system

Abstract

The system according to the embodiment aims to provide language learners with optimal teaching materials and instructors. [Solution] A system according to an embodiment includes a leveling unit, a selection unit, a presentation unit, a feedback collection unit, and an improvement unit. The leveling unit determines the user's level. The selection unit selects appropriate learning materials and instructors based on the level determined by the leveling unit. The presentation unit presents the learning materials and instructors selected by the selection unit to the user. The feedback collection unit collects learning data from the user. The improvement unit analyzes the data collected by the feedback collection unit and improves matching accuracy.

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 that the process of selecting the most suitable teaching materials and instructors for language learners is complicated, making it difficult to provide the best possible match for each learner.

[0005] The system according to the embodiment aims to provide language learners with optimal teaching materials and instructors. [Means for solving the problem]

[0006] The system according to the embodiment includes a leveling unit, a selection unit, a presentation unit, a feedback collection unit, and an improvement unit. The leveling unit determines the level of the user. The selection unit selects appropriate learning materials and instructors based on the level determined by the leveling unit. The presentation unit presents the learning materials and instructors selected by the selection unit to the user. The feedback collection unit collects learning data of the user. The improvement unit analyzes the data collected by the feedback collection unit and improves matching accuracy. [Effects of the Invention]

[0007] The system according to the embodiment can provide language learners with optimal teaching materials and instructors. [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 language learning support system according to an embodiment of the present invention is a system that uses AI to assign optimal learning materials and instructors tailored to a user's level when learning a non-native language. The system begins when a user accesses a portal site and inputs their current level and goals for learning the non-native language. Next, the AI ​​analyzes the user's input information and determines the user's level. Based on this determination, the AI ​​selects optimal learning materials and instructors from a database. The selected learning materials and instructors are presented to the user, who can then select the materials and instructors that best suit them. Furthermore, as the user progresses with their learning using the selected learning materials and instructors, their learning data is fed back to the AI, which analyzes this feedback data and improves the accuracy of matching. In this way, the portal site provides optimal learning materials and instructors tailored to the user's level, improving matching accuracy over time. This allows users to efficiently learn non-native languages.

[0029] A language learning support system according to an embodiment includes a leveling unit, a selection unit, a presentation unit, a feedback collection unit, and an improvement unit. The leveling unit determines a user's level. Examples of the user's level include, but are not limited to, test results, past learning history, and self-assessment. The leveling unit, for example, analyzes information entered by the user on a portal site to determine the user's level. The leveling unit can also determine the user's level using AI. For example, the leveling unit inputs the user's input information to AI, which then outputs the analysis results. The selection unit selects optimal learning materials and instructors from a database based on the level determined by the leveling unit. The database includes, for example, types of learning materials, instructor profiles, and past learning data, but is not limited to these examples. The selection unit selects optimal learning materials and instructors from the database based on the level determined by the leveling unit. The selection unit can also select optimal learning materials and instructors using AI. For example, the selection unit inputs the level determined by the leveling unit to AI, which then outputs optimal learning materials and instructors. The presentation unit presents the learning materials and instructors selected by the selection unit to the user. Examples of presentation methods include, but are not limited to, an online platform, email notification, and dashboard display. For example, the presentation unit presents the learning materials and instructors selected by the selection unit on an online platform. The presentation unit can also use AI for presentation. For example, the presentation unit inputs the learning materials and instructors selected by the selection unit into AI, which then outputs a presentation method. The feedback collection unit collects learning data from the user. Examples of learning data include, but are not limited to, study time, test results, and feedback content. The feedback collection unit collects, for example, data learned by the user. The feedback collection unit can also collect learning data using AI. For example, the feedback collection unit inputs the user's learning data into AI, which then outputs a collection method. The improvement unit analyzes the data collected by the feedback collection unit and improves matching accuracy.Matching accuracy includes, but is not limited to, for example, user satisfaction, learning outcomes, and feedback analysis results. The improvement unit, for example, analyzes data collected by the feedback collection unit to improve matching accuracy. The improvement unit can also improve matching accuracy using AI. For example, the improvement unit inputs data collected by the feedback collection unit into AI, which then outputs the analysis results. As a result, the language learning support system according to the embodiment can provide optimal learning materials and instructors tailored to the user's level and improve matching accuracy over time.

[0030] The leveling unit can analyze the user's input information and determine the user's level. The leveling unit, for example, analyzes information entered by the user into a portal site and determines the user's level. The user's input information includes, but is not limited to, questionnaire responses, test results, and self-assessments. The leveling unit can also analyze the user's input information using AI. For example, the leveling unit inputs the user's input information into AI, which then outputs the analysis results. This enables accurate level determination by analyzing the user's input information. Some or all of the above-described processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's input information into AI, which then outputs the analysis results.

[0031] The selection unit can select appropriate teaching materials and instructors from a database based on the level determined by the leveling unit. For example, the selection unit selects optimal teaching materials and instructors from the database based on the level determined by the leveling unit. The database includes, for example, but is not limited to, types of teaching materials, instructor profiles, and past learning data. The selection unit can also use AI to select optimal teaching materials and instructors. For example, the selection unit inputs the level determined by the leveling unit into AI, which then outputs optimal teaching materials and instructors. This enables effective learning by selecting optimal teaching materials and instructors based on the user's level. Some or all of the above-described processing by the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the level determined by the leveling unit into AI, which then outputs optimal teaching materials and instructors.

[0032] The presentation unit can present the teaching materials and instructors selected by the selection unit to the user. For example, the presentation unit presents the teaching materials and instructors selected by the selection unit on an online platform. Presentation methods include, but are not limited to, online platforms, email notifications, dashboard displays, and the like. The presentation unit can also use AI for presentation. For example, the presentation unit inputs the teaching materials and instructors selected by the selection unit into AI, which then outputs a presentation method. By presenting the selected teaching materials and instructors to the user, the user can select teaching materials and instructors that suit them. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs the teaching materials and instructors selected by the selection unit into AI, which then outputs a presentation method.

[0033] The feedback collection unit can collect the user's learning data. The feedback collection unit, for example, collects data on the user's learning. The learning data includes, but is not limited to, for example, study time, test results, and feedback content. The feedback collection unit can also collect the learning data using AI. For example, the feedback collection unit inputs the user's learning data into AI, which then outputs a collection method. By collecting the user's learning data, the progress and effectiveness of learning can be understood. Some or all of the above-described processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's learning data into AI, which then outputs a collection method.

[0034] The improvement unit can analyze the data collected by the feedback collection unit and improve the matching accuracy. The improvement unit, for example, analyzes the data collected by the feedback collection unit and improves the matching accuracy. Matching accuracy includes, for example, user satisfaction, learning outcomes, feedback analysis results, etc., but is not limited to these examples. The improvement unit can also improve the matching accuracy using AI. For example, the improvement unit inputs the data collected by the feedback collection unit into AI, which then outputs the analysis results. In this way, the matching accuracy can be improved by analyzing the collected data. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the data collected by the feedback collection unit into AI, which then outputs the analysis results.

[0035] The leveling unit can analyze the user's past learning history and select an appropriate level assessment method. The leveling unit can assess the user's current level, for example, based on the percentage of correct answers to questions the user has answered in the past. The leveling unit can also select the optimal level assessment method by referring to the difficulty level of learning materials the user has used in the past. Furthermore, the leveling unit can analyze the user's past learning time and select a level assessment method according to the user's learning progress. This allows for more accurate level assessment by analyzing the user's past learning history. Some or all of the above-mentioned processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's past learning data into AI, which selects the optimal level assessment method.

[0036] When determining the level, the leveling unit can perform filtering based on the user's current learning environment and learning style. For example, if the user is studying in a quiet environment, the leveling unit can prioritize providing problems that require concentration. Furthermore, if the user is studying while on the move, the leveling unit can also provide problems that can be solved in a short time. Furthermore, if the user is studying in a group, the leveling unit can also provide problems that can be solved cooperatively. In this way, filtering based on the learning environment and learning style enables more appropriate level determination. Some or all of the above-mentioned processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's learning environment data into AI, and the AI ​​performs filtering.

[0037] When determining the level, the leveling unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the leveling unit can prioritize providing learning materials related to that area. Furthermore, if the user is traveling, the leveling unit can also provide learning materials related to the language and culture of the travel destination. Furthermore, if the user belongs to a specific school or educational institution, the leveling unit can also provide learning materials based on the institution's curriculum. In this way, by taking geographical location information into consideration, more relevant information can be acquired. Some or all of the above-mentioned processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's geographical location information into AI, which then acquires highly relevant information.

[0038] The leveling unit can analyze the user's social media activity and acquire relevant information when determining the user's level. The leveling unit can, for example, determine the user's level based on the learning content shared by the user on social media. The leveling unit can also provide relevant learning materials from education-related accounts the user follows. Furthermore, the leveling unit can analyze the activity of online communities in which the user participates and provide optimal learning materials. This allows for the acquisition of more relevant information by analyzing social media activity. Some or all of the above-described processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's social media data into AI, which acquires relevant information.

[0039] When selecting learning materials and instructors, the selection unit can adjust the level of detail of the selection based on the user's learning goals. For example, if the user is aiming to learn in a short period of time, the selection unit selects learning materials and instructors that allow for intensive learning. In addition, if the user is aiming for long-term learning, the selection unit can also select learning materials and instructors that can provide continuous support. Furthermore, if the user wants to acquire a specific skill, the selection unit can also select learning materials and instructors that specialize in that skill. In this way, by adjusting the level of detail of the selection based on the learning goals, more appropriate learning materials and instructors can be selected. Some or all of the above-mentioned processing by the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the user's learning goal data into AI, and the AI ​​adjusts the level of detail of the selection.

[0040] When selecting learning materials and instructors, the selection unit can apply different selection algorithms depending on the user's learning style. For example, if the user prefers visual learning, the selection unit selects learning materials and instructors that include a lot of visual materials. Furthermore, if the user prefers auditory learning, the selection unit can also select learning materials and instructors that include a lot of audio materials. Furthermore, if the user prefers practical learning, the selection unit can also select learning materials and instructors that include a lot of practical exercises. In this way, by applying a selection algorithm depending on the learning style, more appropriate learning materials and instructors can be selected. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the user's learning style data into AI, and the AI ​​applies the selection algorithm.

[0041] When selecting learning materials and instructors, the selection unit can determine the selection priority based on the user's submission time. For example, if the user is aiming to learn in a short period of time, the selection unit can prioritize selecting learning materials and instructors that allow for intensive learning. In addition, if the user is aiming for long-term learning, the selection unit can also prioritize selecting learning materials and instructors that can provide continuous support. Furthermore, if the user is studying for a specific exam or event, the selection unit can also prioritize selecting learning materials and instructors that are suited to that period. In this way, by determining the priority based on the submission time, more appropriate learning materials and instructors can be selected. Some or all of the above-mentioned processing by the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the user's submission time data into AI, and the AI ​​determines the priority.

[0042] When selecting learning materials and instructors, the selection unit can adjust the selection order based on the user's relevance. For example, if the user is interested in a particular field, the selection unit can prioritize selecting learning materials and instructors related to that field. Furthermore, if the user wants to acquire a particular skill, the selection unit can also prioritize selecting learning materials and instructors specialized in that skill. Furthermore, if the user prefers a particular learning style, the selection unit can also prioritize selecting learning materials and instructors that match that style. By adjusting the selection order based on relevance, more appropriate learning materials and instructors can be selected. Some or all of the above-described processing by the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the user's relevance data into AI, which then adjusts the selection order.

[0043] The presentation unit can adjust the level of detail of the presentation based on the importance of the teaching materials and instructors when presenting the information. For example, the presentation unit provides detailed information for teaching materials and instructors with high importance. The presentation unit can also provide concise information for teaching materials and instructors with low importance. Furthermore, the presentation unit can also provide balanced information for teaching materials and instructors with medium importance. This allows for more appropriate information presentation by adjusting the level of detail of the presentation based on importance. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs importance data of the teaching materials and instructors into AI, which then adjusts the level of detail of the presentation.

[0044] The presentation unit can apply different presentation algorithms depending on the category of the teaching material or instructor when presenting the information. For example, in the case of language teaching materials, the presentation unit provides a visual presentation method. In addition, in the case of a practical instructor, the presentation unit can also provide a presentation method that makes extensive use of audio and video. Furthermore, in the case of theoretical teaching materials, the presentation unit can also provide a text-based presentation method. This makes it possible to present information more appropriately by applying different presentation algorithms depending on the category. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs category data of the teaching materials and instructors into AI, which then applies the presentation algorithm.

[0045] The presentation unit can determine the presentation priority based on the submission dates of the teaching materials and instructors when presenting the information. For example, the presentation unit prioritizes the presentation of teaching materials and instructors with upcoming submission deadlines. The presentation unit can also postpone the presentation of teaching materials and instructors with more distant submission deadlines. Furthermore, the presentation unit can present teaching materials and instructors with medium submission deadlines in a balanced order. This enables more appropriate information presentation by determining the priority based on the submission dates. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs data on the submission dates of the teaching materials and instructors into AI, which then determines the priority.

[0046] The presentation unit can adjust the order of presentation based on the relevance of the teaching materials and instructors when presenting the information. For example, if a user is interested in a particular field, the presentation unit can prioritize presenting teaching materials and instructors related to that field. Furthermore, if a user wants to acquire a particular skill, the presentation unit can also prioritize presenting teaching materials and instructors specialized in that skill. Furthermore, if a user prefers a particular learning style, the presentation unit can also prioritize presenting teaching materials and instructors that match that style. This allows for more appropriate information presentation by adjusting the order of presentation based on relevance. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs relevance data of teaching materials and instructors into AI, which then adjusts the order of presentation.

[0047] When collecting feedback, the feedback collection unit can analyze the user's past learning data and select an appropriate collection method. The feedback collection unit selects the optimal collection method, for example, based on the content of feedback submitted by the user in the past. The feedback collection unit can also adjust the frequency of feedback based on the user's past learning data. Furthermore, the feedback collection unit can analyze the user's past learning results and customize the content of the feedback. This makes it possible to collect more appropriate feedback by analyzing past learning data. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's past learning data into AI, and the AI ​​selects the collection method.

[0048] When collecting feedback, the feedback collection unit can customize the collection means based on the user's current learning situation. For example, if the user is concentrating on their studies, the feedback collection unit provides feedback that can be answered in a short time. Furthermore, if the user is relaxed, the feedback collection unit can also provide a questionnaire requesting detailed feedback. Furthermore, if the user is studying while on the move, the feedback collection unit can also collect feedback via voice input. This allows for more appropriate feedback collection by customizing the collection means based on the current learning situation. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's current learning situation data into AI, which then customizes the collection means.

[0049] When collecting feedback, the feedback collection unit can select an appropriate collection method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback collection unit prioritizes collecting feedback related to that area. In addition, if the user is traveling, the feedback collection unit can also collect feedback related to the language and culture of the travel destination. Furthermore, if the user belongs to a specific school or educational institution, the feedback collection unit can collect feedback based on the institution's curriculum. This makes it possible to collect more appropriate feedback by taking into account the geographical location information. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's geographical location information into AI, which then selects the collection method.

[0050] When collecting feedback, the feedback collection unit can analyze the user's social media activity and suggest collection methods. For example, the feedback collection unit customizes feedback questions based on learning content shared by the user on social media. The feedback collection unit can also collect relevant feedback from education-related accounts the user follows. Furthermore, the feedback collection unit can analyze activities in online communities in which the user participates and suggest optimal feedback collection methods. This enables more appropriate feedback collection by analyzing social media activity. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's social media data into AI, which then suggests collection methods.

[0051] When making improvements, the improvement unit can analyze the user's past learning data and select an appropriate improvement method. For example, the improvement unit may focus on improving areas in which the user was weak in the past. The improvement unit can also select an effective improvement method based on the user's past learning results. Furthermore, the improvement unit can analyze the user's past learning time and suggest an efficient improvement method. This enables more appropriate improvement by analyzing past learning data. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the user's past learning data into AI, which selects an improvement method.

[0052] During improvement, the improvement unit can customize the improvement measures based on the user's current learning situation. For example, if the user is concentrating on studying, the improvement unit provides a short and effective improvement method. The improvement unit can also provide a detailed improvement method if the user is relaxed. Furthermore, the improvement unit can also provide an easy-to-implement improvement method if the user is studying while on the move. This enables more appropriate improvement by customizing the improvement measures based on the current learning situation. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the user's current learning situation data into AI, which then customizes the improvement measures.

[0053] When making an improvement, the improvement unit can select an appropriate improvement method by taking into account the user's geographical location information. For example, if the user is in a specific region, the improvement unit can provide an improvement method related to that region. Also, if the user is traveling, the improvement unit can provide an improvement method related to the language and culture of the destination. Furthermore, if the user belongs to a specific school or educational institution, the improvement unit can provide an improvement method based on the institution's curriculum. This enables more appropriate improvement by taking geographical location information into consideration. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the user's geographical location information into AI, which then selects an improvement method.

[0054] During improvement, the improvement unit can analyze the user's social media activity and suggest improvement measures. For example, the improvement unit can suggest optimal improvement measures based on the learning content shared by the user on social media. The improvement unit can also provide relevant improvement measures from education-related accounts the user follows. Furthermore, the improvement unit can analyze the activities of online communities in which the user participates and suggest optimal improvement measures. This enables more appropriate improvement by analyzing social media activity. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the user's social media data into AI, which then suggests improvement measures.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The language learning support system may further include a "motivation unit." The motivation unit provides a function to increase the user's motivation to learn. For example, the motivation unit may visualize the progress toward the goals set by the user and display the achievement level in graphs or charts. The motivation unit may also incorporate gamification elements by awarding badges or points when the user achieves a certain study time. Furthermore, the motivation unit may provide encouraging messages to encourage the user to continue studying, or an element of competition with other users. This makes it easier for the user to maintain their motivation to learn, allowing them to acquire the language more effectively.

[0057] The leveling unit can analyze the user's learning style and select the optimal level assessment method. For example, if the user prefers visual learning, it can provide a test that makes extensive use of images and videos. If the user prefers auditory learning, it can provide a test that uses audio. Furthermore, if the user prefers practical learning, it can provide a test that uses actual conversations or simulations. This makes it possible to assess the user's level in accordance with their learning style, resulting in more accurate level assessment.

[0058] The selection unit can analyze the user's learning history and prioritize the selection of teaching materials and instructors that have been effective in the past. For example, if a user has achieved good learning results with specific teaching materials in the past, the unit can select those teaching materials again. Also, if a user has shown high satisfaction with learning with a specific instructor, the unit can prioritize the selection of that instructor. Furthermore, the unit can select complementary teaching materials and instructors for areas in which the user has previously struggled. This allows for more effective learning based on the user's past learning history.

[0059] The presentation unit can propose a study plan based on the user's learning goals. For example, if the user is aiming to acquire knowledge in a short period of time, the presentation unit can propose a schedule that allows for intensive study. If the user is aiming for long-term learning, the presentation unit can also propose a continuous study plan. Furthermore, if the user wants to acquire a specific skill, the presentation unit can also propose a study plan specialized for that skill. This provides an effective study plan that matches the user's learning goals.

[0060] The feedback collection unit can collect the user's learning data in real time and provide instant feedback. For example, it can provide test results and explanations immediately after the user finishes a test. It can also provide instant answers if the user has questions while studying. Furthermore, it can provide real-time feedback on the user's progress and areas for improvement as the user continues studying. This allows the user to immediately check the effectiveness of their learning, making it easier to maintain motivation to study.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The leveling unit determines the user's level. The user's level includes, for example, test results, past learning history, and self-evaluation. The leveling unit can also analyze the information the user enters into the portal site and use AI to determine the user's level. Step 2: The selection unit selects the most suitable teaching materials and instructors from a database based on the level determined by the leveling unit. The database includes information such as the type of teaching materials, instructor profiles, and past learning data. The selection unit can also use AI to select the most suitable teaching materials and instructors. Step 3: The presentation unit presents the learning materials and instructors selected by the selection unit to the user. Methods of presentation include online platforms, email notifications, dashboard displays, etc. The presentation unit can also use AI to present the learning materials and instructors. Step 4: The feedback collection unit collects the user's learning data. The learning data includes study time, test results, feedback content, etc. The feedback collection unit can also collect learning data using AI. Step 5: The improvement unit analyzes the data collected by the feedback collection unit and improves the matching accuracy. Matching accuracy includes user satisfaction, learning outcomes, and feedback analysis results. The improvement unit can also use AI to improve the matching accuracy.

[0063] (Example 2) A language learning support system according to an embodiment of the present invention is a system that uses AI to assign optimal learning materials and instructors tailored to a user's level when learning a non-native language. The system begins when a user accesses a portal site and inputs their current level and goals for learning the non-native language. Next, the AI ​​analyzes the user's input information and determines the user's level. Based on this determination, the AI ​​selects optimal learning materials and instructors from a database. The selected learning materials and instructors are presented to the user, allowing the user to select the materials and instructors that best suit them. Furthermore, as the user progresses with their learning using the selected learning materials and instructors, their learning data is fed back to the AI, which analyzes this feedback data and improves matching accuracy. In this way, the portal site provides optimal learning materials and instructors tailored to the user's level, thereby improving matching accuracy over time. This allows the user to efficiently learn a non-native language. The language learning support system thus provides optimal learning materials and instructors tailored to the user's level, thereby improving matching accuracy over time.

[0064] A language learning support system according to an embodiment includes a leveling unit, a selection unit, a presentation unit, a feedback collection unit, and an improvement unit. The leveling unit determines a user's level. Examples of the user's level include, but are not limited to, test results, past learning history, and self-assessment. The leveling unit, for example, analyzes information entered by the user on a portal site to determine the user's level. The leveling unit can also determine the user's level using AI. For example, the leveling unit inputs the user's input information to AI, which then outputs the analysis results. The selection unit selects optimal learning materials and instructors from a database based on the level determined by the leveling unit. The database includes, for example, types of learning materials, instructor profiles, and past learning data, but is not limited to these examples. The selection unit selects optimal learning materials and instructors from the database based on the level determined by the leveling unit. The selection unit can also select optimal learning materials and instructors using AI. For example, the selection unit inputs the level determined by the leveling unit to AI, which then outputs optimal learning materials and instructors. The presentation unit presents the learning materials and instructors selected by the selection unit to the user. Examples of presentation methods include, but are not limited to, an online platform, email notification, and dashboard display. For example, the presentation unit presents the learning materials and instructors selected by the selection unit on an online platform. The presentation unit can also use AI for presentation. For example, the presentation unit inputs the learning materials and instructors selected by the selection unit into AI, which then outputs a presentation method. The feedback collection unit collects learning data from the user. Examples of learning data include, but are not limited to, study time, test results, and feedback content. The feedback collection unit collects, for example, data learned by the user. The feedback collection unit can also collect learning data using AI. For example, the feedback collection unit inputs the user's learning data into AI, which then outputs a collection method. The improvement unit analyzes the data collected by the feedback collection unit and improves matching accuracy.Matching accuracy includes, but is not limited to, for example, user satisfaction, learning outcomes, and feedback analysis results. The improvement unit, for example, analyzes data collected by the feedback collection unit to improve matching accuracy. The improvement unit can also improve matching accuracy using AI. For example, the improvement unit inputs data collected by the feedback collection unit into AI, which then outputs the analysis results. As a result, the language learning support system according to the embodiment can provide optimal learning materials and instructors tailored to the user's level and improve matching accuracy over time.

[0065] The leveling unit can analyze the user's input information and determine the user's level. The leveling unit, for example, analyzes information entered by the user into a portal site and determines the user's level. The user's input information includes, but is not limited to, questionnaire responses, test results, and self-assessments. The leveling unit can also analyze the user's input information using AI. For example, the leveling unit inputs the user's input information into AI, which then outputs the analysis results. This enables accurate level determination by analyzing the user's input information. Some or all of the above-described processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's input information into AI, which then outputs the analysis results.

[0066] The selection unit can select appropriate teaching materials and instructors from a database based on the level determined by the leveling unit. For example, the selection unit selects optimal teaching materials and instructors from the database based on the level determined by the leveling unit. The database includes, for example, but is not limited to, types of teaching materials, instructor profiles, and past learning data. The selection unit can also use AI to select optimal teaching materials and instructors. For example, the selection unit inputs the level determined by the leveling unit into AI, which then outputs optimal teaching materials and instructors. This enables effective learning by selecting optimal teaching materials and instructors based on the user's level. Some or all of the above-described processing by the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the level determined by the leveling unit into AI, which then outputs optimal teaching materials and instructors.

[0067] The presentation unit can present the teaching materials and instructors selected by the selection unit to the user. For example, the presentation unit presents the teaching materials and instructors selected by the selection unit on an online platform. Presentation methods include, but are not limited to, online platforms, email notifications, dashboard displays, and the like. The presentation unit can also use AI for presentation. For example, the presentation unit inputs the teaching materials and instructors selected by the selection unit into AI, which then outputs a presentation method. By presenting the selected teaching materials and instructors to the user, the user can select teaching materials and instructors that suit them. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs the teaching materials and instructors selected by the selection unit into AI, which then outputs a presentation method.

[0068] The feedback collection unit can collect the user's learning data. The feedback collection unit, for example, collects data on the user's learning. The learning data includes, but is not limited to, for example, study time, test results, and feedback content. The feedback collection unit can also collect the learning data using AI. For example, the feedback collection unit inputs the user's learning data into AI, which then outputs a collection method. By collecting the user's learning data, the progress and effectiveness of learning can be understood. Some or all of the above-described processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's learning data into AI, which then outputs a collection method.

[0069] The improvement unit can analyze the data collected by the feedback collection unit and improve the matching accuracy. The improvement unit, for example, analyzes the data collected by the feedback collection unit and improves the matching accuracy. Matching accuracy includes, for example, user satisfaction, learning outcomes, feedback analysis results, etc., but is not limited to these examples. The improvement unit can also improve the matching accuracy using AI. For example, the improvement unit inputs the data collected by the feedback collection unit into AI, which then outputs the analysis results. In this way, the matching accuracy can be improved by analyzing the collected data. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the data collected by the feedback collection unit into AI, which then outputs the analysis results.

[0070] The leveling unit can estimate the user's emotions and adjust the level determination criteria based on the estimated user emotions. For example, if the user is feeling stressed, the leveling unit can relax the level determination criteria and adjust them to start with easy problems. Furthermore, if the user is relaxed, the leveling unit can also tighten the level determination criteria and adjust them to include more challenging problems. Furthermore, if the user is excited, the leveling unit can set the level determination criteria to a moderate level and provide balanced problems. This allows for more appropriate level determination by adjusting the level determination criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the leveling unit can be performed using AI, or without AI. For example, the leveling unit inputs the user's emotion data into AI, which then adjusts the level determination criteria.

[0071] The leveling unit can analyze the user's past learning history and select an appropriate level assessment method. The leveling unit can assess the user's current level, for example, based on the percentage of correct answers to questions the user has answered in the past. The leveling unit can also select the optimal level assessment method by referring to the difficulty level of learning materials the user has used in the past. Furthermore, the leveling unit can analyze the user's past learning time and select a level assessment method according to the user's learning progress. This allows for more accurate level assessment by analyzing the user's past learning history. Some or all of the above-mentioned processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's past learning data into AI, which selects the optimal level assessment method.

[0072] When determining the level, the leveling unit can perform filtering based on the user's current learning environment and learning style. For example, if the user is studying in a quiet environment, the leveling unit can prioritize providing problems that require concentration. Furthermore, if the user is studying while on the move, the leveling unit can also provide problems that can be solved in a short time. Furthermore, if the user is studying in a group, the leveling unit can also provide problems that can be solved cooperatively. In this way, filtering based on the learning environment and learning style enables more appropriate level determination. Some or all of the above-mentioned processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's learning environment data into AI, and the AI ​​performs filtering.

[0073] The leveling unit can estimate the user's emotions and determine the priority of level determination based on the estimated user emotions. For example, if the user is feeling stressed, the leveling unit can prioritize providing easy problems. Furthermore, if the user is relaxed, the leveling unit can prioritize providing difficult problems. Furthermore, if the user is excited, the leveling unit can prioritize providing balanced problems. This enables more appropriate level determination by determining the priority of level determination based on the user's emotions. Emotion estimation is realized 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. Some or all of the above-mentioned processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's emotion data into AI, and the AI ​​determines the priority of level determination.

[0074] When determining the level, the leveling unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the leveling unit can prioritize providing learning materials related to that area. Furthermore, if the user is traveling, the leveling unit can also provide learning materials related to the language and culture of the travel destination. Furthermore, if the user belongs to a specific school or educational institution, the leveling unit can also provide learning materials based on the institution's curriculum. In this way, by taking geographical location information into consideration, more relevant information can be acquired. Some or all of the above-mentioned processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's geographical location information into AI, which then acquires highly relevant information.

[0075] The leveling unit can analyze the user's social media activity and acquire relevant information when determining the user's level. The leveling unit can, for example, determine the user's level based on the learning content shared by the user on social media. The leveling unit can also provide relevant learning materials from education-related accounts the user follows. Furthermore, the leveling unit can analyze the activity of online communities in which the user participates and provide optimal learning materials. This allows for the acquisition of more relevant information by analyzing social media activity. Some or all of the above-described processing in the leveling unit may be performed using AI, or may be performed without using AI. For example, the leveling unit inputs the user's social media data into AI, which acquires relevant information.

[0076] The selection unit can estimate the user's emotions and adjust the selection criteria for learning materials and instructors based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit selects relaxing learning materials and instructors. Furthermore, if the user is relaxed, the selection unit can select challenging learning materials and instructors. Furthermore, if the user is excited, the selection unit can select engaging learning materials and instructors. By adjusting the selection criteria based on the user's emotions, more appropriate learning materials and instructors can be selected. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without AI. For example, the selection unit inputs the user's emotion data into AI, which then adjusts the selection criteria.

[0077] When selecting learning materials and instructors, the selection unit can adjust the level of detail of the selection based on the user's learning goals. For example, if the user is aiming to learn in a short period of time, the selection unit selects learning materials and instructors that allow for intensive learning. In addition, if the user is aiming for long-term learning, the selection unit can also select learning materials and instructors that can provide continuous support. Furthermore, if the user wants to acquire a specific skill, the selection unit can also select learning materials and instructors that specialize in that skill. In this way, by adjusting the level of detail of the selection based on the learning goals, more appropriate learning materials and instructors can be selected. Some or all of the above-mentioned processing by the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the user's learning goal data into AI, and the AI ​​adjusts the level of detail of the selection.

[0078] When selecting learning materials and instructors, the selection unit can apply different selection algorithms depending on the user's learning style. For example, if the user prefers visual learning, the selection unit selects learning materials and instructors that include a lot of visual materials. Furthermore, if the user prefers auditory learning, the selection unit can also select learning materials and instructors that include a lot of audio materials. Furthermore, if the user prefers practical learning, the selection unit can also select learning materials and instructors that include a lot of practical exercises. In this way, by applying a selection algorithm depending on the learning style, more appropriate learning materials and instructors can be selected. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the user's learning style data into AI, and the AI ​​applies the selection algorithm.

[0079] The selection unit can estimate the user's emotions and determine the priority of the learning materials and instructors to be selected based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can prioritize selecting relaxing learning materials and instructors. Furthermore, if the user is relaxed, the selection unit can also prioritize selecting challenging learning materials and instructors. Furthermore, if the user is excited, the selection unit can also prioritize selecting interesting learning materials and instructors. Thus, by determining the priority based on the user's emotions, more appropriate learning materials and instructors can be selected. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using AI, or may be performed without AI. For example, the selection unit inputs the user's emotion data into AI, which then determines the priority.

[0080] When selecting learning materials and instructors, the selection unit can determine the selection priority based on the user's submission time. For example, if the user is aiming to learn in a short period of time, the selection unit can prioritize selecting learning materials and instructors that allow for intensive learning. In addition, if the user is aiming for long-term learning, the selection unit can also prioritize selecting learning materials and instructors that can provide continuous support. Furthermore, if the user is studying for a specific exam or event, the selection unit can also prioritize selecting learning materials and instructors that are suited to that period. In this way, by determining the priority based on the submission time, more appropriate learning materials and instructors can be selected. Some or all of the above-mentioned processing by the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the user's submission time data into AI, and the AI ​​determines the priority.

[0081] When selecting learning materials and instructors, the selection unit can adjust the selection order based on the user's relevance. For example, if the user is interested in a particular field, the selection unit can prioritize selecting learning materials and instructors related to that field. Furthermore, if the user wants to acquire a particular skill, the selection unit can also prioritize selecting learning materials and instructors specialized in that skill. Furthermore, if the user prefers a particular learning style, the selection unit can also prioritize selecting learning materials and instructors that match that style. By adjusting the selection order based on relevance, more appropriate learning materials and instructors can be selected. Some or all of the above-described processing by the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit inputs the user's relevance data into AI, which then adjusts the selection order.

[0082] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated user's emotions. For example, if the user is feeling stressed, the presentation unit can provide a simple, highly visible presentation method. Furthermore, if the user is relaxed, the presentation unit can provide a presentation method that includes detailed information. Furthermore, if the user is excited, the presentation unit can provide a visually stimulating presentation method. This enables more appropriate information presentation by adjusting the presentation method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without AI. For example, the presentation unit inputs the user's emotion data into AI, which then adjusts the presentation method.

[0083] The presentation unit can adjust the level of detail of the presentation based on the importance of the teaching materials and instructors when presenting the information. For example, the presentation unit provides detailed information for teaching materials and instructors with high importance. The presentation unit can also provide concise information for teaching materials and instructors with low importance. Furthermore, the presentation unit can also provide balanced information for teaching materials and instructors with medium importance. This allows for more appropriate information presentation by adjusting the level of detail of the presentation based on importance. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs importance data of the teaching materials and instructors into AI, which then adjusts the level of detail of the presentation.

[0084] The presentation unit can apply different presentation algorithms depending on the category of the teaching material or instructor when presenting the information. For example, in the case of language teaching materials, the presentation unit provides a visual presentation method. In addition, in the case of a practical instructor, the presentation unit can also provide a presentation method that makes extensive use of audio and video. Furthermore, in the case of theoretical teaching materials, the presentation unit can also provide a text-based presentation method. This makes it possible to present information more appropriately by applying different presentation algorithms depending on the category. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs category data of the teaching materials and instructors into AI, which then applies the presentation algorithm.

[0085] The presentation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated user emotions. For example, if the user is feeling stressed, the presentation unit can provide a short, to-the-point presentation. Furthermore, if the user is relaxed, the presentation unit can provide a longer presentation with detailed explanations. Furthermore, if the user is excited, the presentation unit can provide a visually stimulating presentation. This allows for more appropriate information presentation by adjusting the length of the presentation based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using AI, or without AI. For example, the presentation unit inputs the user's emotion data into AI, which then adjusts the length of the presentation.

[0086] The presentation unit can determine the presentation priority based on the submission dates of the teaching materials and instructors when presenting the information. For example, the presentation unit prioritizes the presentation of teaching materials and instructors with upcoming submission deadlines. The presentation unit can also postpone the presentation of teaching materials and instructors with more distant submission deadlines. Furthermore, the presentation unit can present teaching materials and instructors with medium submission deadlines in a balanced order. This enables more appropriate information presentation by determining the priority based on the submission dates. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs data on the submission dates of the teaching materials and instructors into AI, which then determines the priority.

[0087] The presentation unit can adjust the order of presentation based on the relevance of the teaching materials and instructors when presenting the information. For example, if a user is interested in a particular field, the presentation unit can prioritize presenting teaching materials and instructors related to that field. Furthermore, if a user wants to acquire a particular skill, the presentation unit can also prioritize presenting teaching materials and instructors specialized in that skill. Furthermore, if a user prefers a particular learning style, the presentation unit can also prioritize presenting teaching materials and instructors that match that style. This allows for more appropriate information presentation by adjusting the order of presentation based on relevance. Some or all of the above-described processing in the presentation unit may be performed using AI, or may be performed without using AI. For example, the presentation unit inputs relevance data of teaching materials and instructors into AI, which then adjusts the order of presentation.

[0088] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, if the user is feeling stressed, the feedback collection unit collects feedback in the form of a simple questionnaire. Furthermore, if the user is relaxed, the feedback collection unit can also provide a questionnaire requesting detailed feedback. Furthermore, if the user is excited, the feedback collection unit can also collect feedback in the form of free description. This allows for more appropriate feedback collection by adjusting the feedback collection method based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback collection unit may be performed using AI, or may be performed without AI. For example, the feedback collection unit inputs the user's emotion data into AI, which then adjusts the collection method.

[0089] When collecting feedback, the feedback collection unit can analyze the user's past learning data and select an appropriate collection method. The feedback collection unit selects the optimal collection method, for example, based on the content of feedback submitted by the user in the past. The feedback collection unit can also adjust the frequency of feedback based on the user's past learning data. Furthermore, the feedback collection unit can analyze the user's past learning results and customize the content of the feedback. This makes it possible to collect more appropriate feedback by analyzing past learning data. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's past learning data into AI, and the AI ​​selects the collection method.

[0090] When collecting feedback, the feedback collection unit can customize the collection means based on the user's current learning situation. For example, if the user is concentrating on their studies, the feedback collection unit provides feedback that can be answered in a short time. Furthermore, if the user is relaxed, the feedback collection unit can also provide a questionnaire requesting detailed feedback. Furthermore, if the user is studying while on the move, the feedback collection unit can also collect feedback via voice input. This allows for more appropriate feedback collection by customizing the collection means based on the current learning situation. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's current learning situation data into AI, which then customizes the collection means.

[0091] The feedback collection unit can estimate the user's emotions and determine the priority of feedback collection based on the estimated user's emotions. For example, when the user is stressed, the feedback collection unit can prioritize collecting simple feedback. Furthermore, when the user is relaxed, the feedback collection unit can prioritize collecting detailed feedback. Furthermore, when the user is excited, the feedback collection unit can prioritize collecting free-form feedback. This enables more appropriate feedback collection by determining the priority of feedback collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's emotion data into AI, and the AI ​​determines the priority.

[0092] When collecting feedback, the feedback collection unit can select an appropriate collection method by taking into account the user's geographical location information. For example, if the user is in a specific area, the feedback collection unit prioritizes collecting feedback related to that area. In addition, if the user is traveling, the feedback collection unit can also collect feedback related to the language and culture of the travel destination. Furthermore, if the user belongs to a specific school or educational institution, the feedback collection unit can collect feedback based on the institution's curriculum. This makes it possible to collect more appropriate feedback by taking into account the geographical location information. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's geographical location information into AI, which then selects the collection method.

[0093] When collecting feedback, the feedback collection unit can analyze the user's social media activity and suggest collection methods. For example, the feedback collection unit customizes feedback questions based on learning content shared by the user on social media. The feedback collection unit can also collect relevant feedback from education-related accounts the user follows. Furthermore, the feedback collection unit can analyze activities in online communities in which the user participates and suggest optimal feedback collection methods. This enables more appropriate feedback collection by analyzing social media activity. Some or all of the above-mentioned processing in the feedback collection unit may be performed using AI, or may be performed without using AI. For example, the feedback collection unit inputs the user's social media data into AI, which then suggests collection methods.

[0094] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user emotions. For example, if the user is feeling stressed, the improvement unit can simplify the learning content and provide a relaxing environment. Furthermore, if the user is relaxed, the improvement unit can provide challenging learning content to enhance the learning effect. Furthermore, if the user is excited, the improvement unit can provide interesting learning content to maintain the user's motivation to learn. This enables more appropriate improvement by adjusting the improvement method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the improvement unit may be performed using AI, or may be performed without AI. For example, the improvement unit inputs the user's emotion data into AI, which then adjusts the improvement method.

[0095] When making improvements, the improvement unit can analyze the user's past learning data and select an appropriate improvement method. For example, the improvement unit may focus on improving areas in which the user was weak in the past. The improvement unit can also select an effective improvement method based on the user's past learning results. Furthermore, the improvement unit can analyze the user's past learning time and suggest an efficient improvement method. This enables more appropriate improvement by analyzing past learning data. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the user's past learning data into AI, which selects an improvement method.

[0096] During improvement, the improvement unit can customize the improvement measures based on the user's current learning situation. For example, if the user is concentrating on studying, the improvement unit provides a short and effective improvement method. The improvement unit can also provide a detailed improvement method if the user is relaxed. Furthermore, the improvement unit can also provide an easy-to-implement improvement method if the user is studying while on the move. This enables more appropriate improvement by customizing the improvement measures based on the current learning situation. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the user's current learning situation data into AI, which then customizes the improvement measures.

[0097] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated user emotions. For example, if the user is feeling stressed, the improvement unit can prioritize providing improvement methods that help the user relax. Furthermore, if the user is relaxed, the improvement unit can prioritize providing challenging improvement methods. Furthermore, if the user is excited, the improvement unit can prioritize providing interesting improvement methods. This enables more appropriate improvements by determining the priority of improvements based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the improvement unit can be performed using AI, or can be performed without AI. For example, the improvement unit inputs the user's emotion data into AI, which then determines the priority.

[0098] When making an improvement, the improvement unit can select an appropriate improvement method by taking into account the user's geographical location information. For example, if the user is in a specific region, the improvement unit can provide an improvement method related to that region. Also, if the user is traveling, the improvement unit can provide an improvement method related to the language and culture of the destination. Furthermore, if the user belongs to a specific school or educational institution, the improvement unit can provide an improvement method based on the institution's curriculum. This enables more appropriate improvement by taking geographical location information into consideration. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the user's geographical location information into AI, which then selects an improvement method.

[0099] During improvement, the improvement unit can analyze the user's social media activity and suggest improvement measures. For example, the improvement unit can suggest optimal improvement measures based on the learning content shared by the user on social media. The improvement unit can also provide relevant improvement measures from education-related accounts the user follows. Furthermore, the improvement unit can analyze the activities of online communities in which the user participates and suggest optimal improvement measures. This enables more appropriate improvement by analyzing social media activity. Some or all of the above-mentioned processing in the improvement unit may be performed using AI, or may be performed without using AI. For example, the improvement unit inputs the user's social media data into AI, which then suggests improvement measures. === Hard Collateral 1-1 === Each of the multiple elements, including the leveling unit, selection unit, presentation unit, feedback collection unit, and improvement unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the leveling unit is realized by the control unit 46A of the smart device 14 and analyzes input information from the user to determine the level. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects optimal learning materials and instructors from the database 24. The presentation unit is realized, for example, by the output device 40 of the smart device 14 and presents the selected learning materials and instructors to the user. The feedback collection unit collects learning data using, for example, the camera 42 and microphone 38B of the smart device 14. The improvement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to improve matching accuracy. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned leveling unit, selection unit, presentation unit, feedback collection unit, and improvement unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the leveling unit is realized by the control unit 46A of the smart glasses 214 and analyzes user input information to determine the level. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects optimal teaching materials and instructors from the database 24. The presentation unit is realized, for example, by the speaker 240 of the smart glasses 214 and presents the selected teaching materials and instructors to the user. The feedback collection unit collects learning data using, for example, the camera 42 and microphone 238 of the smart glasses 214. The improvement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to improve matching accuracy. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned leveling unit, selection unit, presentation unit, feedback collection unit, and improvement unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the leveling unit is realized by the control unit 46A of the headset-type terminal 314 and analyzes input information from the user to determine the level. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects optimal learning materials and instructors from the database 24. The presentation unit is realized, for example, by the display 343 of the headset-type terminal 314 and presents the selected learning materials and instructors to the user. The feedback collection unit collects learning data using, for example, the camera 42 and microphone 238 of the headset-type terminal 314. The improvement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to improve matching accuracy. === Hard Collateral 1-4 === Each of the multiple elements including the leveling unit, selection unit, presentation unit, feedback collection unit, and improvement unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the leveling unit is realized by the control unit 46A of the robot 414 and analyzes input information from the user to determine the level. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects optimal teaching materials and instructors from the database 24. The presentation unit is realized, for example, by the speaker 240 of the robot 414 and presents the selected teaching materials and instructors to the user. The feedback collection unit collects learning data using, for example, the camera 42 and microphone 238 of the robot 414. The improvement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to improve matching accuracy.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The language learning support system may further include a "motivation unit." The motivation unit provides a function to increase the user's motivation to learn. For example, the motivation unit may visualize the progress toward the goals set by the user and display the achievement level in graphs or charts. The motivation unit may also incorporate gamification elements by awarding badges or points when the user achieves a certain study time. Furthermore, the motivation unit may provide encouraging messages to encourage the user to continue studying, or an element of competition with other users. This makes it easier for the user to maintain their motivation to learn, allowing them to acquire the language more effectively.

[0102] The leveling unit can analyze the user's learning style and select the optimal level assessment method. For example, if the user prefers visual learning, it can provide a test that makes extensive use of images and videos. If the user prefers auditory learning, it can provide a test that uses audio. Furthermore, if the user prefers practical learning, it can provide a test that uses actual conversations or simulations. This makes it possible to assess the user's level in accordance with their learning style, resulting in more accurate level assessment.

[0103] The selection unit can analyze the user's learning history and prioritize the selection of teaching materials and instructors that have been effective in the past. For example, if a user has achieved good learning results with specific teaching materials in the past, the unit can select those teaching materials again. Also, if a user has shown high satisfaction with learning with a specific instructor, the unit can prioritize the selection of that instructor. Furthermore, the unit can select complementary teaching materials and instructors for areas in which the user has previously struggled. This allows for more effective learning based on the user's past learning history.

[0104] The presentation unit can propose a study plan based on the user's learning goals. For example, if the user is aiming to acquire knowledge in a short period of time, the presentation unit can propose a schedule that allows for intensive study. If the user is aiming for long-term learning, the presentation unit can also propose a continuous study plan. Furthermore, if the user wants to acquire a specific skill, the presentation unit can also propose a study plan specialized for that skill. This provides an effective study plan that matches the user's learning goals.

[0105] The feedback collection unit can collect the user's learning data in real time and provide instant feedback. For example, it can provide test results and explanations immediately after the user finishes a test. It can also provide instant answers if the user has questions while studying. Furthermore, it can provide real-time feedback on the user's progress and areas for improvement as the user continues studying. This allows the user to immediately check the effectiveness of their learning, making it easier to maintain motivation to study.

[0106] The improvement unit can estimate the user's emotions and adjust the learning content based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide relaxing content and easy questions. If the user is relaxed, it can provide challenging content and high-level questions. Furthermore, if the user is excited, it can provide interesting content and balanced questions. This provides learning content that matches the user's emotions, improving the learning effect.

[0107] The leveling unit can estimate the user's emotions and adjust the level determination criteria based on the estimated user's emotions. For example, if the user is feeling stressed, the level determination criteria can be relaxed and adjusted to start with easy problems. Also, if the user is relaxed, the level determination criteria can be tightened and adjusted to include more challenging problems. Furthermore, if the user is excited, the level determination criteria can be set to a medium level and balanced problems can be provided. In this way, adjusting the level determination criteria based on the user's emotions enables more appropriate level determination.

[0108] The selection unit can estimate the user's emotions and adjust the selection criteria for learning materials and instructors based on the estimated user's emotions. For example, if the user is feeling stressed, the selection unit can select learning materials and instructors that are relaxing. If the user is relaxed, the selection unit can select challenging learning materials and instructors. Furthermore, if the user is excited, the selection unit can select interesting learning materials and instructors. In this way, by adjusting the selection criteria based on the user's emotions, more appropriate learning materials and instructors can be selected.

[0109] The presentation unit can estimate the user's emotions and adjust the presentation method based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible presentation method can be provided. If the user is relaxed, a presentation method including detailed information can be provided. Furthermore, if the user is excited, a visually stimulating presentation method can be provided. In this way, by adjusting the presentation method based on the user's emotions, more appropriate information presentation is possible.

[0110] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, if the user is feeling stressed, feedback can be collected in the form of a simple questionnaire. Alternatively, if the user is relaxed, a questionnaire requesting detailed feedback can be provided. Furthermore, if the user is excited, feedback can be collected in the form of free description. In this way, by adjusting the feedback collection method based on the user's emotions, more appropriate feedback collection is possible.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The leveling unit determines the user's level. The user's level includes, for example, test results, past learning history, and self-evaluation. The leveling unit can also analyze the information the user enters into the portal site and use AI to determine the user's level. Step 2: The selection unit selects the most suitable teaching materials and instructors from a database based on the level determined by the leveling unit. The database includes information such as the type of teaching materials, instructor profiles, and past learning data. The selection unit can also use AI to select the most suitable teaching materials and instructors. Step 3: The presentation unit presents the learning materials and instructors selected by the selection unit to the user. Methods of presentation include online platforms, email notifications, dashboard displays, etc. The presentation unit can also use AI to present the learning materials and instructors. Step 4: The feedback collection unit collects the user's learning data. The learning data includes study time, test results, feedback content, etc. The feedback collection unit can also collect learning data using AI. Step 5: The improvement unit analyzes the data collected by the feedback collection unit and improves the matching accuracy. Matching accuracy includes user satisfaction, learning outcomes, and feedback analysis results. The improvement unit can also use AI to improve the matching accuracy.

[0113] 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.

[0114] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 leveling unit for determining the level of a user; a selection unit that selects appropriate teaching materials and instructors based on the level determined by the leveling unit; a presentation unit that presents the teaching materials and lecturers selected by the selection unit to the user; a feedback collection unit that collects user learning data; an improvement unit that analyzes the data collected by the feedback collection unit and improves matching accuracy. A system characterized by:

2. The leveling unit includes: Analyze the user's input information and determine the user's level 2. The system of claim 1.

3. The selection unit Based on the level determined by the leveling unit, appropriate teaching materials and instructors are selected from a database.

2. The system of claim 1.

4. The presentation unit Presenting the teaching materials and lecturers selected by the selection unit to the user 2. The system of claim 1.

5. The feedback collection unit: Collect user learning data 2. The system of claim 1.

6. The improvement unit Analyzing the data collected by the feedback collection unit to improve matching accuracy 2. The system of claim 1.

7. The leveling unit includes: Estimate the user's emotions and adjust the criteria for determining the level based on the estimated user emotions.

2. The system of claim 1.

8. The leveling unit includes: Analyze the user's past learning history and select the appropriate level assessment method 2. The system of claim 1.

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