system

Abstract

The system according to the embodiment aims to centrally collect lesson content and knowledge from educators and utilize them effectively. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a classification unit, and a recommendation unit. The collection unit collects lesson content and wisdom from educators. The analysis unit analyzes the content collected by the collection unit. The classification unit classifies the content analyzed by the analysis unit into predetermined categories. The recommendation unit recommends predetermined content based on the content classified by the classification unit.

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 technology does not adequately centralize and effectively utilize lesson content and knowledge from educators, and there is room for improvement.

[0005] The system according to the embodiment aims to centrally collect lesson content and knowledge from educators and utilize them effectively. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a classification unit, and a recommendation unit. The collection unit collects lesson content and wisdom from educators. The analysis unit analyzes the content collected by the collection unit. The classification unit classifies the content analyzed by the analysis unit into predetermined categories. The recommendation unit recommends predetermined content based on the content classified by the classification unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally collect lesson content and knowledge from educators and utilize them effectively. [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) An educational content provision system according to an embodiment of the present invention centrally collects lesson content and wisdom from educators and provides new learning experiences and experiences to children and adults around the world through AI. The educational content provision system collects lesson content and wisdom from educators, analyzes them using AI, classifies them into appropriate categories, and recommends optimal content based on users' interests and learning needs. For example, the educational content provision system collects lesson content and wisdom from each educator. For example, educators describe their fields of expertise and lesson content in detail and provide them in digital format. Next, the educational content provision system analyzes the collected content and classifies it into appropriate categories using AI. For example, the AI ​​analyzes the content of each piece of content and categorizes it into categories such as arithmetic, physics, and history. Furthermore, when a user inputs their field of interest or the content they want to learn, the AI ​​recommends optimal content. For example, if a user inputs "I want to learn history," the AI ​​recommends optimal lesson content from the "history" category. This allows the educational content provision system to provide new learning experiences and experiences to children and adults around the world. This allows the educational content provision system to efficiently collect, analyze, categorize, and recommend lesson content and wisdom from educators. For example, Japanese elementary school students can take a lecture from an American university professor, or French high school students can experience a class at a Japanese elementary school. This provides more creative and equal learning opportunities, allowing people around the world to acquire new knowledge and skills.

[0029] An educational content provision system according to an embodiment includes a collection unit, an analysis unit, a classification unit, and a recommendation unit. The collection unit collects lesson content and knowledge from educators. The lesson content and knowledge from educators include, but are not limited to, video lectures, text materials, and interactive quizzes. The collection unit collects content from educators, for example, through an online form. The collection unit can also acquire data via an API. The collection unit can also collect content through manual input. For example, the collection unit collects video lectures provided by educators through an online form. The collection unit can also automatically acquire text materials from educators using an API. The collection unit can also collect interactive quizzes manually entered by educators. The analysis unit analyzes the content collected by the collection unit. The analysis can be performed using, for example, text mining, natural language processing, data mining, or other methods, but is not limited to these. For example, the analysis unit analyzes the collected text materials using text mining technology. The analysis unit can also analyze the content of video lectures using natural language processing technology. The analysis unit can also analyze interactive quiz data using data mining technology. The classification unit classifies the content analyzed by the analysis unit into predetermined categories. The categories are classified based on criteria such as, but not limited to, subject, difficulty level, and target age. For example, the classification unit classifies the analyzed content by subject. The classification unit can also classify content by difficulty level. The classification unit can also classify content by target age. The recommendation unit recommends predetermined content based on the content classified by the classification unit. The recommendation is performed using a recommendation algorithm based on, for example, the user's past behavioral history, interests, learning progress, etc., but is not limited to, for example. The recommendation unit recommends optimal content based on the user's past behavioral history. The recommendation unit can also recommend content based on the user's interests. The recommendation unit can also recommend content based on the user's learning progress.As a result, the educational content providing system according to the embodiment can efficiently collect, analyze, classify, and recommend lesson content and wisdom from educators.

[0030] The educational content provision system includes a reception unit that receives user input. The reception unit receives user input. User input includes, but is not limited to, text input, multiple-choice questionnaires, and voice input. For example, the reception unit receives a user's field of interest input in text. The reception unit can also receive the user's desired learning content through a multiple-choice questionnaire. The reception unit can also receive the user's desired learning content using voice input. By receiving user input, content based on the user's interests and desired learning content can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's text input to a generation AI, which can analyze the input content and recommend appropriate content.

[0031] The recommendation unit can make individually tailored recommendations based on the user's learning history and interests. The recommendation unit makes recommendations based on the user's learning history, such as the user's previously taken courses, test scores, and study time. For example, the recommendation unit can recommend related new courses based on the content of courses the user has previously taken. The recommendation unit can also recommend content of an appropriate level of difficulty based on the user's test scores. The recommendation unit can also recommend content that matches the user's learning pace based on the user's study time. Furthermore, the recommendation unit makes recommendations based on the user's interests. For example, the recommendation unit can recommend content in a field of interest based on the user's selected topics, browsing history, survey results, etc. This allows more appropriate content to be recommended based on the user's learning history and interests. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's learning history and interests into a generation AI, which can then recommend optimal content.

[0032] The educational content provision system includes a provision unit that provides a recommendation result to a user. The provision unit provides the recommendation result to the user. The recommendation result may be, for example, in a list format, a graphical display, a notification message, or the like, but is not limited to these examples. For example, the provision unit provides the recommended content to the user in a list format. The provision unit may also visually provide the recommendation result using a graphical display. The provision unit may also notify the user of the recommendation result through a notification message. In this way, by providing the recommendation result to the user, the user can use the optimal content. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or may be performed without using AI. For example, the provision unit may generate the recommendation result in a list format using a generation AI and provide it to the user.

[0033] The collection unit can collect lesson content and knowledge provided by educators in electronic format. Examples of electronic formats include, but are not limited to, PDFs, Word documents, and video files. For example, the collection unit can collect lesson materials in PDF format provided by educators. The collection unit can also collect lecture notes in Word document format provided by educators. The collection unit can also collect video lectures in video file format provided by educators. By collecting lesson content and knowledge provided by educators in digital format, efficient data management becomes possible. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input lesson materials in PDF format provided by educators into a generation AI, which can analyze the content and classify them into appropriate categories.

[0034] The analysis unit can analyze the collected content and classify it into appropriate categories. Examples of appropriate categories include, but are not limited to, by subject, difficulty level, and target age. For example, the analysis unit can classify the collected content by subject. For example, the analysis unit can classify math lesson content into the "Math" category. The analysis unit can also classify content by difficulty level. For example, the analysis unit can classify content into categories such as beginner, intermediate, and advanced. The analysis unit can also classify content by target age. For example, the analysis unit can classify content into categories such as for elementary school students, junior high school students, and high school students. By analyzing the collected content and classifying it into appropriate categories, users can easily find the information they need. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected content into a generation AI, which can analyze the content and classify it into appropriate categories.

[0035] The collection unit can analyze the educator's past lesson delivery history and select a predetermined collection method. For example, the collection unit analyzes the content of lessons previously provided by the educator and selects the most effective collection method. For example, the collection unit selects a collection method using an online form based on the content of lessons previously provided by the educator. The collection unit can also identify an appropriate time period and method for collection based on the educator's past lesson delivery history. For example, the collection unit selects an optimal collection time period based on the date and time of lessons previously provided by the educator. The collection unit can also customize the collection method and perform optimal collection based on the educator's past lesson delivery history. For example, the collection unit selects data acquisition via an API based on the content of lessons previously provided by the educator. This enables the educator's past lesson delivery history to be analyzed to select the optimal collection method and enable efficient collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the educator's past lesson delivery history into a generation AI, which then selects the optimal collection method.

[0036] When collecting lesson content, the collection unit can filter the content based on the educator's field of expertise and the lesson content. The collection unit, for example, collects only relevant lesson content based on the educator's field of expertise. For example, if the educator specializes in mathematics, the collection unit prioritizes collecting lesson content related to mathematics. The collection unit can also prioritize collecting content related to a specific topic based on the educator's lesson content. For example, if the educator teaches physics, the collection unit prioritizes collecting content related to physics. The collection unit can also combine the educator's field of expertise and the lesson content to filter and collect optimal content. For example, if the educator specializes in science and teaches chemistry, the collection unit prioritizes collecting content related to science and chemistry. This allows for efficient collection of highly relevant content by filtering based on the educator's field of expertise and the lesson content. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the educator's field of expertise and the lesson content into a generation AI, which can then filter and collect optimal content.

[0037] When collecting lesson content, the collection unit can select the optimal collection means depending on the educator's input method. For example, when the educator provides lesson content via voice, the collection unit collects the content using voice recognition technology. For example, the collection unit converts the lesson content provided by the educator via voice into text data using voice recognition technology and collects the text data. In addition, when the educator provides lesson content via text, the collection unit can also collect the content using text analysis technology. For example, the collection unit collects the lesson content provided by the educator via text using text analysis technology. In addition, when the educator provides lesson content via video, the collection unit can also collect the content using video analysis technology. For example, the collection unit collects the lesson content provided by the educator via video analysis technology. This enables efficient data collection by selecting the optimal collection means depending on the educator's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the educator's voice data into a generation AI, which then converts the voice data into text data and collects it.

[0038] When collecting lesson content, the collection unit can prioritize collecting highly relevant content by taking into account the geographical location information of the educator. The collection unit, for example, prioritizes collecting content related to a region based on the geographical location information of the educator. For example, if the educator is located in a specific region, the collection unit prioritizes collecting lesson content related to the region. The collection unit can also collect content related to the regional educational curriculum by taking into account the geographical location information of the educator. For example, the collection unit collects lesson content related to the educational curriculum of a specific region by the educator. The collection unit can also prioritize collecting content related to the culture and history of the region based on the geographical location information of the educator. For example, the collection unit collects lesson content related to the culture and history of a specific region by the educator. This makes it possible to provide content appropriate for the region by preferentially collecting highly relevant content by taking into account the geographical location information of the educator. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the educator to a generation AI, which can then prioritize collecting highly relevant content.

[0039] The collection unit may analyze the educator's social media activities and collect related content when collecting lesson content. For example, the collection unit may analyze the educator's social media posts and collect related lesson content. For example, the collection unit may collect related lesson content based on the content posted by the educator on social media. The collection unit may also collect content related to topics in which the educator is interested from the educator's social media activities. For example, the collection unit may collect lesson content related to topics in which the educator has shown interest on social media. The collection unit may also analyze the reactions of the educator's social media followers and collect popular content. For example, the collection unit may collect content that has received many reactions from the educator's followers. This allows for efficient collection of related content by analyzing the educator's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the educator's social media activities into a generation AI, which may collect related content.

[0040] When collecting lesson content, the collection unit can customize the collection method by reflecting the educator's past feedback. The collection unit, for example, improves the collection method based on the educator's past feedback and performs optimal collection. For example, the collection unit improves the collection method based on feedback provided by the educator in the past. The collection unit can also adjust the type and format of content to be collected by reflecting the educator's past feedback. For example, the collection unit adjusts the type and format of content to be collected based on feedback provided by the educator in the past. The collection unit can also customize the timing and means of collection by referring to the educator's past feedback. For example, the collection unit customizes the timing and means of collection based on feedback provided by the educator in the past. This optimizes the collection method by reflecting the educator's past feedback, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the educator's past feedback into a generation AI, which can customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the content. For example, the analysis unit performs a detailed analysis on important content. For example, the analysis unit performs a detailed analysis on important lesson content. The analysis unit can also perform a brief analysis on less important content. For example, the analysis unit performs a brief analysis on less important lesson content. The analysis unit can also gradually adjust the level of detail of the analysis depending on the importance of the content. For example, the analysis unit gradually adjusts the level of detail of the analysis depending on the importance of the content. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input content importance data to a generation AI, which can adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the content category. For example, the analysis unit applies a numerical analysis algorithm to arithmetic content. For example, the analysis unit applies a numerical analysis algorithm to arithmetic lesson content. The analysis unit can also apply a text analysis algorithm to history content. For example, the analysis unit applies a text analysis algorithm to history lesson content. The analysis unit can also apply a simulation analysis algorithm to physics content. For example, the analysis unit applies a simulation analysis algorithm to physics lesson content. This enables efficient data analysis by applying different analysis algorithms depending on the content category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input content category data to a generation AI, which then applies different analysis algorithms.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the educator's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the educator's past analysis results to improve accuracy. For example, the analysis unit adjusts the analysis algorithm based on the educator's past analysis results. The analysis unit can also optimize analysis parameters by referring to the educator's past analysis results. For example, the analysis unit optimizes analysis parameters based on the educator's past analysis results. The analysis unit can also reduce analysis errors by using the educator's past analysis results. For example, the analysis unit reduces analysis errors based on the educator's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the educator's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the educator's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the submission date of the content. The analysis unit, for example, prioritizes analysis of the latest content. For example, the analysis unit prioritizes analysis of the latest lesson content. The analysis unit can also postpone analysis of content submitted earlier. For example, the analysis unit postpones analysis of lesson content submitted earlier. The analysis unit can also gradually adjust the analysis priority based on the submission date. For example, the analysis unit gradually adjusts the analysis priority based on the submission date. In this way, by determining the analysis priority based on the submission date of the content, the latest information can be preferentially analyzed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input content submission date data to the generation AI, and the generation AI can determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the content. For example, the analysis unit prioritizes analysis of highly relevant content. For example, the analysis unit prioritizes analysis of highly relevant lesson content. The analysis unit can also postpone analysis of less relevant content. For example, the analysis unit postpones analysis of less relevant lesson content. The analysis unit can also gradually adjust the order of analysis based on the relevance of the content. For example, the analysis unit gradually adjusts the order of analysis based on the relevance of the content. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input content relevance data to a generation AI, which can adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the educator's level of expertise. For example, if the educator's level of expertise is high, the analysis unit provides an analysis result that uses a lot of technical terms. For example, if the educator's level of expertise is high, the analysis unit provides an analysis result that uses a lot of technical terms. Furthermore, if the educator's level of expertise is low, the analysis unit can provide an analysis result that avoids technical terms. For example, if the educator's level of expertise is low, the analysis unit can provide an analysis result that avoids technical terms. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis result according to the educator's level of expertise. For example, the analysis unit gradually adjusts the use of technical terms in the analysis result according to the educator's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the educator's level of expertise, it is possible to provide an analysis result that is easy for educators to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the educator's level of expertise data into a generation AI, which can adjust the use of technical terms in the analysis.

[0047] The classifier can improve the accuracy of classification by taking into account the interrelationships between content items during classification. The classifier, for example, analyzes the interrelationships between content items and classifies related content items into the same category. For example, the classifier can analyze the interrelationships between content items and classify related lesson content items into the same category. The classifier can also improve the accuracy of classification by taking into account the interrelationships between content items. For example, the classifier can improve the accuracy of classification by taking into account the interrelationships between content items. The classifier can also adjust the classification algorithm based on the interrelationships between content items to improve the accuracy. For example, the classifier can adjust the classification algorithm based on the interrelationships between content items to improve the accuracy. In this way, the accuracy of classification can be improved by taking the interrelationships between content items into consideration. Some or all of the above-described processing in the classifier can be performed using AI, for example, or without AI. For example, the classifier can input content interrelationship data into a generation AI, which can improve the accuracy of classification.

[0048] The classification unit can weight the classification based on the frequency of content provision during classification. For example, the classification unit prioritizes classifying content that is provided frequently. For example, the classification unit prioritizes classifying lesson content that is provided frequently. The classification unit can also postpone classifying content that is provided less frequently. For example, the classification unit postpones classifying lesson content that is provided less frequently. The classification unit can also gradually adjust the weighting of the classification based on the frequency of provision. For example, the classification unit gradually adjusts the weighting of the classification based on the frequency of provision. This enables efficient data classification by weighting the classification based on the frequency of content provision. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input content provision frequency data to a generation AI, which then weights the classification.

[0049] The classification unit can perform classification taking into account the geographical distribution of the content. For example, the classification unit classifies content by region based on the geographical distribution of the content. For example, the classification unit classifies content by region based on the geographical distribution of the content. The classification unit can also classify content related to a regional educational curriculum taking into account the geographical distribution. For example, the classification unit classifies content related to a regional educational curriculum taking into account the geographical distribution. The classification unit can also classify content related to a regional culture or history based on the geographical distribution. For example, the classification unit classifies content related to a regional culture or history based on the geographical distribution. In this way, classification appropriate for a region can be performed by taking into account the geographical distribution of the content. Some or all of the above-described processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input geographical distribution data of the content to a generation AI, which then performs the classification.

[0050] The classification unit can improve the accuracy of classification by referring to literature related to the content during classification. For example, the classification unit refers to literature related to the content and classifies the content into related categories. For example, the classification unit refers to literature related to the content and classifies related lesson content into related categories. The classification unit can also adjust the classification algorithm based on the related literature to improve accuracy. For example, the classification unit adjusts the classification algorithm based on the related literature to improve accuracy. The classification unit can also gradually improve the accuracy of classification by taking into account the related literature. For example, the classification unit gradually improves the accuracy of classification by taking into account the related literature. In this way, the accuracy of classification can be improved by referring to literature related to the content. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input literature data related to the content into a generation AI, which can improve the accuracy of the classification.

[0051] The classification unit can take into consideration the market value of the content when classifying. For example, the classification unit prioritizes classifying content with high market value. For example, the classification unit prioritizes classifying lesson content with high market value. The classification unit can also postpone classifying content with low market value. For example, the classification unit postpones classifying lesson content with low market value. The classification unit can also gradually adjust the weighting of the classification based on the market value. For example, the classification unit gradually adjusts the weighting of the classification based on the market value. This enables efficient data classification by performing classification taking into consideration the market value of the content. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input market value data of the content to a generation AI, which then performs the classification.

[0052] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the content when making a recommendation. For example, the recommendation unit makes a detailed recommendation for important content. For example, the recommendation unit makes a detailed recommendation for important lesson content. The recommendation unit can also make a concise recommendation for less important content. For example, the recommendation unit makes a concise recommendation for less important lesson content. The recommendation unit can also gradually adjust the level of detail of the recommendation depending on the importance of the content. For example, the recommendation unit gradually adjusts the level of detail of the recommendation depending on the importance of the content. This enables efficient data recommendation by adjusting the level of detail of the recommendation based on the importance of the content. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input content importance data to a generation AI, which can adjust the level of detail of the recommendation.

[0053] The recommendation unit can apply different recommendation algorithms depending on the content category when making a recommendation. For example, the recommendation unit applies a numerical analysis algorithm to arithmetic content. For example, the recommendation unit applies a numerical analysis algorithm to arithmetic lesson content. The recommendation unit can also apply a text analysis algorithm to history content. For example, the recommendation unit applies a text analysis algorithm to history lesson content. The recommendation unit can also apply a simulation analysis algorithm to physics content. For example, the recommendation unit applies a simulation analysis algorithm to physics lesson content. This enables efficient data recommendation by applying different recommendation algorithms depending on the content category. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input content category data to a generation AI, which can apply different recommendation algorithms.

[0054] The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results when making recommendations. The recommendation unit, for example, adjusts the recommendation algorithm based on the user's past recommendation results to improve accuracy. For example, the recommendation unit adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also optimize recommendation parameters by referring to the user's past recommendation results. For example, the recommendation unit optimizes recommendation parameters based on the user's past recommendation results. The recommendation unit can also reduce recommendation errors by using the user's past recommendation results. For example, the recommendation unit reduces recommendation errors based on the user's past recommendation results. This makes it possible to improve recommendation accuracy by referring to the user's past recommendation results. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation result data into a generation AI, which can improve the accuracy of recommendations.

[0055] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the submission date of the content. The recommendation unit, for example, prioritizes recommending the latest content. For example, the recommendation unit prioritizes recommending the latest lesson content. The recommendation unit can also postpone recommending content that was submitted earlier. For example, the recommendation unit postpones recommending lesson content that was submitted earlier. The recommendation unit can also gradually adjust the priority of the recommendation based on the submission date. For example, the recommendation unit gradually adjusts the priority of the recommendation based on the submission date. In this way, by determining the priority of the recommendation based on the submission date of the content, the latest information can be preferentially recommended. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the submission date of the content to a generation AI, which can then determine the priority of the recommendation.

[0056] The recommendation unit can adjust the order of recommendations based on the relevance of the content when making a recommendation. For example, the recommendation unit prioritizes recommending highly relevant content. For example, the recommendation unit prioritizes recommending highly relevant lesson content. The recommendation unit can also postpone recommending less relevant content. For example, the recommendation unit postpones recommending less relevant lesson content. The recommendation unit can also gradually adjust the order of recommendations based on the relevance of the content. For example, the recommendation unit gradually adjusts the order of recommendations based on the relevance of the content. This enables efficient data recommendation by adjusting the order of recommendations based on the relevance of the content. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input content relevance data to a generation AI, which can adjust the order of recommendations.

[0057] The recommendation unit can adjust the use of technical terminology in the recommendation depending on the user's level of expertise when making a recommendation. For example, if the user's level of expertise is high, the recommendation unit provides recommended content that uses a lot of technical terminology. For example, if the user's level of expertise is high, the recommendation unit can provide recommended content that uses a lot of technical terminology. Furthermore, if the user's level of expertise is low, the recommendation unit can provide recommended content that avoids technical terminology. For example, if the user's level of expertise is low, the recommendation unit can provide recommended content that avoids technical terminology. Furthermore, the recommendation unit can gradually adjust the use of technical terminology in the recommended content depending on the user's level of expertise. For example, the recommendation unit gradually adjusts the use of technical terminology in the recommended content depending on the user's level of expertise. This allows the recommendation results to be provided that are easy for the user to understand by adjusting the use of technical terminology in the recommendation depending on the user's level of expertise. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input the user's level of expertise data into a generation AI, which can adjust the use of technical terminology in the recommendation.

[0058] When receiving input, the reception unit can select the optimal reception method by referring to the user's past input history. For example, the reception unit automatically displays content that the user has frequently input in the past as candidates. For example, the reception unit automatically displays content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit preferentially suggests input methods that the user has used in the past. The reception unit can also predict and suggest content to be used in a specific time period based on the user's past input history. For example, the reception unit predicts and suggests content to be used in a specific time period based on the user's past input history. This allows the optimal reception method to be selected by referring to the user's past input history, enabling efficient input reception. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data to a generation AI, which can select the optimal reception method.

[0059] The reception unit can customize the received content based on the user's current learning situation when receiving input. The reception unit, for example, analyzes the user's current learning situation and suggests optimal input content. For example, the reception unit analyzes the user's current learning situation and suggests optimal input content. The reception unit can also customize the input content based on the user's learning progress. For example, the reception unit customizes the input content based on the user's learning progress. The reception unit can also gradually adjust the input content taking into account the user's learning situation. For example, the reception unit gradually adjusts the input content taking into account the user's learning situation. This enables efficient input reception by customizing the received content based on the user's current learning situation. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current learning situation data to a generation AI, which can then customize the received content.

[0060] When accepting input, the acceptance unit can select the optimal acceptance method by taking into account the user's device information. For example, if the user is using a smartphone, the acceptance unit provides an input method tailored to the screen size. For example, if the user is using a smartphone, the acceptance unit provides an input method tailored to the screen size. Furthermore, if the user is using a tablet, the acceptance unit can provide an input method optimized for a large screen. For example, if the user is using a tablet, the acceptance unit can provide an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the acceptance unit can provide an input method that is simple and highly visible. For example, if the user is using a smartwatch, the acceptance unit provides an input method that is simple and highly visible. This enables the optimal acceptance method to be selected by taking into account the user's device information, enabling efficient input acceptance. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's device information data into a generation AI, which can select the optimal acceptance method.

[0061] When receiving input, the reception unit can make the received content multilingual according to the user's language setting. The reception unit automatically sets the input content based on, for example, the language setting of the user's device. For example, the reception unit automatically sets the input content based on the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. For example, the reception unit provides a language switching function when the user uses multiple languages. The reception unit can also provide the input content in a specific language when the user selects that language. For example, the reception unit provides the input content in that language when the user selects a specific language. This enables efficient input reception by making the received content multilingual according to the user's language setting. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's language setting data to a generation AI, which can then make the received content multilingual.

[0062] The providing unit can select the optimal providing method by referring to the user's past providing history when providing the content. The providing unit selects the optimal providing method, for example, based on the user's past providing history. For example, the providing unit selects the optimal providing method based on the user's past providing history. The providing unit can also identify a time period and method suitable for providing the content from the user's past providing history. For example, the providing unit identifies a time period and method suitable for providing the content from the user's past providing history. The providing unit can also customize the providing method by referring to the user's past providing history. For example, the providing unit customizes the providing method by referring to the user's past providing history. This enables the optimal providing method to be selected by referring to the user's past providing history, thereby enabling efficient providing. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past providing history data into a generation AI, which can select the optimal providing method.

[0063] The providing unit can customize the content to be provided based on the user's current learning situation at the time of providing the content. The providing unit, for example, analyzes the user's current learning situation and proposes optimal content to be provided. For example, the providing unit analyzes the user's current learning situation and proposes optimal content to be provided. The providing unit can also customize the content to be provided based on the user's learning progress. For example, the providing unit customizes the content to be provided based on the user's learning progress. The providing unit can also gradually adjust the content to be provided, taking into account the user's learning situation. For example, the providing unit gradually adjusts the content to be provided, taking into account the user's learning situation. This enables efficient provision by customizing the content to be provided based on the user's current learning situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current learning situation data into a generating AI, which can customize the content to be provided.

[0064] The providing unit can select the optimal delivery method by taking into account the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit provides a delivery method tailored to the screen size. For example, if the user is using a smartphone, the providing unit provides a delivery method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible delivery method. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible delivery method. This enables the optimal delivery method to be selected by taking into account the user's device information, enabling efficient delivery. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into a generation AI, which can select the optimal delivery method.

[0065] The providing unit can make the provided content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the provided content based on, for example, the language setting of the user's device. For example, the providing unit automatically sets the provided content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides a language switching function when the user uses multiple languages. The providing unit can also provide the provided content in a specific language when the user selects that language. For example, the providing unit provides the provided content in that language when the user selects a specific language. This enables efficient provision by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI, which can then make the provided content multilingual.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The educational content provision system can also analyze a user's learning style and provide the most appropriate content format. For example, a user who prefers visual learning can be provided with content that makes heavy use of videos and infographics. A user who prefers auditory learning can be provided with content in the form of podcasts or audiobooks. Furthermore, a user who prefers hands-on learning can be provided with content that includes interactive quizzes and simulations. This maximizes learning effectiveness by providing the most appropriate content format according to the user's learning style.

[0068] The educational content provision system can further analyze the user's learning environment and provide the optimal learning environment. For example, if the user prefers to study in a quiet environment, a noise-canceling function can be provided. Also, if the user is more likely to concentrate during a certain time of day, it can recommend studying during that time. Furthermore, if the user prefers to study on a specific device, it can provide content optimized for that device. This can improve learning effectiveness by providing the optimal learning environment according to the user's learning environment.

[0069] The educational content provision system can also monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if the user is progressing ahead of schedule, the system can provide the next step early. If the user is falling behind schedule, the system can review the learning plan and provide additional resources to support progress. Furthermore, if the user is struggling with a particular topic, the system can provide supplementary materials related to that topic. This maximizes learning effectiveness by providing a flexible learning plan that adapts to the user's learning progress.

[0070] The educational content provision system can also provide a dashboard that visualizes the user's learning progress based on their learning history. For example, it can display the topics the user has studied in the past and the goals they have achieved in graphs and charts. It can also display the user's learning pace and progress in real time. It can also present the user with the next topic and goal they should study. This makes the user's learning progress visible, which can increase motivation to learn and support effective learning.

[0071] The educational content providing system can also provide a reminder function to support learning progress based on the user's learning history. For example, if a user plans to study a specific topic, a reminder can be sent the day before. Also, if the user has stopped studying, a reminder can be sent to encourage the user to resume. Furthermore, if the user achieves a learning goal, a reminder can be sent to celebrate the achievement. In this way, by providing a reminder function to support the user's learning progress, continuous learning can be promoted.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects lesson content and knowledge from educators. The lesson content and knowledge from educators may include video lectures, text materials, interactive quizzes, etc. The collection unit collects content through methods such as online forms, APIs, and manual input. For example, the collection unit may collect video lectures provided by educators through an online form, automatically obtain text materials from educators using APIs, and collect interactive quizzes manually entered by educators. Step 2: The analysis unit analyzes the content collected by the collection unit. The analysis is performed using methods such as text mining, natural language processing, and data mining. For example, the analysis unit may use text mining technology to analyze the collected text materials, natural language processing technology to analyze the content of video lectures, and data mining technology to analyze the data of interactive quizzes. Step 3: The classification unit classifies the content analyzed by the analysis unit into predetermined categories. The categories are classified by subject, difficulty level, target age, etc. For example, the classification unit can classify the analyzed content by subject, difficulty level, and target age. Step 4: The recommendation unit recommends predetermined content based on the content classified by the classification unit. Recommendations are made using a recommendation algorithm based on the user's past behavioral history, interests, learning progress, etc. For example, the recommendation unit can recommend optimal content based on the user's past behavioral history, content based on the user's interests, and content based on the user's learning progress.

[0074] (Example 2) An educational content provision system according to an embodiment of the present invention centrally collects lesson content and wisdom from educators and provides new learning experiences and experiences to children and adults around the world through AI. The educational content provision system collects lesson content and wisdom from educators, analyzes them using AI, classifies them into appropriate categories, and recommends optimal content based on users' interests and learning needs. For example, the educational content provision system collects lesson content and wisdom from each educator. For example, educators describe their fields of expertise and lesson content in detail and provide them in digital format. Next, the educational content provision system analyzes the collected content and classifies it into appropriate categories using AI. For example, the AI ​​analyzes the content of each piece of content and categorizes it into categories such as arithmetic, physics, and history. Furthermore, when a user inputs their field of interest or the content they want to learn, the AI ​​recommends optimal content. For example, if a user inputs "I want to learn history," the AI ​​recommends optimal lesson content from the "history" category. This allows the educational content provision system to provide new learning experiences and experiences to children and adults around the world. This allows the educational content provision system to efficiently collect, analyze, categorize, and recommend lesson content and wisdom from educators. For example, Japanese elementary school students can take a lecture from an American university professor, or French high school students can experience a class at a Japanese elementary school. This provides more creative and equal learning opportunities, allowing people around the world to acquire new knowledge and skills.

[0075] An educational content provision system according to an embodiment includes a collection unit, an analysis unit, a classification unit, and a recommendation unit. The collection unit collects lesson content and knowledge from educators. The lesson content and knowledge from educators include, but are not limited to, video lectures, text materials, and interactive quizzes. The collection unit collects content from educators, for example, through an online form. The collection unit can also acquire data via an API. The collection unit can also collect content through manual input. For example, the collection unit collects video lectures provided by educators through an online form. The collection unit can also automatically acquire text materials from educators using an API. The collection unit can also collect interactive quizzes manually entered by educators. The analysis unit analyzes the content collected by the collection unit. The analysis can be performed using, for example, text mining, natural language processing, data mining, or other methods, but is not limited to these. For example, the analysis unit analyzes the collected text materials using text mining technology. The analysis unit can also analyze the content of video lectures using natural language processing technology. The analysis unit can also analyze interactive quiz data using data mining technology. The classification unit classifies the content analyzed by the analysis unit into predetermined categories. The categories are classified based on criteria such as, but not limited to, subject, difficulty level, and target age. For example, the classification unit classifies the analyzed content by subject. The classification unit can also classify content by difficulty level. The classification unit can also classify content by target age. The recommendation unit recommends predetermined content based on the content classified by the classification unit. The recommendation is performed using a recommendation algorithm based on, for example, the user's past behavioral history, interests, learning progress, etc., but is not limited to, for example. The recommendation unit recommends optimal content based on the user's past behavioral history. The recommendation unit can also recommend content based on the user's interests. The recommendation unit can also recommend content based on the user's learning progress.As a result, the educational content providing system according to the embodiment can efficiently collect, analyze, classify, and recommend lesson content and wisdom from educators.

[0076] The educational content provision system includes a reception unit that receives user input. The reception unit receives user input. User input includes, but is not limited to, text input, multiple-choice questionnaires, and voice input. For example, the reception unit receives a user's field of interest input in text. The reception unit can also receive the user's desired learning content through a multiple-choice questionnaire. The reception unit can also receive the user's desired learning content using voice input. By receiving user input, content based on the user's interests and desired learning content can be provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's text input to a generation AI, which can analyze the input content and recommend appropriate content.

[0077] The recommendation unit can make individually tailored recommendations based on the user's learning history and interests. The recommendation unit makes recommendations based on the user's learning history, such as the user's previously taken courses, test scores, and study time. For example, the recommendation unit can recommend related new courses based on the content of courses the user has previously taken. The recommendation unit can also recommend content of an appropriate level of difficulty based on the user's test scores. The recommendation unit can also recommend content that matches the user's learning pace based on the user's study time. Furthermore, the recommendation unit makes recommendations based on the user's interests. For example, the recommendation unit can recommend content in a field of interest based on the user's selected topics, browsing history, survey results, etc. This allows more appropriate content to be recommended based on the user's learning history and interests. Some or all of the above-described processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input the user's learning history and interests into a generation AI, which can then recommend optimal content.

[0078] The educational content provision system includes a provision unit that provides a recommendation result to a user. The provision unit provides the recommendation result to the user. The recommendation result may be, for example, in a list format, a graphical display, a notification message, or the like, but is not limited to these examples. For example, the provision unit provides the recommended content to the user in a list format. The provision unit may also visually provide the recommendation result using a graphical display. The provision unit may also notify the user of the recommendation result through a notification message. In this way, by providing the recommendation result to the user, the user can use the optimal content. Some or all of the above-described processing in the provision unit may be performed, for example, using AI or may be performed without using AI. For example, the provision unit may generate the recommendation result in a list format using a generation AI and provide it to the user.

[0079] The collection unit can collect lesson content and knowledge provided by educators in electronic format. Examples of electronic formats include, but are not limited to, PDFs, Word documents, and video files. For example, the collection unit can collect lesson materials in PDF format provided by educators. The collection unit can also collect lecture notes in Word document format provided by educators. The collection unit can also collect video lectures in video file format provided by educators. By collecting lesson content and knowledge provided by educators in digital format, efficient data management becomes possible. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input lesson materials in PDF format provided by educators into a generation AI, which can analyze the content and classify them into appropriate categories.

[0080] The analysis unit can analyze the collected content and classify it into appropriate categories. Examples of appropriate categories include, but are not limited to, by subject, difficulty level, and target age. For example, the analysis unit can classify the collected content by subject. For example, the analysis unit can classify math lesson content into the "Math" category. The analysis unit can also classify content by difficulty level. For example, the analysis unit can classify content into categories such as beginner, intermediate, and advanced. The analysis unit can also classify content by target age. For example, the analysis unit can classify content into categories such as for elementary school students, junior high school students, and high school students. By analyzing the collected content and classifying it into appropriate categories, users can easily find the information they need. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected content into a generation AI, which can analyze the content and classify it into appropriate categories.

[0081] The collection unit can analyze the educator's emotions and adjust the timing of collecting lesson content based on the analyzed educator's emotions. For example, if the educator is relaxed, the collection unit flexibly sets the collection timing and collects content during a time when the educator can concentrate best. For example, the collection unit collects lesson content during a time when the educator is relaxed. Furthermore, if the educator is stressed, the collection unit can adjust the collection timing and collect content during a time when the educator can relax. For example, the collection unit collects content to avoid times when the educator is stressed. Furthermore, if the educator is busy, the collection unit can adjust the collection timing to match the educator's schedule. For example, the collection unit sets the collection timing to match the educator's schedule. This reduces the burden on the educator by adjusting the collection timing according to the educator's emotions, enabling efficient collection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the educator's emotional data into the generation AI, and the generation AI may adjust the collection timing.

[0082] The collection unit can analyze the educator's past lesson delivery history and select a predetermined collection method. For example, the collection unit analyzes the content of lessons previously provided by the educator and selects the most effective collection method. For example, the collection unit selects a collection method using an online form based on the content of lessons previously provided by the educator. The collection unit can also identify an appropriate time period and method for collection based on the educator's past lesson delivery history. For example, the collection unit selects an optimal collection time period based on the date and time of lessons previously provided by the educator. The collection unit can also customize the collection method and perform optimal collection based on the educator's past lesson delivery history. For example, the collection unit selects data acquisition via an API based on the content of lessons previously provided by the educator. This enables the educator's past lesson delivery history to be analyzed to select the optimal collection method and enable efficient collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the educator's past lesson delivery history into a generation AI, which then selects the optimal collection method.

[0083] When collecting lesson content, the collection unit can filter the content based on the educator's field of expertise and the lesson content. The collection unit, for example, collects only relevant lesson content based on the educator's field of expertise. For example, if the educator specializes in mathematics, the collection unit prioritizes collecting lesson content related to mathematics. The collection unit can also prioritize collecting content related to a specific topic based on the educator's lesson content. For example, if the educator teaches physics, the collection unit prioritizes collecting content related to physics. The collection unit can also combine the educator's field of expertise and the lesson content to filter and collect optimal content. For example, if the educator specializes in science and teaches chemistry, the collection unit prioritizes collecting content related to science and chemistry. This allows for efficient collection of highly relevant content by filtering based on the educator's field of expertise and the lesson content. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the educator's field of expertise and the lesson content into a generation AI, which can then filter and collect optimal content.

[0084] When collecting lesson content, the collection unit can select the optimal collection means depending on the educator's input method. For example, when the educator provides lesson content via voice, the collection unit collects the content using voice recognition technology. For example, the collection unit converts the lesson content provided by the educator via voice into text data using voice recognition technology and collects the text data. In addition, when the educator provides lesson content via text, the collection unit can also collect the content using text analysis technology. For example, the collection unit collects the lesson content provided by the educator via text using text analysis technology. In addition, when the educator provides lesson content via video, the collection unit can also collect the content using video analysis technology. For example, the collection unit collects the lesson content provided by the educator via video analysis technology. This enables efficient data collection by selecting the optimal collection means depending on the educator's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the educator's voice data into a generation AI, which then converts the voice data into text data and collects it.

[0085] The collection unit can analyze the educator's emotions and determine the priority of content to be collected based on the analyzed educator's emotions. For example, when the educator is relaxed, the collection unit prioritizes collecting important content. For example, the collection unit collects important lesson content during times when the educator is relaxed. The collection unit can also prioritize collecting easy content when the educator is stressed. For example, the collection unit collects easy lesson content during times when the educator is stressed. The collection unit can also prioritize collecting content that can be collected in a short time when the educator is busy. For example, the collection unit collects lesson content that can be collected in a short time during times when the educator is busy. This enables efficient data collection by determining the priority of content to be collected based on the educator's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input educator's emotional data into the generation AI and determine the priority of the content that the generation AI will collect.

[0086] When collecting lesson content, the collection unit can prioritize collecting highly relevant content by taking into account the geographical location information of the educator. The collection unit, for example, prioritizes collecting content related to a region based on the geographical location information of the educator. For example, if the educator is located in a specific region, the collection unit prioritizes collecting lesson content related to the region. The collection unit can also collect content related to the regional educational curriculum by taking into account the geographical location information of the educator. For example, the collection unit collects lesson content related to the educational curriculum of a specific region by the educator. The collection unit can also prioritize collecting content related to the culture and history of the region based on the geographical location information of the educator. For example, the collection unit collects lesson content related to the culture and history of a specific region by the educator. This makes it possible to provide content appropriate for the region by preferentially collecting highly relevant content by taking into account the geographical location information of the educator. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the educator to a generation AI, which can then prioritize collecting highly relevant content.

[0087] The collection unit may analyze the educator's social media activities and collect related content when collecting lesson content. For example, the collection unit may analyze the educator's social media posts and collect related lesson content. For example, the collection unit may collect related lesson content based on the content posted by the educator on social media. The collection unit may also collect content related to topics in which the educator is interested from the educator's social media activities. For example, the collection unit may collect lesson content related to topics in which the educator has shown interest on social media. The collection unit may also analyze the reactions of the educator's social media followers and collect popular content. For example, the collection unit may collect content that has received many reactions from the educator's followers. This allows for efficient collection of related content by analyzing the educator's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the educator's social media activities into a generation AI, which may collect related content.

[0088] When collecting lesson content, the collection unit can customize the collection method by reflecting the educator's past feedback. The collection unit, for example, improves the collection method based on the educator's past feedback and performs optimal collection. For example, the collection unit improves the collection method based on feedback provided by the educator in the past. The collection unit can also adjust the type and format of content to be collected by reflecting the educator's past feedback. For example, the collection unit adjusts the type and format of content to be collected based on feedback provided by the educator in the past. The collection unit can also customize the timing and means of collection by referring to the educator's past feedback. For example, the collection unit customizes the timing and means of collection based on feedback provided by the educator in the past. This optimizes the collection method by reflecting the educator's past feedback, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the educator's past feedback into a generation AI, which can customize the collection method.

[0089] The analysis unit can analyze the educator's emotions and adjust the way the analysis is presented based on the analyzed educator's emotions. For example, when the educator is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit provides detailed analysis results during times when the educator is relaxed. The analysis unit can also provide concise and to-the-point analysis results when the educator is stressed. For example, the analysis unit provides concise analysis results during times when the educator is stressed. The analysis unit can also provide analysis results that can be understood in a short time when the educator is busy. For example, the analysis unit provides analysis results that can be understood in a short time when the educator is busy. In this way, by adjusting the way the analysis is presented based on the educator's emotions, it is possible to provide analysis results that are easy for educators to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the educator's emotional data into the generation AI, which can then adjust the way the analysis is expressed.

[0090] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the content. For example, the analysis unit performs a detailed analysis on important content. For example, the analysis unit performs a detailed analysis on important lesson content. The analysis unit can also perform a brief analysis on less important content. For example, the analysis unit performs a brief analysis on less important lesson content. The analysis unit can also gradually adjust the level of detail of the analysis depending on the importance of the content. For example, the analysis unit gradually adjusts the level of detail of the analysis depending on the importance of the content. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input content importance data to a generation AI, which can adjust the level of detail of the analysis.

[0091] During analysis, the analysis unit can apply different analysis algorithms depending on the content category. For example, the analysis unit applies a numerical analysis algorithm to arithmetic content. For example, the analysis unit applies a numerical analysis algorithm to arithmetic lesson content. The analysis unit can also apply a text analysis algorithm to history content. For example, the analysis unit applies a text analysis algorithm to history lesson content. The analysis unit can also apply a simulation analysis algorithm to physics content. For example, the analysis unit applies a simulation analysis algorithm to physics lesson content. This enables efficient data analysis by applying different analysis algorithms depending on the content category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input content category data to a generation AI, which then applies different analysis algorithms.

[0092] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the educator's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the educator's past analysis results to improve accuracy. For example, the analysis unit adjusts the analysis algorithm based on the educator's past analysis results. The analysis unit can also optimize analysis parameters by referring to the educator's past analysis results. For example, the analysis unit optimizes analysis parameters based on the educator's past analysis results. The analysis unit can also reduce analysis errors by using the educator's past analysis results. For example, the analysis unit reduces analysis errors based on the educator's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the educator's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the educator's past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0093] The analysis unit can analyze the educator's emotions and adjust the length of the analysis based on the analyzed educator's emotions. For example, when the educator is relaxed, the analysis unit performs a detailed analysis and provides a longer analysis result. For example, the analysis unit performs a detailed analysis and provides a longer analysis result during times when the educator is relaxed. The analysis unit can also provide a concise and short analysis result when the educator is stressed. For example, the analysis unit provides a concise and short analysis result during times when the educator is stressed. The analysis unit can also provide an analysis result that can be understood in a short time when the educator is busy. For example, the analysis unit provides an analysis result that can be understood in a short time during times when the educator is busy. In this way, by adjusting the length of the analysis based on the educator's emotions, it is possible to provide an analysis result that is easy for the educator to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the educator's emotional data into the generation AI, which can then adjust the length of the analysis.

[0094] During analysis, the analysis unit can determine the analysis priority based on the submission date of the content. The analysis unit, for example, prioritizes analysis of the latest content. For example, the analysis unit prioritizes analysis of the latest lesson content. The analysis unit can also postpone analysis of content submitted earlier. For example, the analysis unit postpones analysis of lesson content submitted earlier. The analysis unit can also gradually adjust the analysis priority based on the submission date. For example, the analysis unit gradually adjusts the analysis priority based on the submission date. In this way, by determining the analysis priority based on the submission date of the content, the latest information can be preferentially analyzed. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input content submission date data to the generation AI, and the generation AI can determine the analysis priority.

[0095] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the content. For example, the analysis unit prioritizes analysis of highly relevant content. For example, the analysis unit prioritizes analysis of highly relevant lesson content. The analysis unit can also postpone analysis of less relevant content. For example, the analysis unit postpones analysis of less relevant lesson content. The analysis unit can also gradually adjust the order of analysis based on the relevance of the content. For example, the analysis unit gradually adjusts the order of analysis based on the relevance of the content. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input content relevance data to a generation AI, which can adjust the order of analysis.

[0096] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the educator's level of expertise. For example, if the educator's level of expertise is high, the analysis unit provides an analysis result that uses a lot of technical terms. For example, if the educator's level of expertise is high, the analysis unit provides an analysis result that uses a lot of technical terms. Furthermore, if the educator's level of expertise is low, the analysis unit can provide an analysis result that avoids technical terms. For example, if the educator's level of expertise is low, the analysis unit can provide an analysis result that avoids technical terms. Furthermore, the analysis unit can gradually adjust the use of technical terms in the analysis result according to the educator's level of expertise. For example, the analysis unit gradually adjusts the use of technical terms in the analysis result according to the educator's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the educator's level of expertise, it is possible to provide an analysis result that is easy for educators to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the educator's level of expertise data into a generation AI, which can adjust the use of technical terms in the analysis.

[0097] The classification unit can analyze the educator's emotions and adjust the classification criteria based on the analyzed educator's emotions. For example, the classification unit applies detailed classification criteria when the educator is relaxed. For example, the classification unit applies detailed classification criteria to times when the educator is relaxed. The classification unit can also apply simple classification criteria when the educator is stressed. For example, the classification unit applies simple classification criteria to times when the educator is stressed. The classification unit can also apply criteria that can be used to quickly classify when the educator is busy. For example, the classification unit applies criteria that can be used to quickly classify when the educator is busy. In this way, by adjusting the classification criteria based on the educator's emotions, it is possible to provide classification results that are easy for educators to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 classification unit can be performed, for example, using AI, or without using AI. For example, the classification unit can input the educator's emotional data into the generation AI, which can then adjust the classification criteria.

[0098] The classifier can improve the accuracy of classification by taking into account the interrelationships between content items during classification. The classifier, for example, analyzes the interrelationships between content items and classifies related content items into the same category. For example, the classifier can analyze the interrelationships between content items and classify related lesson content items into the same category. The classifier can also improve the accuracy of classification by taking into account the interrelationships between content items. For example, the classifier can improve the accuracy of classification by taking into account the interrelationships between content items. The classifier can also adjust the classification algorithm based on the interrelationships between content items to improve the accuracy. For example, the classifier can adjust the classification algorithm based on the interrelationships between content items to improve the accuracy. In this way, the accuracy of classification can be improved by taking the interrelationships between content items into consideration. Some or all of the above-described processing in the classifier can be performed using AI, for example, or without AI. For example, the classifier can input content interrelationship data into a generation AI, which can improve the accuracy of classification.

[0099] The classification unit can weight the classification based on the frequency of content provision during classification. For example, the classification unit prioritizes classifying content that is provided frequently. For example, the classification unit prioritizes classifying lesson content that is provided frequently. The classification unit can also postpone classifying content that is provided less frequently. For example, the classification unit postpones classifying lesson content that is provided less frequently. The classification unit can also gradually adjust the weighting of the classification based on the frequency of provision. For example, the classification unit gradually adjusts the weighting of the classification based on the frequency of provision. This enables efficient data classification by weighting the classification based on the frequency of content provision. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input content provision frequency data to a generation AI, which then weights the classification.

[0100] The classification unit can analyze the educator's emotions and adjust the order in which the classification results are displayed based on the analyzed educator's emotions. For example, when the educator is relaxed, the classification unit prioritizes displaying detailed classification results. For example, the classification unit prioritizes displaying detailed classification results during times when the educator is relaxed. The classification unit can also prioritize displaying concise classification results during times when the educator is stressed. For example, the classification unit prioritizes displaying concise classification results during times when the educator is stressed. The classification unit can also prioritize displaying classification results that can be checked in a short time during times when the educator is busy. For example, the classification unit prioritizes displaying classification results that can be checked in a short time during times when the educator is busy. In this way, by adjusting the order in which the classification results are displayed based on the educator's emotions, classification results that are easy for educators to understand can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative 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-mentioned processing in the classification unit can be performed using, for example, AI, or without AI. For example, the classification unit can input the educator's emotional data into the generation AI and adjust the order in which the generation AI displays the classification results.

[0101] The classification unit can perform classification taking into account the geographical distribution of the content. For example, the classification unit classifies content by region based on the geographical distribution of the content. For example, the classification unit classifies content by region based on the geographical distribution of the content. The classification unit can also classify content related to a regional educational curriculum taking into account the geographical distribution. For example, the classification unit classifies content related to a regional educational curriculum taking into account the geographical distribution. The classification unit can also classify content related to a regional culture or history based on the geographical distribution. For example, the classification unit classifies content related to a regional culture or history based on the geographical distribution. In this way, classification appropriate for a region can be performed by taking into account the geographical distribution of the content. Some or all of the above-described processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input geographical distribution data of the content to a generation AI, which then performs the classification.

[0102] The classification unit can improve the accuracy of classification by referring to literature related to the content during classification. For example, the classification unit refers to literature related to the content and classifies the content into related categories. For example, the classification unit refers to literature related to the content and classifies related lesson content into related categories. The classification unit can also adjust the classification algorithm based on the related literature to improve accuracy. For example, the classification unit adjusts the classification algorithm based on the related literature to improve accuracy. The classification unit can also gradually improve the accuracy of classification by taking into account the related literature. For example, the classification unit gradually improves the accuracy of classification by taking into account the related literature. In this way, the accuracy of classification can be improved by referring to literature related to the content. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input literature data related to the content into a generation AI, which can improve the accuracy of the classification.

[0103] The classification unit can take into consideration the market value of the content when classifying. For example, the classification unit prioritizes classifying content with high market value. For example, the classification unit prioritizes classifying lesson content with high market value. The classification unit can also postpone classifying content with low market value. For example, the classification unit postpones classifying lesson content with low market value. The classification unit can also gradually adjust the weighting of the classification based on the market value. For example, the classification unit gradually adjusts the weighting of the classification based on the market value. This enables efficient data classification by performing classification taking into consideration the market value of the content. Some or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input market value data of the content to a generation AI, which then performs the classification.

[0104] The recommendation unit can analyze the user's emotions and adjust the way recommendations are presented based on the analyzed user's emotions. For example, when the user is relaxed, the recommendation unit provides detailed recommendations. For example, the recommendation unit provides detailed recommendations during times when the user is relaxed. The recommendation unit can also provide concise and to-the-point recommendations when the user is stressed. For example, the recommendation unit provides concise and to-the-point recommendations during times when the user is stressed. The recommendation unit can also provide recommendations that can be understood in a short time when the user is busy. For example, the recommendation unit provides recommendations that can be understood in a short time when the user is busy. This allows the recommendation presentation to be adjusted based on the user's emotions, thereby providing recommendations that are easy for the user to understand. 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 these examples. Some or all of the above-described processing in the recommendation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recommendation unit can input the user's emotional data into the generation AI, which can then adjust the way the recommendation is expressed.

[0105] The recommendation unit can adjust the level of detail of the recommendation based on the importance of the content when making a recommendation. For example, the recommendation unit makes a detailed recommendation for important content. For example, the recommendation unit makes a detailed recommendation for important lesson content. The recommendation unit can also make a concise recommendation for less important content. For example, the recommendation unit makes a concise recommendation for less important lesson content. The recommendation unit can also gradually adjust the level of detail of the recommendation depending on the importance of the content. For example, the recommendation unit gradually adjusts the level of detail of the recommendation depending on the importance of the content. This enables efficient data recommendation by adjusting the level of detail of the recommendation based on the importance of the content. Some or all of the above-mentioned processing in the recommendation unit may be performed using, or without, AI. For example, the recommendation unit can input content importance data to a generation AI, which can adjust the level of detail of the recommendation.

[0106] The recommendation unit can apply different recommendation algorithms depending on the content category when making a recommendation. For example, the recommendation unit applies a numerical analysis algorithm to arithmetic content. For example, the recommendation unit applies a numerical analysis algorithm to arithmetic lesson content. The recommendation unit can also apply a text analysis algorithm to history content. For example, the recommendation unit applies a text analysis algorithm to history lesson content. The recommendation unit can also apply a simulation analysis algorithm to physics content. For example, the recommendation unit applies a simulation analysis algorithm to physics lesson content. This enables efficient data recommendation by applying different recommendation algorithms depending on the content category. Some or all of the above-mentioned processing in the recommendation unit may be performed using AI, for example, or may be performed without using AI. For example, the recommendation unit can input content category data to a generation AI, which can apply different recommendation algorithms.

[0107] The recommendation unit can improve the accuracy of recommendations by referring to the user's past recommendation results when making recommendations. The recommendation unit, for example, adjusts the recommendation algorithm based on the user's past recommendation results to improve accuracy. For example, the recommendation unit adjusts the recommendation algorithm based on the user's past recommendation results. The recommendation unit can also optimize recommendation parameters by referring to the user's past recommendation results. For example, the recommendation unit optimizes recommendation parameters based on the user's past recommendation results. The recommendation unit can also reduce recommendation errors by using the user's past recommendation results. For example, the recommendation unit reduces recommendation errors based on the user's past recommendation results. This makes it possible to improve recommendation accuracy by referring to the user's past recommendation results. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input the user's past recommendation result data into a generation AI, which can improve the accuracy of recommendations.

[0108] The recommendation unit can analyze the user's emotions and adjust the length of the recommendation based on the analyzed user's emotions. For example, when the user is relaxed, the recommendation unit makes detailed recommendations and provides longer recommendation content. For example, when the user is relaxed, the recommendation unit makes detailed recommendations and provides longer recommendation content. The recommendation unit can also provide concise and short recommendation content when the user is stressed. For example, the recommendation unit can provide concise and short recommendation content when the user is stressed. The recommendation unit can also provide recommendation content that can be understood in a short time when the user is busy. For example, the recommendation unit provides recommendation content that can be understood in a short time when the user is busy. In this way, by adjusting the length of the recommendation based on the user's emotions, it is possible to provide recommendation results that are easy for the user to understand. 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-mentioned processing in the recommendation unit may be performed using, for example, AI, or without AI. For example, the recommendation unit can input the user's emotional data into the generation AI, which can then adjust the length of the recommendation.

[0109] When making a recommendation, the recommendation unit can determine the priority of the recommendation based on the submission date of the content. The recommendation unit, for example, prioritizes recommending the latest content. For example, the recommendation unit prioritizes recommending the latest lesson content. The recommendation unit can also postpone recommending content that was submitted earlier. For example, the recommendation unit postpones recommending lesson content that was submitted earlier. The recommendation unit can also gradually adjust the priority of the recommendation based on the submission date. For example, the recommendation unit gradually adjusts the priority of the recommendation based on the submission date. In this way, by determining the priority of the recommendation based on the submission date of the content, the latest information can be preferentially recommended. Some or all of the above-mentioned processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input data on the submission date of the content to a generation AI, which can then determine the priority of the recommendation.

[0110] The recommendation unit can adjust the order of recommendations based on the relevance of the content when making a recommendation. For example, the recommendation unit prioritizes recommending highly relevant content. For example, the recommendation unit prioritizes recommending highly relevant lesson content. The recommendation unit can also postpone recommending less relevant content. For example, the recommendation unit postpones recommending less relevant lesson content. The recommendation unit can also gradually adjust the order of recommendations based on the relevance of the content. For example, the recommendation unit gradually adjusts the order of recommendations based on the relevance of the content. This enables efficient data recommendation by adjusting the order of recommendations based on the relevance of the content. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without using AI. For example, the recommendation unit can input content relevance data to a generation AI, which can adjust the order of recommendations.

[0111] The recommendation unit can adjust the use of technical terminology in the recommendation depending on the user's level of expertise when making a recommendation. For example, if the user's level of expertise is high, the recommendation unit provides recommended content that uses a lot of technical terminology. For example, if the user's level of expertise is high, the recommendation unit can provide recommended content that uses a lot of technical terminology. Furthermore, if the user's level of expertise is low, the recommendation unit can provide recommended content that avoids technical terminology. For example, if the user's level of expertise is low, the recommendation unit can provide recommended content that avoids technical terminology. Furthermore, the recommendation unit can gradually adjust the use of technical terminology in the recommended content depending on the user's level of expertise. For example, the recommendation unit gradually adjusts the use of technical terminology in the recommended content depending on the user's level of expertise. This allows the recommendation results to be provided that are easy for the user to understand by adjusting the use of technical terminology in the recommendation depending on the user's level of expertise. Some or all of the above-described processing in the recommendation unit may be performed using, for example, AI, or may be performed without AI. For example, the recommendation unit can input the user's level of expertise data into a generation AI, which can adjust the use of technical terminology in the recommendation.

[0112] The reception unit can analyze the user's emotions and adjust the input reception method based on the analyzed user's emotions. For example, when the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, the reception unit provides a simple interface and minimizes input steps during times when the user is feeling stressed. The reception unit can also provide detailed input options and suggest a customizable input method when the user is relaxed. For example, the reception unit can provide detailed input options and suggest a customizable input method during times when the user is relaxed. The reception unit can also prioritize voice input when the user is in a hurry, allowing for quick input. For example, the reception unit can prioritize voice input during times when the user is in a hurry, allowing for quick input. This allows for an easy-to-use interface to be provided by adjusting the input reception method 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 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data to the generation AI, and the generation AI may adjust the method of receiving the input.

[0113] When receiving input, the reception unit can select the optimal reception method by referring to the user's past input history. For example, the reception unit automatically displays content that the user has frequently input in the past as candidates. For example, the reception unit automatically displays content that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit preferentially suggests input methods that the user has used in the past. The reception unit can also predict and suggest content to be used in a specific time period based on the user's past input history. For example, the reception unit predicts and suggests content to be used in a specific time period based on the user's past input history. This allows the optimal reception method to be selected by referring to the user's past input history, enabling efficient input reception. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's past input history data to a generation AI, which can select the optimal reception method.

[0114] The reception unit can customize the received content based on the user's current learning situation when receiving input. The reception unit, for example, analyzes the user's current learning situation and suggests optimal input content. For example, the reception unit analyzes the user's current learning situation and suggests optimal input content. The reception unit can also customize the input content based on the user's learning progress. For example, the reception unit customizes the input content based on the user's learning progress. The reception unit can also gradually adjust the input content taking into account the user's learning situation. For example, the reception unit gradually adjusts the input content taking into account the user's learning situation. This enables efficient input reception by customizing the received content based on the user's current learning situation. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's current learning situation data to a generation AI, which can then customize the received content.

[0115] The reception unit can analyze the user's emotions and determine the priority of input reception based on the analyzed user's emotions. For example, when the user is relaxed, the reception unit prioritizes receiving important input content. For example, the reception unit prioritizes receiving important input content during times when the user is relaxed. The reception unit can also prioritize receiving simple input content when the user is stressed. For example, the reception unit prioritizes receiving simple input content during times when the user is stressed. The reception unit can also prioritize receiving content that can be quickly entered when the user is in a hurry. For example, the reception unit prioritizes receiving content that can be quickly entered during times when the user is in a hurry. This enables efficient input reception by determining the priority of input reception based on the user's emotions. 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 reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of input reception.

[0116] When accepting input, the acceptance unit can select the optimal acceptance method by taking into account the user's device information. For example, if the user is using a smartphone, the acceptance unit provides an input method tailored to the screen size. For example, if the user is using a smartphone, the acceptance unit provides an input method tailored to the screen size. Furthermore, if the user is using a tablet, the acceptance unit can provide an input method optimized for a large screen. For example, if the user is using a tablet, the acceptance unit can provide an input method optimized for a large screen. Furthermore, if the user is using a smartwatch, the acceptance unit can provide an input method that is simple and highly visible. For example, if the user is using a smartwatch, the acceptance unit provides an input method that is simple and highly visible. This enables the optimal acceptance method to be selected by taking into account the user's device information, enabling efficient input acceptance. Some or all of the above-described processing in the acceptance unit may be performed using, for example, AI, or may be performed without using AI. For example, the acceptance unit can input the user's device information data into a generation AI, which can select the optimal acceptance method.

[0117] When receiving input, the reception unit can make the received content multilingual according to the user's language setting. The reception unit automatically sets the input content based on, for example, the language setting of the user's device. For example, the reception unit automatically sets the input content based on the language setting of the user's device. The reception unit can also provide a language switching function when the user uses multiple languages. For example, the reception unit provides a language switching function when the user uses multiple languages. The reception unit can also provide the input content in a specific language when the user selects that language. For example, the reception unit provides the input content in that language when the user selects a specific language. This enables efficient input reception by making the received content multilingual according to the user's language setting. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's language setting data to a generation AI, which can then make the received content multilingual.

[0118] The providing unit can analyze the user's emotions and adjust the method of provision based on the analyzed user's emotions. For example, when the user is relaxed, the providing unit provides detailed content to be provided. For example, the providing unit provides detailed content to be provided during times when the user is relaxed. The providing unit can also provide concise and to-the-point content to be provided when the user is stressed. For example, the providing unit provides concise and to-the-point content to be provided during times when the user is stressed. The providing unit can also provide content that can be understood in a short time when the user is busy. For example, the providing unit provides content that can be understood in a short time when the user is busy. In this way, by adjusting the method of provision based on the user's emotions, it is possible to provide content that is easy for the user to understand. 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-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI, and the generating AI can adjust the method of providing.

[0119] The providing unit can select the optimal providing method by referring to the user's past providing history when providing the content. The providing unit selects the optimal providing method, for example, based on the user's past providing history. For example, the providing unit selects the optimal providing method based on the user's past providing history. The providing unit can also identify a time period and method suitable for providing the content from the user's past providing history. For example, the providing unit identifies a time period and method suitable for providing the content from the user's past providing history. The providing unit can also customize the providing method by referring to the user's past providing history. For example, the providing unit customizes the providing method by referring to the user's past providing history. This enables the optimal providing method to be selected by referring to the user's past providing history, thereby enabling efficient providing. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past providing history data into a generation AI, which can select the optimal providing method.

[0120] The providing unit can customize the content to be provided based on the user's current learning situation at the time of providing the content. The providing unit, for example, analyzes the user's current learning situation and proposes optimal content to be provided. For example, the providing unit analyzes the user's current learning situation and proposes optimal content to be provided. The providing unit can also customize the content to be provided based on the user's learning progress. For example, the providing unit customizes the content to be provided based on the user's learning progress. The providing unit can also gradually adjust the content to be provided, taking into account the user's learning situation. For example, the providing unit gradually adjusts the content to be provided, taking into account the user's learning situation. This enables efficient provision by customizing the content to be provided based on the user's current learning situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current learning situation data into a generating AI, which can customize the content to be provided.

[0121] The providing unit can analyze the user's emotions and determine a priority of content provision based on the analyzed user's emotions. For example, when the user is relaxed, the providing unit prioritizes providing important content. For example, the providing unit prioritizes providing important content during times when the user is relaxed. The providing unit can also prioritize providing simple content when the user is stressed. For example, the providing unit prioritizes providing simple content during times when the user is stressed. The providing unit can also prioritize providing content that can be provided quickly when the user is in a hurry. For example, the providing unit prioritizes providing content that can be provided quickly during times when the user is in a hurry. This enables efficient provision by determining the priority of content provision 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 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 providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotional data into the generating AI, and the generating AI can determine the priority of provision.

[0122] The providing unit can select the optimal delivery method by taking into account the user's device information when providing the information. For example, if the user is using a smartphone, the providing unit provides a delivery method tailored to the screen size. For example, if the user is using a smartphone, the providing unit provides a delivery method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a delivery method optimized for a large screen. For example, if the user is using a tablet, the providing unit can provide a delivery method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible delivery method. For example, if the user is using a smartwatch, the providing unit provides a simple and highly visible delivery method. This enables the optimal delivery method to be selected by taking into account the user's device information, enabling efficient delivery. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into a generation AI, which can select the optimal delivery method.

[0123] The providing unit can make the provided content multilingual according to the user's language setting when providing the content. The providing unit automatically sets the provided content based on, for example, the language setting of the user's device. For example, the providing unit automatically sets the provided content based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides a language switching function when the user uses multiple languages. The providing unit can also provide the provided content in a specific language when the user selects that language. For example, the providing unit provides the provided content in that language when the user selects a specific language. This enables efficient provision by making the provided content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generation AI, which can then make the provided content multilingual. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, classification unit, recommendation unit, reception unit, provision unit, and emotion analysis function described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect lesson content and wisdom from educators using the control unit 46A of the smart device 14. Furthermore, the analysis unit, classification unit, recommendation unit, reception unit, provision unit, and emotion analysis function are realized, for example, by the specific processing unit 290 of the data processing device 12. Furthermore, the provision unit can provide the recommendation results to the user using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, classification unit, recommendation unit, reception unit, provision unit, and emotion analysis function described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect lesson content and wisdom from educators via the control unit 46A of the smart glasses 214. Furthermore, the analysis unit, classification unit, recommendation unit, reception unit, provision unit, and emotion analysis function are realized, for example, by the specific processing unit 290 of the data processing device 12. Furthermore, the provision unit can provide the recommendation result to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, classification unit, recommendation unit, reception unit, provision unit, and emotion analysis function described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect lesson content and wisdom from educators using the control unit 46A of the headset type terminal 314. Furthermore, the analysis unit, classification unit, recommendation unit, reception unit, provision unit, and emotion analysis function are realized, for example, by the specific processing unit 290 of the data processing device 12. Furthermore, the provision unit can provide recommendation results to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, classification unit, recommendation unit, reception unit, provision unit, and emotion analysis function is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect lesson content and wisdom from educators using the control unit 46A of the robot 414. Furthermore, the analysis unit, classification unit, recommendation unit, reception unit, provision unit, and emotion analysis function are realized, for example, by the specific processing unit 290 of the data processing device 12. Furthermore, the provision unit can provide the recommendation results to the user using the speaker 240 of the robot 414.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The educational content provision system can also analyze a user's learning style and provide the most appropriate content format. For example, a user who prefers visual learning can be provided with content that makes heavy use of videos and infographics. A user who prefers auditory learning can be provided with content in the form of podcasts or audiobooks. Furthermore, a user who prefers hands-on learning can be provided with content that includes interactive quizzes and simulations. This maximizes learning effectiveness by providing the most appropriate content format according to the user's learning style.

[0126] The educational content providing system can further analyze the user's emotions and adjust the learning progress based on the analyzed user's emotions. For example, if the user is feeling stressed, the learning pace can be slowed down. Also, if the user is relaxed, the learning pace can be accelerated. Furthermore, if the user is excited, a challenging task can be provided. In this way, the effectiveness of learning can be improved by adjusting the learning progress according to the user's emotions.

[0127] The educational content provision system can also provide feedback to increase the user's motivation to learn based on the user's learning history. For example, if a user achieves a certain learning goal, the system can provide praise or a badge. If the user is struggling with their learning, the system can also send an encouraging message. Furthermore, the system can provide points or rewards as an incentive to the user to continue learning. This can increase the user's motivation to learn and encourage continuous learning.

[0128] The educational content provision system can further analyze the user's learning environment and provide the optimal learning environment. For example, if the user prefers to study in a quiet environment, a noise-canceling function can be provided. Also, if the user is more likely to concentrate during a certain time of day, it can recommend studying during that time. Furthermore, if the user prefers to study on a specific device, it can provide content optimized for that device. This can improve learning effectiveness by providing the optimal learning environment according to the user's learning environment.

[0129] The educational content provision system can also monitor the user's learning progress in real time and adjust the learning plan as needed. For example, if the user is progressing ahead of schedule, the system can provide the next step early. If the user is falling behind schedule, the system can review the learning plan and provide additional resources to support progress. Furthermore, if the user is struggling with a particular topic, the system can provide supplementary materials related to that topic. This maximizes learning effectiveness by providing a flexible learning plan that adapts to the user's learning progress.

[0130] The educational content providing system can further analyze the user's emotions and adjust the difficulty of the learning content based on the analyzed user's emotions. For example, if the user is feeling stressed, the difficulty can be lowered. If the user is feeling relaxed, the difficulty can be increased. Furthermore, if the user is excited, a more challenging task can be provided. In this way, adjusting the difficulty of the learning content according to the user's emotions can improve the effectiveness of learning.

[0131] The educational content provision system can also provide a dashboard that visualizes the user's learning progress based on their learning history. For example, it can display the topics the user has studied in the past and the goals they have achieved in graphs and charts. It can also display the user's learning pace and progress in real time. It can also present the user with the next topic and goal they should study. This makes the user's learning progress visible, which can increase motivation to learn and support effective learning.

[0132] The educational content providing system can further analyze the user's emotions and adjust the learning feedback based on the analyzed user's emotions. For example, if the user is feeling stressed, the system can provide gentle feedback. If the user is relaxed, the system can provide detailed feedback. If the user is excited, the system can provide challenging feedback. In this way, the effectiveness of learning can be improved by providing feedback according to the user's emotions.

[0133] The educational content providing system can also provide a reminder function to support learning progress based on the user's learning history. For example, if a user plans to study a specific topic, a reminder can be sent the day before. Also, if the user has stopped studying, a reminder can be sent to encourage the user to resume. Furthermore, if the user achieves a learning goal, a reminder can be sent to celebrate the achievement. In this way, by providing a reminder function to support the user's learning progress, continuous learning can be promoted.

[0134] The educational content providing system can further analyze the user's emotions and customize the learning interface based on the analyzed user's emotions. For example, if the user is feeling stressed, a simple and intuitive interface can be provided. If the user is relaxed, more detailed options can be provided. Furthermore, if the user is excited, an interface with many interactive elements can be provided. In this way, the effectiveness of learning can be improved by providing an interface that corresponds to the user's emotions.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection unit collects lesson content and knowledge from educators. The lesson content and knowledge from educators may include video lectures, text materials, interactive quizzes, etc. The collection unit collects content through methods such as online forms, APIs, and manual input. For example, the collection unit may collect video lectures provided by educators through an online form, automatically obtain text materials from educators using APIs, and collect interactive quizzes manually entered by educators. Step 2: The analysis unit analyzes the content collected by the collection unit. The analysis is performed using methods such as text mining, natural language processing, and data mining. For example, the analysis unit may use text mining technology to analyze the collected text materials, natural language processing technology to analyze the content of video lectures, and data mining technology to analyze the data of interactive quizzes. Step 3: The classification unit classifies the content analyzed by the analysis unit into predetermined categories. The categories are classified by subject, difficulty level, target age, etc. For example, the classification unit can classify the analyzed content by subject, difficulty level, and target age. Step 4: The recommendation unit recommends predetermined content based on the content classified by the classification unit. Recommendations are made using a recommendation algorithm based on the user's past behavioral history, interests, learning progress, etc. For example, the recommendation unit can recommend optimal content based on the user's past behavioral history, content based on the user's interests, and content based on the user's learning progress.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] 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.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] [Explanation of symbols]

[0209] 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 collection department that collects lesson content and wisdom from educators, an analysis unit that analyzes the content collected by the collection unit; a classification unit that classifies the content analyzed by the analysis unit into predetermined categories; a recommendation unit that recommends predetermined content based on the content classified by the classification unit. A system characterized by:

2. A reception unit that receives user input is provided.

2. The system of claim 1.

3. The recommendation unit Make personalized recommendations based on your learning history and interests 2. The system of claim 1.

4. A provision unit is provided to provide the recommendation results to the user.

2. The system of claim 1.

5. The collecting unit Collecting lesson content and wisdom provided by educators in digital format 2. The system of claim 1.

6. The analysis unit Analyze the collected content and categorize it appropriately 2. The system of claim 1.

7. The collecting unit Analyzes educators' emotions and adjusts the timing of collecting lesson content based on the analyzed educators' emotions.

2. The system of claim 1.

8. The collecting unit Analyze the educator's past teaching history and select a predetermined collection method 2. The system of claim 1.

9. The collecting unit When collecting lesson content, filter it based on the educator's area of ​​expertise and the content of the lesson.

2. The system of claim 1.

Images