system

Abstract

The system according to this embodiment aims to provide a customized learning plan tailored to the user's language proficiency. [Solution] The system according to the embodiment comprises an evaluation unit, a generation unit, a feedback unit, and an adjustment unit. The evaluation unit evaluates the user's language ability. The generation unit automatically generates an individually optimized learning plan based on the results evaluated by the evaluation unit. The feedback unit supports learning in an interactive format based on the learning plan generated by the generation unit and provides feedback in real time. The adjustment unit adjusts the learning plan according to the user's progress based on the feedback provided by the feedback unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 a customized learning plan according to the user's language ability, and the learning effect cannot be obtained sufficiently.

[0005] The system according to the embodiment aims to provide a customized learning plan according to the user's language ability.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an evaluation unit, a generation unit, a feedback unit, and an adjustment unit. The evaluation unit evaluates the user's language ability. The generation unit automatically generates an individually optimized learning plan based on the results evaluated by the evaluation unit. The feedback unit supports learning in an interactive manner based on the learning plan generated by the generation unit and provides feedback in real time. The adjustment unit adjusts the learning plan according to the user's progress based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a customized learning plan tailored to the user's language proficiency. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The language learning system according to an embodiment of the present invention is a system that uses AI to provide a customized learning plan tailored to each user's language ability. This system evaluates the user's current language ability using an AI algorithm that evaluates the user's language ability. Next, it automatically generates an individually optimized learning plan based on the evaluation results. This learning plan is adjusted in real time according to the user's progress. Furthermore, an interactive AI tutor supports the user's learning by interacting with them and provides real-time feedback. This allows the user to learn a language efficiently and maintain their motivation to learn. For example, an AI algorithm is used to evaluate the user's language ability. This algorithm analyzes the user's past learning data and test results to evaluate the user's current language ability. For example, it evaluates the user's vocabulary and grammar comprehension based on the user's past test scores and learning history. Next, it automatically generates an individually optimized learning plan based on the evaluation results. This learning plan includes content to reinforce the user's weaknesses and is adjusted in real time according to the user's progress. For example, if the user has difficulty with a particular grammatical item, a learning plan that focuses on that grammatical item is generated. Furthermore, an interactive AI tutor supports learning by interacting with the user. The AI ​​tutor monitors the user's learning progress in real time and provides appropriate feedback. For example, if the user gives an incorrect answer, it explains the reason and provides hints to guide the user to the correct answer. This system allows users to learn the language efficiently and maintain their motivation. For example, regular tests are conducted and the results are provided as feedback so that users can feel their learning progress. This allows users to feel their own progress and increases their motivation to learn. In addition, the AI ​​agent continues to evolve based on the user's learning data. The AI ​​model is updated to provide more effective learning plans by analyzing the user's learning history and feedback. As a result, users always receive the latest learning plans and can learn the language efficiently.In this way, AI-powered language learning agents can improve user learning efficiency and motivation by providing customized learning plans tailored to the user's language proficiency and offering real-time support from an interactive AI tutor. This allows the language learning system to provide customized learning plans tailored to the user's language proficiency, support learning in an interactive format, and adjust the plan according to the user's progress.

[0029] The language learning system according to the embodiment comprises an evaluation unit, a generation unit, a feedback unit, and an adjustment unit. The evaluation unit evaluates the user's language ability. The evaluation unit evaluates the user's current language ability by, for example, analyzing the user's past learning data and test results. The evaluation unit evaluates the user's vocabulary and grammatical comprehension based on, for example, the user's scores on tests taken in the past and their learning history. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit inputs the user's past learning data into the AI, and the AI ​​analyzes the data and outputs an evaluation result. The generation unit automatically generates an individually optimized learning plan based on the results evaluated by the evaluation unit. For example, the generation unit generates a learning plan that includes content to reinforce the user's weaknesses. For example, if the user has difficulty with a particular grammatical item, the generation unit generates a learning plan that focuses on that grammatical item. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the results evaluated by the evaluation unit into the AI, and the AI ​​generates a learning plan. The feedback unit supports learning in an interactive format based on the learning plan generated by the generation unit and provides real-time feedback. For example, if the user gives an incorrect answer, the feedback unit explains the reason and provides hints to guide the user to the correct answer. For example, the feedback unit conducts periodic tests so that the user can feel the progress of their learning and provides feedback on the results. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's answer data into the AI, and the AI ​​generates feedback. The adjustment unit adjusts the learning plan according to the user's progress based on the feedback provided by the feedback unit. For example, the adjustment unit changes the content and order of the learning plan according to the user's progress. For example, if the user has difficulty with a particular grammar item, the adjustment unit adjusts the learning plan to focus on that grammar item.Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit inputs feedback data provided by the feedback unit to the AI, and the AI ​​adjusts the learning plan. As a result, the language learning system according to the embodiment can provide a customized learning plan tailored to the user's language ability, support learning in an interactive format, and adjust the plan according to the user's progress.

[0030] The evaluation unit assesses the user's language ability. For example, the evaluation unit analyzes the user's past learning data and test results to assess the user's current language ability. Specifically, it evaluates the user's vocabulary and grammatical comprehension based on the user's past test scores and learning history. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit inputs the user's past learning data into the AI, which analyzes the data and outputs evaluation results. The AI ​​uses natural language processing technology to analyze the user's text data and evaluate the frequency of vocabulary use and grammatical accuracy. Furthermore, the AI ​​can also analyze the user's pronunciation data using speech recognition technology to evaluate the accuracy and fluency of pronunciation. This allows the evaluation unit to comprehensively evaluate the user's language ability and provide detailed evaluation results. The evaluation unit can also analyze the user's learning history in chronological order to grasp the progress and trends of learning. For example, it can analyze what learning content the user tackled and what results they achieved during a specific period to evaluate the effectiveness of their learning. This allows the evaluation unit to clearly identify the user's learning strengths and weaknesses and conduct evaluations tailored to individual learning needs. Furthermore, the evaluation unit can collect user self-assessments and feedback and reflect them in the evaluation results. This enables a comprehensive evaluation that takes into account the user's subjective learning experience and self-assessment.

[0031] The generation unit automatically generates an individually optimized learning plan based on the results evaluated by the evaluation unit. For example, the generation unit generates a learning plan that includes content to reinforce the user's weaknesses. Specifically, if the user has difficulty with a particular grammatical item, the generation unit generates a learning plan that focuses on that grammatical item. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit inputs the results evaluated by the evaluation unit into the AI, and the AI ​​generates the learning plan. The AI ​​uses an algorithm to determine the optimal learning content and order based on the user's evaluation results. For example, the AI ​​considers the user's vocabulary and grammatical understanding and selects appropriate level learning materials and practice problems. The AI ​​can also customize the format and method of the learning plan according to the user's learning style and preferences. For example, if the user prefers visual learning, the AI ​​generates a learning plan that includes many images and videos. This allows the generation unit to provide a learning plan that meets the user's individual needs and supports effective learning. Furthermore, the generation unit can monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if a user demonstrates a high level of understanding of a particular learning topic, the system generates a plan for them to move on to the next step. Furthermore, if a user is experiencing difficulties in their learning, the system provides supplementary materials or additional practice problems. This allows the generation unit to respond flexibly to the user's learning progress, maximizing the effectiveness of their learning.

[0032] The feedback unit supports learning in an interactive format based on the learning plan generated by the generation unit and provides real-time feedback. For example, if the user gives an incorrect answer, the feedback unit explains the reason and provides hints to guide the user to the correct answer. Specifically, if the user answers a grammar question incorrectly, the feedback unit explains the grammatical rule again and shows the correct usage of the grammar. Also, if the user answers a vocabulary question incorrectly, the feedback unit explains the meaning and usage of the word in detail and provides example sentences to deepen understanding. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit inputs the user's answer data into the AI, and the AI ​​generates the feedback. The AI ​​analyzes the user's answers using natural language processing technology and generates appropriate feedback. For example, the AI ​​identifies grammatical errors in the user's answer and explains the cause of the error. The AI ​​can also evaluate the content of the user's answer and suggest more appropriate expressions and phrasing. In this way, the feedback unit can provide the user with specific and useful feedback and improve the quality of learning. Furthermore, the feedback unit can periodically evaluate the user's learning progress and provide feedback on the results. For example, the feedback unit can conduct regular tests and provide feedback to the user on the results. This allows the user to understand their learning progress and maintain their motivation to learn. The feedback unit can also provide personalized feedback based on the user's learning history. For example, it can provide further feedback on problems the user previously got wrong or topics they struggle with to deepen their understanding. In this way, the feedback unit can continuously support the user's learning and achieve effective learning.

[0033] The adjustment unit adjusts the learning plan according to the user's progress based on the feedback provided by the feedback unit. For example, the adjustment unit changes the content and order of the learning plan according to the user's progress. Specifically, if the user has difficulty with a particular grammatical item, the adjustment unit adjusts the learning plan to focus on that grammatical item. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit inputs the feedback data provided by the feedback unit into the AI, and the AI ​​adjusts the learning plan. The AI ​​analyzes the user's feedback data and determines the optimal way to adjust the learning plan. For example, if the user shows a high level of understanding of a particular grammatical item, that item is excluded from the learning plan, and a plan is generated to move to the next step. Also, if the user has difficulty with a particular vocabulary, the adjustment unit adjusts the learning plan to focus on that vocabulary. This allows the adjustment unit to respond flexibly to the user's learning situation and maximize the effectiveness of learning. Furthermore, the adjustment unit can also customize the learning plan according to the user's learning style and preferences. For example, if a user prefers visual learning, the system generates a learning plan that includes many images and videos. Similarly, if a user prefers interactive learning, the system generates a learning plan that includes many interactive practice problems. This allows the system to provide learning plans tailored to the user's individual needs and support effective learning. The system can also analyze the user's learning history chronologically to understand learning progress and trends. For example, it can analyze what learning content a user tackled and what results they achieved during a specific period, and evaluate the effectiveness of their learning. This allows the system to clearly identify the user's strengths and weaknesses in learning and make adjustments to meet their individual learning needs.

[0034] The evolution unit evolves based on the user's learning data. For example, the evolution unit analyzes the user's learning history and feedback to improve the system's accuracy. For example, the evolution unit collects the user's learning data and applies an evolution algorithm to evolve the system. Some or all of the above processes in the evolution unit may be performed using AI, or not using AI. For example, the evolution unit inputs the user's learning data into the AI, which analyzes the data to determine the direction of evolution. This improves the system's accuracy by evolving based on the user's learning data.

[0035] The analysis unit analyzes the user's learning history and feedback. For example, the analysis unit collects the user's learning history and feedback, and performs analysis based on the data collection method, analysis method, and evaluation criteria. For example, the analysis unit evaluates the progress and effectiveness of learning based on the user's learning history. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs the user's learning history data into the AI, which analyzes the data and outputs evaluation results. This allows for the provision of more effective learning plans by analyzing the user's learning history and feedback.

[0036] The testing department conducts periodic tests and provides feedback. The testing department conducts tests periodically based on, for example, test intervals and schedules. The testing department conducts tests periodically to understand the user's learning progress and provides feedback on the results. Some or all of the above processes in the testing department may be performed using AI, or not using AI. For example, the testing department inputs the user's test result data into the AI, which analyzes the data and generates feedback. This makes it easier to understand the user's learning progress by conducting periodic tests and providing feedback.

[0037] The evaluation unit analyzes the user's past learning data and test results to assess the user's current language ability. For example, the evaluation unit collects the user's past learning data and test results and performs an evaluation based on the data collection method, analysis method, and evaluation criteria. For example, the evaluation unit evaluates the user's vocabulary and grammatical comprehension based on the user's past test scores and learning history. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs the user's past learning data into the AI, which analyzes the data and outputs the evaluation results. This allows for an accurate assessment of the user's current language ability by analyzing the user's past learning data and test results.

[0038] The generation unit generates a learning plan that includes content to reinforce the user's weaknesses. For example, the generation unit identifies the user's weaknesses and generates a plan that includes learning content to reinforce those weaknesses. For example, if the user has difficulty with a particular grammatical item, the generation unit generates a learning plan that focuses on that grammatical item. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit inputs the user's weakness data into the AI, and the AI ​​analyzes the data to generate a learning plan. This enables effective learning by generating a learning plan that includes content to reinforce the user's weaknesses.

[0039] The feedback unit explains the reason for an incorrect answer and provides hints to guide the user to the correct answer. For example, when a user gives an incorrect answer, the feedback unit provides a detailed explanation of the reason and provides hints to guide the user to the correct answer. For example, when a user gives an incorrect answer, the feedback unit explains the reason and provides hints to guide the user to the correct answer. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's answer data into the AI, and the AI ​​analyzes the data and generates feedback. This improves the learning effect by explaining the reason for an incorrect answer and providing hints to guide the user to the correct answer.

[0040] The adjustment unit adjusts the learning plan in real time according to the user's progress. For example, the adjustment unit changes the content and order of the learning plan according to the user's progress. For example, if the user has difficulty with a particular grammar item, the adjustment unit adjusts the learning plan to focus on that grammar item. Some or all of the above processes in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit inputs the user's progress data into the AI, and the AI ​​analyzes the data and adjusts the learning plan. This makes effective learning possible by adjusting the learning plan in real time according to the user's progress.

[0041] The evaluation unit analyzes the user's past learning data and selects the optimal evaluation method. For example, the evaluation unit collects the user's past learning data and selects the optimal evaluation method based on the data collection method, analysis method, and evaluation criteria. For example, the evaluation unit prioritizes selecting evaluation methods in which the user has previously achieved high scores. For example, the evaluation unit avoids evaluation methods in which the user has previously struggled and selects an alternative method. Some or all of the above processes in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs the user's past learning data into the AI, and the AI ​​analyzes the data to select the optimal evaluation method. This allows the optimal evaluation method to be selected by analyzing the user's past learning data.

[0042] The evaluation unit customizes the evaluation content based on the user's current living situation and areas of interest during the evaluation process. For example, the evaluation unit provides evaluation content related to topics the user is interested in. For example, the evaluation unit adjusts the evaluation content to suit the user's living situation. For example, the evaluation unit customizes the evaluation content based on the user's areas of interest. Some or all of the above processes in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs data on the user's living situation and areas of interest into the AI, and the AI ​​analyzes the data to customize the evaluation content. This makes it possible to perform more effective evaluations by customizing the evaluation content based on the user's current living situation and areas of interest.

[0043] The evaluation unit prioritizes evaluation items that are highly relevant based on the user's geographical location information during the evaluation process. For example, if the user is in a specific region, the evaluation unit prioritizes evaluation items related to that region. For example, if the user is traveling, the evaluation unit provides evaluation items related to the travel destination. For example, if the user is at home, the evaluation unit prioritizes providing evaluation items that can be performed at home. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit inputs the user's geographical location information into the AI, and the AI ​​analyzes the data to determine the evaluation items. This allows for a more effective evaluation by prioritizing highly relevant evaluation items while considering the user's geographical location information.

[0044] The evaluation unit analyzes the user's social media activity during the evaluation process and adds relevant evaluation items. For example, the evaluation unit provides evaluation items related to topics the user has shown interest in on social media. For example, the evaluation unit customizes evaluation items based on the user's social media activity history. For example, the evaluation unit adds evaluation items related to accounts the user follows on social media. Some or all of the above processes in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs the user's social media activity data into the AI, which analyzes the data to determine the evaluation items. This allows for the addition of relevant evaluation items by analyzing the user's social media activity.

[0045] The generation unit adjusts the level of detail of the learning plan based on the user's weaknesses when generating the learning plan. For example, the generation unit provides a learning plan that focuses on grammatical items that the user finds difficult. For example, the generation unit provides a learning plan that includes a detailed vocabulary list to strengthen the user's vocabulary. For example, the generation unit provides a learning plan that includes pronunciation practice to improve the user's pronunciation. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit inputs user weakness data into the AI, and the AI ​​analyzes the data to adjust the level of detail of the learning plan. This allows for effective learning by adjusting the level of detail of the plan based on the user's weaknesses.

[0046] The generation unit applies different generation algorithms depending on the user's learning style when generating a learning plan. For example, if the user is a visual learner, the generation unit provides a learning plan that includes a lot of visual content. For example, if the user is an auditory learner, the generation unit provides a learning plan that includes a lot of audio content. For example, if the user is an experiential learner, the generation unit provides a learning plan that includes a lot of practical exercises. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit inputs the user's learning style data into the AI, and the AI ​​analyzes the data and applies the optimal generation algorithm. This enables effective learning by applying different generation algorithms depending on the user's learning style.

[0047] The generation unit determines the priority of learning plans based on the user's learning history when generating learning plans. For example, the generation unit provides items that require priority review based on what the user has learned in the past. For example, the generation unit provides items that have not yet been learned based on the user's learning history. For example, the generation unit analyzes the user's learning history and provides the most effective learning order. Some or all of the above processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit inputs the user's learning history data into the AI, and the AI ​​analyzes the data to determine the priority of the plans. This enables effective learning by determining the priority of plans based on the user's learning history.

[0048] The generation unit adjusts the order of learning plans based on user relevance when generating them. For example, the generation unit prioritizes providing content related to topics the user is interested in. For example, the generation unit prioritizes providing content that is highly relevant to the user's learning goals. For example, the generation unit prioritizes providing content that is highly relevant to the user's learning style. Some or all of the above processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit inputs user relevance data into the AI, and the AI ​​analyzes the data to adjust the order of the plans. This allows for effective learning by adjusting the order of plans based on user relevance.

[0049] The feedback unit provides optimal feedback based on the user's past response history when providing feedback. For example, the feedback unit provides detailed feedback on items the user has answered incorrectly in the past. For example, the feedback unit provides appropriate feedback based on the user's past response history. For example, the feedback unit analyzes the user's past response history and provides the most effective feedback. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's past response history data into the AI, and the AI ​​analyzes the data to generate optimal feedback. This allows the system to provide optimal feedback by referring to the user's past response history.

[0050] The feedback unit applies different feedback methods depending on the user's learning style when providing feedback. For example, if the user is a visual learner, the feedback unit provides feedback that includes a lot of visual content. For example, if the user is an auditory learner, the feedback unit provides feedback that includes a lot of audio content. For example, if the user is an experiential learner, the feedback unit provides feedback that includes a lot of practical exercises. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's learning style data into the AI, and the AI ​​analyzes the data and applies the optimal feedback method. This makes it possible to provide effective feedback by applying different feedback methods depending on the user's learning style.

[0051] The feedback unit prioritizes providing highly relevant feedback based on the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit prioritizes providing feedback related to that region. For example, if the user is traveling, the feedback unit provides feedback related to their travel destination. For example, if the user is at home, the feedback unit prioritizes providing feedback that can be done at home. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit inputs the user's geographical location information into the AI, and the AI ​​analyzes the data to determine the content of the feedback. This makes it possible to provide more effective feedback by prioritizing highly relevant feedback while considering the user's geographical location information.

[0052] The feedback unit analyzes the user's social media activity when providing feedback and adds relevant feedback. For example, the feedback unit provides feedback related to topics the user has shown interest in on social media. For example, the feedback unit customizes feedback based on the user's social media activity history. For example, the feedback unit adds feedback related to accounts the user follows on social media. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit inputs the user's social media activity data into AI, which analyzes the data to determine the content of the feedback. This allows for the addition of relevant feedback by analyzing the user's social media activity.

[0053] The adjustment unit makes optimal adjustments to the learning plan based on the user's progress data. For example, the adjustment unit adjusts the content of the learning plan based on the user's progress data. For example, the adjustment unit analyzes the user's progress data and provides the most effective adjustment method. For example, the adjustment unit adjusts the priority of the learning plan based on the user's progress data. Some or all of the above processes in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit inputs the user's progress data into the AI, which analyzes the data and makes optimal adjustments. This allows for optimal adjustments to be made by referring to the user's progress data.

[0054] The adjustment unit applies different adjustment algorithms depending on the user's learning style when adjusting the learning plan. For example, if the user is a visual learner, the adjustment unit will make adjustments that include a lot of visual content. For example, if the user is an auditory learner, the adjustment unit will make adjustments that include a lot of audio content. For example, if the user is an experiential learner, the adjustment unit will make adjustments that include a lot of practical exercises. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit inputs the user's learning style data into the AI, and the AI ​​analyzes the data and applies the optimal adjustment algorithm. This makes effective learning possible by applying different adjustment algorithms depending on the user's learning style.

[0055] The adjustment unit prioritizes highly relevant adjustments based on the user's geographical location when adjusting the learning plan. For example, if the user is in a specific region, the adjustment unit prioritizes adjustments related to that region. For example, if the user is traveling, the adjustment unit makes adjustments related to the travel destination. For example, if the user is at home, the adjustment unit prioritizes adjustments that can be performed at home. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit inputs the user's geographical location information into the AI, and the AI ​​analyzes the data to determine the adjustment content. This allows for more effective learning by prioritizing highly relevant adjustments while considering the user's geographical location information.

[0056] The adjustment unit analyzes the user's social media activity when adjusting the learning plan and adds relevant adjustments. For example, the adjustment unit makes adjustments related to topics the user has shown interest in on social media. For example, the adjustment unit customizes adjustments based on the user's social media activity history. For example, the adjustment unit adds adjustments related to accounts the user follows on social media. Some or all of the above processes in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit inputs the user's social media activity data into the AI, which analyzes the data and determines the adjustments. This allows for the addition of relevant adjustments by analyzing the user's social media activity.

[0057] The evolution unit selects the optimal evolution method based on the user's past learning data during evolution. For example, the evolution unit collects the user's past learning data and selects the optimal evolution method based on the data collection method, analysis method, and evaluation criteria. For example, the evolution unit selects the optimal evolution method based on the user's past learning data. For example, the evolution unit analyzes the user's past learning data and selects the most effective evolution method. Some or all of the above processes in the evolution unit may be performed using AI, or not using AI. For example, the evolution unit inputs the user's past learning data into the AI, and the AI ​​analyzes the data to select the optimal evolution method. This allows the optimal evolution method to be selected by referring to the user's past learning data.

[0058] The evolution unit weights evolutionary data based on the user's learning history during evolution. For example, the evolution unit weights evolutionary data based on the user's learning history. For example, the evolution unit analyzes the user's learning history and weights the evolutionary data in the most effective way. For example, the evolution unit refers to the user's learning history to determine the weighting of the evolutionary data. Some or all of the above processes in the evolution unit may be performed using AI, or not using AI. For example, the evolution unit inputs the user's learning history data into the AI, and the AI ​​analyzes the data and weights the evolutionary data. This makes more effective evolution possible by weighting the evolutionary data based on the user's learning history.

[0059] The analysis unit selects the optimal analysis method based on the user's past learning data during analysis. For example, the analysis unit collects the user's past learning data and selects the optimal analysis method based on the data collection method, analysis method, and evaluation criteria. For example, the analysis unit selects the optimal analysis method based on the user's past learning data. For example, the analysis unit analyzes the user's past learning data and selects the most effective analysis method. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs the user's past learning data into AI, and the AI ​​analyzes the data and selects the optimal analysis method. This allows the optimal analysis method to be selected by referring to the user's past learning data.

[0060] The analysis department prioritizes highly relevant analyses based on the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis department prioritizes analyses related to that region. For example, if the user is traveling, the analysis department performs analyses related to the travel destination. For example, if the user is at home, the analysis department prioritizes analyses that can be performed at home. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department inputs the user's geographical location information into the AI, and the AI ​​analyzes the data to determine the content of the analysis. This allows for more effective analysis by prioritizing highly relevant analyses while considering the user's geographical location information.

[0061] The testing unit provides the optimal test based on the user's past test results during test execution. For example, the testing unit collects the user's past test results and provides the optimal test based on the data collection method, analysis method, and evaluation criteria. For example, the testing unit provides the optimal test based on the user's past test results. For example, the testing unit analyzes the user's past test results and provides the most effective test. Some or all of the above processes in the testing unit may be performed using AI, or not using AI. For example, the testing unit inputs the user's past test result data into AI, and the AI ​​analyzes the data to provide the optimal test. This allows the testing unit to provide the optimal test by referring to the user's past test results.

[0062] The testing unit weights the test content based on the user's learning history when conducting the test. For example, the testing unit weights the test content based on the user's learning history. For example, the testing unit analyzes the user's learning history and weights the test content to be most effective. For example, the testing unit refers to the user's learning history to determine the weighting of the test content. Some or all of the above processes in the testing unit may be performed using AI, or not using AI. For example, the testing unit inputs the user's learning history data into the AI, and the AI ​​analyzes the data and weights the test content. This makes it possible to conduct more effective tests by weighting the test content based on the user's learning history.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The evaluation unit can estimate the user's learning style and adjust the evaluation method based on that estimated style. For example, if the user is a visual learner, the evaluation unit can select an evaluation method that includes many visual elements. If the user is an auditory learner, it can select an evaluation method that uses a lot of audio. Furthermore, if the user is an experiential learner, it can select an evaluation method that includes practical exercises. By providing an evaluation method that matches the user's learning style, more effective evaluation becomes possible.

[0065] The analytics department can also prioritize highly relevant analyses based on the user's geographical location. For example, if a user is in a specific region, it can prioritize analyses related to that region. Similarly, if a user is traveling, it can prioritize analyses related to their travel destination. Furthermore, if a user is at home, it can prioritize analyses that can be performed at home. This allows for more effective analysis by prioritizing highly relevant analyses based on the user's geographical location.

[0066] The evaluation unit can analyze users' social media activity and add relevant evaluation items. For example, it can provide evaluation items related to topics that users have shown interest in on social media. It can also customize evaluation items based on users' social media activity history. Furthermore, it can add evaluation items related to accounts that users follow on social media. This allows for the addition of relevant evaluation items by analyzing users' social media activity.

[0067] The feedback system can also apply different feedback methods depending on the user's learning style. For example, if the user is a visual learner, feedback can be provided that includes a lot of visual content. If the user is an auditory learner, feedback can be provided that includes a lot of audio content. Furthermore, if the user is an experiential learner, feedback can be provided that includes a lot of practical exercises. By applying different feedback methods according to the user's learning style, effective feedback becomes possible.

[0068] The evolutionary component can also weight evolutionary data based on the user's learning history. For example, it can weight evolutionary data based on the user's learning history. It can also analyze the user's learning history to determine the most effective weighting of evolutionary data. Furthermore, it can determine the weighting of evolutionary data by referring to the user's learning history. This allows for more effective evolution by weighting evolutionary data based on the user's learning history.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The evaluation unit assesses the user's language ability. The evaluation unit analyzes the user's past learning data and test results to assess the user's current language ability. For example, it assesses the user's vocabulary and grammar comprehension based on the user's past test scores and learning history. The processing in the evaluation unit may or may not be performed using AI. Step 2: The generation unit automatically generates an individually optimized learning plan based on the results evaluated by the evaluation unit. The generation unit generates a learning plan that includes content to reinforce the user's weaknesses. For example, if the user has difficulty with a particular grammar item, the generation unit will generate a learning plan that focuses on that grammar item. The processing in the generation unit may or may not be performed using AI. Step 3: The feedback unit supports learning interactively based on the learning plan generated by the generation unit and provides real-time feedback. If the user gives an incorrect answer, the feedback unit explains the reason and provides hints to guide the user to the correct answer. Furthermore, it conducts periodic tests and provides feedback on the results so that the user can feel the progress of their learning. Processing in the feedback unit may or may not be performed using AI. Step 4: The adjustment unit adjusts the learning plan according to the user's progress based on the feedback provided by the feedback unit. The adjustment unit changes the content and order of the learning plan according to the user's progress. For example, if the user has difficulty with a particular grammar item, the adjustment unit will adjust the learning plan to focus on that grammar item. The processing in the adjustment unit may or may not be performed using AI.

[0071] (Example of form 2) The language learning system according to an embodiment of the present invention is a system that uses AI to provide a customized learning plan tailored to each user's language ability. This system evaluates the user's current language ability using an AI algorithm that evaluates the user's language ability. Next, it automatically generates an individually optimized learning plan based on the evaluation results. This learning plan is adjusted in real time according to the user's progress. Furthermore, an interactive AI tutor supports the user's learning by interacting with them and provides real-time feedback. This allows the user to learn a language efficiently and maintain their motivation to learn. For example, an AI algorithm is used to evaluate the user's language ability. This algorithm analyzes the user's past learning data and test results to evaluate the user's current language ability. For example, it evaluates the user's vocabulary and grammar comprehension based on the user's past test scores and learning history. Next, it automatically generates an individually optimized learning plan based on the evaluation results. This learning plan includes content to reinforce the user's weaknesses and is adjusted in real time according to the user's progress. For example, if the user has difficulty with a particular grammatical item, a learning plan that focuses on that grammatical item is generated. Furthermore, an interactive AI tutor supports learning by interacting with the user. The AI ​​tutor monitors the user's learning progress in real time and provides appropriate feedback. For example, if the user gives an incorrect answer, it explains the reason and provides hints to guide the user to the correct answer. This system allows users to learn the language efficiently and maintain their motivation. For example, regular tests are conducted and the results are provided as feedback so that users can feel their learning progress. This allows users to feel their own progress and increases their motivation to learn. In addition, the AI ​​agent continues to evolve based on the user's learning data. The AI ​​model is updated to provide more effective learning plans by analyzing the user's learning history and feedback. As a result, users always receive the latest learning plans and can learn the language efficiently.In this way, AI-powered language learning agents can improve user learning efficiency and motivation by providing customized learning plans tailored to the user's language proficiency and offering real-time support from an interactive AI tutor. This allows the language learning system to provide customized learning plans tailored to the user's language proficiency, support learning in an interactive format, and adjust the plan according to the user's progress.

[0072] The language learning system according to the embodiment comprises an evaluation unit, a generation unit, a feedback unit, and an adjustment unit. The evaluation unit evaluates the user's language ability. The evaluation unit evaluates the user's current language ability by, for example, analyzing the user's past learning data and test results. The evaluation unit evaluates the user's vocabulary and grammatical comprehension based on, for example, the user's scores on tests taken in the past and their learning history. Some or all of the above-described processes in the evaluation unit may be performed using, for example, AI, or not using AI. For example, the evaluation unit inputs the user's past learning data into the AI, and the AI ​​analyzes the data and outputs an evaluation result. The generation unit automatically generates an individually optimized learning plan based on the results evaluated by the evaluation unit. For example, the generation unit generates a learning plan that includes content to reinforce the user's weaknesses. For example, if the user has difficulty with a particular grammatical item, the generation unit generates a learning plan that focuses on that grammatical item. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit inputs the results evaluated by the evaluation unit into the AI, and the AI ​​generates a learning plan. The feedback unit supports learning in an interactive format based on the learning plan generated by the generation unit and provides real-time feedback. For example, if the user gives an incorrect answer, the feedback unit explains the reason and provides hints to guide the user to the correct answer. For example, the feedback unit conducts periodic tests so that the user can feel the progress of their learning and provides feedback on the results. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's answer data into the AI, and the AI ​​generates feedback. The adjustment unit adjusts the learning plan according to the user's progress based on the feedback provided by the feedback unit. For example, the adjustment unit changes the content and order of the learning plan according to the user's progress. For example, if the user has difficulty with a particular grammar item, the adjustment unit adjusts the learning plan to focus on that grammar item.Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit inputs feedback data provided by the feedback unit to the AI, and the AI ​​adjusts the learning plan. As a result, the language learning system according to the embodiment can provide a customized learning plan tailored to the user's language ability, support learning in an interactive format, and adjust the plan according to the user's progress.

[0073] The evaluation unit assesses the user's language ability. For example, the evaluation unit analyzes the user's past learning data and test results to assess the user's current language ability. Specifically, it evaluates the user's vocabulary and grammatical comprehension based on the user's past test scores and learning history. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit inputs the user's past learning data into the AI, which analyzes the data and outputs evaluation results. The AI ​​uses natural language processing technology to analyze the user's text data and evaluate the frequency of vocabulary use and grammatical accuracy. Furthermore, the AI ​​can also analyze the user's pronunciation data using speech recognition technology to evaluate the accuracy and fluency of pronunciation. This allows the evaluation unit to comprehensively evaluate the user's language ability and provide detailed evaluation results. The evaluation unit can also analyze the user's learning history in chronological order to grasp the progress and trends of learning. For example, it can analyze what learning content the user tackled and what results they achieved during a specific period to evaluate the effectiveness of their learning. This allows the evaluation unit to clearly identify the user's learning strengths and weaknesses and conduct evaluations tailored to individual learning needs. Furthermore, the evaluation unit can collect user self-assessments and feedback and reflect them in the evaluation results. This enables a comprehensive evaluation that takes into account the user's subjective learning experience and self-assessment.

[0074] The generation unit automatically generates an individually optimized learning plan based on the results evaluated by the evaluation unit. For example, the generation unit generates a learning plan that includes content to reinforce the user's weaknesses. Specifically, if the user has difficulty with a particular grammatical item, the generation unit generates a learning plan that focuses on that grammatical item. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit inputs the results evaluated by the evaluation unit into the AI, and the AI ​​generates the learning plan. The AI ​​uses an algorithm to determine the optimal learning content and order based on the user's evaluation results. For example, the AI ​​considers the user's vocabulary and grammatical understanding and selects appropriate level learning materials and practice problems. The AI ​​can also customize the format and method of the learning plan according to the user's learning style and preferences. For example, if the user prefers visual learning, the AI ​​generates a learning plan that includes many images and videos. This allows the generation unit to provide a learning plan that meets the user's individual needs and supports effective learning. Furthermore, the generation unit can monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if a user demonstrates a high level of understanding of a particular learning topic, the system generates a plan for them to move on to the next step. Furthermore, if a user is experiencing difficulties in their learning, the system provides supplementary materials or additional practice problems. This allows the generation unit to respond flexibly to the user's learning progress, maximizing the effectiveness of their learning.

[0075] The feedback unit supports learning in an interactive format based on the learning plan generated by the generation unit and provides real-time feedback. For example, if the user gives an incorrect answer, the feedback unit explains the reason and provides hints to guide the user to the correct answer. Specifically, if the user answers a grammar question incorrectly, the feedback unit explains the grammatical rule again and shows the correct usage of the grammar. Also, if the user answers a vocabulary question incorrectly, the feedback unit explains the meaning and usage of the word in detail and provides example sentences to deepen understanding. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit inputs the user's answer data into the AI, and the AI ​​generates the feedback. The AI ​​analyzes the user's answers using natural language processing technology and generates appropriate feedback. For example, the AI ​​identifies grammatical errors in the user's answer and explains the cause of the error. The AI ​​can also evaluate the content of the user's answer and suggest more appropriate expressions and phrasing. In this way, the feedback unit can provide the user with specific and useful feedback and improve the quality of learning. Furthermore, the feedback unit can periodically evaluate the user's learning progress and provide feedback on the results. For example, the feedback unit can conduct regular tests and provide feedback to the user on the results. This allows the user to understand their learning progress and maintain their motivation to learn. The feedback unit can also provide personalized feedback based on the user's learning history. For example, it can provide further feedback on problems the user previously got wrong or topics they struggle with to deepen their understanding. In this way, the feedback unit can continuously support the user's learning and achieve effective learning.

[0076] The adjustment unit adjusts the learning plan according to the user's progress based on the feedback provided by the feedback unit. For example, the adjustment unit changes the content and order of the learning plan according to the user's progress. Specifically, if the user has difficulty with a particular grammatical item, the adjustment unit adjusts the learning plan to focus on that grammatical item. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit inputs the feedback data provided by the feedback unit into the AI, and the AI ​​adjusts the learning plan. The AI ​​analyzes the user's feedback data and determines the optimal way to adjust the learning plan. For example, if the user shows a high level of understanding of a particular grammatical item, that item is excluded from the learning plan, and a plan is generated to move to the next step. Also, if the user has difficulty with a particular vocabulary, the adjustment unit adjusts the learning plan to focus on that vocabulary. This allows the adjustment unit to respond flexibly to the user's learning situation and maximize the effectiveness of learning. Furthermore, the adjustment unit can also customize the learning plan according to the user's learning style and preferences. For example, if a user prefers visual learning, the system generates a learning plan that includes many images and videos. Similarly, if a user prefers interactive learning, the system generates a learning plan that includes many interactive practice problems. This allows the system to provide learning plans tailored to the user's individual needs and support effective learning. The system can also analyze the user's learning history chronologically to understand learning progress and trends. For example, it can analyze what learning content a user tackled and what results they achieved during a specific period, and evaluate the effectiveness of their learning. This allows the system to clearly identify the user's strengths and weaknesses in learning and make adjustments to meet their individual learning needs.

[0077] The evolution unit evolves based on the user's learning data. For example, the evolution unit analyzes the user's learning history and feedback to improve the system's accuracy. For example, the evolution unit collects the user's learning data and applies an evolution algorithm to evolve the system. Some or all of the above processes in the evolution unit may be performed using AI, or not using AI. For example, the evolution unit inputs the user's learning data into the AI, which analyzes the data to determine the direction of evolution. This improves the system's accuracy by evolving based on the user's learning data.

[0078] The analysis unit analyzes the user's learning history and feedback. For example, the analysis unit collects the user's learning history and feedback, and performs analysis based on the data collection method, analysis method, and evaluation criteria. For example, the analysis unit evaluates the progress and effectiveness of learning based on the user's learning history. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs the user's learning history data into the AI, which analyzes the data and outputs evaluation results. This allows for the provision of more effective learning plans by analyzing the user's learning history and feedback.

[0079] The testing department conducts periodic tests and provides feedback. The testing department conducts tests periodically based on, for example, test intervals and schedules. The testing department conducts tests periodically to understand the user's learning progress and provides feedback on the results. Some or all of the above processes in the testing department may be performed using AI, or not using AI. For example, the testing department inputs the user's test result data into the AI, which analyzes the data and generates feedback. This makes it easier to understand the user's learning progress by conducting periodic tests and providing feedback.

[0080] The evaluation unit analyzes the user's past learning data and test results to assess the user's current language ability. For example, the evaluation unit collects the user's past learning data and test results and performs an evaluation based on the data collection method, analysis method, and evaluation criteria. For example, the evaluation unit evaluates the user's vocabulary and grammatical comprehension based on the user's past test scores and learning history. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs the user's past learning data into the AI, which analyzes the data and outputs the evaluation results. This allows for an accurate assessment of the user's current language ability by analyzing the user's past learning data and test results.

[0081] The generation unit generates a learning plan that includes content to reinforce the user's weaknesses. For example, the generation unit identifies the user's weaknesses and generates a plan that includes learning content to reinforce those weaknesses. For example, if the user has difficulty with a particular grammatical item, the generation unit generates a learning plan that focuses on that grammatical item. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit inputs the user's weakness data into the AI, and the AI ​​analyzes the data to generate a learning plan. This enables effective learning by generating a learning plan that includes content to reinforce the user's weaknesses.

[0082] The feedback unit explains the reason for an incorrect answer and provides hints to guide the user to the correct answer. For example, when a user gives an incorrect answer, the feedback unit provides a detailed explanation of the reason and provides hints to guide the user to the correct answer. For example, when a user gives an incorrect answer, the feedback unit explains the reason and provides hints to guide the user to the correct answer. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's answer data into the AI, and the AI ​​analyzes the data and generates feedback. This improves the learning effect by explaining the reason for an incorrect answer and providing hints to guide the user to the correct answer.

[0083] The adjustment unit adjusts the learning plan in real time according to the user's progress. For example, the adjustment unit changes the content and order of the learning plan according to the user's progress. For example, if the user has difficulty with a particular grammar item, the adjustment unit adjusts the learning plan to focus on that grammar item. Some or all of the above processes in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit inputs the user's progress data into the AI, and the AI ​​analyzes the data and adjusts the learning plan. This makes effective learning possible by adjusting the learning plan in real time according to the user's progress.

[0084] The evaluation unit estimates the user's emotions and adjusts the timing of evaluations based on the estimated emotions. For example, if the user is stressed, the evaluation unit reduces the frequency of evaluations and performs them at times when the user can relax. For example, if the user is focused, the evaluation unit increases the frequency of evaluations to quickly grasp the progress of learning. For example, if the user is tired, the evaluation unit postpones the evaluation and performs it again after rest. Some or all of the above processes in the evaluation unit are implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows evaluations to be performed at more appropriate times by adjusting the timing of evaluations based on the user's emotions.

[0085] The evaluation unit analyzes the user's past learning data and selects the optimal evaluation method. For example, the evaluation unit collects the user's past learning data and selects the optimal evaluation method based on the data collection method, analysis method, and evaluation criteria. For example, the evaluation unit prioritizes selecting evaluation methods in which the user has previously achieved high scores. For example, the evaluation unit avoids evaluation methods in which the user has previously struggled and selects an alternative method. Some or all of the above processes in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs the user's past learning data into the AI, and the AI ​​analyzes the data to select the optimal evaluation method. This allows the optimal evaluation method to be selected by analyzing the user's past learning data.

[0086] The evaluation unit customizes the evaluation content based on the user's current living situation and areas of interest during the evaluation process. For example, the evaluation unit provides evaluation content related to topics the user is interested in. For example, the evaluation unit adjusts the evaluation content to suit the user's living situation. For example, the evaluation unit customizes the evaluation content based on the user's areas of interest. Some or all of the above processes in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs data on the user's living situation and areas of interest into the AI, and the AI ​​analyzes the data to customize the evaluation content. This makes it possible to perform more effective evaluations by customizing the evaluation content based on the user's current living situation and areas of interest.

[0087] The evaluation unit estimates the user's emotions and determines the evaluation priority based on the estimated emotions. For example, if the user is relaxed, the evaluation unit prioritizes important evaluation items. If the user is tense, the evaluation unit starts with easy evaluation items. If the user is focused, the evaluation unit prioritizes difficult evaluation items. Some or all of the above processing in the evaluation unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for a more appropriate evaluation by determining the evaluation priority based on the user's emotions.

[0088] The evaluation unit prioritizes evaluation items that are highly relevant based on the user's geographical location information during the evaluation process. For example, if the user is in a specific region, the evaluation unit prioritizes evaluation items related to that region. For example, if the user is traveling, the evaluation unit provides evaluation items related to the travel destination. For example, if the user is at home, the evaluation unit prioritizes providing evaluation items that can be performed at home. Some or all of the above processing in the evaluation unit may be performed using AI, or not. For example, the evaluation unit inputs the user's geographical location information into the AI, and the AI ​​analyzes the data to determine the evaluation items. This allows for a more effective evaluation by prioritizing highly relevant evaluation items while considering the user's geographical location information.

[0089] The evaluation unit analyzes the user's social media activity during the evaluation process and adds relevant evaluation items. For example, the evaluation unit provides evaluation items related to topics the user has shown interest in on social media. For example, the evaluation unit customizes evaluation items based on the user's social media activity history. For example, the evaluation unit adds evaluation items related to accounts the user follows on social media. Some or all of the above processes in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit inputs the user's social media activity data into the AI, which analyzes the data to determine the evaluation items. This allows for the addition of relevant evaluation items by analyzing the user's social media activity.

[0090] The generation unit estimates the user's emotions and adjusts the presentation of the learning plan based on the estimated emotions. For example, if the user is relaxed, the generation unit provides a learning plan with detailed explanations. If the user is in a hurry, the generation unit provides a concise and to-the-point learning plan. If the user is excited, the generation unit provides a learning plan with visually stimulating effects. Some or all of the above processing in the generation unit is implemented using emotion estimation functions, such as 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. This allows for more effective learning by adjusting the presentation of the learning plan based on the user's emotions.

[0091] The generation unit adjusts the level of detail of the learning plan based on the user's weaknesses when generating the learning plan. For example, the generation unit provides a learning plan that focuses on grammatical items that the user finds difficult. For example, the generation unit provides a learning plan that includes a detailed vocabulary list to strengthen the user's vocabulary. For example, the generation unit provides a learning plan that includes pronunciation practice to improve the user's pronunciation. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit inputs user weakness data into the AI, and the AI ​​analyzes the data to adjust the level of detail of the learning plan. This allows for effective learning by adjusting the level of detail of the plan based on the user's weaknesses.

[0092] The generation unit applies different generation algorithms depending on the user's learning style when generating a learning plan. For example, if the user is a visual learner, the generation unit provides a learning plan that includes a lot of visual content. For example, if the user is an auditory learner, the generation unit provides a learning plan that includes a lot of audio content. For example, if the user is an experiential learner, the generation unit provides a learning plan that includes a lot of practical exercises. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit inputs the user's learning style data into the AI, and the AI ​​analyzes the data and applies the optimal generation algorithm. This enables effective learning by applying different generation algorithms depending on the user's learning style.

[0093] The generation unit estimates the user's emotions and adjusts the length of the learning plan based on the estimated emotions. For example, if the user is tired, the generation unit provides a learning plan that can be completed in a short time. For example, if the user is focused, the generation unit provides a learning plan that can be completed in a long time. For example, if the user is relaxed, the generation unit provides a learning plan of a moderate length. Some or all of the above processing in the generation unit is implemented using emotion estimation functions, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to learn more effectively by adjusting the length of the learning plan based on the user's emotions.

[0094] The generation unit determines the priority of learning plans based on the user's learning history when generating learning plans. For example, the generation unit provides items that require priority review based on what the user has learned in the past. For example, the generation unit provides items that have not yet been learned based on the user's learning history. For example, the generation unit analyzes the user's learning history and provides the most effective learning order. Some or all of the above processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit inputs the user's learning history data into the AI, and the AI ​​analyzes the data to determine the priority of the plans. This enables effective learning by determining the priority of plans based on the user's learning history.

[0095] The generation unit adjusts the order of learning plans based on user relevance when generating them. For example, the generation unit prioritizes providing content related to topics the user is interested in. For example, the generation unit prioritizes providing content that is highly relevant to the user's learning goals. For example, the generation unit prioritizes providing content that is highly relevant to the user's learning style. Some or all of the above processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit inputs user relevance data into the AI, and the AI ​​analyzes the data to adjust the order of the plans. This allows for effective learning by adjusting the order of plans based on user relevance.

[0096] The feedback unit estimates the user's emotions and adjusts the way it presents the feedback based on those emotions. For example, if the user is relaxed, the feedback unit provides detailed feedback. If the user is in a hurry, the feedback unit provides concise and to-the-point feedback. If the user is excited, the feedback unit provides feedback with visually stimulating effects. Some or all of the above processing in the feedback unit is implemented using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective feedback by adjusting the way it presents the feedback based on the user's emotions.

[0097] The feedback unit provides optimal feedback based on the user's past response history when providing feedback. For example, the feedback unit provides detailed feedback on items the user has answered incorrectly in the past. For example, the feedback unit provides appropriate feedback based on the user's past response history. For example, the feedback unit analyzes the user's past response history and provides the most effective feedback. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's past response history data into the AI, and the AI ​​analyzes the data to generate optimal feedback. This allows the system to provide optimal feedback by referring to the user's past response history.

[0098] The feedback unit applies different feedback methods depending on the user's learning style when providing feedback. For example, if the user is a visual learner, the feedback unit provides feedback that includes a lot of visual content. For example, if the user is an auditory learner, the feedback unit provides feedback that includes a lot of audio content. For example, if the user is an experiential learner, the feedback unit provides feedback that includes a lot of practical exercises. Some or all of the above processing in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit inputs the user's learning style data into the AI, and the AI ​​analyzes the data and applies the optimal feedback method. This makes it possible to provide effective feedback by applying different feedback methods depending on the user's learning style.

[0099] The feedback unit estimates the user's emotions and prioritizes feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit prioritizes important feedback. If the user is tense, the feedback unit starts with simple feedback. If the user is focused, the feedback unit prioritizes more difficult feedback. Some or all of the above processing in the feedback unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more appropriate feedback by prioritizing feedback based on the user's emotions.

[0100] The feedback unit prioritizes providing highly relevant feedback based on the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit prioritizes providing feedback related to that region. For example, if the user is traveling, the feedback unit provides feedback related to their travel destination. For example, if the user is at home, the feedback unit prioritizes providing feedback that can be done at home. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit inputs the user's geographical location information into the AI, and the AI ​​analyzes the data to determine the content of the feedback. This makes it possible to provide more effective feedback by prioritizing highly relevant feedback while considering the user's geographical location information.

[0101] The feedback unit analyzes the user's social media activity when providing feedback and adds relevant feedback. For example, the feedback unit provides feedback related to topics the user has shown interest in on social media. For example, the feedback unit customizes feedback based on the user's social media activity history. For example, the feedback unit adds feedback related to accounts the user follows on social media. Some or all of the above processes in the feedback unit may be performed using AI or not. For example, the feedback unit inputs the user's social media activity data into AI, which analyzes the data to determine the content of the feedback. This allows for the addition of relevant feedback by analyzing the user's social media activity.

[0102] The adjustment unit estimates the user's emotions and modifies the learning plan adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit makes detailed adjustments. For example, if the user is in a hurry, the adjustment unit makes concise adjustments. For example, if the user is excited, the adjustment unit adds visually stimulating effects to the adjustments. Some or all of the above processing in the adjustment unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to learn more effectively by changing the learning plan adjustment method based on the user's emotions.

[0103] The adjustment unit makes optimal adjustments to the learning plan based on the user's progress data. For example, the adjustment unit adjusts the content of the learning plan based on the user's progress data. For example, the adjustment unit analyzes the user's progress data and provides the most effective adjustment method. For example, the adjustment unit adjusts the priority of the learning plan based on the user's progress data. Some or all of the above processes in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit inputs the user's progress data into the AI, which analyzes the data and makes optimal adjustments. This allows for optimal adjustments to be made by referring to the user's progress data.

[0104] The adjustment unit applies different adjustment algorithms depending on the user's learning style when adjusting the learning plan. For example, if the user is a visual learner, the adjustment unit will make adjustments that include a lot of visual content. For example, if the user is an auditory learner, the adjustment unit will make adjustments that include a lot of audio content. For example, if the user is an experiential learner, the adjustment unit will make adjustments that include a lot of practical exercises. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit inputs the user's learning style data into the AI, and the AI ​​analyzes the data and applies the optimal adjustment algorithm. This makes effective learning possible by applying different adjustment algorithms depending on the user's learning style.

[0105] The adjustment unit estimates the user's emotions and changes the frequency of adjustments to the learning plan based on the estimated emotions. For example, if the user is relaxed, the adjustment unit makes frequent adjustments. For example, if the user is in a hurry, the adjustment unit reduces the frequency of adjustments. For example, if the user is excited, the adjustment unit makes adjustments at a moderate frequency. Some or all of the above processing in the adjustment unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to learn more effectively by changing the frequency of adjustments to the learning plan based on the user's emotions.

[0106] The adjustment unit prioritizes highly relevant adjustments based on the user's geographical location when adjusting the learning plan. For example, if the user is in a specific region, the adjustment unit prioritizes adjustments related to that region. For example, if the user is traveling, the adjustment unit makes adjustments related to the travel destination. For example, if the user is at home, the adjustment unit prioritizes adjustments that can be performed at home. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit inputs the user's geographical location information into the AI, and the AI ​​analyzes the data to determine the adjustment content. This allows for more effective learning by prioritizing highly relevant adjustments while considering the user's geographical location information.

[0107] The adjustment unit analyzes the user's social media activity when adjusting the learning plan and adds relevant adjustments. For example, the adjustment unit makes adjustments related to topics the user has shown interest in on social media. For example, the adjustment unit customizes adjustments based on the user's social media activity history. For example, the adjustment unit adds adjustments related to accounts the user follows on social media. Some or all of the above processes in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit inputs the user's social media activity data into the AI, which analyzes the data and determines the adjustments. This allows for the addition of relevant adjustments by analyzing the user's social media activity.

[0108] The evolutionary unit estimates the user's emotions and adjusts the direction of evolution based on the estimated emotions. For example, if the user is relaxed, the evolutionary unit performs detailed evolution. For example, if the user is in a hurry, the evolutionary unit performs concise evolution. For example, if the user is excited, the evolutionary unit performs evolution with visually stimulating effects. Some or all of the above processing in the evolutionary unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective evolution by adjusting the direction of evolution based on the user's emotions.

[0109] The evolution unit selects the optimal evolution method based on the user's past learning data during evolution. For example, the evolution unit collects the user's past learning data and selects the optimal evolution method based on the data collection method, analysis method, and evaluation criteria. For example, the evolution unit selects the optimal evolution method based on the user's past learning data. For example, the evolution unit analyzes the user's past learning data and selects the most effective evolution method. Some or all of the above processes in the evolution unit may be performed using AI, or not using AI. For example, the evolution unit inputs the user's past learning data into the AI, and the AI ​​analyzes the data to select the optimal evolution method. This allows the optimal evolution method to be selected by referring to the user's past learning data.

[0110] The evolution unit estimates the user's emotions and adjusts the frequency of evolution based on the estimated emotions. For example, the evolution unit evolves frequently when the user is relaxed. For example, the evolution unit reduces the frequency of evolution when the user is in a hurry. For example, the evolution unit evolves at a moderate frequency when the user is excited. Some or all of the above processing in the evolution unit is implemented using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective evolution by adjusting the frequency of evolution based on the user's emotions.

[0111] The evolution unit weights evolutionary data based on the user's learning history during evolution. For example, the evolution unit weights evolutionary data based on the user's learning history. For example, the evolution unit analyzes the user's learning history and weights the evolutionary data in the most effective way. For example, the evolution unit refers to the user's learning history to determine the weighting of the evolutionary data. Some or all of the above processes in the evolution unit may be performed using AI, or not using AI. For example, the evolution unit inputs the user's learning history data into the AI, and the AI ​​analyzes the data and weights the evolutionary data. This makes more effective evolution possible by weighting the evolutionary data based on the user's learning history.

[0112] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. If the user is in a hurry, the analysis unit performs a concise analysis. If the user is excited, the analysis unit adds visually stimulating effects to the analysis. Some or all of the above processing in the analysis unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective analysis by adjusting the analysis method based on the user's emotions.

[0113] The analysis unit selects the optimal analysis method based on the user's past learning data during analysis. For example, the analysis unit collects the user's past learning data and selects the optimal analysis method based on the data collection method, analysis method, and evaluation criteria. For example, the analysis unit selects the optimal analysis method based on the user's past learning data. For example, the analysis unit analyzes the user's past learning data and selects the most effective analysis method. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs the user's past learning data into AI, and the AI ​​analyzes the data and selects the optimal analysis method. This allows the optimal analysis method to be selected by referring to the user's past learning data.

[0114] The analysis unit estimates the user's emotions and determines the priority of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will prioritize important analysis items. If the user is tense, the analysis unit will start with simpler analysis items. If the user is focused, the analysis unit will prioritize more difficult analysis items. Some or all of the above processes in the analysis unit are implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective analysis by prioritizing the analysis based on the user's emotions.

[0115] The analysis department prioritizes highly relevant analyses based on the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis department prioritizes analyses related to that region. For example, if the user is traveling, the analysis department performs analyses related to the travel destination. For example, if the user is at home, the analysis department prioritizes analyses that can be performed at home. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department inputs the user's geographical location information into the AI, and the AI ​​analyzes the data to determine the content of the analysis. This allows for more effective analysis by prioritizing highly relevant analyses while considering the user's geographical location information.

[0116] The testing unit estimates the user's emotions and adjusts the test content based on the estimated emotions. For example, if the user is relaxed, the testing unit provides a detailed test. If the user is in a hurry, the testing unit provides a concise and to-the-point test. If the user is excited, the testing unit provides a test with visually stimulating effects. Some or all of the above processing in the testing unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective testing by adjusting the test content based on the user's emotions.

[0117] The testing unit provides the optimal test based on the user's past test results during test execution. For example, the testing unit collects the user's past test results and provides the optimal test based on the data collection method, analysis method, and evaluation criteria. For example, the testing unit provides the optimal test based on the user's past test results. For example, the testing unit analyzes the user's past test results and provides the most effective test. Some or all of the above processes in the testing unit may be performed using AI, or not using AI. For example, the testing unit inputs the user's past test result data into AI, and the AI ​​analyzes the data to provide the optimal test. This allows the testing unit to provide the optimal test by referring to the user's past test results.

[0118] The testing unit estimates the user's emotions and adjusts the frequency of tests based on the estimated emotions. For example, if the user is relaxed, the testing unit conducts tests frequently. For example, if the user is in a hurry, the testing unit reduces the frequency of tests. For example, if the user is excited, the testing unit conducts tests at a moderate frequency. Some or all of the above processing in the testing unit is implemented using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows for more effective testing by adjusting the frequency of tests based on the user's emotions.

[0119] The testing unit weights the test content based on the user's learning history when conducting the test. For example, the testing unit weights the test content based on the user's learning history. For example, the testing unit analyzes the user's learning history and weights the test content to be most effective. For example, the testing unit refers to the user's learning history to determine the weighting of the test content. Some or all of the above processes in the testing unit may be performed using AI, or not using AI. For example, the testing unit inputs the user's learning history data into the AI, and the AI ​​analyzes the data and weights the test content. This makes it possible to conduct more effective tests by weighting the test content based on the user's learning history.

[0120] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0121] The evaluation unit can estimate the user's learning style and adjust the evaluation method based on that estimated style. For example, if the user is a visual learner, the evaluation unit can select an evaluation method that includes many visual elements. If the user is an auditory learner, it can select an evaluation method that uses a lot of audio. Furthermore, if the user is an experiential learner, it can select an evaluation method that includes practical exercises. By providing an evaluation method that matches the user's learning style, more effective evaluation becomes possible.

[0122] The evolutionary component can also estimate the user's emotions and adjust the direction of evolution based on those emotions. For example, if the user is relaxed, the evolutionary component can perform detailed evolution. If the user is in a hurry, the evolutionary component can perform concise evolution. Furthermore, if the user is excited, the evolutionary component can perform evolution with visually stimulating effects. By adjusting the direction of evolution based on the user's emotions, more effective evolution becomes possible.

[0123] The analytics department can also prioritize highly relevant analyses based on the user's geographical location. For example, if a user is in a specific region, it can prioritize analyses related to that region. Similarly, if a user is traveling, it can prioritize analyses related to their travel destination. Furthermore, if a user is at home, it can prioritize analyses that can be performed at home. This allows for more effective analysis by prioritizing highly relevant analyses based on the user's geographical location.

[0124] The testing unit can also estimate the user's emotions and adjust the test content based on those estimates. For example, if the user is relaxed, a detailed test can be provided. If the user is in a hurry, a concise and to-the-point test can be provided. Furthermore, if the user is excited, a test with visually stimulating effects can be provided. By adjusting the test content based on the user's emotions, more effective testing becomes possible.

[0125] The evaluation unit can analyze users' social media activity and add relevant evaluation items. For example, it can provide evaluation items related to topics that users have shown interest in on social media. It can also customize evaluation items based on users' social media activity history. Furthermore, it can add evaluation items related to accounts that users follow on social media. This allows for the addition of relevant evaluation items by analyzing users' social media activity.

[0126] The generation unit can also estimate the user's emotions and adjust the presentation of the learning plan based on those emotions. For example, if the user is relaxed, it can provide a learning plan with detailed explanations. If the user is in a hurry, it can provide a concise and to-the-point learning plan. Furthermore, if the user is excited, it can provide a learning plan with visually stimulating effects. By adjusting the presentation of the learning plan based on the user's emotions, more effective learning becomes possible.

[0127] The feedback system can also apply different feedback methods depending on the user's learning style. For example, if the user is a visual learner, feedback can be provided that includes a lot of visual content. If the user is an auditory learner, feedback can be provided that includes a lot of audio content. Furthermore, if the user is an experiential learner, feedback can be provided that includes a lot of practical exercises. By applying different feedback methods according to the user's learning style, effective feedback becomes possible.

[0128] The adjustment unit can estimate the user's emotions and change how the learning plan is adjusted based on those emotions. For example, if the user is relaxed, detailed adjustments can be made. If the user is in a hurry, simple adjustments can be made. Furthermore, if the user is excited, adjustments can be made with visually stimulating effects. By changing how the learning plan is adjusted based on the user's emotions, more effective learning becomes possible.

[0129] The evolutionary component can also weight evolutionary data based on the user's learning history. For example, it can weight evolutionary data based on the user's learning history. It can also analyze the user's learning history to determine the most effective weighting of evolutionary data. Furthermore, it can determine the weighting of evolutionary data by referring to the user's learning history. This allows for more effective evolution by weighting evolutionary data based on the user's learning history.

[0130] The analysis unit can also estimate the user's emotions and prioritize the analysis based on those emotions. For example, if the user is relaxed, important analysis items can be prioritized. If the user is tense, the analysis can start with simpler items. Furthermore, if the user is focused, more difficult analysis items can be prioritized. By prioritizing analysis based on the user's emotions, more effective analysis becomes possible.

[0131] The following briefly describes the processing flow for example form 2.

[0132] Step 1: The evaluation unit assesses the user's language ability. The evaluation unit analyzes the user's past learning data and test results to assess the user's current language ability. For example, it assesses the user's vocabulary and grammar comprehension based on the user's past test scores and learning history. The processing in the evaluation unit may or may not be performed using AI. Step 2: The generation unit automatically generates an individually optimized learning plan based on the results evaluated by the evaluation unit. The generation unit generates a learning plan that includes content to reinforce the user's weaknesses. For example, if the user has difficulty with a particular grammar item, the generation unit will generate a learning plan that focuses on that grammar item. The processing in the generation unit may or may not be performed using AI. Step 3: The feedback unit supports learning interactively based on the learning plan generated by the generation unit and provides real-time feedback. If the user gives an incorrect answer, the feedback unit explains the reason and provides hints to guide the user to the correct answer. Furthermore, it conducts periodic tests and provides feedback on the results so that the user can feel the progress of their learning. Processing in the feedback unit may or may not be performed using AI. Step 4: The adjustment unit adjusts the learning plan according to the user's progress based on the feedback provided by the feedback unit. The adjustment unit changes the content and order of the learning plan according to the user's progress. For example, if the user has difficulty with a particular grammar item, the adjustment unit will adjust the learning plan to focus on that grammar item. The processing in the adjustment unit may or may not be performed using AI.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the evaluation unit, generation unit, feedback unit, adjustment unit, evolution unit, analysis unit, test unit, and sentiment estimation function, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the smart device 14 and evaluates the user's current language ability by analyzing the user's past learning data and test results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates an individually optimized learning plan based on the evaluation results. The feedback unit is implemented by the control unit 46A of the smart device 14 and supports learning in an interactive format and provides feedback in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan according to the user's progress. The evolution unit is implemented by the control unit 46A of the smart device 14 and improves the accuracy of the system based on the user's learning data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning history and feedback. The testing unit is implemented, for example, by the control unit 46A of the smart device 14, which performs periodic tests and provides feedback. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which adjusts the timing of evaluations based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the evaluation unit, generation unit, feedback unit, adjustment unit, evolution unit, analysis unit, test unit, and sentiment estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the smart glasses 214 and evaluates the user's current language ability by analyzing the user's past learning data and test results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates an individually optimized learning plan based on the evaluation results. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and supports learning in an interactive format and provides feedback in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan according to the user's progress. The evolution unit is implemented by the control unit 46A of the smart glasses 214 and improves the accuracy of the system based on the user's learning data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning history and feedback. The testing unit is implemented, for example, by the control unit 46A of the smart glasses 214, which performs periodic tests and provides feedback. The emotion estimation function is implemented, for example, by the identification processing unit 290 of the data processing device 12, which adjusts the timing of evaluation based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0153] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the evaluation unit, generation unit, feedback unit, adjustment unit, evolution unit, analysis unit, test unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the headset terminal 314 and evaluates the user's current language ability by analyzing the user's past learning data and test results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates an individually optimized learning plan based on the evaluation results. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and supports learning in an interactive format and provides feedback in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan according to the user's progress. The evolution unit is implemented by the control unit 46A of the headset terminal 314 and improves the accuracy of the system based on the user's learning data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning history and feedback. The testing unit is implemented, for example, by the control unit 46A of the headset terminal 314, which performs periodic tests and provides feedback. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which adjusts the timing of evaluation based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0169] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] Each of the multiple elements described above, including the evaluation unit, generation unit, feedback unit, adjustment unit, evolution unit, analysis unit, test unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the evaluation unit is implemented by the control unit 46A of the robot 414 and evaluates the user's current language ability by analyzing the user's past learning data and test results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates an individually optimized learning plan based on the evaluation results. The feedback unit is implemented by the control unit 46A of the robot 414 and supports learning in an interactive format and provides feedback in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the learning plan according to the user's progress. The evolution unit is implemented by the control unit 46A of the robot 414 and improves the accuracy of the system based on the user's learning data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's learning history and feedback. The testing unit is implemented, for example, by the control unit 46A of the robot 414, which performs periodic tests and provides feedback. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which adjusts the timing of evaluations based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] (Note 1) An evaluation unit that evaluates the user's language ability, A generation unit that automatically generates individually optimized learning plans based on the results evaluated by the evaluation unit, A feedback unit provides interactive learning support and real-time feedback based on the learning plan generated by the generation unit, The system includes an adjustment unit that adjusts the learning plan according to the user's progress based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features. (Note 2) It features a part that evolves based on user learning data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an analysis unit that analyzes the user's learning history and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a testing unit that conducts regular tests and provides feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The evaluation unit, We analyze the user's past learning data and test results to assess their current language ability. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is It includes a section that generates a learning plan that includes content to reinforce the user's weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is The system includes a section that explains the reason for an incorrect answer and provides hints to help the user arrive at the correct answer. The system described in Appendix 1, characterized by the features described herein. (Note 8) The adjustment unit is, It includes a section that adjusts the learning plan in real time according to the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 9) The evaluation unit, It includes a unit that estimates the user's emotions and adjusts the timing of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit, It includes a section that analyzes the user's past learning data and selects the optimal evaluation method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit, The system includes a component that customizes the evaluation content based on the user's current lifestyle and areas of interest during the evaluation process. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, It includes a unit that estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, The system includes a component that prioritizes evaluation items based on the user's geographical location information during the evaluation process. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, The system includes a section that analyzes the user's social media activity during evaluation and adds relevant evaluation items. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It includes a section that estimates the user's emotions and adjusts the way the learning plan is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system includes a component that adjusts the level of detail in the learning plan based on the user's weaknesses when generating the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is The system includes a component that applies different generation algorithms depending on the user's learning style when generating a learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It includes a section that estimates the user's emotions and adjusts the length of the learning plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system includes a component that determines the priority of learning plans based on the user's learning history when generating learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is The system includes a component that adjusts the order of learning plans based on user relevance during plan generation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is It includes a unit that estimates the user's emotions and adjusts the way feedback is expressed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is The system includes a section that provides optimal feedback based on the user's past response history when providing feedback. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is The system includes a component that applies different feedback methods depending on the user's learning style when providing feedback. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is It includes a unit that estimates the user's emotions and determines the priority of feedback based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is The system includes a component that prioritizes providing highly relevant feedback based on the user's geographical location information when they submit feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is The system includes a section that analyzes the user's social media activity and adds relevant feedback when providing feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, It includes a section that estimates the user's emotions and modifies the learning plan adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, The system includes a component that performs optimal adjustments to the learning plan based on the user's progress data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, The system includes a section that applies different adjustment algorithms depending on the user's learning style when adjusting the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 30) The adjustment unit is, It includes a section that estimates the user's emotions and changes the frequency of adjustments to the learning plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, The system includes a component that prioritizes highly relevant adjustments based on the user's geographical location information when adjusting the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, The system includes a section that analyzes the user's social media activity and adds relevant adjustments when adjusting the learning plan. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned evolutionary section is It includes a section that estimates user emotions and adjusts the direction of evolution based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned evolutionary section is During evolution, it includes a section that selects the optimal evolution method based on the user's past learning data. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned evolutionary section is It includes a unit that estimates the user's emotions and adjusts the frequency of evolution based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned evolutionary section is During evolution, it includes a section that weights evolutionary data based on the user's learning history. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned analysis unit is It includes a unit that estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned analysis unit is During analysis, the system includes a component that selects the optimal analysis method based on the user's past learning data. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned analysis unit is It includes a unit that estimates the user's emotions and determines the priority of analysis based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned analysis unit is The system includes a section that prioritizes highly relevant analyses based on the user's geographical location information during analysis. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned test unit is It includes a unit that estimates the user's emotions and adjusts the test content based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned test unit is The system includes a component that provides the optimal test based on the user's past test results during testing. The system described in Appendix 4, characterized by the features described herein. (Note 43) The aforementioned test unit is It includes a unit that estimates the user's emotions and adjusts the test frequency based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned test unit is The system includes a component that weights the test content based on the user's learning history during test execution. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0205] 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. An evaluation unit that evaluates the user's language ability, A generation unit that automatically generates individually optimized learning plans based on the results evaluated by the evaluation unit, A feedback unit provides interactive learning support and real-time feedback based on the learning plan generated by the generation unit, The system includes an adjustment unit that adjusts the learning plan according to the user's progress based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features.

2. It features an evolutionary unit that evolves based on user learning data. The system according to feature 1.

3. It includes an analysis unit that analyzes the user's learning history and feedback. The system according to feature 1.

4. It includes a testing unit that conducts regular tests and provides feedback. The system according to feature 1.

5. The evaluation unit described above, We analyze the user's past learning data and test results to assess their current language ability. The system according to feature 1.

6. The generating unit is It includes a section that generates a learning plan that includes content to reinforce the user's weaknesses. The system according to feature 1.

7. The aforementioned feedback unit is The system includes a section that explains the reason for an incorrect answer and provides hints to help the user arrive at the correct answer. The system according to feature 1.

8. The adjustment unit is, It includes a section that adjusts the learning plan in real time according to the user's progress. The system according to feature 1.

9. The evaluation unit described above, It includes a unit that estimates the user's emotions and adjusts the timing of evaluations based on the estimated user emotions. The system according to feature 1.

Images