System

Abstract

An object of a system according to an embodiment is to provide a teaching material customized according to a skill level and a learning objective of a user.SOLUTION: A system includes a reception unit, a generation unit, a customization unit, and a provision unit. The reception unit inputs a skill level and a learning objective of the user. The generation unit analyzes a video clip focusing on a specific game or player on the basis of the information input by the reception unit and generates a question. The customization unit customizes the question generated by the generation unit in accordance with the skill level and the learning objective of the user. The providing unit provides the question customized by the customizing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult to provide customized learning materials according to a user's skill level and learning goals.

[0005] The system according to the embodiment aims to provide customized learning materials according to the skill level and learning goals of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a customization unit, and a provision unit. The reception unit inputs the user's skill level and learning goals. The generation unit analyzes video clips focusing on specific matches or players based on the information input by the reception unit, and generates questions. The customization unit customizes the questions generated by the generation unit to suit the user's skill level and learning goals. The provision unit provides the questions customized by the customization unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized learning materials according to the skill level and learning goals of the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A question generation system according to an embodiment of the present invention analyzes video clips focusing on specific games or players based on a user's skill level and learning goals, and generates and provides questions based on the user's skill level and learning goals. The question generation system inputs the user's skill level and learning goals, analyzes video clips focusing on specific games or players, and generates questions based on the content of the video clips. The generated questions are customized and provided to the user. For example, the question generation system allows a user to input their own skill level and learning goals. For example, a beginner user can select questions related to basic techniques or strategies, while an advanced user can select questions related to advanced techniques or strategies. This information is input into an AI. The AI ​​then analyzes the input information and generates questions optimal for the user. The AI ​​analyzes video clips focusing on specific games or players and creates questions based on the content of the video clips. For example, a video clip related to a specific player's dribbling technique can be analyzed to generate questions related to that technique. The generated questions are customized to the user's skill level and learning goals. For example, a beginner user can be provided with questions related to basic techniques, while an advanced user can be provided with questions related to advanced techniques. The question generation system also includes a data collection unit that allows coaches to track players' progress. A coach can check a player's progress and generate custom problems that focus on specific problems. For example, if a specific player has issues with dribbling technique, a coach can generate custom problems related to that technique and provide them to the player. This allows the problem generation system to efficiently provide learning materials tailored to the user's skill level and learning goals. The coach can also effectively manage the player's progress and provide custom problems that focus on specific techniques and strategies. This allows the problem generation system to efficiently provide learning materials tailored to the user's skill level and learning goals. The coach can also effectively manage the player's progress and provide custom problems that focus on specific techniques and strategies. This is expected to improve the player's skills.

[0029] A question generation system according to an embodiment includes a receiving unit, a generating unit, a customizing unit, and a providing unit. The receiving unit inputs a user's skill level and learning goals. Examples of the user's skill level include, but are not limited to, beginner, intermediate, and advanced. The receiving unit provides, for example, an interface through which the user inputs their skill level and learning goals. The generating unit analyzes video clips focusing on specific matches or players based on the information input by the receiving unit and generates questions. The generating unit analyzes video clips using, for example, AI and generates questions based on the content of the video clips. For example, the generating unit analyzes video clips related to a specific player's dribbling technique and generates questions related to that technique. The customizing unit customizes the questions generated by the generating unit to suit the user's skill level and learning goals. For example, the customizing unit adjusts the questions generated using AI to suit the user's skill level and learning goals. For example, the customizing unit provides questions related to basic techniques to a beginner user and questions related to advanced techniques to an advanced user. The providing unit provides the questions customized by the customizing unit. The providing unit provides the user with questions customized using, for example, AI. For example, the providing unit delivers the customized questions to the user's device. This allows the question generation system according to the embodiment to generate and provide questions tailored to the user's skill level and learning goals.

[0030] The question generation system further includes a data collection unit that allows the coach to track the player's progress. The data collection unit collects data that the coach uses to track the player's progress. The data collection unit collects data such as the player's learning history and test results. For example, the data collection unit collects data such as the correct answer rate and answer time for questions answered by the player. The data collection unit can also collect data such as the player's feedback and comments. For example, the data collection unit collects feedback and comments provided by the player and provides the feedback and comments to the coach. This allows the coach to grasp the player's progress in detail and provide appropriate guidance. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the player's learning history and test results into AI, which can analyze the data and grasp the player's progress.

[0031] The problem generation system further includes a generation unit that generates custom problems focused on specific problems based on the data collected by the data collection unit. The generation unit generates custom problems focused on specific problems based on the data collected by the data collection unit. The generation unit, for example, analyzes the collected data using AI and generates custom problems focused on specific problems. For example, if a specific player has issues with dribbling technique, the generation unit generates custom problems related to that technique. For example, the generation unit inputs player progress data to the AI, which analyzes the data and generates custom problems. This allows the generation unit to provide custom problems tailored to the player's progress.

[0032] The reception unit can analyze the user's past learning history and suggest an appropriate input method. For example, the reception unit retrieves the user's past learning history from a database and analyzes it using AI. For example, the reception unit automatically displays the skill level and learning goals that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests the skill level and learning goals to be used in a specific time period based on the user's past learning history. This allows the reception unit to suggest the optimal input method based on the user's past learning history.

[0033] When inputting a skill level or learning goal, the reception unit can filter the input based on the user's current learning situation and areas of interest. The reception unit, for example, retrieves the user's current learning situation from a database and analyzes it using AI. For example, the reception unit analyzes the user's current learning situation and preferentially displays related skill levels and learning goals. The reception unit can also filter and display related skill levels and learning goals based on the user's areas of interest. For example, the reception unit combines the user's past learning history and current learning situation to suggest optimal skill levels and learning goals. This allows the reception unit to suggest optimal skill levels and learning goals based on the user's current learning situation and areas of interest.

[0034] When inputting a skill level or learning goal, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the skill level or learning goal using voice recognition technology. For example, the reception unit automatically analyzes the voice using voice recognition software and saves it as text. Furthermore, if the user selects text input, the reception unit can provide a text box to input the skill level or learning goal. For example, if the user selects image input, the reception unit inputs the skill level or learning goal using image analysis technology. This allows the reception unit to provide the optimal input means according to the user's input method.

[0035] When inputting a skill level or learning goal, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information from GPS data or an IP address and analyzes it using AI. For example, the reception unit can prioritize displaying skill levels and learning goals related to a region based on the user's geographical location information. The reception unit can also prioritize displaying information related to local educational institutions and training centers based on the user's geographical location information. For example, the reception unit can prioritize displaying information related to local sporting events and training sessions based on the user's geographical location information. This allows the reception unit to provide highly relevant information based on the user's geographical location information.

[0036] When inputting a skill level or learning goal, the reception unit can analyze the user's social media activity and input related information. The reception unit, for example, analyzes the content of the user's social media posts using AI. For example, the reception unit can suggest related skill levels and learning goals based on the content of the user's social media posts. The reception unit can also suggest related skill levels and learning goals by referring to the activities of the user's friends on social media. For example, the reception unit can suggest related skill levels and learning goals based on the user's social media check-in information. This allows the reception unit to provide related information based on the user's social media activity.

[0037] The reception unit can customize the input method by reflecting the user's past feedback when inputting the skill level or learning goal. The reception unit, for example, retrieves the user's past feedback from a database and analyzes it using AI. For example, the reception unit suggests the optimal input method based on the feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and improve the input interface. For example, the reception unit simplifies the input procedure by referring to the user's past feedback. This allows the reception unit to customize the input method based on the user's past feedback.

[0038] When generating questions, the generation unit can adjust the level of detail of the generated questions based on the importance of the video clip. For example, the generation unit evaluates the importance based on the number of views and ratings of the video clip, and adjusts the level of detail of the questions using AI. For example, the generation unit generates detailed questions based on an important video clip. The generation unit can also generate simplified questions based on a video clip with a low level of importance. The generation unit can also adjust the difficulty of the questions according to the importance of the video clip. For example, the generation unit generates more detailed questions the more times a video clip is viewed, and generates simplified questions if the number of views is low. This allows the generation unit to adjust the level of detail of the questions based on the importance of the video clip.

[0039] When generating questions, the generation unit can apply an appropriate generation algorithm depending on the category of the video clip. For example, the generation unit classifies the category of the video clip using AI and applies an appropriate generation algorithm. For example, the generation unit can apply an algorithm that generates technical questions to a video clip related to technique. The generation unit can also apply an algorithm that generates strategic questions to a video clip related to strategy. The generation unit can also apply an algorithm that generates questions related to a specific player to a video clip related to the player. For example, the generation unit can apply an algorithm that generates technical questions to a video clip related to technique, and an algorithm that generates strategic questions to a video clip related to strategy. This allows the generation unit to apply the optimal generation algorithm depending on the category of the video clip.

[0040] When generating questions, the generation unit can improve the accuracy of the generation by referring to the user's past question answer results. For example, the generation unit retrieves the user's past question answer results from a database and analyzes them using AI. For example, the generation unit prioritizes generating questions with a high correct answer rate based on the user's past question answer results. The generation unit can also generate questions related to weak areas based on the user's past question answer results. The generation unit can also adjust the difficulty of questions by referring to the user's past question answer results. For example, the generation unit prioritizes generating questions with a high correct answer rate based on the user's past question answer results, and generates questions related to weak areas. This allows the generation unit to improve the accuracy of generation based on the user's past question answer results.

[0041] When generating questions, the generation unit can determine the priority of generation based on the shooting date of the video clip. For example, the generation unit obtains the shooting date of the video clip from a database and analyzes it using AI. For example, the generation unit generates questions preferentially based on the latest video clip. The generation unit can also generate questions based on older video clips as needed. The generation unit can also adjust the order of question generation according to the shooting date of the video clip. For example, the generation unit generates questions preferentially based on the latest video clip, and generates questions based on older video clips as needed. This allows the generation unit to determine the priority of generation based on the shooting date of the video clip.

[0042] When generating questions, the generation unit can adjust the order of generation based on the relevance of the video clips. For example, the generation unit analyzes the content of the video clips using AI and evaluates the relevance. For example, the generation unit generates questions preferentially based on highly relevant video clips. The generation unit can also generate questions as needed based on less relevant video clips. The generation unit can also adjust the order of generation of questions based on the relevance of the video clips. For example, the generation unit generates questions preferentially based on highly relevant video clips, and generates questions as needed based on less relevant video clips. This allows the generation unit to adjust the order of generation based on the relevance of the video clips.

[0043] When generating questions, the generation unit can adjust the use of technical terms in the generated questions according to the user's level of expertise. For example, the generation unit obtains the user's level of expertise from a database and analyzes it using AI. For example, the generation unit generates questions with fewer technical terms for a novice user. The generation unit can also generate questions with more technical terms for an advanced user. The generation unit can also adjust the frequency of use of technical terms according to the user's level of expertise. For example, the generation unit generates questions with fewer technical terms for a novice user and generates questions with more technical terms for an advanced user. This allows the generation unit to use appropriate technical terms according to the user's level of expertise.

[0044] During customization, the customization unit can analyze the user's past learning history and select an appropriate customization method. For example, the customization unit retrieves the user's past learning history from a database and analyzes it using AI. For example, the customization unit proposes optimal customization options based on the user's past learning history. The customization unit can also analyze the user's past learning history and improve the customization method. For example, the customization unit refers to the user's past learning history to simplify the customization procedure. This allows the customization unit to select the optimal customization method based on the user's past learning history.

[0045] During customization, the customization unit can provide an appropriate customization means based on the user's current learning situation. For example, the customization unit obtains the user's current learning situation from a database and analyzes it using AI. For example, the customization unit analyzes the user's current learning situation and suggests optimal customization options. The customization unit can also adjust the customization means based on the user's current learning situation. For example, the customization unit simplifies the customization procedure by referring to the user's current learning situation. This allows the customization unit to provide the optimal customization means based on the user's current learning situation.

[0046] The customization unit can improve the customization method by reflecting user feedback during customization. For example, the customization unit obtains user feedback from a database and analyzes it using AI. For example, the customization unit improves the customization options based on the user feedback. The customization unit can also analyze the user feedback and adjust the customization method. For example, the customization unit refers to the user feedback and simplifies the customization procedure. This allows the customization unit to improve the customization method based on the user feedback.

[0047] During customization, the customization unit can select an appropriate customization method based on the user's geographic location information. For example, the customization unit obtains the user's geographic location information from GPS data or an IP address and analyzes it using AI. For example, the customization unit can prioritize displaying customization options related to a region based on the user's geographic location information. The customization unit can also prioritize displaying information related to local educational institutions and training centers based on the user's geographic location information. For example, the customization unit can prioritize displaying information related to local sporting events and training sessions based on the user's geographic location information. This allows the customization unit to provide the optimal customization method based on the user's geographic location information.

[0048] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit uses AI to analyze the user's social media posts. For example, the customization unit can suggest related customization options based on the user's social media posts. The customization unit can also suggest related customization options based on the activity of the user's friends on social media. For example, the customization unit can suggest related customization options based on the user's social media check-in information. This allows the customization unit to provide optimal customization methods based on the user's social media activity.

[0049] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. For example, the customization unit retrieves the user's past feedback from a database and analyzes it using AI. For example, the customization unit suggests optimal customization options based on feedback provided by the user in the past. The customization unit can also analyze the user's past feedback and improve the customization method. For example, the customization unit refers to the user's past feedback to simplify the customization procedure. This allows the customization unit to provide the optimal customization method based on the user's past feedback.

[0050] At the time of provision, the provision unit can analyze the user's past learning history and select an appropriate provision method. For example, the provision unit retrieves the user's past learning history from a database and analyzes it using AI. For example, the provision unit proposes an optimal provision method based on the user's past learning history. The provision unit can also analyze the user's past learning history and improve the means of provision. For example, the provision unit simplifies the provision procedure by referring to the user's past learning history. This allows the provision unit to provide the optimal provision method based on the user's past learning history.

[0051] The providing unit can customize the means of provision based on the user's current learning situation at the time of provision. For example, the providing unit obtains the user's current learning situation from a database and analyzes it using AI. For example, the providing unit analyzes the user's current learning situation and proposes the optimal provision method. The providing unit can also adjust the means of provision based on the user's current learning situation. For example, the providing unit simplifies the provision procedure by referring to the user's current learning situation. This allows the providing unit to provide the optimal means of provision based on the user's current learning situation.

[0052] The providing unit can improve the method of providing the information by reflecting the user's feedback at the time of providing the information. For example, the providing unit obtains the user's feedback from a database and analyzes it using AI. For example, the providing unit improves the method of providing the information based on the user's feedback. The providing unit can also analyze the user's feedback and adjust the means of providing the information. For example, the providing unit refers to the user's feedback and simplifies the procedure of providing the information. This allows the providing unit to improve the method of providing the information based on the user's feedback.

[0053] The providing unit can select an appropriate provision method based on the user's geographical location information at the time of provision. For example, the providing unit obtains the user's geographical location information from GPS data or an IP address and analyzes it using AI. For example, the providing unit can prioritize providing information related to a region based on the user's geographical location information. The providing unit can also prioritize providing information related to local educational institutions and training centers based on the user's geographical location information. For example, the providing unit can prioritize providing information related to local sporting events and training sessions based on the user's geographical location information. This allows the providing unit to provide the optimal provision method based on the user's geographical location information.

[0054] At the time of providing information, the providing unit can analyze the user's social media activity and suggest a means of providing the information. For example, the providing unit analyzes the content posted by the user on social media using AI. For example, the providing unit provides related information based on the content posted by the user on social media. The providing unit can also provide related information by referring to the activities of the user's friends on social media. For example, the providing unit provides related information based on the user's check-in information on social media. This allows the providing unit to provide the optimal means of providing the information based on the user's social media activity.

[0055] The providing unit can customize the method of providing information by reflecting the user's past feedback when providing information. The providing unit, for example, retrieves the user's past feedback from a database and analyzes it using AI. For example, the providing unit suggests the optimal method of providing information based on the feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and improve the means of providing information. For example, the providing unit simplifies the procedure of providing information by referring to the user's past feedback. This allows the providing unit to provide the optimal method of providing information based on the user's past feedback.

[0056] When collecting data, the data collection unit can analyze the user's past learning history and select the optimal collection method. For example, the data collection unit retrieves the user's past learning history from a database and analyzes it using AI. For example, the data collection unit proposes the optimal data collection method based on the user's past learning history. The data collection unit can also analyze the user's past learning history and improve data collection methods. For example, the data collection unit refers to the user's past learning history to simplify the data collection procedure. This allows the data collection unit to provide the optimal data collection method based on the user's past learning history.

[0057] When collecting data, the data collection unit can customize the collection means based on the user's current learning situation. For example, the data collection unit obtains the user's current learning situation from a database and analyzes it using AI. For example, the data collection unit analyzes the user's current learning situation and proposes the optimal data collection method. The data collection unit can also adjust the data collection means based on the user's current learning situation. For example, the data collection unit simplifies the data collection procedure by referring to the user's current learning situation. This allows the data collection unit to provide the optimal data collection means based on the user's current learning situation.

[0058] The data collection unit can improve the data collection method by reflecting user feedback when collecting data. For example, the data collection unit obtains user feedback from a database and analyzes it using AI. For example, the data collection unit improves the data collection method based on the user feedback. The data collection unit can also analyze the user feedback and adjust the means of data collection. For example, the data collection unit refers to the user feedback and simplifies the data collection procedure. This allows the data collection unit to improve the data collection method based on the user feedback.

[0059] When collecting data, the data collection unit can select an appropriate collection method based on the user's geographical location information. For example, the data collection unit obtains the user's geographical location information from GPS data or an IP address and analyzes it using AI. For example, the data collection unit prioritizes collecting data related to a region based on the user's geographical location information. The data collection unit can also prioritize collecting data related to local educational institutions and training centers based on the user's geographical location information. For example, the data collection unit prioritizes collecting data related to local sporting events and training sessions based on the user's geographical location information. This allows the data collection unit to provide the optimal data collection method based on the user's geographical location information.

[0060] When collecting data, the data collection unit can analyze the user's social media activities and suggest collection methods. The data collection unit, for example, uses AI to analyze the content of the user's social media posts. For example, the data collection unit collects related data based on the content of the user's social media posts. The data collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the data collection unit collects related data based on the user's social media check-in information. This allows the data collection unit to provide optimal data collection methods based on the user's social media activities.

[0061] When collecting data, the data collection unit can customize the collection method by reflecting the user's past feedback. For example, the data collection unit retrieves the user's past feedback from a database and analyzes it using AI. For example, the data collection unit proposes an optimal data collection method based on feedback provided by the user in the past. The data collection unit can also analyze the user's past feedback and improve the means of data collection. For example, the data collection unit refers to the user's past feedback to simplify the data collection procedure. This allows the data collection unit to provide an optimal data collection method based on the user's past feedback.

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

[0063] When the user inputs their skill level or learning goals, the reception unit can analyze the user's past learning history and suggest an appropriate input method. For example, the reception unit retrieves the user's past learning history from a database and analyzes it using AI. This makes it possible to automatically display as candidates the skill level and learning goals that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, it can predict and suggest the skill level and learning goals to be used at a specific time period based on the user's past learning history. This allows the reception unit to suggest the optimal input method based on the user's past learning history.

[0064] The reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, the reception unit obtains the user's geographical location information from GPS data or IP address and analyzes it using AI. This makes it possible to prioritize display of skill levels and learning goals related to the local area. It is also possible to prioritize display of information related to local educational institutions and training centers. Furthermore, it is possible to prioritize display of information related to local sporting events and training sessions. This allows the reception unit to provide highly relevant information based on the user's geographical location information.

[0065] The generator can adjust the level of detail of the generated questions based on the importance of the video clip. For example, the generator evaluates the importance based on the number of views and ratings of the video clip, and adjusts the level of detail of the questions using AI. This makes it possible to generate detailed questions based on important video clips. It is also possible to generate simplified questions based on video clips with low importance. Furthermore, it is possible to adjust the difficulty of the questions according to the importance of the video clip. This makes it possible for the generator to adjust the level of detail of the questions based on the importance of the video clip.

[0066] The customization unit can analyze the user's past learning history and select an appropriate customization method. For example, the customization unit retrieves the user's past learning history from a database and analyzes it using AI. This makes it possible to propose optimal customization options based on the user's past learning history. The customization unit can also analyze the user's past learning history and improve customization methods. Furthermore, the customization procedure can be simplified by referring to the user's past learning history. This allows the customization unit to select the optimal customization method based on the user's past learning history.

[0067] The providing unit can analyze the user's social media activity and suggest a means of provision. For example, the providing unit can analyze the content of the user's social media posts using AI. This makes it possible to provide related information based on the content of the user's social media posts. It can also provide related information by referring to the activities of the user's friends on social media. Furthermore, it can provide related information based on the user's social media check-in information. This allows the providing unit to provide the optimal means of provision based on the user's social media activity.

[0068] The data collection unit can customize the collection method by reflecting the user's past feedback. For example, the data collection unit retrieves the user's past feedback from a database and analyzes it using AI. This makes it possible to propose the optimal data collection method based on the feedback the user has provided in the past. The data collection unit can also analyze the user's past feedback and improve the data collection method. Furthermore, the data collection procedure can be simplified by referring to the user's past feedback. This makes it possible for the data collection unit to provide the optimal data collection method based on the user's past feedback.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit inputs the user's skill level and learning goals. The user's skill level may be, for example, beginner, intermediate, or advanced. The reception unit provides an interface for the user to input their skill level and learning goals. Step 2: The generator analyzes video clips focusing on specific matches or players based on the information input by the receiver, and generates questions. For example, the generator analyzes the video clips using AI and generates questions based on the content of the video clips. For example, the generator analyzes a video clip about a specific player's dribbling technique and generates questions related to that technique. Step 3: The customization unit customizes the questions generated by the generation unit to suit the user's skill level and learning goals. For example, the customization unit adjusts questions generated using AI to suit the user's skill level and learning goals. For example, it provides questions related to basic techniques to a beginner user and questions related to advanced techniques to an advanced user. Step 4: The providing unit provides the problem customized by the customization unit. The providing unit provides the problem customized by the customization unit to the user, for example, by using AI. For example, the providing unit delivers the customized problem to the user's device.

[0071] (Example 2) A question generation system according to an embodiment of the present invention analyzes video clips focusing on specific games or players based on a user's skill level and learning goals, and generates and provides questions based on the user's skill level and learning goals. The question generation system inputs the user's skill level and learning goals, analyzes video clips focusing on specific games or players, and generates questions based on the content of the video clips. The generated questions are customized and provided to the user. For example, the question generation system allows a user to input their own skill level and learning goals. For example, a beginner user can select questions related to basic techniques or strategies, while an advanced user can select questions related to advanced techniques or strategies. This information is input into an AI. The AI ​​then analyzes the input information and generates questions optimal for the user. The AI ​​analyzes video clips focusing on specific games or players and creates questions based on the content of the video clips. For example, a video clip related to a specific player's dribbling technique can be analyzed to generate questions related to that technique. The generated questions are customized to the user's skill level and learning goals. For example, a beginner user can be provided with questions related to basic techniques, while an advanced user can be provided with questions related to advanced techniques. The question generation system also includes a data collection unit that allows coaches to track players' progress. A coach can check a player's progress and generate custom problems that focus on specific problems. For example, if a specific player has issues with dribbling technique, a coach can generate custom problems related to that technique and provide them to the player. This allows the problem generation system to efficiently provide learning materials tailored to the user's skill level and learning goals. The coach can also effectively manage the player's progress and provide custom problems that focus on specific techniques and strategies. This allows the problem generation system to efficiently provide learning materials tailored to the user's skill level and learning goals. The coach can also effectively manage the player's progress and provide custom problems that focus on specific techniques and strategies. This is expected to improve the player's skills.

[0072] A question generation system according to an embodiment includes a receiving unit, a generating unit, a customizing unit, and a providing unit. The receiving unit inputs a user's skill level and learning goals. Examples of the user's skill level include, but are not limited to, beginner, intermediate, and advanced. The receiving unit provides, for example, an interface through which the user inputs their skill level and learning goals. The generating unit analyzes video clips focusing on specific matches or players based on the information input by the receiving unit and generates questions. The generating unit analyzes video clips using, for example, AI and generates questions based on the content of the video clips. For example, the generating unit analyzes video clips related to a specific player's dribbling technique and generates questions related to that technique. The customizing unit customizes the questions generated by the generating unit to suit the user's skill level and learning goals. For example, the customizing unit adjusts the questions generated using AI to suit the user's skill level and learning goals. For example, the customizing unit provides questions related to basic techniques to a beginner user and questions related to advanced techniques to an advanced user. The providing unit provides the questions customized by the customizing unit. The providing unit provides the user with questions customized using, for example, AI. For example, the providing unit delivers the customized questions to the user's device. This allows the question generation system according to the embodiment to generate and provide questions tailored to the user's skill level and learning goals.

[0073] The question generation system further includes a data collection unit that allows the coach to track the player's progress. The data collection unit collects data that the coach uses to track the player's progress. The data collection unit collects data such as the player's learning history and test results. For example, the data collection unit collects data such as the correct answer rate and answer time for questions answered by the player. The data collection unit can also collect data such as the player's feedback and comments. For example, the data collection unit collects feedback and comments provided by the player and provides the feedback and comments to the coach. This allows the coach to grasp the player's progress in detail and provide appropriate guidance. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input the player's learning history and test results into AI, which can analyze the data and grasp the player's progress.

[0074] The problem generation system further includes a generation unit that generates custom problems focused on specific problems based on the data collected by the data collection unit. The generation unit generates custom problems focused on specific problems based on the data collected by the data collection unit. The generation unit, for example, analyzes the collected data using AI and generates custom problems focused on specific problems. For example, if a specific player has issues with dribbling technique, the generation unit generates custom problems related to that technique. For example, the generation unit inputs player progress data to the AI, which analyzes the data and generates custom problems. This allows the generation unit to provide custom problems tailored to the player's progress.

[0075] The reception unit can estimate the user's emotions and adjust the input method for the skill level and learning goals based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This allows the reception unit to adjust the input method according to the user's emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. If the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. If the user is in a hurry, the reception unit prioritizes voice input, allowing the user to quickly input their skill level and learning goals. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0076] The reception unit can analyze the user's past learning history and suggest an appropriate input method. For example, the reception unit retrieves the user's past learning history from a database and analyzes it using AI. For example, the reception unit automatically displays the skill level and learning goals that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests the skill level and learning goals to be used in a specific time period based on the user's past learning history. This allows the reception unit to suggest the optimal input method based on the user's past learning history.

[0077] When inputting a skill level or learning goal, the reception unit can filter the input based on the user's current learning situation and areas of interest. The reception unit, for example, retrieves the user's current learning situation from a database and analyzes it using AI. For example, the reception unit analyzes the user's current learning situation and preferentially displays related skill levels and learning goals. The reception unit can also filter and display related skill levels and learning goals based on the user's areas of interest. For example, the reception unit combines the user's past learning history and current learning situation to suggest optimal skill levels and learning goals. This allows the reception unit to suggest optimal skill levels and learning goals based on the user's current learning situation and areas of interest.

[0078] When inputting a skill level or learning goal, the reception unit can select an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the skill level or learning goal using voice recognition technology. For example, the reception unit automatically analyzes the voice using voice recognition software and saves it as text. Furthermore, if the user selects text input, the reception unit can provide a text box to input the skill level or learning goal. For example, if the user selects image input, the reception unit inputs the skill level or learning goal using image analysis technology. This allows the reception unit to provide the optimal input means according to the user's input method.

[0079] The reception unit can estimate the user's emotions and determine the priority of the skill levels and learning goals to be input based on the estimated user emotions. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates the emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates the emotion score based on heart rate fluctuations. This allows the reception unit to determine the priority of skill levels and learning goals according to the user's emotions. For example, if the user is nervous, the reception unit prioritizes displaying basic skill levels and learning goals. If the user is relaxed, the reception unit prioritizes displaying detailed skill levels and learning goals. If the user is in a hurry, the reception unit prioritizes displaying the most important skill levels and learning goals. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] When inputting a skill level or learning goal, the reception unit can prioritize inputting highly relevant information taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information from GPS data or an IP address and analyzes it using AI. For example, the reception unit can prioritize displaying skill levels and learning goals related to a region based on the user's geographical location information. The reception unit can also prioritize displaying information related to local educational institutions and training centers based on the user's geographical location information. For example, the reception unit can prioritize displaying information related to local sporting events and training sessions based on the user's geographical location information. This allows the reception unit to provide highly relevant information based on the user's geographical location information.

[0081] When inputting a skill level or learning goal, the reception unit can analyze the user's social media activity and input related information. The reception unit, for example, analyzes the content of the user's social media posts using AI. For example, the reception unit can suggest related skill levels and learning goals based on the content of the user's social media posts. The reception unit can also suggest related skill levels and learning goals by referring to the activities of the user's friends on social media. For example, the reception unit can suggest related skill levels and learning goals based on the user's social media check-in information. This allows the reception unit to provide related information based on the user's social media activity.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting the skill level or learning goal. The reception unit, for example, retrieves the user's past feedback from a database and analyzes it using AI. For example, the reception unit suggests the optimal input method based on the feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and improve the input interface. For example, the reception unit simplifies the input procedure by referring to the user's past feedback. This allows the reception unit to customize the input method based on the user's past feedback.

[0083] The generation unit can estimate the user's emotions and adjust the problem generation method based on the estimated user emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation unit to adjust the problem generation method according to the user's emotions. For example, if the user is relaxed, the generation unit generates problems that proceed at a leisurely pace. If the user is in a hurry, the generation unit generates problems that emphasize the shortest route. If the user is excited, the generation unit generates problems with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] When generating questions, the generation unit can adjust the level of detail of the generated questions based on the importance of the video clip. For example, the generation unit evaluates the importance based on the number of views and ratings of the video clip, and adjusts the level of detail of the questions using AI. For example, the generation unit generates detailed questions based on an important video clip. The generation unit can also generate simplified questions based on a video clip with a low level of importance. The generation unit can also adjust the difficulty of the questions according to the importance of the video clip. For example, the generation unit generates more detailed questions the more times a video clip is viewed, and generates simplified questions if the number of views is low. This allows the generation unit to adjust the level of detail of the questions based on the importance of the video clip.

[0085] When generating questions, the generation unit can apply an appropriate generation algorithm depending on the category of the video clip. For example, the generation unit classifies the category of the video clip using AI and applies an appropriate generation algorithm. For example, the generation unit can apply an algorithm that generates technical questions to a video clip related to technique. The generation unit can also apply an algorithm that generates strategic questions to a video clip related to strategy. The generation unit can also apply an algorithm that generates questions related to a specific player to a video clip related to the player. For example, the generation unit can apply an algorithm that generates technical questions to a video clip related to technique, and an algorithm that generates strategic questions to a video clip related to strategy. This allows the generation unit to apply the optimal generation algorithm depending on the category of the video clip.

[0086] When generating questions, the generation unit can improve the accuracy of the generation by referring to the user's past question answer results. For example, the generation unit retrieves the user's past question answer results from a database and analyzes them using AI. For example, the generation unit prioritizes generating questions with a high correct answer rate based on the user's past question answer results. The generation unit can also generate questions related to weak areas based on the user's past question answer results. The generation unit can also adjust the difficulty of questions by referring to the user's past question answer results. For example, the generation unit prioritizes generating questions with a high correct answer rate based on the user's past question answer results, and generates questions related to weak areas. This allows the generation unit to improve the accuracy of generation based on the user's past question answer results.

[0087] The generation unit can estimate the user's emotions and adjust the length of the questions to be generated based on the estimated user emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the generation unit to adjust the length of the questions according to the user's emotions. For example, if the user is in a hurry, the generation unit generates short, to-the-point questions. If the user is relaxed, the generation unit generates longer questions with detailed explanations. If the user is excited, the generation unit generates questions with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] When generating questions, the generation unit can determine the priority of generation based on the shooting date of the video clip. For example, the generation unit obtains the shooting date of the video clip from a database and analyzes it using AI. For example, the generation unit generates questions preferentially based on the latest video clip. The generation unit can also generate questions based on older video clips as needed. The generation unit can also adjust the order of question generation according to the shooting date of the video clip. For example, the generation unit generates questions preferentially based on the latest video clip, and generates questions based on older video clips as needed. This allows the generation unit to determine the priority of generation based on the shooting date of the video clip.

[0089] When generating questions, the generation unit can adjust the order of generation based on the relevance of the video clips. For example, the generation unit analyzes the content of the video clips using AI and evaluates the relevance. For example, the generation unit generates questions preferentially based on highly relevant video clips. The generation unit can also generate questions as needed based on less relevant video clips. The generation unit can also adjust the order of generation of questions based on the relevance of the video clips. For example, the generation unit generates questions preferentially based on highly relevant video clips, and generates questions as needed based on less relevant video clips. This allows the generation unit to adjust the order of generation based on the relevance of the video clips.

[0090] When generating questions, the generation unit can adjust the use of technical terms in the generated questions according to the user's level of expertise. For example, the generation unit obtains the user's level of expertise from a database and analyzes it using AI. For example, the generation unit generates questions with fewer technical terms for a novice user. The generation unit can also generate questions with more technical terms for an advanced user. The generation unit can also adjust the frequency of use of technical terms according to the user's level of expertise. For example, the generation unit generates questions with fewer technical terms for a novice user and generates questions with more technical terms for an advanced user. This allows the generation unit to use appropriate technical terms according to the user's level of expertise.

[0091] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the customization unit calculates an emotion score based on changes in facial expressions. The customization unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the customization unit analyzes the tone and speed of the voice and calculates an emotion score. The customization unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the customization unit calculates an emotion score based on heart rate fluctuations. This allows the customization unit to adjust the customization method according to the user's emotions. For example, if the user is relaxed, the customization unit provides detailed customization options. If the user is in a hurry, the customization unit provides simplified customization options. If the user is excited, the customization unit provides visually stimulating customization options. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0092] During customization, the customization unit can analyze the user's past learning history and select an appropriate customization method. For example, the customization unit retrieves the user's past learning history from a database and analyzes it using AI. For example, the customization unit proposes optimal customization options based on the user's past learning history. The customization unit can also analyze the user's past learning history and improve the customization method. For example, the customization unit refers to the user's past learning history to simplify the customization procedure. This allows the customization unit to select the optimal customization method based on the user's past learning history.

[0093] During customization, the customization unit can provide an appropriate customization means based on the user's current learning situation. For example, the customization unit obtains the user's current learning situation from a database and analyzes it using AI. For example, the customization unit analyzes the user's current learning situation and suggests optimal customization options. The customization unit can also adjust the customization means based on the user's current learning situation. For example, the customization unit simplifies the customization procedure by referring to the user's current learning situation. This allows the customization unit to provide the optimal customization means based on the user's current learning situation.

[0094] The customization unit can improve the customization method by reflecting user feedback during customization. For example, the customization unit obtains user feedback from a database and analyzes it using AI. For example, the customization unit improves the customization options based on the user feedback. The customization unit can also analyze the user feedback and adjust the customization method. For example, the customization unit refers to the user feedback and simplifies the customization procedure. This allows the customization unit to improve the customization method based on the user feedback.

[0095] The customization unit can estimate the user's emotions and determine customization priorities based on the estimated user emotions. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the customization unit calculates an emotion score based on changes in facial expressions. The customization unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the customization unit analyzes the tone and speed of the voice and calculates an emotion score. The customization unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the customization unit calculates an emotion score based on heart rate fluctuations. This allows the customization unit to determine customization priorities based on the user's emotions. For example, if the user is nervous, the customization unit prioritizes displaying basic customization options. If the user is relaxed, the customization unit prioritizes displaying detailed customization options. If the user is in a hurry, the customization unit prioritizes displaying the most important customization options. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] During customization, the customization unit can select an appropriate customization method based on the user's geographic location information. For example, the customization unit obtains the user's geographic location information from GPS data or an IP address and analyzes it using AI. For example, the customization unit can prioritize displaying customization options related to a region based on the user's geographic location information. The customization unit can also prioritize displaying information related to local educational institutions and training centers based on the user's geographic location information. For example, the customization unit can prioritize displaying information related to local sporting events and training sessions based on the user's geographic location information. This allows the customization unit to provide the optimal customization method based on the user's geographic location information.

[0097] During customization, the customization unit can analyze the user's social media activity and suggest customization methods. For example, the customization unit uses AI to analyze the user's social media posts. For example, the customization unit can suggest related customization options based on the user's social media posts. The customization unit can also suggest related customization options based on the activity of the user's friends on social media. For example, the customization unit can suggest related customization options based on the user's social media check-in information. This allows the customization unit to provide optimal customization methods based on the user's social media activity.

[0098] During customization, the customization unit can customize the customization method by reflecting the user's past feedback. For example, the customization unit retrieves the user's past feedback from a database and analyzes it using AI. For example, the customization unit suggests optimal customization options based on feedback provided by the user in the past. The customization unit can also analyze the user's past feedback and improve the customization method. For example, the customization unit refers to the user's past feedback to simplify the customization procedure. This allows the customization unit to provide the optimal customization method based on the user's past feedback.

[0099] The providing unit can estimate the user's emotion and adjust the presentation method based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows the providing unit to adjust the presentation method according to the user's emotion. For example, if the user is relaxed, the providing unit provides a presentation method that includes detailed information. If the user is in a hurry, the providing unit provides a simplified presentation method. If the user is excited, the providing unit provides a visually stimulating presentation method. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0100] At the time of provision, the provision unit can analyze the user's past learning history and select an appropriate provision method. For example, the provision unit retrieves the user's past learning history from a database and analyzes it using AI. For example, the provision unit proposes an optimal provision method based on the user's past learning history. The provision unit can also analyze the user's past learning history and improve the means of provision. For example, the provision unit simplifies the provision procedure by referring to the user's past learning history. This allows the provision unit to provide the optimal provision method based on the user's past learning history.

[0101] The providing unit can customize the means of provision based on the user's current learning situation at the time of provision. For example, the providing unit obtains the user's current learning situation from a database and analyzes it using AI. For example, the providing unit analyzes the user's current learning situation and proposes the optimal provision method. The providing unit can also adjust the means of provision based on the user's current learning situation. For example, the providing unit simplifies the provision procedure by referring to the user's current learning situation. This allows the providing unit to provide the optimal means of provision based on the user's current learning situation.

[0102] The providing unit can improve the method of providing the information by reflecting the user's feedback at the time of providing the information. For example, the providing unit obtains the user's feedback from a database and analyzes it using AI. For example, the providing unit improves the method of providing the information based on the user's feedback. The providing unit can also analyze the user's feedback and adjust the means of providing the information. For example, the providing unit refers to the user's feedback and simplifies the procedure of providing the information. This allows the providing unit to improve the method of providing the information based on the user's feedback.

[0103] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This allows the providing unit to determine the priority of information to be provided based on the user's emotions. For example, if the user is nervous, the providing unit prioritizes providing basic information. If the user is relaxed, the providing unit prioritizes providing detailed information. If the user is in a hurry, the providing unit prioritizes providing the most important information. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0104] The providing unit can select an appropriate provision method based on the user's geographical location information at the time of provision. For example, the providing unit obtains the user's geographical location information from GPS data or an IP address and analyzes it using AI. For example, the providing unit can prioritize providing information related to a region based on the user's geographical location information. The providing unit can also prioritize providing information related to local educational institutions and training centers based on the user's geographical location information. For example, the providing unit can prioritize providing information related to local sporting events and training sessions based on the user's geographical location information. This allows the providing unit to provide the optimal provision method based on the user's geographical location information.

[0105] At the time of providing information, the providing unit can analyze the user's social media activity and suggest a means of providing the information. For example, the providing unit analyzes the content posted by the user on social media using AI. For example, the providing unit provides related information based on the content posted by the user on social media. The providing unit can also provide related information by referring to the activities of the user's friends on social media. For example, the providing unit provides related information based on the user's check-in information on social media. This allows the providing unit to provide the optimal means of providing the information based on the user's social media activity.

[0106] The providing unit can customize the method of providing information by reflecting the user's past feedback when providing information. The providing unit, for example, retrieves the user's past feedback from a database and analyzes it using AI. For example, the providing unit suggests the optimal method of providing information based on the feedback provided by the user in the past. The providing unit can also analyze the user's past feedback and improve the means of providing information. For example, the providing unit simplifies the procedure of providing information by referring to the user's past feedback. This allows the providing unit to provide the optimal method of providing information based on the user's past feedback.

[0107] The data collection unit can estimate the user's emotion and adjust the data collection method based on the estimated user's emotion. For example, the data collection unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on changes in facial expression. The data collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the data collection unit analyzes the tone and speed of the voice and calculates an emotion score. The data collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on heart rate fluctuations. This allows the data collection unit to adjust the data collection method according to the user's emotion. For example, if the user is relaxed, the data collection unit provides a detailed data collection method. If the user is in a hurry, the data collection unit provides a simplified data collection method. If the user is excited, the data collection unit provides a visually stimulating data collection method. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] When collecting data, the data collection unit can analyze the user's past learning history and select the optimal collection method. For example, the data collection unit retrieves the user's past learning history from a database and analyzes it using AI. For example, the data collection unit proposes the optimal data collection method based on the user's past learning history. The data collection unit can also analyze the user's past learning history and improve data collection methods. For example, the data collection unit refers to the user's past learning history to simplify the data collection procedure. This allows the data collection unit to provide the optimal data collection method based on the user's past learning history.

[0109] When collecting data, the data collection unit can customize the collection means based on the user's current learning situation. For example, the data collection unit obtains the user's current learning situation from a database and analyzes it using AI. For example, the data collection unit analyzes the user's current learning situation and proposes the optimal data collection method. The data collection unit can also adjust the data collection means based on the user's current learning situation. For example, the data collection unit simplifies the data collection procedure by referring to the user's current learning situation. This allows the data collection unit to provide the optimal data collection means based on the user's current learning situation.

[0110] The data collection unit can improve the data collection method by reflecting user feedback when collecting data. For example, the data collection unit obtains user feedback from a database and analyzes it using AI. For example, the data collection unit improves the data collection method based on the user feedback. The data collection unit can also analyze the user feedback and adjust the means of data collection. For example, the data collection unit refers to the user feedback and simplifies the data collection procedure. This allows the data collection unit to improve the data collection method based on the user feedback.

[0111] The data collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, the data collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the data collection unit analyzes the tone and speed of the voice and calculates an emotion score. The data collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the data collection unit calculates an emotion score based on heart rate fluctuations. This allows the data collection unit to determine the priority of data to be collected based on the user's emotions. For example, if the user is nervous, the data collection unit prioritizes collecting basic data. If the user is relaxed, the data collection unit prioritizes collecting detailed data. If the user is in a hurry, the data collection unit prioritizes collecting the most important data. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] When collecting data, the data collection unit can select an appropriate collection method based on the user's geographical location information. For example, the data collection unit obtains the user's geographical location information from GPS data or an IP address and analyzes it using AI. For example, the data collection unit prioritizes collecting data related to a region based on the user's geographical location information. The data collection unit can also prioritize collecting data related to local educational institutions and training centers based on the user's geographical location information. For example, the data collection unit prioritizes collecting data related to local sporting events and training sessions based on the user's geographical location information. This allows the data collection unit to provide the optimal data collection method based on the user's geographical location information.

[0113] When collecting data, the data collection unit can analyze the user's social media activities and suggest collection methods. The data collection unit, for example, uses AI to analyze the content of the user's social media posts. For example, the data collection unit collects related data based on the content of the user's social media posts. The data collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the data collection unit collects related data based on the user's social media check-in information. This allows the data collection unit to provide optimal data collection methods based on the user's social media activities.

[0114] When collecting data, the data collection unit can customize the collection method by reflecting the user's past feedback. For example, the data collection unit retrieves the user's past feedback from a database and analyzes it using AI. For example, the data collection unit proposes an optimal data collection method based on feedback provided by the user in the past. The data collection unit can also analyze the user's past feedback and improve the means of data collection. For example, the data collection unit refers to the user's past feedback to simplify the data collection procedure. This allows the data collection unit to provide an optimal data collection method based on the user's past feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, customization unit, provision unit, and data collection unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can input a user's skill level and learning goals using the reception device 38 of the smart device 14. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12 and generate questions by analyzing video clips focusing on specific games or players. For example, the customization unit can be implemented by the specific processing unit 290 of the data processing device 12 and customize the generated questions to suit the user's skill level and learning goals. For example, the provision unit can provide the customized questions to the user using the output device 40 of the smart device 14. For example, the data collection unit can collect data tracking a player's progress using the camera 42 or microphone 38B of the smart device 14. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12 and generate custom questions based on the collected data. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, generation unit, customization unit, provision unit, and data collection unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can input the user's skill level and learning goals using the microphone 238 of the smart glasses 214. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12 and generate questions by analyzing video clips focusing on specific games or players. For example, the customization unit can be implemented by the specific processing unit 290 of the data processing device 12 and customize the generated questions to suit the user's skill level and learning goals. For example, the provision unit can provide the customized questions to the user using the speaker 240 of the smart glasses 214. For example, the data collection unit can collect data tracking the player's progress using the camera 42 and microphone 238 of the smart glasses 214. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12 and generate custom questions based on the collected data. === Hard Collateral 1-3 === Each of the above-described elements, including the reception unit, generation unit, customization unit, provision unit, and data collection unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit can input the user's skill level and learning goals using the microphone 238 of the headset terminal 314. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12 and generate questions by analyzing video clips focusing on specific games or players. For example, the customization unit can be implemented by the specific processing unit 290 of the data processing device 12 and customize the generated questions to suit the user's skill level and learning goals. For example, the provision unit can provide the customized questions to the user using the display 343 of the headset terminal 314. For example, the data collection unit can collect data tracking the player's progress using the camera 42 and microphone 238 of the headset terminal 314. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12 and generate custom questions based on the collected data. === Hard Collateral 1-4 === Each of the above-described elements, including the reception unit, generation unit, customization unit, provision unit, and data collection unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can input the user's skill level and learning goals using the microphone 238 of the robot 414. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12 and analyze video clips focusing on specific games or players to generate questions. For example, the customization unit can be implemented by the specific processing unit 290 of the data processing device 12 and customize the generated questions to suit the user's skill level and learning goals. For example, the provision unit can provide the customized questions to the user using the speaker 240 of the robot 414. For example, the data collection unit can collect data tracking the player's progress using the camera 42 and microphone 238 of the robot 414. For example, the generation unit can be implemented by the specific processing unit 290 of the data processing device 12 and generate custom questions based on the collected data.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] When the user inputs their skill level or learning goals, the reception unit can analyze the user's past learning history and suggest an appropriate input method. For example, the reception unit retrieves the user's past learning history from a database and analyzes it using AI. This makes it possible to automatically display as candidates the skill level and learning goals that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, it can predict and suggest the skill level and learning goals to be used at a specific time period based on the user's past learning history. This allows the reception unit to suggest the optimal input method based on the user's past learning history.

[0117] The generator can estimate the user's emotions and adjust the problem generation method based on the estimated user emotions. For example, the generator can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. As a result, if the user is relaxed, the generator can generate problems that proceed at a leisurely pace. If the user is in a hurry, the generator can generate problems that emphasize the shortest route. Furthermore, if the user is excited, the generator can generate problems with visually stimulating effects. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI.

[0118] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. For example, the customization unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. As a result, if the user is relaxed, the customization unit can provide detailed customization options. If the user is in a hurry, the customization unit can provide simplified customization options. Furthermore, if the user is excited, the customization unit can provide visually stimulating customization options. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI, etc.

[0119] The providing unit can estimate the user's emotion and adjust the presentation method based on the estimated user emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. As a result, if the user is relaxed, the providing unit can provide a presentation method that includes detailed information. If the user is in a hurry, the providing unit can provide a simplified presentation method. Furthermore, if the user is excited, the providing unit can provide a visually stimulating presentation method. Emotion estimation is realized using an emotion estimation function using an emotion engine or generative AI, etc.

[0120] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user emotions. For example, the data collection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. As a result, if the user is relaxed, the data collection unit can provide a detailed data collection method. If the user is in a hurry, the data collection unit can provide a simplified data collection method. Furthermore, if the user is excited, the data collection unit can provide a visually stimulating data collection method. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI, etc.

[0121] The reception unit can prioritize input of highly relevant information taking into account the user's geographical location information. For example, the reception unit obtains the user's geographical location information from GPS data or IP address and analyzes it using AI. This makes it possible to prioritize display of skill levels and learning goals related to the local area. It is also possible to prioritize display of information related to local educational institutions and training centers. Furthermore, it is possible to prioritize display of information related to local sporting events and training sessions. This allows the reception unit to provide highly relevant information based on the user's geographical location information.

[0122] The generator can adjust the level of detail of the generated questions based on the importance of the video clip. For example, the generator evaluates the importance based on the number of views and ratings of the video clip, and adjusts the level of detail of the questions using AI. This makes it possible to generate detailed questions based on important video clips. It is also possible to generate simplified questions based on video clips with low importance. Furthermore, it is possible to adjust the difficulty of the questions according to the importance of the video clip. This makes it possible for the generator to adjust the level of detail of the questions based on the importance of the video clip.

[0123] The customization unit can analyze the user's past learning history and select an appropriate customization method. For example, the customization unit retrieves the user's past learning history from a database and analyzes it using AI. This makes it possible to propose optimal customization options based on the user's past learning history. The customization unit can also analyze the user's past learning history and improve customization methods. Furthermore, the customization procedure can be simplified by referring to the user's past learning history. This allows the customization unit to select the optimal customization method based on the user's past learning history.

[0124] The providing unit can analyze the user's social media activity and suggest a means of provision. For example, the providing unit can analyze the content of the user's social media posts using AI. This makes it possible to provide related information based on the content of the user's social media posts. It can also provide related information by referring to the activities of the user's friends on social media. Furthermore, it can provide related information based on the user's social media check-in information. This allows the providing unit to provide the optimal means of provision based on the user's social media activity.

[0125] The data collection unit can customize the collection method by reflecting the user's past feedback. For example, the data collection unit retrieves the user's past feedback from a database and analyzes it using AI. This makes it possible to propose the optimal data collection method based on the feedback the user has provided in the past. The data collection unit can also analyze the user's past feedback and improve the data collection method. Furthermore, the data collection procedure can be simplified by referring to the user's past feedback. This makes it possible for the data collection unit to provide the optimal data collection method based on the user's past feedback.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The reception unit inputs the user's skill level and learning goals. The user's skill level may be, for example, beginner, intermediate, or advanced. The reception unit provides an interface for the user to input their skill level and learning goals. Step 2: The generator analyzes video clips focusing on specific matches or players based on the information input by the receiver, and generates questions. For example, the generator analyzes the video clips using AI and generates questions based on the content of the video clips. For example, the generator analyzes a video clip about a specific player's dribbling technique and generates questions related to that technique. Step 3: The customization unit customizes the questions generated by the generation unit to suit the user's skill level and learning goals. For example, the customization unit adjusts questions generated using AI to suit the user's skill level and learning goals. For example, it provides questions related to basic techniques to a beginner user and questions related to advanced techniques to an advanced user. Step 4: The providing unit provides the problem customized by the customization unit. The providing unit provides the problem customized by the customization unit to the user, for example, by using AI. For example, the providing unit delivers the customized problem to the user's device.

[0128] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0171] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0172] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0175] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0177] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0184] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0185] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0189] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0190] 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.

[0191] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0192] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0193] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0194] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0197] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section for inputting the user's skill level and learning goals; a generation unit that analyzes a video clip focusing on a specific game or player based on the information input by the reception unit and generates questions; a customization unit that customizes the questions generated by the generation unit to suit the skill level and learning goals of the user; a providing unit that provides questions customized by the customization unit; Equipped with A system characterized by:

2. Includes data collection for coaches to track player progress 2. The system of claim 1.

3. A generator generates custom questions focused on specific problems based on the data collected by the data collector.

3. The system of claim 2.

4. The reception unit Inferring user emotions and adjusting the skill level and learning goal input method based on the estimated user emotions 2. The system of claim 1.

5. The reception unit Analyzes the user's past learning history and suggests appropriate input methods 2. The system of claim 1.

6. The reception unit Filter based on the user's current learning status and interests when entering skill levels and learning goals 2. The system of claim 1.

7. The reception unit Select the appropriate input method depending on the user's input method when entering skill level and learning goals.

2. The system of claim 1.

8. The reception unit Estimate the user's emotions and prioritize the skill level and learning goals to be input based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit Prioritize relevant information based on the user's geographic location when entering skill levels and learning goals 2. The system of claim 1.

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