calls, and speech recognition technology. The practice unit can set various scenarios, from everyday conversation to business English, and allow practice with native-like pronunciation. The practice unit uses a generation AI to evaluate the learner's pronunciation in real time and provides specific feedback on pronunciation corrections and areas for improvement. The evaluation unit evaluates the learner's pronunciation based on the conversation practice provided by the practice unit and provides feedback on corrections and areas for improvement. For example, the evaluation unit provides voice analysis results, specific pronunciation guidance, and suggestions for practice methods. The evaluation unit evaluates the learner's pronunciation in real time and provides specific feedback on corrections and areas for improvement. The tracking unit tracks the learner's progress based on the information provided by the evaluation unit, analyzes weaknesses, and suggests learning content. For example, the tracking unit records learning history, analyzes test results, and records learning time. The tracking unit tracks the learner's progress, analyzes weaknesses, and suggests learning content accordingly. Furthermore, the tracking unit provides progress reports and a sense of accomplishment to maintain motivation. As a result, the learning support system according to this embodiment can provide learners with a personalized learning experience and achieve efficient English learning.

[0064] The generation unit creates a customized learning plan based on the learner's current English level, goals, and interests. Specifically, the generation unit utilizes multiple data sources to comprehensively evaluate the learner's English level. For example, it collects results from past English tests, self-assessment sheets, and teacher evaluation comments, and integrates this information to calculate an overall English level. Furthermore, the generation unit conducts interviews and questionnaires with learners to clarify specific goals in order to set detailed objectives. For example, it sets goals that meet the learner's needs, such as improving TOEFL scores, mastering everyday conversation, or mastering business English. The generation unit also collects information on the learner's past learning history, hobbies, and interests to identify their interests. For example, if a learner is interested in movies or music, it incorporates materials and topics related to those interests into the learning plan. Based on this information, the generation unit selects learning content, allocates learning time, and chooses materials to provide the learner with the optimal learning plan. In addition, the generation unit utilizes AI technology to evaluate the effectiveness of the learning plan in real time and revise the plan as needed. For example, the learning plan can be dynamically adjusted based on the learner's progress and feedback, providing an optimal learning environment at all times. This allows the generation unit to provide learners with a personalized learning experience, supporting efficient English language learning.

[0065] The practice section provides real-time conversation practice based on learning plans created by the generation section. Specifically, the practice section uses online chat, video calls, and speech recognition technology to allow learners to experience real-life conversation situations. For example, online chat allows learners to practice English conversation in a text-based format, while video calls provide an environment where learners can practice conversation face-to-face with native speakers. By using speech recognition technology, learners' pronunciation and intonation can be evaluated in real time, and feedback can be provided immediately. The practice section utilizes generative AI to evaluate learners' pronunciation in real time and provide specific feedback on corrections and areas for improvement. For example, when a learner pronounces a specific word or phrase, the generative AI analyzes the pronunciation and suggests the correct pronunciation method and areas for improvement. In addition, the practice section allows learners to practice with a variety of scenarios, from everyday conversation to business English. For example, in everyday conversation scenarios, learners can learn practical English by recreating real-life situations such as shopping, ordering at a restaurant, and conversations with friends. Business English scenarios allow learners to practice essential English skills needed in business settings, such as meetings, presentations, and email correspondence. This provides practical conversation practice for learners, improving their English communication abilities.

[0066] The evaluation unit assesses learners' pronunciation based on conversation practice provided by the practice unit and provides feedback on corrections and areas for improvement. Specifically, the evaluation unit uses speech analysis technology to analyze learners' pronunciation in detail. For example, it analyzes the audio data produced by learners and evaluates the accuracy of pronunciation at the phoneme level, as well as intonation and rhythm. Based on the analysis results, the evaluation unit proposes specific pronunciation guidance and practice methods. For example, if the pronunciation of a particular phoneme is inaccurate, it will show the correct way to pronounce that phoneme and suggest practice methods. Furthermore, the evaluation unit can evaluate learners' pronunciation in real time and provide immediate feedback. For example, while a learner is practicing pronunciation, the evaluation unit can immediately point out areas for correction and suggest ways to improve. In addition, the evaluation unit evaluates the progress of learners' pronunciation based on past pronunciation data and practice history and provides advice for long-term pronunciation improvement. For example, it evaluates the degree of improvement compared to past pronunciation data and proposes future practice strategies. In this way, the evaluation unit can provide learners with specific and effective pronunciation guidance and support their pronunciation improvement.

[0067] The tracking unit tracks learners' progress based on feedback from the evaluation unit, analyzes their weaknesses, and proposes learning content. Specifically, the tracking unit centrally manages learners' learning history, test results, and practice data, and records their progress in detail. For example, it collects information such as which materials learners used, how long they spent studying, and what test results they obtained, and stores this information in a database. Based on this data, the tracking unit analyzes learners' weaknesses and proposes specific improvement measures. For example, if a learner has weaknesses in specific pronunciation or grammar, it proposes practice methods and materials to overcome those weaknesses. The tracking unit also monitors learners' progress in real time and modifies the learning plan as needed. For example, if a learner is behind schedule towards their goal, it reviews the learning plan and proposes more effective learning methods. Furthermore, the tracking unit provides progress reports and a sense of accomplishment to maintain learners' motivation. For example, when a learner achieves a certain goal, it provides a progress report to help them feel a sense of accomplishment. It also provides incentives to encourage learners to continue learning and increases their motivation. This allows the tracking unit to closely monitor the learner's progress, analyze their weaknesses, suggest effective learning content, and support the learner's English language learning.

[0068] The generation unit can estimate the learner's emotions and adjust the difficulty level of the learning plan based on the estimated emotions. For example, if the learner is stressed, the generation unit's generating AI may prioritize providing easier tasks. The generation unit can also provide more difficult tasks if the learner is relaxed. Furthermore, if the learner is excited, the generation unit can provide challenging tasks. This allows for the provision of a learning plan of appropriate difficulty level according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input learner emotion data into the generation AI and have the generation AI perform emotion estimation.

[0069] The generation unit can analyze the learner's past learning history and select the optimal learning plan. For example, the generation unit's AI can create a learning plan that prioritizes topics the learner has struggled with in the past. The generation unit can also have the AI ​​create a learning plan that strengthens topics the learner has excelled at in the past. Furthermore, the generation unit can have the AI ​​create a learning plan with an appropriate pace based on the learner's past learning speed. This allows the generation unit to provide the learner with the optimal learning plan based on their past learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learner's past learning history data into the generation AI and have the generation AI select the optimal learning plan.

[0070] The generation unit can filter learning plans based on the learner's current lifestyle and areas of interest. For example, the generation unit can have the AI ​​create a learning plan that includes topics related to the learner's current job. The generation unit can also have the AI ​​create a learning plan that includes topics related to the learner's hobbies. Furthermore, the generation unit can have the AI ​​create a learning plan that is tailored to the learner's lifestyle. This allows the generation unit to provide learning plans that are appropriate to the learner's lifestyle and areas of interest. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the learner's lifestyle and areas of interest into the generation AI and have the generation AI perform the filtering.

[0071] The generation unit can estimate the learner's emotions and prioritize learning plans based on the estimated emotions. For example, if the learner is stressed, the generation unit can prioritize providing relaxing topics. If the learner is relaxed, the generation unit can also prioritize providing challenging topics. Furthermore, if the learner is excited, the generation unit can prioritize providing challenging topics. This allows for the provision of learning plans with priorities tailored to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input learner emotion data into a generation AI and have the generation AI perform emotion estimation.

[0072] The generation unit can prioritize and incorporate highly relevant content when creating a learning plan, taking into account the learner's geographical location. For example, the generation unit can provide topics related to the culture and history of the area where the learner lives using its generating AI. The generation unit can also have its generating AI create a learning plan that includes information about the learner's travel destinations. Furthermore, based on the learner's geographical location, the generation unit can also provide topics related to local news and events using its generating AI. This allows for the provision of learning plans tailored to the learner's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learner's geographical location information into the generating AI and have the generating AI select highly relevant content.

[0073] The generation unit can analyze the learner's social media activity and incorporate relevant content when creating a learning plan. For example, the generation unit's AI can create a learning plan that includes topics the learner has shown interest in on social media. The generation unit can also provide topics related to the posts of influencers the learner follows. Furthermore, the generation unit can create an optimal learning plan based on the learner's social media activity patterns. This allows the generation unit to provide a learning plan tailored to the learner's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learner's social media activity data into the generation AI and have the generation AI select relevant content.

[0074] The practice unit can estimate the learner's emotions and adjust the conversation practice scenario based on the estimated emotions. For example, if the learner is nervous, the practice unit can have the generative AI provide a relaxing scenario. If the learner is relaxed, the practice unit can also have the generative AI provide a more challenging scenario. Furthermore, if the learner is excited, the practice unit can have the generative AI provide a challenging scenario. This allows for conversation practice with scenarios appropriate to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the practice unit may be performed using AI or not using AI. For example, the practice unit can input learner emotion data into the generative AI and have the generative AI perform emotion estimation.

[0075] The practice unit can select the most suitable scenario during conversation practice by referring to the learner's past practice history. For example, the practice unit can prioritize providing scenarios that the learner has struggled with in the past. It can also provide practice to reinforce scenarios that the learner has excelled at in the past. Furthermore, the practice unit can provide scenarios at an appropriate pace based on the learner's past practice speed. This allows the practice unit to provide the learner with the most suitable scenario based on their past practice history. Some or all of the above processing in the practice unit may be performed using AI, for example, or not. For example, the practice unit can input the learner's past practice history data into a generating AI and have the generating AI select the most suitable scenario.

[0076] The practice unit can customize scenarios during conversation practice based on the learner's current life circumstances. For example, the practice unit can provide scenarios related to the learner's current job. It can also provide scenarios related to the learner's hobbies. Furthermore, the practice unit can provide scenarios that match the learner's daily routine. This allows for conversation practice with scenarios tailored to the learner's life circumstances. Some or all of the above processing in the practice unit may be performed using AI, for example, or without AI. For example, the practice unit can input the learner's life circumstances data into a generating AI and have the generating AI perform the scenario customization.

[0077] The practice unit can estimate the learner's emotions and prioritize conversation practice based on the estimated emotions. For example, if the learner is stressed, the practice unit can prioritize providing relaxing scenarios using the generative AI. If the learner is relaxed, the practice unit can also prioritize providing more challenging scenarios using the generative AI. Furthermore, if the learner is excited, the practice unit can prioritize providing challenging scenarios using the generative AI. This allows for conversation practice to be provided with priorities tailored to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the practice unit may be performed using AI or not. For example, the practice unit can input learner emotion data into the generative AI and have the generative AI perform emotion estimation.

[0078] The practice unit can prioritize providing highly relevant scenarios during conversation practice, taking into account the learner's geographical location. For example, the practice unit can provide scenarios related to the culture and history of the area where the learner lives. It can also provide scenarios that include information about the learner's travel destinations. Furthermore, based on the learner's geographical location, the practice unit can provide scenarios related to local news and events. This allows for conversation practice with scenarios tailored to the learner's geographical location. Some or all of the above processing in the practice unit may be performed using AI, for example, or without AI. For example, the practice unit can input the learner's geographical location information into a generating AI and have the generating AI select highly relevant scenarios.

[0079] The practice unit can analyze the learner's social media activity during conversation practice and provide relevant scenarios. For example, the practice unit can provide scenarios that include topics the learner has shown interest in on social media. It can also provide scenarios related to the content of posts by influencers the learner follows. Furthermore, the practice unit can provide optimal scenarios based on the learner's social media activity patterns. This allows for conversation practice with scenarios based on the learner's social media activity. Some or all of the above processing in the practice unit may be performed using AI, for example, or not. For example, the practice unit can input the learner's social media activity data into a generating AI and have the generating AI select relevant scenarios.

[0080] The evaluation unit can estimate the learner's emotions and adjust the pronunciation evaluation criteria based on the estimated emotions. For example, if the learner is nervous, the evaluation unit can have the generating AI apply lenient evaluation criteria. The evaluation unit can also have the generating AI apply strict evaluation criteria if the learner is relaxed. Furthermore, if the learner is excited, the evaluation unit can have the generating AI apply challenging evaluation criteria. This allows for pronunciation evaluation using appropriate criteria according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input learner emotion data into a generating AI and have the generating AI perform emotion estimation.

[0081] The evaluation unit can improve the accuracy of its evaluation by referring to the learner's past pronunciation history during pronunciation evaluation. For example, the evaluation unit can focus on evaluating pronunciations that the learner has struggled with in the past. The evaluation unit can also perform evaluations to reinforce pronunciations that the learner has excelled at in the past. Furthermore, the evaluation unit can apply appropriate evaluation criteria based on the learner's past pronunciation history. This allows the evaluation unit to provide the learner with the most optimal pronunciation evaluation based on their past pronunciation history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's past pronunciation history data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0082] The evaluation unit can customize the evaluation criteria based on the learner's current life circumstances when evaluating pronunciation. For example, the evaluation unit may focus on evaluating pronunciation related to the learner's current job. It can also evaluate pronunciation related to the learner's hobbies. Furthermore, the evaluation unit can apply evaluation criteria that are tailored to the learner's daily routine. This allows for pronunciation evaluation based on criteria that are appropriate to the learner's life circumstances. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input learner life circumstances data into a generating AI and have the generating AI perform the customization of evaluation criteria.

[0083] The evaluation unit can estimate the learner's emotions and determine the priority of pronunciation evaluation based on the estimated learner's emotions. For example, if the learner is stressed, the evaluation unit can have the generating AI prioritize evaluating pronunciations that help them relax. Furthermore, if the learner is relaxed, the evaluation unit can have the generating AI prioritize evaluating pronunciations that are more difficult. Additionally, if the learner is excited, the evaluation unit can have the generating AI prioritize evaluating challenging pronunciations. This allows for pronunciation evaluation with priorities tailored to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can input learner emotion data into a generating AI and have the generating AI perform emotion estimation.

[0084] The evaluation unit can apply highly relevant evaluation criteria when evaluating pronunciation, taking into account the learner's geographical location. For example, the evaluation unit can apply evaluation criteria that take into account the accent and dialect of the region where the learner lives. It can also apply evaluation criteria that take into account the accent and dialect of the place the learner is traveling to. Furthermore, the evaluation unit can apply evaluation criteria related to local pronunciation based on the learner's geographical location. This allows pronunciation evaluation to be performed using criteria based on the learner's geographical location. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's geographical location information into a generating AI and have the generating AI perform the application of highly relevant evaluation criteria.

[0085] The evaluation unit can analyze the learner's social media activity and apply relevant evaluation criteria during pronunciation evaluation. For example, the evaluation unit may focus on evaluating pronunciations that the learner has shown interest in on social media. It can also apply evaluation criteria related to the pronunciations of influencers the learner follows. Furthermore, the evaluation unit can apply the most appropriate evaluation criteria based on the learner's social media activity patterns. This allows for pronunciation evaluation based on criteria derived from the learner's social media activity. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's social media activity data into a generating AI and have the generating AI apply the relevant evaluation criteria.

[0086] The tracking unit can estimate the learner's emotions and adjust the progress tracking method based on the estimated learner's emotions. For example, if the learner is stressed, the tracking unit can have the generative AI provide a simple progress report. If the learner is relaxed, the tracking unit can have the generative AI provide a more detailed progress report. Furthermore, if the learner is excited, the tracking unit can have the generative AI provide a more challenging progress report. This allows for progress tracking in an appropriate manner according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input learner emotion data into the generative AI and have the generative AI perform emotion estimation.

[0087] The tracking unit can select the optimal tracking method by referring to the learner's past learning history when tracking progress. For example, the tracking unit can focus on tracking topics that the learner has struggled with in the past. It can also track topics that the learner has excelled at in the past to reinforce those areas. Furthermore, the tracking unit can track at an appropriate pace based on the learner's past learning pace. This allows the tracking unit to provide the learner with optimal progress tracking based on their past learning history. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the learner's past learning history data into a generating AI and have the generating AI select the optimal tracking method.

[0088] The tracking unit can customize the tracking method based on the learner's current life circumstances when tracking progress. For example, the tracking unit can focus on tracking topics related to the learner's current work. It can also track topics related to the learner's hobbies. Furthermore, the tracking unit can provide a tracking method that matches the learner's daily rhythm. This allows progress tracking to be performed in a way that suits the learner's life circumstances. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input learner life circumstances data into a generating AI and have the generating AI perform the customization of the tracking method.

[0089] The tracking unit can estimate the learner's emotions and determine the priority of progress tracking based on the estimated learner's emotions. For example, if the learner is stressed, the tracking unit can prioritize tracking topics that the generative AI can help them relax. If the learner is relaxed, the tracking unit can also prioritize tracking topics that the generative AI finds challenging. Furthermore, if the learner is excited, the tracking unit can prioritize tracking challenging topics. This allows for progress tracking with priorities tailored to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input learner emotion data into the generative AI and have the generative AI perform emotion estimation.

[0090] The tracking unit can select the optimal tracking method when tracking progress, taking into account the learner's geographical location. For example, the tracking unit can track topics related to the culture and history of the area where the learner lives. It can also track topics that include information about the learner's travel destinations. Furthermore, based on the learner's geographical location, the tracking unit can track topics related to local news and events. This allows for progress tracking in a way that is based on the learner's geographical location. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the learner's geographical location information into a generating AI and have the generating AI select the optimal tracking method.

[0091] The tracking unit can analyze the learner's social media activity and apply relevant tracking methods when tracking progress. For example, the tracking unit can track topics that the learner has shown interest in on social media. It can also track topics related to the content of posts by influencers that the learner follows. Furthermore, the tracking unit can provide the optimal tracking method based on the learner's social media activity patterns. This allows progress tracking to be performed in a way that is based on the learner's social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the learner's social media activity data into a generating AI and have the generating AI apply relevant tracking methods.

[0092] The system can estimate the learner's emotions and select a device to connect with based on the estimated emotions. For example, if the learner is relaxed, the system may prioritize connecting with a smartphone. It may also prioritize connecting with a tablet if the learner is stressed. Furthermore, if the learner is excited, the system may prioritize connecting with a robot. This allows learning to be provided using a device appropriate to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input learner emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The system can select the optimal connection method by referring to the learner's past device usage history during connection. For example, the system may prioritize connection to devices that the learner has frequently used in the past. The system can also select a connection method based on the learner's past device usage patterns. Furthermore, the system can provide the optimal connection method based on the learner's past device usage history. This allows the system to provide the learner with the most suitable connection method based on their past device usage history. Some or all of the above processes in the system may be performed using AI, for example, or without AI. For example, the system can input the learner's past device usage history data into a generating AI and have the generating AI select the optimal connection method.

[0094] The system can estimate the learner's emotions and adjust the frequency of interaction based on the estimated emotions. For example, if the learner is stressed, the system may set the interaction frequency low. Conversely, if the learner is relaxed, the system may set the interaction frequency high. Furthermore, if the learner is excited, the system may set the interaction frequency to a moderate level. This allows for device interaction at a frequency appropriate to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI, or not. For example, the system can input learner emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The system can select the optimal collaboration method when collaborating, taking into account the learner's device information. For example, if the learner is using a smartphone, the system can provide a collaboration method optimized for smartphones. The system can also provide a collaboration method optimized for tablets if the learner is using a tablet. Furthermore, if the learner is using a robot, the system can provide a collaboration method optimized for robots. This allows the system to provide the optimal collaboration method based on the learner's device information. Some or all of the above-described processes in the system may be performed using AI, for example, or without AI. For example, the system can input the learner's device information into a generating AI and have the generating AI select the optimal collaboration method.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] A learning support system can estimate a learner's emotions and adjust the feedback method of the learning plan based on the estimated emotions. For example, if a learner is stressed, the generative AI can provide feedback in gentle language. If the learner is relaxed, the generative AI can also provide detailed feedback. Furthermore, if the learner is excited, the generative AI can provide challenging feedback. This allows for the provision of appropriate feedback according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning support system may be performed using AI, for example, or not using AI. For example, the learning support system can input learner emotion data into the generative AI and have the generative AI perform emotion estimation.

[0098] The learning support system can analyze a learner's past learning history and select the optimal learning plan. For example, the generating AI can create a learning plan that prioritizes topics the learner has struggled with in the past. It can also create a learning plan that strengthens topics the learner has excelled at in the past. Furthermore, the generating AI can create a learning plan with an appropriate pace based on the learner's past learning speed. This allows the system to provide the learner with the most suitable learning plan based on their past learning history. Some or all of the above processing in the generation unit may be performed using AI, or without AI. For example, the generation unit can input the learner's past learning history data into the generating AI and have the generating AI select the optimal learning plan.

[0099] The learning support system can filter learning materials based on the learner's current living situation and areas of interest. For example, the generating AI can create a learning plan that includes topics related to the learner's current job. It can also create a learning plan that includes topics related to the learner's hobbies. Furthermore, the generating AI can create a learning plan that matches the learner's daily routine. This allows the system to provide learning plans tailored to the learner's living situation and areas of interest. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the learner's living situation and areas of interest into the generating AI and have the generating AI perform the filtering.

[0100] The learning support system can prioritize and incorporate highly relevant content by considering the learner's geographical location. For example, the generating AI can provide topics related to the culture and history of the area where the learner lives. The generating AI can also create learning plans that include information about the learner's travel destinations. Furthermore, based on the learner's geographical location, the generating AI can provide topics related to local news and events. This allows for the provision of learning plans tailored to the learner's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the learner's geographical location information into the generating AI and have the generating AI select highly relevant content.

[0101] The learning support system can analyze learners' social media activity and incorporate relevant content. For example, the generating AI can create a learning plan that includes topics the learner has shown interest in on social media. The generating AI can also provide topics related to the posts of influencers the learner follows. Furthermore, the generating AI can create an optimal learning plan based on the learner's social media activity patterns. This allows the system to provide learning plans tailored to the learner's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input learner's social media activity data into the generating AI and have the generating AI select relevant content.

[0102] The learning support system can estimate the learner's emotions and adjust the conversation practice scenario based on the estimated emotions. For example, if the learner is nervous, the generative AI can provide a relaxing scenario. If the learner is relaxed, the generative AI can also provide a more challenging scenario. Furthermore, if the learner is excited, the generative AI can provide a challenging scenario. This allows the system to provide conversation practice with scenarios appropriate to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the practice section may be performed using AI, for example, or not using AI. For example, the practice section can input the learner's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0103] The learning support system can select the optimal scenario during conversation practice by referring to the learner's past practice history. For example, it can prioritize providing scenarios that the learner has struggled with in the past. It can also provide practice to reinforce scenarios that the learner has excelled at in the past. Furthermore, it can provide scenarios with an appropriate pace based on the learner's past practice speed. This allows the system to provide the optimal scenario for the learner based on their past practice history. Some or all of the above processing in the practice section may be performed using AI, for example, or without AI. For example, the practice section can input the learner's past practice history data into a generating AI and have the generating AI select the optimal scenario.

[0104] The learning support system can customize scenarios during conversation practice based on the learner's current life circumstances. For example, the learner can provide scenarios related to their current job. They can also provide scenarios related to their hobbies. Furthermore, it can provide scenarios that match the learner's daily routine. This allows for conversation practice with scenarios tailored to the learner's life circumstances. Some or all of the above processing in the practice section may be performed using AI, for example, or without AI. For example, the practice section can input the learner's life circumstances data into a generating AI and have the generating AI perform the scenario customization.

[0105] The learning support system can estimate the learner's emotions and adjust the pronunciation evaluation criteria based on the estimated emotions. For example, if the learner is nervous, the generating AI can apply lenient evaluation criteria. If the learner is relaxed, the generating AI can apply strict evaluation criteria. Furthermore, if the learner is excited, the generating AI can apply challenging evaluation criteria. This allows for pronunciation evaluation with appropriate criteria according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the learner's emotion data into the generating AI and have the generating AI perform emotion estimation.

[0106] The learning support system can improve the accuracy of pronunciation evaluations by referring to the learner's past pronunciation history. For example, it can focus on evaluating pronunciations that the learner has struggled with in the past. It can also perform evaluations to reinforce pronunciations that the learner has excelled at in the past. Furthermore, it can apply appropriate evaluation criteria based on the learner's past pronunciation history. This allows the system to provide learners with optimal pronunciation evaluations based on their past pronunciation history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the learner's past pronunciation history data into a generating AI and have the generating AI perform improvements to the evaluation accuracy.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The generation unit creates a customized learning plan based on the learner's current English level, goals, and interests. For example, it assesses the learner's English level based on test results, self-assessments, and teacher evaluations, and sets goals such as achieving a TOEFL score, mastering everyday conversation, or mastering business English. It also identifies the learner's interests from questionnaires, past learning history, hobbies, etc., and customizes the learning plan based on this information. Step 2: The practice unit provides real-time conversation practice based on the learning plan created by the generation unit. For example, it uses online chat, video calls, and speech recognition technology to conduct real-time conversation practice, setting up various scenarios from everyday conversation to business English, with pronunciation like a native speaker. Step 3: The evaluation department assesses the learner's pronunciation based on the conversation practice provided by the practice department and provides feedback on corrections and areas for improvement. This may include voice analysis results, specific pronunciation guidance, and suggestions for practice methods. Step 4: The tracking unit tracks the learner's progress based on the information provided by the evaluation unit, analyzes weaknesses, and suggests learning content. For example, it tracks progress by recording learning history, analyzing test results, and recording study time, analyzes weaknesses, and suggests appropriate learning content. It also provides progress reports and a sense of accomplishment to maintain motivation.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the generation unit, practice unit, evaluation unit, and tracking unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The practice unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The evaluation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The tracking unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the generation unit, practice unit, evaluation unit, and tracking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The practice unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The tracking unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the generation unit, practice unit, evaluation unit, and tracking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The practice unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The tracking unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the generation unit, practice unit, evaluation unit, and tracking unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The practice unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The tracking unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A generation unit that creates a customized learning plan based on the learner's current English level, goals, and interests, A practice unit provides real-time conversation practice based on the learning plan created by the generation unit, An evaluation unit evaluates the learner's pronunciation based on the conversation practice provided by the aforementioned practice unit, and provides feedback on corrections and areas for improvement in pronunciation. The system includes a tracking unit that tracks the learner's progress based on the information fed back by the evaluation unit, analyzes weaknesses, and proposes learning content. A system characterized by the following features. (Note 2) The generating unit is The system estimates the learner's emotions and adjusts the difficulty level of the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Analyze the learner's past learning history and select the optimal learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is When creating a learning plan, filter the information based on the learner's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The system estimates learners' emotions and prioritizes learning plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is When creating a learning plan, prioritize incorporating highly relevant content by considering the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is When creating a learning plan, analyze the learner's social media activity and incorporate relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned practice unit, The system estimates the learner's emotions and adjusts the conversation practice scenario based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned practice unit, During conversation practice, the system selects the most suitable scenario by referring to the learner's past practice history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned practice unit, During conversation practice, customize the scenario based on the learner's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned practice unit, The system estimates the learner's emotions and prioritizes conversation practice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned practice unit, During conversation practice, the system prioritizes providing highly relevant scenarios, taking into account the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned practice unit, During conversation practice, analyze learners' social media activity and provide relevant scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, The system estimates the learner's emotions and adjusts the pronunciation evaluation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, When evaluating pronunciation, referencing the learner's past pronunciation history improves the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, When evaluating pronunciation, customize the evaluation criteria based on the learner's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The evaluation unit, The system estimates the learner's emotions and determines the priority of pronunciation evaluation based on the estimated learner's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The evaluation unit, When evaluating pronunciation, we apply highly relevant evaluation criteria by taking into account the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, During pronunciation assessment, analyze learners' social media activity and apply relevant assessment criteria. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned tracking unit is We estimate learners' emotions and adjust progress tracking methods based on the estimated learners' emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned tracking unit is When tracking progress, the system selects the optimal tracking method by referring to the learner's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tracking unit is When tracking progress, customize the tracking method based on the learner's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tracking unit is The system estimates learners' emotions and prioritizes progress tracking based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tracking unit is When tracking progress, the optimal tracking method is selected considering the learner's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tracking unit is When tracking progress, analyze learners' social media activity and apply relevant tracking methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned system, The system estimates the learner's emotions and selects a compatible device based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned system, During integration, the system selects the optimal integration method by referring to the learner's past device usage history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned system, The system estimates the learner's emotions and adjusts the frequency of interaction based on the estimated learner emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned system, When integrating, the optimal integration method is selected considering the learner's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that creates a customized learning plan based on the learner's current English level, goals, and interests, A practice unit provides real-time conversation practice based on the learning plan created by the generation unit, An evaluation unit evaluates the learner's pronunciation based on the conversation practice provided by the aforementioned practice unit, and provides feedback on corrections and areas for improvement in pronunciation. The system includes a tracking unit that tracks the learner's progress based on the information fed back by the evaluation unit, analyzes weaknesses, and proposes learning content. A system characterized by the following features.

2. The generating unit is The system estimates the learner's emotions and adjusts the difficulty level of the learning plan based on those estimated emotions. The system according to feature 1.

3. The generating unit is Analyze the learner's past learning history and select the optimal learning plan. The system according to feature 1.

4. The generating unit is When creating a learning plan, filter the information based on the learner's current living situation and areas of interest. The system according to feature 1.

5. The generating unit is The system estimates learners' emotions and prioritizes learning plans based on those estimated emotions. The system according to feature 1.

6. The generating unit is When creating a learning plan, prioritize incorporating highly relevant content by considering the learner's geographical location. The system according to feature 1.

7. The generating unit is When creating a learning plan, analyze the learner's social media activity and incorporate relevant content. The system according to feature 1.

8. The aforementioned practice unit, The system estimates the learner's emotions and adjusts the conversation practice scenario based on those estimated emotions. The system according to feature 1.

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